From 637eb5a647e01d3bd929bdf1e325342ccdb4f8ac Mon Sep 17 00:00:00 2001
From: cateban <cardoesteban@gmail.com>
Date: Tue, 24 Mar 2020 17:09:15 -0600
Subject: [PATCH 1/4] =?UTF-8?q?Generaci=C3=B3n=20de=20conflicto?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .DS_Store                                     | Bin 10244 -> 10244 bytes
 module-1/.DS_Store                            | Bin 20484 -> 30724 bytes
 .../lab-resolving-git-conflicts/.DS_Store     | Bin 6148 -> 6148 bytes
 .../your-code/about-me.md                     |   1 +
 4 files changed, 1 insertion(+)

diff --git a/.DS_Store b/.DS_Store
index eeb51dee3138810c0c743611b5b74659c2415f4e..aa4d854adf805272f54c2ae505d50cc7b66fc536 100644
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diff --git a/module-1/.DS_Store b/module-1/.DS_Store
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diff --git a/module-1/lab-resolving-git-conflicts/.DS_Store b/module-1/lab-resolving-git-conflicts/.DS_Store
index 045803ad77123517852baedb8f0c1791aa7a9f72..56adc1dda90059fbb0dc4dcba0bb5e32030c7ecf 100644
GIT binary patch
delta 126
zcmZoMXfc@J&&abeU^g=(&t@JLV<yg&;^ds9{QMlo$#$$_0y*i1!O8i#1q@)oodg!l
t&3AE0DJ@B6V7RqF*xy$SDwu*H$X}3wtX*KTEvxuudp1GF&Fmb1`2mZoA$$M;

delta 32
ocmZoMXfc@J&&a+pU^g=(`(_>%W2VjD*aR6THf-O_&heKY0Hs9=W&i*H

diff --git a/module-1/lab-resolving-git-conflicts/your-code/about-me.md b/module-1/lab-resolving-git-conflicts/your-code/about-me.md
index 30a999d..14a2186 100755
--- a/module-1/lab-resolving-git-conflicts/your-code/about-me.md
+++ b/module-1/lab-resolving-git-conflicts/your-code/about-me.md
@@ -1,3 +1,4 @@
+QUE VIVA EL CONFLICTO! mentira, no queremos que viva. 
 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque viverra laoreet lorem et dapibus. Integer auctor dignissim egestas. Ut id purus neque. Pellentesque imperdiet lacus in libero laoreet, at tempus felis tristique. Cras fermentum erat a dui vulputate gravida. Nulla aliquet nisi interdum nulla pretium, ac vestibulum diam congue. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Phasellus lacus risus, sodales vitae viverra quis, maximus ac ipsum. Sed consequat viverra mattis. Curabitur iaculis varius mollis.
 
 Ut porttitor iaculis tellus bibendum euismod. Morbi porta, ante nec tempus porta, felis mi faucibus lacus, sed tristique purus nunc sed est. Aenean pulvinar urna ut lacus interdum aliquam. Pellentesque sit amet magna accumsan, sagittis metus a, volutpat velit. Mauris vitae ex vehicula, posuere nisi sed, sagittis nunc. Ut scelerisque, mi non tristique tristique, mi enim luctus nunc, eu mattis sem quam auctor nunc. Donec lobortis tellus eget blandit ultricies. Vivamus euismod metus eget leo blandit, at malesuada magna efficitur. Praesent sodales faucibus mi, ullamcorper ultrices orci. Vivamus maximus malesuada massa, nec placerat leo feugiat vel. Nam vitae eleifend enim. Nullam interdum ipsum velit, vitae faucibus lectus blandit euismod.

From 26a8b822421a32db3a0522b2340eb4064cdf92b5 Mon Sep 17 00:00:00 2001
From: cateban <cardoesteban@gmail.com>
Date: Tue, 24 Mar 2020 17:49:19 -0600
Subject: [PATCH 2/4] .ds_store

---
 .DS_Store          | Bin 10244 -> 10244 bytes
 module-1/.DS_Store | Bin 20484 -> 34820 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/.DS_Store b/.DS_Store
index eeb51dee3138810c0c743611b5b74659c2415f4e..269f82615c654728f6b1448faa9ce71366fc1667 100644
GIT binary patch
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diff --git a/module-1/.DS_Store b/module-1/.DS_Store
index dc90db086a0689164ee8b0d896f89f418fb35e18..3d2f9282664e7965397d179b523456f0f11c9c99 100644
GIT binary patch
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From 2093b8e4d8bad8a8b82394f4626bda972803e017 Mon Sep 17 00:00:00 2001
From: cateban <cardoesteban@gmail.com>
Date: Thu, 2 Apr 2020 17:10:14 -0600
Subject: [PATCH 3/4] .dsstore

---
 module-1/.DS_Store           | Bin 34820 -> 45060 bytes
 module-1/lab-numpy/.DS_Store | Bin 6148 -> 8196 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/module-1/.DS_Store b/module-1/.DS_Store
index 3d2f9282664e7965397d179b523456f0f11c9c99..16da8f8299fd59403ca26865af54f5807c902b0b 100644
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From 61cb34144121a74c62ad096744c39c30de614a31 Mon Sep 17 00:00:00 2001
From: cateban <cardoesteban@gmail.com>
Date: Mon, 20 Apr 2020 12:19:56 -0500
Subject: [PATCH 4/4] python-bi-project

---
 .DS_Store                                     |  Bin 10244 -> 14340 bytes
 module-1/.DS_Store                            |  Bin 45060 -> 45060 bytes
 module-2/.DS_Store                            |  Bin 18436 -> 30724 bytes
 module-2/python-bi-project/.DS_Store          |  Bin 0 -> 10244 bytes
 .../Project - Final-checkpoint.ipynb          | 2923 +++++++++++++++
 .../.ipynb_checkpoints/draft-checkpoint.ipynb |    6 +
 .../geocoder testing-checkpoint.ipynb         |    6 +
 .../MapaFinal.html                            |   91 +
 .../MapaFinalRestaurantes.html                |  131 +
 .../Project - Final.ipynb                     | 2923 +++++++++++++++
 .../python-bi-project - Esteban/README.md     |   43 +
 .../python-bi-project - Esteban/draft.ipynb   | 3121 +++++++++++++++++
 .../geocoder testing.ipynb                    | 1136 ++++++
 .../rest_data_ca.pkl                          |  Bin 0 -> 3453929 bytes
 14 files changed, 10380 insertions(+)
 create mode 100644 module-2/python-bi-project/.DS_Store
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/Project - Final-checkpoint.ipynb
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/draft-checkpoint.ipynb
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/geocoder testing-checkpoint.ipynb
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/MapaFinal.html
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/MapaFinalRestaurantes.html
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/Project - Final.ipynb
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/README.md
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/draft.ipynb
 create mode 100644 module-2/python-bi-project/python-bi-project - Esteban/geocoder testing.ipynb
 create mode 100755 module-2/python-bi-project/python-bi-project - Esteban/rest_data_ca.pkl

diff --git a/.DS_Store b/.DS_Store
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diff --git a/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/Project - Final-checkpoint.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/Project - Final-checkpoint.ipynb
new file mode 100644
index 0000000..adca703
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/Project - Final-checkpoint.ipynb	
@@ -0,0 +1,2923 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pymongo\n",
+    "from pymongo import MongoClient\n",
+    "from tqdm import tqdm\n",
+    "from tqdm import trange\n",
+    "import folium\n",
+    "from folium import plugins\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "warnings.simplefilter('ignore')\n",
+    "import re\n",
+    "import pickle\n",
+    "from opencage.geocoder import OpenCageGeocode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating the Mongo conection\n",
+    "cliente=MongoClient('mongodb://localhost:27017')\n",
+    "db = cliente.companies\n",
+    "col = db.companies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>permalink</th>\n",
+       "      <th>crunchbase_url</th>\n",
+       "      <th>homepage_url</th>\n",
+       "      <th>blog_url</th>\n",
+       "      <th>blog_feed_url</th>\n",
+       "      <th>twitter_username</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>...</th>\n",
+       "      <th>offices</th>\n",
+       "      <th>milestones</th>\n",
+       "      <th>video_embeds</th>\n",
+       "      <th>screenshots</th>\n",
+       "      <th>external_links</th>\n",
+       "      <th>partners</th>\n",
+       "      <th>deadpooled_month</th>\n",
+       "      <th>deadpooled_day</th>\n",
+       "      <th>deadpooled_url</th>\n",
+       "      <th>ipo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8a</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>abc2</td>\n",
+       "      <td>http://www.crunchbase.com/company/wetpaint</td>\n",
+       "      <td>http://wetpaint-inc.com</td>\n",
+       "      <td>http://digitalquarters.net/</td>\n",
+       "      <td>http://digitalquarters.net/feed/</td>\n",
+       "      <td>BachelrWetpaint</td>\n",
+       "      <td>web</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': '', 'address1': '710 - 2nd Av...</td>\n",
+       "      <td>[{'id': 5869, 'description': 'Wetpaint named i...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 86], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://www.geekwire.com/201...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8b</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>abc3</td>\n",
+       "      <td>http://www.crunchbase.com/company/adventnet</td>\n",
+       "      <td>http://adventnet.com</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>manageengine</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 94], 'assets/imag...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8c</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>abc4</td>\n",
+       "      <td>http://www.crunchbase.com/company/zoho</td>\n",
+       "      <td>http://zoho.com</td>\n",
+       "      <td>http://blogs.zoho.com/</td>\n",
+       "      <td>http://blogs.zoho.com/feed</td>\n",
+       "      <td>zoho</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 388, 'description': 'Zoho Reaches 2 Mi...</td>\n",
+       "      <td>[{'embed_code': '&lt;object width=\"430\" height=\"2...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'external_url': 'http://www.online-tech-tips...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8d</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>http://www.crunchbase.com/company/digg</td>\n",
+       "      <td>http://www.digg.com</td>\n",
+       "      <td>http://blog.digg.com/</td>\n",
+       "      <td>http://blog.digg.com/?feed=rss2</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "      <td>[{'id': 9588, 'description': 'Another Digg Exe...</td>\n",
+       "      <td>[{'embed_code': '&lt;embed src=\"http://blip.tv/pl...</td>\n",
+       "      <td>[{'available_sizes': [[[117, 150], 'assets/ima...</td>\n",
+       "      <td>[{'external_url': 'http://www.sociableblog.com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8e</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>http://www.crunchbase.com/company/facebook</td>\n",
+       "      <td>http://facebook.com</td>\n",
+       "      <td>http://blog.facebook.com</td>\n",
+       "      <td>http://blog.facebook.com/atom.php</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 108, 'description': 'Facebook adds com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 68], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://latimesblogs.latimes...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>{'valuation_amount': 104000000000, 'valuation_...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id       name permalink  \\\n",
+       "0  52cdef7c4bab8bd675297d8a   Wetpaint      abc2   \n",
+       "1  52cdef7c4bab8bd675297d8b  AdventNet      abc3   \n",
+       "2  52cdef7c4bab8bd675297d8c       Zoho      abc4   \n",
+       "3  52cdef7c4bab8bd675297d8d       Digg      digg   \n",
+       "4  52cdef7c4bab8bd675297d8e   Facebook  facebook   \n",
+       "\n",
+       "                                crunchbase_url             homepage_url  \\\n",
+       "0   http://www.crunchbase.com/company/wetpaint  http://wetpaint-inc.com   \n",
+       "1  http://www.crunchbase.com/company/adventnet     http://adventnet.com   \n",
+       "2       http://www.crunchbase.com/company/zoho          http://zoho.com   \n",
+       "3       http://www.crunchbase.com/company/digg      http://www.digg.com   \n",
+       "4   http://www.crunchbase.com/company/facebook      http://facebook.com   \n",
+       "\n",
+       "                      blog_url                      blog_feed_url  \\\n",
+       "0  http://digitalquarters.net/   http://digitalquarters.net/feed/   \n",
+       "1                                                                   \n",
+       "2       http://blogs.zoho.com/         http://blogs.zoho.com/feed   \n",
+       "3        http://blog.digg.com/    http://blog.digg.com/?feed=rss2   \n",
+       "4     http://blog.facebook.com  http://blog.facebook.com/atom.php   \n",
+       "\n",
+       "  twitter_username category_code  number_of_employees  ...  \\\n",
+       "0  BachelrWetpaint           web                 47.0  ...   \n",
+       "1     manageengine    enterprise                600.0  ...   \n",
+       "2             zoho      software               1600.0  ...   \n",
+       "3             digg          news                 60.0  ...   \n",
+       "4         facebook        social               5299.0  ...   \n",
+       "\n",
+       "                                             offices  \\\n",
+       "0  [{'description': '', 'address1': '710 - 2nd Av...   \n",
+       "1  [{'description': 'Headquarters', 'address1': '...   \n",
+       "2  [{'description': 'Headquarters', 'address1': '...   \n",
+       "3  [{'description': None, 'address1': '135 Missis...   \n",
+       "4  [{'description': 'Headquarters', 'address1': '...   \n",
+       "\n",
+       "                                          milestones  \\\n",
+       "0  [{'id': 5869, 'description': 'Wetpaint named i...   \n",
+       "1                                                 []   \n",
+       "2  [{'id': 388, 'description': 'Zoho Reaches 2 Mi...   \n",
+       "3  [{'id': 9588, 'description': 'Another Digg Exe...   \n",
+       "4  [{'id': 108, 'description': 'Facebook adds com...   \n",
+       "\n",
+       "                                        video_embeds  \\\n",
+       "0                                                 []   \n",
+       "1                                                 []   \n",
+       "2  [{'embed_code': '<object width=\"430\" height=\"2...   \n",
+       "3  [{'embed_code': '<embed src=\"http://blip.tv/pl...   \n",
+       "4                                                 []   \n",
+       "\n",
+       "                                         screenshots  \\\n",
+       "0  [{'available_sizes': [[[150, 86], 'assets/imag...   \n",
+       "1  [{'available_sizes': [[[150, 94], 'assets/imag...   \n",
+       "2                                                 []   \n",
+       "3  [{'available_sizes': [[[117, 150], 'assets/ima...   \n",
+       "4  [{'available_sizes': [[[150, 68], 'assets/imag...   \n",
+       "\n",
+       "                                      external_links partners  \\\n",
+       "0  [{'external_url': 'http://www.geekwire.com/201...       []   \n",
+       "1                                                 []       []   \n",
+       "2  [{'external_url': 'http://www.online-tech-tips...       []   \n",
+       "3  [{'external_url': 'http://www.sociableblog.com...       []   \n",
+       "4  [{'external_url': 'http://latimesblogs.latimes...       []   \n",
+       "\n",
+       "  deadpooled_month deadpooled_day deadpooled_url  \\\n",
+       "0              NaN            NaN            NaN   \n",
+       "1              NaN            NaN            NaN   \n",
+       "2              NaN            NaN            NaN   \n",
+       "3              NaN            NaN           None   \n",
+       "4              NaN            NaN                  \n",
+       "\n",
+       "                                                 ipo  \n",
+       "0                                                NaN  \n",
+       "1                                                NaN  \n",
+       "2                                                NaN  \n",
+       "3                                               None  \n",
+       "4  {'valuation_amount': 104000000000, 'valuation_...  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cole = col.find({})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 18801 entries, 0 to 18800\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   _id                  18801 non-null  object \n",
+      " 1   name                 18801 non-null  object \n",
+      " 2   permalink            18801 non-null  object \n",
+      " 3   crunchbase_url       18801 non-null  object \n",
+      " 4   homepage_url         16895 non-null  object \n",
+      " 5   blog_url             16890 non-null  object \n",
+      " 6   blog_feed_url        16757 non-null  object \n",
+      " 7   twitter_username     11383 non-null  object \n",
+      " 8   category_code        16050 non-null  object \n",
+      " 9   number_of_employees  8889 non-null   float64\n",
+      " 10  founded_year         13136 non-null  float64\n",
+      " 11  founded_month        7898 non-null   float64\n",
+      " 12  founded_day          5569 non-null   float64\n",
+      " 13  deadpooled_year      929 non-null    float64\n",
+      " 14  tag_list             14748 non-null  object \n",
+      " 15  alias_list           8932 non-null   object \n",
+      " 16  email_address        16793 non-null  object \n",
+      " 17  phone_number         16778 non-null  object \n",
+      " 18  description          12360 non-null  object \n",
+      " 19  created_at           18801 non-null  object \n",
+      " 20  updated_at           18801 non-null  object \n",
+      " 21  overview             16820 non-null  object \n",
+      " 22  image                16175 non-null  object \n",
+      " 23  products             18801 non-null  object \n",
+      " 24  relationships        18801 non-null  object \n",
+      " 25  competitions         18801 non-null  object \n",
+      " 26  providerships        18801 non-null  object \n",
+      " 27  total_money_raised   18801 non-null  object \n",
+      " 28  funding_rounds       18801 non-null  object \n",
+      " 29  investments          18801 non-null  object \n",
+      " 30  acquisition          2677 non-null   object \n",
+      " 31  acquisitions         18801 non-null  object \n",
+      " 32  offices              18801 non-null  object \n",
+      " 33  milestones           18801 non-null  object \n",
+      " 34  video_embeds         18801 non-null  object \n",
+      " 35  screenshots          18801 non-null  object \n",
+      " 36  external_links       18801 non-null  object \n",
+      " 37  partners             18801 non-null  object \n",
+      " 38  deadpooled_month     765 non-null    float64\n",
+      " 39  deadpooled_day       648 non-null    float64\n",
+      " 40  deadpooled_url       1079 non-null   object \n",
+      " 41  ipo                  390 non-null    object \n",
+      "dtypes: float64(7), object(35)\n",
+      "memory usage: 6.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1\n",
+    "#### Define client type: as I mentioned in the README ,they want to be sorrounded with Small and Medium Business, they want to have a coffee shop nearby and they want to be in San Diego California"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2. \n",
+    "#### Correctly query the data I want. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>founded_year</th>\n",
+       "      <th>deadpooled_year</th>\n",
+       "      <th>total_money_raised</th>\n",
+       "      <th>offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$45M</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$2.43B</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>web</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$0</td>\n",
+       "      <td>[{'description': None, 'address1': '959 Skyway...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Geni</td>\n",
+       "      <td>web</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$16.5M</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Flektor</td>\n",
+       "      <td>games_video</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$0</td>\n",
+       "      <td>[{'description': None, 'address1': '8536 Natio...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name category_code  number_of_employees  founded_year deadpooled_year  \\\n",
+       "0      Digg          news                 60.0        2004.0            None   \n",
+       "1  Facebook        social               5299.0        2004.0            None   \n",
+       "2   Postini           web                  NaN        1999.0            None   \n",
+       "3      Geni           web                 18.0        2006.0            None   \n",
+       "4   Flektor   games_video                  NaN           NaN            None   \n",
+       "\n",
+       "  total_money_raised                                            offices  \n",
+       "0               $45M  [{'description': None, 'address1': '135 Missis...  \n",
+       "1             $2.43B  [{'description': 'Headquarters', 'address1': '...  \n",
+       "2                 $0  [{'description': None, 'address1': '959 Skyway...  \n",
+       "3             $16.5M  [{'description': 'Headquarters', 'address1': '...  \n",
+       "4                 $0  [{'description': None, 'address1': '8536 Natio...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Selecting only the ones that have offices and do not have deadpool year\n",
+    "cole = col.find({'offices': {'$not': {'$size':0}},\n",
+    "                'deadpooled_year':{'$type':'null'}},\n",
+    "                {'_id':0 ,'name':1,'category_code':1,'number_of_employees':1,\n",
+    "                 'offices':1,'founded_year':1,'deadpooled_year':1,'total_money_raised':1})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 12957 entries, 0 to 12956\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   name                 12957 non-null  object \n",
+      " 1   category_code        12310 non-null  object \n",
+      " 2   number_of_employees  7039 non-null   float64\n",
+      " 3   founded_year         10630 non-null  float64\n",
+      " 4   deadpooled_year      0 non-null      object \n",
+      " 5   total_money_raised   12957 non-null  object \n",
+      " 6   offices              12957 non-null  object \n",
+      "dtypes: float64(2), object(5)\n",
+      "memory usage: 708.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12957, 7)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3\n",
+    "#### Transforming data according to client needs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12957/12957 [02:05<00:00, 102.86it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Generate a list with dataframes with office information.\n",
+    "#adding a column with name, total employees, category and number of offices\n",
+    "\n",
+    "lista_df=[] \n",
+    "\n",
+    "for i in tqdm(range(len(data.offices))):\n",
+    "    df = pd.DataFrame(data.iloc[i].offices)\n",
+    "    df['name'] = str(data.loc[i,['name'][0]])\n",
+    "    df['total_employees'] = data.iloc[i].number_of_employees\n",
+    "    df['category'] = str(data.loc[i,['category_code'][0]])\n",
+    "    df['total_offices'] = len(data.iloc[i].offices)\n",
+    "    lista_df.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12957"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lista_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>1601 Willow Road</td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.416</td>\n",
+       "      <td>-122.152</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Europe HQ</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Dublin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>IRL</td>\n",
+       "      <td>53.3441</td>\n",
+       "      <td>-6.26749</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>340 Madison Ave</td>\n",
+       "      <td></td>\n",
+       "      <td>10017</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7557</td>\n",
+       "      <td>-73.9792</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    description                    address1 address2 zip_code           city  \\\n",
+       "0          None          135 Mississippi St             94107  San Francisco   \n",
+       "1  Headquarters            1601 Willow Road             94025     Menlo Park   \n",
+       "2     Europe HQ                                                       Dublin   \n",
+       "3      New York             340 Madison Ave             10017       New York   \n",
+       "4          None  959 Skyway Road, Suite 200     None    94070     San Carlos   \n",
+       "\n",
+       "  state_code country_code latitude longitude      name  total_employees  \\\n",
+       "0         CA          USA  37.7647  -122.395      Digg             60.0   \n",
+       "1         CA          USA   37.416  -122.152  Facebook           5299.0   \n",
+       "2       None          IRL  53.3441  -6.26749  Facebook           5299.0   \n",
+       "3         NY          USA  40.7557  -73.9792  Facebook           5299.0   \n",
+       "4         CA          USA  37.5069  -122.248   Postini              NaN   \n",
+       "\n",
+       "  category  total_offices  \n",
+       "0     news              1  \n",
+       "1   social              3  \n",
+       "2   social              3  \n",
+       "3   social              3  \n",
+       "4      web              1  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final = pd.concat(lista_df).reset_index(drop=True)\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15848, 13)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country_code\n",
+       "USA    9652\n",
+       "GBR    1327\n",
+       "CAN     591\n",
+       "IND     509\n",
+       "DEU     392\n",
+       "FRA     359\n",
+       "AUS     263\n",
+       "ESP     232\n",
+       "ISR     213\n",
+       "CHN     196\n",
+       "Name: name, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.groupby('country_code')['name'].count().sort_values(ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 15848/15848 [00:01<00:00, 8747.27it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#genero una lista con los geopuntos en formato de diccionario\n",
+    "lista_points = []\n",
+    "for i in tqdm(range(len(final.longitude))):\n",
+    "    \n",
+    "    lista_points.append(dict({'type':'Point',\n",
+    "                          'coordinates': [final.longitude[i],\n",
+    "                                         final.latitude[i]]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15848"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lista_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#agrego los puntos al DataFrame \n",
+    "final['principal'] = pd.Series(lista_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['description', 'address1', 'address2', 'zip_code', 'city', 'state_code',\n",
+       "       'country_code', 'latitude', 'longitude', 'name', 'total_employees',\n",
+       "       'category', 'total_offices', 'principal'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15848, 14)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reordeno el dataframe\n",
+    "final = final[['name','total_employees','category','total_offices','description','address1',\n",
+    "              'address2','zip_code','city','state_code','country_code','latitude','longitude','principal']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.394523,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>1601 Willow Road</td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.416</td>\n",
+       "      <td>-122.152</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.151801,...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Europe HQ</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Dublin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>IRL</td>\n",
+       "      <td>53.3441</td>\n",
+       "      <td>-6.26749</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-6.267494, 5...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>340 Madison Ave</td>\n",
+       "      <td></td>\n",
+       "      <td>10017</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7557</td>\n",
+       "      <td>-73.9792</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-73.9792469,...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.247573,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name  total_employees category  total_offices   description  \\\n",
+       "0      Digg             60.0     news              1          None   \n",
+       "1  Facebook           5299.0   social              3  Headquarters   \n",
+       "2  Facebook           5299.0   social              3     Europe HQ   \n",
+       "3  Facebook           5299.0   social              3      New York   \n",
+       "4   Postini              NaN      web              1          None   \n",
+       "\n",
+       "                     address1 address2 zip_code           city state_code  \\\n",
+       "0          135 Mississippi St             94107  San Francisco         CA   \n",
+       "1            1601 Willow Road             94025     Menlo Park         CA   \n",
+       "2                                                       Dublin       None   \n",
+       "3             340 Madison Ave             10017       New York         NY   \n",
+       "4  959 Skyway Road, Suite 200     None    94070     San Carlos         CA   \n",
+       "\n",
+       "  country_code latitude longitude  \\\n",
+       "0          USA  37.7647  -122.395   \n",
+       "1          USA   37.416  -122.152   \n",
+       "2          IRL  53.3441  -6.26749   \n",
+       "3          USA  40.7557  -73.9792   \n",
+       "4          USA  37.5069  -122.248   \n",
+       "\n",
+       "                                           principal company_size  \n",
+       "0  {'type': 'Point', 'coordinates': [-122.394523,...          SMB  \n",
+       "1  {'type': 'Point', 'coordinates': [-122.151801,...        JUMBO  \n",
+       "2  {'type': 'Point', 'coordinates': [-6.267494, 5...        JUMBO  \n",
+       "3  {'type': 'Point', 'coordinates': [-73.9792469,...        JUMBO  \n",
+       "4  {'type': 'Point', 'coordinates': [-122.247573,...          SMB  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#agrego columna JUMBO o SMB dependiendo de la cantidad de empleados\n",
+    "final['company_size'] = np.where(final.total_employees>1000,'JUMBO','SMB')\n",
+    "\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.394523,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.247573,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Geni</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>9229 W. Sunset Blvd.</td>\n",
+       "      <td></td>\n",
+       "      <td>90069</td>\n",
+       "      <td>West Hollywood</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.0904</td>\n",
+       "      <td>-118.393</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.393064,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Flektor</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>games_video</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8536 National Blvd, Suite A</td>\n",
+       "      <td>None</td>\n",
+       "      <td>90232</td>\n",
+       "      <td>Culver City</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.026</td>\n",
+       "      <td>-118.38</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.379768,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Fox Interactive Media</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td></td>\n",
+       "      <td>407 N Maple Dr</td>\n",
+       "      <td></td>\n",
+       "      <td>90210</td>\n",
+       "      <td>Beverly Hills</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.0762</td>\n",
+       "      <td>-118.394</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.39417, ...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name  total_employees     category  total_offices  \\\n",
+       "0                   Digg             60.0         news              1   \n",
+       "4                Postini              NaN          web              1   \n",
+       "5                   Geni             18.0          web              1   \n",
+       "6                Flektor              NaN  games_video              1   \n",
+       "7  Fox Interactive Media              0.0          web              1   \n",
+       "\n",
+       "    description                     address1 address2 zip_code  \\\n",
+       "0          None           135 Mississippi St             94107   \n",
+       "4          None   959 Skyway Road, Suite 200     None    94070   \n",
+       "5  Headquarters         9229 W. Sunset Blvd.             90069   \n",
+       "6          None  8536 National Blvd, Suite A     None    90232   \n",
+       "7                             407 N Maple Dr             90210   \n",
+       "\n",
+       "             city state_code country_code latitude longitude  \\\n",
+       "0   San Francisco         CA          USA  37.7647  -122.395   \n",
+       "4      San Carlos         CA          USA  37.5069  -122.248   \n",
+       "5  West Hollywood         CA          USA  34.0904  -118.393   \n",
+       "6     Culver City         CA          USA   34.026   -118.38   \n",
+       "7   Beverly Hills         CA          USA  34.0762  -118.394   \n",
+       "\n",
+       "                                           principal company_size  \n",
+       "0  {'type': 'Point', 'coordinates': [-122.394523,...          SMB  \n",
+       "4  {'type': 'Point', 'coordinates': [-122.247573,...          SMB  \n",
+       "5  {'type': 'Point', 'coordinates': [-118.393064,...          SMB  \n",
+       "6  {'type': 'Point', 'coordinates': [-118.379768,...          SMB  \n",
+       "7  {'type': 'Point', 'coordinates': [-118.39417, ...          SMB  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#elimino a las empresas JUMBO\n",
+    "final = final[final.company_size == 'SMB']\n",
+    "final.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15383, 15)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final.dropna(subset=['latitude','longitude'],inplace=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9919, 15)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4\n",
+    "#### Create a new collection with the final DataFrame in MongoCompass so we can add a 2dsphere index. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymongo.results.InsertManyResult at 0x124059240>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#inserto en compass el data frame resultante en una nueva coleccion\n",
+    "db.project.insert_many(final.to_dict('records')) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'principal_2dsphere'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creo el índice 2dsphere para poder hacer geoqueries\n",
+    "db.project.create_index([('principal', '2dsphere')])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5\n",
+    "\n",
+    "#### Make geoqueries of the new collection with client conditions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cole = db.project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#funcion que me permite encontrar puntos cercanos con radio por default de 1000 metros\n",
+    "def find_near(geopoint, radio=10000):\n",
+    "    \n",
+    "    return cole.find({'principal':{\n",
+    "        '$near': {'$geometry': geopoint, '$maxDistance':radio}\n",
+    "    }})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#inicializo la conexión con geocoder\n",
+    "key = 'd8314354b31d46a298e0f21ffb920a34'\n",
+    "geocoder = OpenCageGeocode(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#función que permite al usuario elegir una dirección y en base a la misma devuelve los datos necesarios para hacer la geoquery\n",
+    "def getting_coor():\n",
+    "    try:\n",
+    "        x = input('Where would you like your office? ')\n",
+    "        m = input('Whats the max distance you would like to evaluate: ')\n",
+    "        results = geocoder.geocode(x)\n",
+    "        fcoordinates=[]\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        bandera = results[0]['annotations']['flag']\n",
+    "        country = results[0]['components']['country']\n",
+    "        city=results[0]['components']['city']\n",
+    "        localidad = country+', '+city\n",
+    "        fcoordinates.append(coordinates)\n",
+    "        fcoordinates.append(m)\n",
+    "        fcoordinates.append(localidad)\n",
+    "        fcoordinates.append(x)\n",
+    "        fcoordinates.append(bandera)\n",
+    "        return fcoordinates\n",
+    "    except:\n",
+    "        return 'Not a valid address'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? San Diego California\n",
+      "Whats the max distance you would like to evaluate: 2500\n",
+      "Amount of offices near San Diego California are: 24\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x113948100>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#este podría ser el código principal que solicita el input del usuario y devuelve el mapa\n",
+    "\n",
+    "coordenadas = getting_coor() #acá inicializa la funcion creada arriba\n",
+    "longlat = coordenadas[0]\n",
+    "latlong = longlat[::-1]\n",
+    "radio = int(coordenadas[1])\n",
+    "pais = coordenadas[4]\n",
+    "localidad = coordenadas[2]\n",
+    "direccion_solicitud = coordenadas[3]\n",
+    "\n",
+    "address={'type': 'Point', 'coordinates':longlat}  # geopoint\n",
+    "\n",
+    "ratio=radio  # radio de busqueda\n",
+    "\n",
+    "n_ofi=find_near(address, ratio).count()\n",
+    "ofi = find_near(address, ratio)\n",
+    "ofi=pd.DataFrame(ofi)\n",
+    "\n",
+    "#creating map object\n",
+    "tooltip = 'Location you chose: {} \\n {}'.format(direccion_solicitud,localidad)\n",
+    "mapa=folium.Map(latlong, zoom_start=12, width='100%', height='100%')\n",
+    "datamapa=ofi[['latitude', 'longitude']].values\n",
+    "mapa.add_child(plugins.HeatMap(datamapa, radius=15))\n",
+    "folium.Marker(latlong, tooltip=tooltip, icon=folium.Icon()).add_to(mapa)\n",
+    "mapa.save('MapaFinal.html')\n",
+    "print('Amount of offices near {} are: {}'.format(direccion_solicitud,n_ofi))\n",
+    "mapa\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6\n",
+    "#### Get more information about restaurants in the area and display them with the heatmap mentioned above and marking where the restaurants of coffee shops are. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Found a DB in Kaggle with California restaurants. I proceed to explore and clean data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://www.kaggle.com/vaibhavsxn/restaurants-in-california\n",
+    "\n",
+    "with open('rest_data_ca.pkl', 'rb') as rest:\n",
+    "    data = pickle.load(rest)\n",
+    "    \n",
+    "dfrest = pd.DataFrame(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 52477 entries, 0 to 56157\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   Cuisine     52477 non-null  object\n",
+      " 1   address     52477 non-null  object\n",
+      " 2   name        52477 non-null  object\n",
+      " 3   postalCode  52477 non-null  object\n",
+      " 4   Tag         52477 non-null  object\n",
+      " 5   ratings     52477 non-null  int64 \n",
+      "dtypes: int64(1), object(5)\n",
+      "memory usage: 2.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dfrest.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "All          47851\n",
+       "Mexican       4530\n",
+       "Fast Food       64\n",
+       "Veg             32\n",
+       "Name: Cuisine, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.Cuisine.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Food    52477\n",
+       "Name: Tag, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.Tag.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    26428\n",
+       "3    26049\n",
+       "Name: ratings, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.ratings.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>925 Blossom Hill Road Oakridge Mall</td>\n",
+       "      <td>Buca Di Beppo - San Jose - Oakridge</td>\n",
+       "      <td>95123</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1640 Camino Del Rio N</td>\n",
+       "      <td>Tilted Kilt Mission Valley</td>\n",
+       "      <td>92108</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1231 Park St</td>\n",
+       "      <td>The Fire Den</td>\n",
+       "      <td>94501</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26423</th>\n",
+       "      <td>All</td>\n",
+       "      <td>6719 LA TIJERA BLVD # A-B</td>\n",
+       "      <td>CHIPOTLE #3094</td>\n",
+       "      <td>90045</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26424</th>\n",
+       "      <td>All</td>\n",
+       "      <td>100 1/2 7TH ST</td>\n",
+       "      <td>BRAAZO PIZZA</td>\n",
+       "      <td>90014</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26425</th>\n",
+       "      <td>All</td>\n",
+       "      <td>1244 WESTWOOD BLVD</td>\n",
+       "      <td>POKE'ME</td>\n",
+       "      <td>90024</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26426</th>\n",
+       "      <td>All</td>\n",
+       "      <td>217 W TEMPLE AVE</td>\n",
+       "      <td>HALL OF JUSTICE</td>\n",
+       "      <td>90012</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26427</th>\n",
+       "      <td>All</td>\n",
+       "      <td>6741 HOLLYWOOD BLVD</td>\n",
+       "      <td>JUICY WINGZ</td>\n",
+       "      <td>90028</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26428 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Cuisine                              address  \\\n",
+       "0      Fast Food  925 Blossom Hill Road Oakridge Mall   \n",
+       "1      Fast Food                       555 Soquel Ave   \n",
+       "2      Fast Food                       9822 Libra Ave   \n",
+       "3      Fast Food                1640 Camino Del Rio N   \n",
+       "4      Fast Food                         1231 Park St   \n",
+       "...          ...                                  ...   \n",
+       "26423        All            6719 LA TIJERA BLVD # A-B   \n",
+       "26424        All                       100 1/2 7TH ST   \n",
+       "26425        All                   1244 WESTWOOD BLVD   \n",
+       "26426        All                     217 W TEMPLE AVE   \n",
+       "26427        All                  6741 HOLLYWOOD BLVD   \n",
+       "\n",
+       "                                      name postalCode   Tag  ratings  \n",
+       "0      Buca Di Beppo - San Jose - Oakridge      95123  Food        4  \n",
+       "1                      Ristorante Italiano      95062  Food        4  \n",
+       "2                            Milanos Pizza      95827  Food        4  \n",
+       "3               Tilted Kilt Mission Valley      92108  Food        4  \n",
+       "4                             The Fire Den      94501  Food        4  \n",
+       "...                                    ...        ...   ...      ...  \n",
+       "26423                       CHIPOTLE #3094      90045  Food        4  \n",
+       "26424                         BRAAZO PIZZA      90014  Food        4  \n",
+       "26425                              POKE'ME      90024  Food        4  \n",
+       "26426                      HALL OF JUSTICE      90012  Food        4  \n",
+       "26427                          JUICY WINGZ      90028  Food        4  \n",
+       "\n",
+       "[26428 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#removing restaurants with low ratings\n",
+    "dfrest = dfrest[dfrest.ratings>=4]\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using REGEX so I can categorize the restaurant type\n",
+    "\n",
+    "def typerest(x):\n",
+    "    if re.search(r'pizza',x.lower()) or re.search(r'pasta',x.lower()) or re.search(r'italia',x.lower()):\n",
+    "        x='Italian'\n",
+    "    elif re.search(r'burger',x.lower()):\n",
+    "        x='Burger'\n",
+    "    elif re.search(r'coffee',x.lower()):\n",
+    "        x='CoffeeShop'\n",
+    "    elif re.search(r'coffee',x.lower()):\n",
+    "        x='CoffeeShop'\n",
+    "    elif re.search(r'pie',x.lower()) or  re.search(r'cake',x.lower()) or re.search(r'dess?ert',x.lower()):\n",
+    "        x='Dessert'\n",
+    "    elif re.search(r'mex',x.lower()) or  re.search(r'taco',x.lower()):\n",
+    "        x='Mexican'\n",
+    "    elif re.search(r'sushi',x.lower()) or  re.search(r'poke',x.lower()) or re.search(r'dumpling',x.lower())\\\n",
+    "        or re.search(r'asia',x.lower()) or re.search(r'china?e?',x.lower()) or re.search(r'japan',x.lower())\\\n",
+    "        or re.search(r'korea',x.lower()):\n",
+    "        x='Asian'\n",
+    "    else:\n",
+    "        x='other'\n",
+    "        \n",
+    "    return x  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 26428/26428 [00:00<00:00, 27249.91it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#applying the above function to the data frame using TQDM\n",
+    "tqdm.pandas()\n",
+    "dfrest['type'] = dfrest['name'].progress_apply(typerest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>925 Blossom Hill Road Oakridge Mall</td>\n",
+       "      <td>Buca Di Beppo - San Jose - Oakridge</td>\n",
+       "      <td>95123</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1640 Camino Del Rio N</td>\n",
+       "      <td>Tilted Kilt Mission Valley</td>\n",
+       "      <td>92108</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1231 Park St</td>\n",
+       "      <td>The Fire Den</td>\n",
+       "      <td>94501</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cuisine                              address  \\\n",
+       "0  Fast Food  925 Blossom Hill Road Oakridge Mall   \n",
+       "1  Fast Food                       555 Soquel Ave   \n",
+       "2  Fast Food                       9822 Libra Ave   \n",
+       "3  Fast Food                1640 Camino Del Rio N   \n",
+       "4  Fast Food                         1231 Park St   \n",
+       "\n",
+       "                                  name postalCode   Tag  ratings     type  \n",
+       "0  Buca Di Beppo - San Jose - Oakridge      95123  Food        4    other  \n",
+       "1                  Ristorante Italiano      95062  Food        4  Italian  \n",
+       "2                        Milanos Pizza      95827  Food        4  Italian  \n",
+       "3           Tilted Kilt Mission Valley      92108  Food        4    other  \n",
+       "4                         The Fire Den      94501  Food        4    other  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "other         22147\n",
+       "Mexican        1165\n",
+       "Asian           929\n",
+       "Italian         833\n",
+       "CoffeeShop      632\n",
+       "Burger          552\n",
+       "Dessert         170\n",
+       "Name: type, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>5365 Alton Pkwy</td>\n",
+       "      <td>Zpizza</td>\n",
+       "      <td>92604</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>6593 Collins Dr</td>\n",
+       "      <td>Custom Pie</td>\n",
+       "      <td>93021</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Dessert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>820 N Alta Ave</td>\n",
+       "      <td>You-be's U-bake Pizza</td>\n",
+       "      <td>93618</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cuisine          address                   name postalCode   Tag  \\\n",
+       "0  Fast Food   555 Soquel Ave    Ristorante Italiano      95062  Food   \n",
+       "1  Fast Food   9822 Libra Ave          Milanos Pizza      95827  Food   \n",
+       "2  Fast Food  5365 Alton Pkwy                 Zpizza      92604  Food   \n",
+       "3  Fast Food  6593 Collins Dr             Custom Pie      93021  Food   \n",
+       "4  Fast Food   820 N Alta Ave  You-be's U-bake Pizza      93618  Food   \n",
+       "\n",
+       "   ratings     type  \n",
+       "0        4  Italian  \n",
+       "1        4  Italian  \n",
+       "2        4  Italian  \n",
+       "3        4  Dessert  \n",
+       "4        4  Italian  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Because of the request number restriction, \n",
+    "#Im deleting every \"other\" type restaurant in database\n",
+    "dfrest = dfrest[dfrest.type != 'other']\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type  \n",
+       "0  Food        4  Mexican  \n",
+       "1  Food        4   Burger  \n",
+       "2  Food        4   Burger  \n",
+       "3  Food        4  Mexican  \n",
+       "4  Food        4   Burger  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#getting a sample of 2000 restaurantes because of api restriction\n",
+    "dfrest = dfrest.sample(2000)\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2000, 7)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to obtain latitude and longitude of the addresses in DF\n",
+    "def gettingres_coor(x):\n",
+    "    try:\n",
+    "        results = geocoder.geocode(x)\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        return coordinates\n",
+    "    except:\n",
+    "        return np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2000/2000 [39:35<00:00,  1.19s/it]  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#applying function to DF\n",
+    "tqdm.pandas()\n",
+    "dfrest['coordinates'] = dfrest.address.progress_apply(gettingres_coor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#removing nan values\n",
+    "dfrest.dropna(inplace=True)\n",
+    "dfrest.reset_index(drop = True, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting columns\n",
+    "dfrest['latitude'] = dfrest.coordinates.apply(lambda x:x[1])\n",
+    "dfrest['longitude'] = dfrest.coordinates.apply(lambda x:x[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-117.7520488, 34.0684236]</td>\n",
+       "      <td>34.068424</td>\n",
+       "      <td>-117.752049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.3018267, 34.1799755]</td>\n",
+       "      <td>34.179975</td>\n",
+       "      <td>-118.301827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.0734294, 34.0840807]</td>\n",
+       "      <td>34.084081</td>\n",
+       "      <td>-118.073429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-118.5031071, 34.2285216]</td>\n",
+       "      <td>34.228522</td>\n",
+       "      <td>-118.503107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-75.926111, 42.105103]</td>\n",
+       "      <td>42.105103</td>\n",
+       "      <td>-75.926111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type                 coordinates   latitude   longitude  \n",
+       "0  Food        4  Mexican  [-117.7520488, 34.0684236]  34.068424 -117.752049  \n",
+       "1  Food        4   Burger  [-118.3018267, 34.1799755]  34.179975 -118.301827  \n",
+       "2  Food        4   Burger  [-118.0734294, 34.0840807]  34.084081 -118.073429  \n",
+       "3  Food        4  Mexican  [-118.5031071, 34.2285216]  34.228522 -118.503107  \n",
+       "4  Food        4   Burger     [-75.926111, 42.105103]  42.105103  -75.926111  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1995/1995 [00:00<00:00, 4321.81it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#getting a list of the geopoints\n",
+    "lista_pointsres = []\n",
+    "for i in tqdm(range(len(dfrest.latitude))):\n",
+    "    lista_pointsres.append(dict({'type':'Point',\n",
+    "                          'coordinates': [dfrest.longitude[i],\n",
+    "                                         dfrest.latitude[i]]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#adding geopoints to main DF\n",
+    "dfrest['principal'] = pd.Series(lista_pointsres)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-117.7520488, 34.0684236]</td>\n",
+       "      <td>34.068424</td>\n",
+       "      <td>-117.752049</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.7520488...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.3018267, 34.1799755]</td>\n",
+       "      <td>34.179975</td>\n",
+       "      <td>-118.301827</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.3018267...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.0734294, 34.0840807]</td>\n",
+       "      <td>34.084081</td>\n",
+       "      <td>-118.073429</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.0734294...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-118.5031071, 34.2285216]</td>\n",
+       "      <td>34.228522</td>\n",
+       "      <td>-118.503107</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.5031071...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-75.926111, 42.105103]</td>\n",
+       "      <td>42.105103</td>\n",
+       "      <td>-75.926111</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-75.926111, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type                 coordinates   latitude   longitude  \\\n",
+       "0  Food        4  Mexican  [-117.7520488, 34.0684236]  34.068424 -117.752049   \n",
+       "1  Food        4   Burger  [-118.3018267, 34.1799755]  34.179975 -118.301827   \n",
+       "2  Food        4   Burger  [-118.0734294, 34.0840807]  34.084081 -118.073429   \n",
+       "3  Food        4  Mexican  [-118.5031071, 34.2285216]  34.228522 -118.503107   \n",
+       "4  Food        4   Burger     [-75.926111, 42.105103]  42.105103  -75.926111   \n",
+       "\n",
+       "                                           principal  \n",
+       "0  {'type': 'Point', 'coordinates': [-117.7520488...  \n",
+       "1  {'type': 'Point', 'coordinates': [-118.3018267...  \n",
+       "2  {'type': 'Point', 'coordinates': [-118.0734294...  \n",
+       "3  {'type': 'Point', 'coordinates': [-118.5031071...  \n",
+       "4  {'type': 'Point', 'coordinates': [-75.926111, ...  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymongo.results.InsertManyResult at 0x124635f40>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#inserting to Compass\n",
+    "db.restaurantes.insert_many(dfrest.to_dict('records')) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'principal_2dsphere'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating 2dsphere index\n",
+    "db.restaurantes.create_index([('principal', '2dsphere')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to find restaurants near clients input\n",
+    "coleres = db.restaurantes\n",
+    "def find_near_res(geopoint, radio=10000):\n",
+    "    \n",
+    "    return coleres.find({'principal':{\n",
+    "        '$near': {'$geometry': geopoint, '$maxDistance':radio}\n",
+    "    }})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? San Diego California\n",
+      "Whats the max distance you would like to evaluate: 2500\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:00<00:00, 411.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount of offices near San Diego California are: 24\n",
+      "Amount of restaurants near San Diego California are: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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            <div class="folium-map" id="map_49035a569c114d2ca44d51939b8a581a" ></div>
        
</body>
<script>    
    
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                [32.7174209, -117.1627714],
                {}
            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
        
    
            var icon_5c9d2637f04d4d008ad3f6ed158f1080 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
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            marker_d97c507df05b47cdb7eee6e163609e1e.bindTooltip(
                `<div>
                     Location you chose: San Diego California 
 United States of America, San Diego
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_78a4740d141f4fe1a230778308432eb7 = L.marker(
                [32.7156373, -117.1701501],
                {}
            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
        
    
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            marker_78a4740d141f4fe1a230778308432eb7.setIcon(icon_2dbef31c0d8346b1be98975b704c3dc8);
        
    
            marker_78a4740d141f4fe1a230778308432eb7.bindTooltip(
                `<div>
                     FRANK'S BURGER
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_32327acdde63418cbdda3c3a29f6265f = L.marker(
                [32.706318, -117.14241],
                {}
            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
        
    
            var icon_f78d5b0c520b4dd4beb4c4742dd12b44 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_32327acdde63418cbdda3c3a29f6265f.setIcon(icon_f78d5b0c520b4dd4beb4c4742dd12b44);
        
    
            marker_32327acdde63418cbdda3c3a29f6265f.bindTooltip(
                `<div>
                     STARBUCKS COFFEE #545
                 </div>`,
                {"sticky": true}
            );
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x12c9dfac0>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#main code to ask client where they want the office and getting all the needed information\n",
+    "\n",
+    "coordenadas = getting_coor() #function discribed above\n",
+    "longlat = coordenadas[0]\n",
+    "latlong = longlat[::-1]\n",
+    "radio = int(coordenadas[1])\n",
+    "pais = coordenadas[4]\n",
+    "localidad = coordenadas[2]\n",
+    "direccion_solicitud = coordenadas[3]\n",
+    "\n",
+    "address={'type': 'Point', 'coordinates':longlat}  # geopoint\n",
+    "\n",
+    "ratio=radio  # radio de busqueda\n",
+    "\n",
+    "#query for offices\n",
+    "n_ofi=find_near(address, ratio).count()\n",
+    "ofi = find_near(address, ratio)\n",
+    "ofi=pd.DataFrame(ofi)\n",
+    "\n",
+    "#query for restaurants\n",
+    "n_res=find_near_res(address, ratio).count()\n",
+    "restaurant = find_near_res(address, ratio)\n",
+    "restaurant = pd.DataFrame(restaurant)\n",
+    "\n",
+    "#creating map object\n",
+    "tooltip = 'Location you chose: {} \\n {}'.format(direccion_solicitud,localidad)\n",
+    "mapa=folium.Map(latlong, zoom_start=11)\n",
+    "datamapa=ofi[['latitude', 'longitude']].values\n",
+    "mapa.add_child(plugins.HeatMap(datamapa, radius=15))\n",
+    "folium.Marker(latlong, tooltip=tooltip, icon=folium.Icon(color='red')).add_to(mapa)\n",
+    "for i in trange(len(restaurant.latitude)):\n",
+    "    folium.Marker([restaurant.latitude[i],restaurant.longitude[i]],\n",
+    "                      tooltip = restaurant.name[i], icon = folium.Icon()).add_to(mapa)\n",
+    "\n",
+    "mapa.save('MapaFinalRestaurantes.html')\n",
+    "print('Amount of offices near {} are: {}'.format(direccion_solicitud,n_ofi))\n",
+    "print('Amount of restaurants near {} are: {}'.format(direccion_solicitud,n_res))\n",
+    "mapa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5e9db680e283983c43b35a8e</td>\n",
+       "      <td>All</td>\n",
+       "      <td>740 W BROADWAY</td>\n",
+       "      <td>FRANK'S BURGER</td>\n",
+       "      <td>91204</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-117.1701501, 32.7156373]</td>\n",
+       "      <td>32.715637</td>\n",
+       "      <td>-117.17015</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.1701501...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5e9db680e283983c43b35e67</td>\n",
+       "      <td>All</td>\n",
+       "      <td>3995 24th St</td>\n",
+       "      <td>STARBUCKS COFFEE #545</td>\n",
+       "      <td>94114</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>CoffeeShop</td>\n",
+       "      <td>[-117.14241, 32.706318]</td>\n",
+       "      <td>32.706318</td>\n",
+       "      <td>-117.14241</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.14241, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id Cuisine         address                   name  \\\n",
+       "0  5e9db680e283983c43b35a8e     All  740 W BROADWAY         FRANK'S BURGER   \n",
+       "1  5e9db680e283983c43b35e67     All    3995 24th St  STARBUCKS COFFEE #545   \n",
+       "\n",
+       "  postalCode   Tag  ratings        type                 coordinates  \\\n",
+       "0      91204  Food        4      Burger  [-117.1701501, 32.7156373]   \n",
+       "1      94114  Food        4  CoffeeShop     [-117.14241, 32.706318]   \n",
+       "\n",
+       "    latitude  longitude                                          principal  \n",
+       "0  32.715637 -117.17015  {'type': 'Point', 'coordinates': [-117.1701501...  \n",
+       "1  32.706318 -117.14241  {'type': 'Point', 'coordinates': [-117.14241, ...  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restaurant.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/draft-checkpoint.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/draft-checkpoint.ipynb
new file mode 100644
index 0000000..7fec515
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/draft-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/geocoder testing-checkpoint.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/geocoder testing-checkpoint.ipynb
new file mode 100644
index 0000000..7fec515
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/.ipynb_checkpoints/geocoder testing-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/MapaFinal.html b/module-2/python-bi-project/python-bi-project - Esteban/MapaFinal.html
new file mode 100644
index 0000000..99fce43
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/MapaFinal.html	
@@ -0,0 +1,91 @@
+<!DOCTYPE html>
+<head>    
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+    
+        <script>
+            L_NO_TOUCH = false;
+            L_DISABLE_3D = false;
+        </script>
+    
+    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
+    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
+    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
+    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
+    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_8cc0ff0f9c7d4971a861bfed81c1b4e8 {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+            </style>
+        
+    <script src="https://leaflet.github.io/Leaflet.heat/dist/leaflet-heat.js"></script>
+</head>
+<body>    
+    
+            <div class="folium-map" id="map_8cc0ff0f9c7d4971a861bfed81c1b4e8" ></div>
+        
+</body>
+<script>    
+    
+            var map_8cc0ff0f9c7d4971a861bfed81c1b4e8 = L.map(
+                "map_8cc0ff0f9c7d4971a861bfed81c1b4e8",
+                {
+                    center: [32.7174209, -117.1627714],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 11,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_536e5b47706a4d1683351fcbc416d242 = L.tileLayer(
+                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            ).addTo(map_8cc0ff0f9c7d4971a861bfed81c1b4e8);
+        
+    
+            var heat_map_dae2083c55f34dc0abe712a77bdd28bf = L.heatLayer(
+                [[32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718379, -117.160661], [32.71537, -117.163353], [32.717797, -117.158793], [32.717797, -117.158793], [32.717244, -117.167105], [32.716294, -117.166932], [32.715414, -117.158798], [32.7194538, -117.1673934], [32.72038, -117.166941], [32.7160497, -117.1574252], [32.7161896, -117.168697], [32.720656, -117.167831], [32.7154, -117.1565], [32.7154, -117.1565], [32.736369, -117.160619]],
+                {"blur": 15, "max": 1.0, "maxZoom": 18, "minOpacity": 0.5, "radius": 15}
+            ).addTo(map_8cc0ff0f9c7d4971a861bfed81c1b4e8);
+        
+    
+            var marker_1001beb3c7834d509b3706c550604a32 = L.marker(
+                [32.7174209, -117.1627714],
+                {}
+            ).addTo(map_8cc0ff0f9c7d4971a861bfed81c1b4e8);
+        
+    
+            var icon_e50d4fb6bdfa4f9e8e20757660531969 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
+            );
+            marker_1001beb3c7834d509b3706c550604a32.setIcon(icon_e50d4fb6bdfa4f9e8e20757660531969);
+        
+    
+            marker_1001beb3c7834d509b3706c550604a32.bindTooltip(
+                `<div>
+                     Location you chose: San Diego California 
+ United States of America, San Diego
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
\ No newline at end of file
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/MapaFinalRestaurantes.html b/module-2/python-bi-project/python-bi-project - Esteban/MapaFinalRestaurantes.html
new file mode 100644
index 0000000..75c1123
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/MapaFinalRestaurantes.html	
@@ -0,0 +1,131 @@
+<!DOCTYPE html>
+<head>    
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+    
+        <script>
+            L_NO_TOUCH = false;
+            L_DISABLE_3D = false;
+        </script>
+    
+    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
+    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
+    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
+    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
+    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_49035a569c114d2ca44d51939b8a581a {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+            </style>
+        
+    <script src="https://leaflet.github.io/Leaflet.heat/dist/leaflet-heat.js"></script>
+</head>
+<body>    
+    
+            <div class="folium-map" id="map_49035a569c114d2ca44d51939b8a581a" ></div>
+        
+</body>
+<script>    
+    
+            var map_49035a569c114d2ca44d51939b8a581a = L.map(
+                "map_49035a569c114d2ca44d51939b8a581a",
+                {
+                    center: [32.7174209, -117.1627714],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 11,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_d39b389ab2894d578493f3ee3e649783 = L.tileLayer(
+                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
+        
+    
+            var heat_map_376033be480d431685884cf6a5e126da = L.heatLayer(
+                [[32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718834, -117.163841], [32.718379, -117.160661], [32.71537, -117.163353], [32.717797, -117.158793], [32.717797, -117.158793], [32.717244, -117.167105], [32.716294, -117.166932], [32.715414, -117.158798], [32.7194538, -117.1673934], [32.72038, -117.166941], [32.7160497, -117.1574252], [32.7161896, -117.168697], [32.720656, -117.167831], [32.7154, -117.1565], [32.7154, -117.1565], [32.736369, -117.160619]],
+                {"blur": 15, "max": 1.0, "maxZoom": 18, "minOpacity": 0.5, "radius": 15}
+            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
+        
+    
+            var marker_d97c507df05b47cdb7eee6e163609e1e = L.marker(
+                [32.7174209, -117.1627714],
+                {}
+            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
+        
+    
+            var icon_5c9d2637f04d4d008ad3f6ed158f1080 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
+            );
+            marker_d97c507df05b47cdb7eee6e163609e1e.setIcon(icon_5c9d2637f04d4d008ad3f6ed158f1080);
+        
+    
+            marker_d97c507df05b47cdb7eee6e163609e1e.bindTooltip(
+                `<div>
+                     Location you chose: San Diego California 
+ United States of America, San Diego
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_78a4740d141f4fe1a230778308432eb7 = L.marker(
+                [32.7156373, -117.1701501],
+                {}
+            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
+        
+    
+            var icon_2dbef31c0d8346b1be98975b704c3dc8 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
+            );
+            marker_78a4740d141f4fe1a230778308432eb7.setIcon(icon_2dbef31c0d8346b1be98975b704c3dc8);
+        
+    
+            marker_78a4740d141f4fe1a230778308432eb7.bindTooltip(
+                `<div>
+                     FRANK'S BURGER
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_32327acdde63418cbdda3c3a29f6265f = L.marker(
+                [32.706318, -117.14241],
+                {}
+            ).addTo(map_49035a569c114d2ca44d51939b8a581a);
+        
+    
+            var icon_f78d5b0c520b4dd4beb4c4742dd12b44 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
+            );
+            marker_32327acdde63418cbdda3c3a29f6265f.setIcon(icon_f78d5b0c520b4dd4beb4c4742dd12b44);
+        
+    
+            marker_32327acdde63418cbdda3c3a29f6265f.bindTooltip(
+                `<div>
+                     STARBUCKS COFFEE #545
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
\ No newline at end of file
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/Project - Final.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/Project - Final.ipynb
new file mode 100644
index 0000000..adca703
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/Project - Final.ipynb	
@@ -0,0 +1,2923 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pymongo\n",
+    "from pymongo import MongoClient\n",
+    "from tqdm import tqdm\n",
+    "from tqdm import trange\n",
+    "import folium\n",
+    "from folium import plugins\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "warnings.simplefilter('ignore')\n",
+    "import re\n",
+    "import pickle\n",
+    "from opencage.geocoder import OpenCageGeocode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating the Mongo conection\n",
+    "cliente=MongoClient('mongodb://localhost:27017')\n",
+    "db = cliente.companies\n",
+    "col = db.companies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>permalink</th>\n",
+       "      <th>crunchbase_url</th>\n",
+       "      <th>homepage_url</th>\n",
+       "      <th>blog_url</th>\n",
+       "      <th>blog_feed_url</th>\n",
+       "      <th>twitter_username</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>...</th>\n",
+       "      <th>offices</th>\n",
+       "      <th>milestones</th>\n",
+       "      <th>video_embeds</th>\n",
+       "      <th>screenshots</th>\n",
+       "      <th>external_links</th>\n",
+       "      <th>partners</th>\n",
+       "      <th>deadpooled_month</th>\n",
+       "      <th>deadpooled_day</th>\n",
+       "      <th>deadpooled_url</th>\n",
+       "      <th>ipo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8a</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>abc2</td>\n",
+       "      <td>http://www.crunchbase.com/company/wetpaint</td>\n",
+       "      <td>http://wetpaint-inc.com</td>\n",
+       "      <td>http://digitalquarters.net/</td>\n",
+       "      <td>http://digitalquarters.net/feed/</td>\n",
+       "      <td>BachelrWetpaint</td>\n",
+       "      <td>web</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': '', 'address1': '710 - 2nd Av...</td>\n",
+       "      <td>[{'id': 5869, 'description': 'Wetpaint named i...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 86], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://www.geekwire.com/201...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8b</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>abc3</td>\n",
+       "      <td>http://www.crunchbase.com/company/adventnet</td>\n",
+       "      <td>http://adventnet.com</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>manageengine</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 94], 'assets/imag...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8c</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>abc4</td>\n",
+       "      <td>http://www.crunchbase.com/company/zoho</td>\n",
+       "      <td>http://zoho.com</td>\n",
+       "      <td>http://blogs.zoho.com/</td>\n",
+       "      <td>http://blogs.zoho.com/feed</td>\n",
+       "      <td>zoho</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 388, 'description': 'Zoho Reaches 2 Mi...</td>\n",
+       "      <td>[{'embed_code': '&lt;object width=\"430\" height=\"2...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'external_url': 'http://www.online-tech-tips...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8d</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>http://www.crunchbase.com/company/digg</td>\n",
+       "      <td>http://www.digg.com</td>\n",
+       "      <td>http://blog.digg.com/</td>\n",
+       "      <td>http://blog.digg.com/?feed=rss2</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "      <td>[{'id': 9588, 'description': 'Another Digg Exe...</td>\n",
+       "      <td>[{'embed_code': '&lt;embed src=\"http://blip.tv/pl...</td>\n",
+       "      <td>[{'available_sizes': [[[117, 150], 'assets/ima...</td>\n",
+       "      <td>[{'external_url': 'http://www.sociableblog.com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8e</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>http://www.crunchbase.com/company/facebook</td>\n",
+       "      <td>http://facebook.com</td>\n",
+       "      <td>http://blog.facebook.com</td>\n",
+       "      <td>http://blog.facebook.com/atom.php</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 108, 'description': 'Facebook adds com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 68], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://latimesblogs.latimes...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>{'valuation_amount': 104000000000, 'valuation_...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id       name permalink  \\\n",
+       "0  52cdef7c4bab8bd675297d8a   Wetpaint      abc2   \n",
+       "1  52cdef7c4bab8bd675297d8b  AdventNet      abc3   \n",
+       "2  52cdef7c4bab8bd675297d8c       Zoho      abc4   \n",
+       "3  52cdef7c4bab8bd675297d8d       Digg      digg   \n",
+       "4  52cdef7c4bab8bd675297d8e   Facebook  facebook   \n",
+       "\n",
+       "                                crunchbase_url             homepage_url  \\\n",
+       "0   http://www.crunchbase.com/company/wetpaint  http://wetpaint-inc.com   \n",
+       "1  http://www.crunchbase.com/company/adventnet     http://adventnet.com   \n",
+       "2       http://www.crunchbase.com/company/zoho          http://zoho.com   \n",
+       "3       http://www.crunchbase.com/company/digg      http://www.digg.com   \n",
+       "4   http://www.crunchbase.com/company/facebook      http://facebook.com   \n",
+       "\n",
+       "                      blog_url                      blog_feed_url  \\\n",
+       "0  http://digitalquarters.net/   http://digitalquarters.net/feed/   \n",
+       "1                                                                   \n",
+       "2       http://blogs.zoho.com/         http://blogs.zoho.com/feed   \n",
+       "3        http://blog.digg.com/    http://blog.digg.com/?feed=rss2   \n",
+       "4     http://blog.facebook.com  http://blog.facebook.com/atom.php   \n",
+       "\n",
+       "  twitter_username category_code  number_of_employees  ...  \\\n",
+       "0  BachelrWetpaint           web                 47.0  ...   \n",
+       "1     manageengine    enterprise                600.0  ...   \n",
+       "2             zoho      software               1600.0  ...   \n",
+       "3             digg          news                 60.0  ...   \n",
+       "4         facebook        social               5299.0  ...   \n",
+       "\n",
+       "                                             offices  \\\n",
+       "0  [{'description': '', 'address1': '710 - 2nd Av...   \n",
+       "1  [{'description': 'Headquarters', 'address1': '...   \n",
+       "2  [{'description': 'Headquarters', 'address1': '...   \n",
+       "3  [{'description': None, 'address1': '135 Missis...   \n",
+       "4  [{'description': 'Headquarters', 'address1': '...   \n",
+       "\n",
+       "                                          milestones  \\\n",
+       "0  [{'id': 5869, 'description': 'Wetpaint named i...   \n",
+       "1                                                 []   \n",
+       "2  [{'id': 388, 'description': 'Zoho Reaches 2 Mi...   \n",
+       "3  [{'id': 9588, 'description': 'Another Digg Exe...   \n",
+       "4  [{'id': 108, 'description': 'Facebook adds com...   \n",
+       "\n",
+       "                                        video_embeds  \\\n",
+       "0                                                 []   \n",
+       "1                                                 []   \n",
+       "2  [{'embed_code': '<object width=\"430\" height=\"2...   \n",
+       "3  [{'embed_code': '<embed src=\"http://blip.tv/pl...   \n",
+       "4                                                 []   \n",
+       "\n",
+       "                                         screenshots  \\\n",
+       "0  [{'available_sizes': [[[150, 86], 'assets/imag...   \n",
+       "1  [{'available_sizes': [[[150, 94], 'assets/imag...   \n",
+       "2                                                 []   \n",
+       "3  [{'available_sizes': [[[117, 150], 'assets/ima...   \n",
+       "4  [{'available_sizes': [[[150, 68], 'assets/imag...   \n",
+       "\n",
+       "                                      external_links partners  \\\n",
+       "0  [{'external_url': 'http://www.geekwire.com/201...       []   \n",
+       "1                                                 []       []   \n",
+       "2  [{'external_url': 'http://www.online-tech-tips...       []   \n",
+       "3  [{'external_url': 'http://www.sociableblog.com...       []   \n",
+       "4  [{'external_url': 'http://latimesblogs.latimes...       []   \n",
+       "\n",
+       "  deadpooled_month deadpooled_day deadpooled_url  \\\n",
+       "0              NaN            NaN            NaN   \n",
+       "1              NaN            NaN            NaN   \n",
+       "2              NaN            NaN            NaN   \n",
+       "3              NaN            NaN           None   \n",
+       "4              NaN            NaN                  \n",
+       "\n",
+       "                                                 ipo  \n",
+       "0                                                NaN  \n",
+       "1                                                NaN  \n",
+       "2                                                NaN  \n",
+       "3                                               None  \n",
+       "4  {'valuation_amount': 104000000000, 'valuation_...  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cole = col.find({})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 18801 entries, 0 to 18800\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   _id                  18801 non-null  object \n",
+      " 1   name                 18801 non-null  object \n",
+      " 2   permalink            18801 non-null  object \n",
+      " 3   crunchbase_url       18801 non-null  object \n",
+      " 4   homepage_url         16895 non-null  object \n",
+      " 5   blog_url             16890 non-null  object \n",
+      " 6   blog_feed_url        16757 non-null  object \n",
+      " 7   twitter_username     11383 non-null  object \n",
+      " 8   category_code        16050 non-null  object \n",
+      " 9   number_of_employees  8889 non-null   float64\n",
+      " 10  founded_year         13136 non-null  float64\n",
+      " 11  founded_month        7898 non-null   float64\n",
+      " 12  founded_day          5569 non-null   float64\n",
+      " 13  deadpooled_year      929 non-null    float64\n",
+      " 14  tag_list             14748 non-null  object \n",
+      " 15  alias_list           8932 non-null   object \n",
+      " 16  email_address        16793 non-null  object \n",
+      " 17  phone_number         16778 non-null  object \n",
+      " 18  description          12360 non-null  object \n",
+      " 19  created_at           18801 non-null  object \n",
+      " 20  updated_at           18801 non-null  object \n",
+      " 21  overview             16820 non-null  object \n",
+      " 22  image                16175 non-null  object \n",
+      " 23  products             18801 non-null  object \n",
+      " 24  relationships        18801 non-null  object \n",
+      " 25  competitions         18801 non-null  object \n",
+      " 26  providerships        18801 non-null  object \n",
+      " 27  total_money_raised   18801 non-null  object \n",
+      " 28  funding_rounds       18801 non-null  object \n",
+      " 29  investments          18801 non-null  object \n",
+      " 30  acquisition          2677 non-null   object \n",
+      " 31  acquisitions         18801 non-null  object \n",
+      " 32  offices              18801 non-null  object \n",
+      " 33  milestones           18801 non-null  object \n",
+      " 34  video_embeds         18801 non-null  object \n",
+      " 35  screenshots          18801 non-null  object \n",
+      " 36  external_links       18801 non-null  object \n",
+      " 37  partners             18801 non-null  object \n",
+      " 38  deadpooled_month     765 non-null    float64\n",
+      " 39  deadpooled_day       648 non-null    float64\n",
+      " 40  deadpooled_url       1079 non-null   object \n",
+      " 41  ipo                  390 non-null    object \n",
+      "dtypes: float64(7), object(35)\n",
+      "memory usage: 6.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1\n",
+    "#### Define client type: as I mentioned in the README ,they want to be sorrounded with Small and Medium Business, they want to have a coffee shop nearby and they want to be in San Diego California"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2. \n",
+    "#### Correctly query the data I want. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>founded_year</th>\n",
+       "      <th>deadpooled_year</th>\n",
+       "      <th>total_money_raised</th>\n",
+       "      <th>offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$45M</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$2.43B</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>web</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$0</td>\n",
+       "      <td>[{'description': None, 'address1': '959 Skyway...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Geni</td>\n",
+       "      <td>web</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$16.5M</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Flektor</td>\n",
+       "      <td>games_video</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>$0</td>\n",
+       "      <td>[{'description': None, 'address1': '8536 Natio...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name category_code  number_of_employees  founded_year deadpooled_year  \\\n",
+       "0      Digg          news                 60.0        2004.0            None   \n",
+       "1  Facebook        social               5299.0        2004.0            None   \n",
+       "2   Postini           web                  NaN        1999.0            None   \n",
+       "3      Geni           web                 18.0        2006.0            None   \n",
+       "4   Flektor   games_video                  NaN           NaN            None   \n",
+       "\n",
+       "  total_money_raised                                            offices  \n",
+       "0               $45M  [{'description': None, 'address1': '135 Missis...  \n",
+       "1             $2.43B  [{'description': 'Headquarters', 'address1': '...  \n",
+       "2                 $0  [{'description': None, 'address1': '959 Skyway...  \n",
+       "3             $16.5M  [{'description': 'Headquarters', 'address1': '...  \n",
+       "4                 $0  [{'description': None, 'address1': '8536 Natio...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Selecting only the ones that have offices and do not have deadpool year\n",
+    "cole = col.find({'offices': {'$not': {'$size':0}},\n",
+    "                'deadpooled_year':{'$type':'null'}},\n",
+    "                {'_id':0 ,'name':1,'category_code':1,'number_of_employees':1,\n",
+    "                 'offices':1,'founded_year':1,'deadpooled_year':1,'total_money_raised':1})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 12957 entries, 0 to 12956\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   name                 12957 non-null  object \n",
+      " 1   category_code        12310 non-null  object \n",
+      " 2   number_of_employees  7039 non-null   float64\n",
+      " 3   founded_year         10630 non-null  float64\n",
+      " 4   deadpooled_year      0 non-null      object \n",
+      " 5   total_money_raised   12957 non-null  object \n",
+      " 6   offices              12957 non-null  object \n",
+      "dtypes: float64(2), object(5)\n",
+      "memory usage: 708.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12957, 7)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3\n",
+    "#### Transforming data according to client needs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12957/12957 [02:05<00:00, 102.86it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Generate a list with dataframes with office information.\n",
+    "#adding a column with name, total employees, category and number of offices\n",
+    "\n",
+    "lista_df=[] \n",
+    "\n",
+    "for i in tqdm(range(len(data.offices))):\n",
+    "    df = pd.DataFrame(data.iloc[i].offices)\n",
+    "    df['name'] = str(data.loc[i,['name'][0]])\n",
+    "    df['total_employees'] = data.iloc[i].number_of_employees\n",
+    "    df['category'] = str(data.loc[i,['category_code'][0]])\n",
+    "    df['total_offices'] = len(data.iloc[i].offices)\n",
+    "    lista_df.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12957"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lista_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>1601 Willow Road</td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.416</td>\n",
+       "      <td>-122.152</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Europe HQ</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Dublin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>IRL</td>\n",
+       "      <td>53.3441</td>\n",
+       "      <td>-6.26749</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>340 Madison Ave</td>\n",
+       "      <td></td>\n",
+       "      <td>10017</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7557</td>\n",
+       "      <td>-73.9792</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    description                    address1 address2 zip_code           city  \\\n",
+       "0          None          135 Mississippi St             94107  San Francisco   \n",
+       "1  Headquarters            1601 Willow Road             94025     Menlo Park   \n",
+       "2     Europe HQ                                                       Dublin   \n",
+       "3      New York             340 Madison Ave             10017       New York   \n",
+       "4          None  959 Skyway Road, Suite 200     None    94070     San Carlos   \n",
+       "\n",
+       "  state_code country_code latitude longitude      name  total_employees  \\\n",
+       "0         CA          USA  37.7647  -122.395      Digg             60.0   \n",
+       "1         CA          USA   37.416  -122.152  Facebook           5299.0   \n",
+       "2       None          IRL  53.3441  -6.26749  Facebook           5299.0   \n",
+       "3         NY          USA  40.7557  -73.9792  Facebook           5299.0   \n",
+       "4         CA          USA  37.5069  -122.248   Postini              NaN   \n",
+       "\n",
+       "  category  total_offices  \n",
+       "0     news              1  \n",
+       "1   social              3  \n",
+       "2   social              3  \n",
+       "3   social              3  \n",
+       "4      web              1  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final = pd.concat(lista_df).reset_index(drop=True)\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15848, 13)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country_code\n",
+       "USA    9652\n",
+       "GBR    1327\n",
+       "CAN     591\n",
+       "IND     509\n",
+       "DEU     392\n",
+       "FRA     359\n",
+       "AUS     263\n",
+       "ESP     232\n",
+       "ISR     213\n",
+       "CHN     196\n",
+       "Name: name, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.groupby('country_code')['name'].count().sort_values(ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 15848/15848 [00:01<00:00, 8747.27it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#genero una lista con los geopuntos en formato de diccionario\n",
+    "lista_points = []\n",
+    "for i in tqdm(range(len(final.longitude))):\n",
+    "    \n",
+    "    lista_points.append(dict({'type':'Point',\n",
+    "                          'coordinates': [final.longitude[i],\n",
+    "                                         final.latitude[i]]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15848"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lista_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#agrego los puntos al DataFrame \n",
+    "final['principal'] = pd.Series(lista_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['description', 'address1', 'address2', 'zip_code', 'city', 'state_code',\n",
+       "       'country_code', 'latitude', 'longitude', 'name', 'total_employees',\n",
+       "       'category', 'total_offices', 'principal'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15848, 14)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reordeno el dataframe\n",
+    "final = final[['name','total_employees','category','total_offices','description','address1',\n",
+    "              'address2','zip_code','city','state_code','country_code','latitude','longitude','principal']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.394523,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>1601 Willow Road</td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.416</td>\n",
+       "      <td>-122.152</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.151801,...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Europe HQ</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Dublin</td>\n",
+       "      <td>None</td>\n",
+       "      <td>IRL</td>\n",
+       "      <td>53.3441</td>\n",
+       "      <td>-6.26749</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-6.267494, 5...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>340 Madison Ave</td>\n",
+       "      <td></td>\n",
+       "      <td>10017</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7557</td>\n",
+       "      <td>-73.9792</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-73.9792469,...</td>\n",
+       "      <td>JUMBO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.247573,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name  total_employees category  total_offices   description  \\\n",
+       "0      Digg             60.0     news              1          None   \n",
+       "1  Facebook           5299.0   social              3  Headquarters   \n",
+       "2  Facebook           5299.0   social              3     Europe HQ   \n",
+       "3  Facebook           5299.0   social              3      New York   \n",
+       "4   Postini              NaN      web              1          None   \n",
+       "\n",
+       "                     address1 address2 zip_code           city state_code  \\\n",
+       "0          135 Mississippi St             94107  San Francisco         CA   \n",
+       "1            1601 Willow Road             94025     Menlo Park         CA   \n",
+       "2                                                       Dublin       None   \n",
+       "3             340 Madison Ave             10017       New York         NY   \n",
+       "4  959 Skyway Road, Suite 200     None    94070     San Carlos         CA   \n",
+       "\n",
+       "  country_code latitude longitude  \\\n",
+       "0          USA  37.7647  -122.395   \n",
+       "1          USA   37.416  -122.152   \n",
+       "2          IRL  53.3441  -6.26749   \n",
+       "3          USA  40.7557  -73.9792   \n",
+       "4          USA  37.5069  -122.248   \n",
+       "\n",
+       "                                           principal company_size  \n",
+       "0  {'type': 'Point', 'coordinates': [-122.394523,...          SMB  \n",
+       "1  {'type': 'Point', 'coordinates': [-122.151801,...        JUMBO  \n",
+       "2  {'type': 'Point', 'coordinates': [-6.267494, 5...        JUMBO  \n",
+       "3  {'type': 'Point', 'coordinates': [-73.9792469,...        JUMBO  \n",
+       "4  {'type': 'Point', 'coordinates': [-122.247573,...          SMB  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#agrego columna JUMBO o SMB dependiendo de la cantidad de empleados\n",
+    "final['company_size'] = np.where(final.total_employees>1000,'JUMBO','SMB')\n",
+    "\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.394523,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Postini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>959 Skyway Road, Suite 200</td>\n",
+       "      <td>None</td>\n",
+       "      <td>94070</td>\n",
+       "      <td>San Carlos</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5069</td>\n",
+       "      <td>-122.248</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.247573,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Geni</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>9229 W. Sunset Blvd.</td>\n",
+       "      <td></td>\n",
+       "      <td>90069</td>\n",
+       "      <td>West Hollywood</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.0904</td>\n",
+       "      <td>-118.393</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.393064,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Flektor</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>games_video</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8536 National Blvd, Suite A</td>\n",
+       "      <td>None</td>\n",
+       "      <td>90232</td>\n",
+       "      <td>Culver City</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.026</td>\n",
+       "      <td>-118.38</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.379768,...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Fox Interactive Media</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>1</td>\n",
+       "      <td></td>\n",
+       "      <td>407 N Maple Dr</td>\n",
+       "      <td></td>\n",
+       "      <td>90210</td>\n",
+       "      <td>Beverly Hills</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.0762</td>\n",
+       "      <td>-118.394</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.39417, ...</td>\n",
+       "      <td>SMB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name  total_employees     category  total_offices  \\\n",
+       "0                   Digg             60.0         news              1   \n",
+       "4                Postini              NaN          web              1   \n",
+       "5                   Geni             18.0          web              1   \n",
+       "6                Flektor              NaN  games_video              1   \n",
+       "7  Fox Interactive Media              0.0          web              1   \n",
+       "\n",
+       "    description                     address1 address2 zip_code  \\\n",
+       "0          None           135 Mississippi St             94107   \n",
+       "4          None   959 Skyway Road, Suite 200     None    94070   \n",
+       "5  Headquarters         9229 W. Sunset Blvd.             90069   \n",
+       "6          None  8536 National Blvd, Suite A     None    90232   \n",
+       "7                             407 N Maple Dr             90210   \n",
+       "\n",
+       "             city state_code country_code latitude longitude  \\\n",
+       "0   San Francisco         CA          USA  37.7647  -122.395   \n",
+       "4      San Carlos         CA          USA  37.5069  -122.248   \n",
+       "5  West Hollywood         CA          USA  34.0904  -118.393   \n",
+       "6     Culver City         CA          USA   34.026   -118.38   \n",
+       "7   Beverly Hills         CA          USA  34.0762  -118.394   \n",
+       "\n",
+       "                                           principal company_size  \n",
+       "0  {'type': 'Point', 'coordinates': [-122.394523,...          SMB  \n",
+       "4  {'type': 'Point', 'coordinates': [-122.247573,...          SMB  \n",
+       "5  {'type': 'Point', 'coordinates': [-118.393064,...          SMB  \n",
+       "6  {'type': 'Point', 'coordinates': [-118.379768,...          SMB  \n",
+       "7  {'type': 'Point', 'coordinates': [-118.39417, ...          SMB  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#elimino a las empresas JUMBO\n",
+    "final = final[final.company_size == 'SMB']\n",
+    "final.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15383, 15)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final.dropna(subset=['latitude','longitude'],inplace=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9919, 15)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4\n",
+    "#### Create a new collection with the final DataFrame in MongoCompass so we can add a 2dsphere index. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymongo.results.InsertManyResult at 0x124059240>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#inserto en compass el data frame resultante en una nueva coleccion\n",
+    "db.project.insert_many(final.to_dict('records')) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'principal_2dsphere'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creo el índice 2dsphere para poder hacer geoqueries\n",
+    "db.project.create_index([('principal', '2dsphere')])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5\n",
+    "\n",
+    "#### Make geoqueries of the new collection with client conditions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cole = db.project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#funcion que me permite encontrar puntos cercanos con radio por default de 1000 metros\n",
+    "def find_near(geopoint, radio=10000):\n",
+    "    \n",
+    "    return cole.find({'principal':{\n",
+    "        '$near': {'$geometry': geopoint, '$maxDistance':radio}\n",
+    "    }})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#inicializo la conexión con geocoder\n",
+    "key = 'd8314354b31d46a298e0f21ffb920a34'\n",
+    "geocoder = OpenCageGeocode(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#función que permite al usuario elegir una dirección y en base a la misma devuelve los datos necesarios para hacer la geoquery\n",
+    "def getting_coor():\n",
+    "    try:\n",
+    "        x = input('Where would you like your office? ')\n",
+    "        m = input('Whats the max distance you would like to evaluate: ')\n",
+    "        results = geocoder.geocode(x)\n",
+    "        fcoordinates=[]\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        bandera = results[0]['annotations']['flag']\n",
+    "        country = results[0]['components']['country']\n",
+    "        city=results[0]['components']['city']\n",
+    "        localidad = country+', '+city\n",
+    "        fcoordinates.append(coordinates)\n",
+    "        fcoordinates.append(m)\n",
+    "        fcoordinates.append(localidad)\n",
+    "        fcoordinates.append(x)\n",
+    "        fcoordinates.append(bandera)\n",
+    "        return fcoordinates\n",
+    "    except:\n",
+    "        return 'Not a valid address'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? San Diego California\n",
+      "Whats the max distance you would like to evaluate: 2500\n",
+      "Amount of offices near San Diego California are: 24\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x113948100>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#este podría ser el código principal que solicita el input del usuario y devuelve el mapa\n",
+    "\n",
+    "coordenadas = getting_coor() #acá inicializa la funcion creada arriba\n",
+    "longlat = coordenadas[0]\n",
+    "latlong = longlat[::-1]\n",
+    "radio = int(coordenadas[1])\n",
+    "pais = coordenadas[4]\n",
+    "localidad = coordenadas[2]\n",
+    "direccion_solicitud = coordenadas[3]\n",
+    "\n",
+    "address={'type': 'Point', 'coordinates':longlat}  # geopoint\n",
+    "\n",
+    "ratio=radio  # radio de busqueda\n",
+    "\n",
+    "n_ofi=find_near(address, ratio).count()\n",
+    "ofi = find_near(address, ratio)\n",
+    "ofi=pd.DataFrame(ofi)\n",
+    "\n",
+    "#creating map object\n",
+    "tooltip = 'Location you chose: {} \\n {}'.format(direccion_solicitud,localidad)\n",
+    "mapa=folium.Map(latlong, zoom_start=12, width='100%', height='100%')\n",
+    "datamapa=ofi[['latitude', 'longitude']].values\n",
+    "mapa.add_child(plugins.HeatMap(datamapa, radius=15))\n",
+    "folium.Marker(latlong, tooltip=tooltip, icon=folium.Icon()).add_to(mapa)\n",
+    "mapa.save('MapaFinal.html')\n",
+    "print('Amount of offices near {} are: {}'.format(direccion_solicitud,n_ofi))\n",
+    "mapa\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6\n",
+    "#### Get more information about restaurants in the area and display them with the heatmap mentioned above and marking where the restaurants of coffee shops are. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Found a DB in Kaggle with California restaurants. I proceed to explore and clean data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://www.kaggle.com/vaibhavsxn/restaurants-in-california\n",
+    "\n",
+    "with open('rest_data_ca.pkl', 'rb') as rest:\n",
+    "    data = pickle.load(rest)\n",
+    "    \n",
+    "dfrest = pd.DataFrame(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 52477 entries, 0 to 56157\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   Cuisine     52477 non-null  object\n",
+      " 1   address     52477 non-null  object\n",
+      " 2   name        52477 non-null  object\n",
+      " 3   postalCode  52477 non-null  object\n",
+      " 4   Tag         52477 non-null  object\n",
+      " 5   ratings     52477 non-null  int64 \n",
+      "dtypes: int64(1), object(5)\n",
+      "memory usage: 2.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dfrest.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "All          47851\n",
+       "Mexican       4530\n",
+       "Fast Food       64\n",
+       "Veg             32\n",
+       "Name: Cuisine, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.Cuisine.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Food    52477\n",
+       "Name: Tag, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.Tag.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    26428\n",
+       "3    26049\n",
+       "Name: ratings, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.ratings.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>925 Blossom Hill Road Oakridge Mall</td>\n",
+       "      <td>Buca Di Beppo - San Jose - Oakridge</td>\n",
+       "      <td>95123</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1640 Camino Del Rio N</td>\n",
+       "      <td>Tilted Kilt Mission Valley</td>\n",
+       "      <td>92108</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1231 Park St</td>\n",
+       "      <td>The Fire Den</td>\n",
+       "      <td>94501</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26423</th>\n",
+       "      <td>All</td>\n",
+       "      <td>6719 LA TIJERA BLVD # A-B</td>\n",
+       "      <td>CHIPOTLE #3094</td>\n",
+       "      <td>90045</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26424</th>\n",
+       "      <td>All</td>\n",
+       "      <td>100 1/2 7TH ST</td>\n",
+       "      <td>BRAAZO PIZZA</td>\n",
+       "      <td>90014</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26425</th>\n",
+       "      <td>All</td>\n",
+       "      <td>1244 WESTWOOD BLVD</td>\n",
+       "      <td>POKE'ME</td>\n",
+       "      <td>90024</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26426</th>\n",
+       "      <td>All</td>\n",
+       "      <td>217 W TEMPLE AVE</td>\n",
+       "      <td>HALL OF JUSTICE</td>\n",
+       "      <td>90012</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26427</th>\n",
+       "      <td>All</td>\n",
+       "      <td>6741 HOLLYWOOD BLVD</td>\n",
+       "      <td>JUICY WINGZ</td>\n",
+       "      <td>90028</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26428 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Cuisine                              address  \\\n",
+       "0      Fast Food  925 Blossom Hill Road Oakridge Mall   \n",
+       "1      Fast Food                       555 Soquel Ave   \n",
+       "2      Fast Food                       9822 Libra Ave   \n",
+       "3      Fast Food                1640 Camino Del Rio N   \n",
+       "4      Fast Food                         1231 Park St   \n",
+       "...          ...                                  ...   \n",
+       "26423        All            6719 LA TIJERA BLVD # A-B   \n",
+       "26424        All                       100 1/2 7TH ST   \n",
+       "26425        All                   1244 WESTWOOD BLVD   \n",
+       "26426        All                     217 W TEMPLE AVE   \n",
+       "26427        All                  6741 HOLLYWOOD BLVD   \n",
+       "\n",
+       "                                      name postalCode   Tag  ratings  \n",
+       "0      Buca Di Beppo - San Jose - Oakridge      95123  Food        4  \n",
+       "1                      Ristorante Italiano      95062  Food        4  \n",
+       "2                            Milanos Pizza      95827  Food        4  \n",
+       "3               Tilted Kilt Mission Valley      92108  Food        4  \n",
+       "4                             The Fire Den      94501  Food        4  \n",
+       "...                                    ...        ...   ...      ...  \n",
+       "26423                       CHIPOTLE #3094      90045  Food        4  \n",
+       "26424                         BRAAZO PIZZA      90014  Food        4  \n",
+       "26425                              POKE'ME      90024  Food        4  \n",
+       "26426                      HALL OF JUSTICE      90012  Food        4  \n",
+       "26427                          JUICY WINGZ      90028  Food        4  \n",
+       "\n",
+       "[26428 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#removing restaurants with low ratings\n",
+    "dfrest = dfrest[dfrest.ratings>=4]\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using REGEX so I can categorize the restaurant type\n",
+    "\n",
+    "def typerest(x):\n",
+    "    if re.search(r'pizza',x.lower()) or re.search(r'pasta',x.lower()) or re.search(r'italia',x.lower()):\n",
+    "        x='Italian'\n",
+    "    elif re.search(r'burger',x.lower()):\n",
+    "        x='Burger'\n",
+    "    elif re.search(r'coffee',x.lower()):\n",
+    "        x='CoffeeShop'\n",
+    "    elif re.search(r'coffee',x.lower()):\n",
+    "        x='CoffeeShop'\n",
+    "    elif re.search(r'pie',x.lower()) or  re.search(r'cake',x.lower()) or re.search(r'dess?ert',x.lower()):\n",
+    "        x='Dessert'\n",
+    "    elif re.search(r'mex',x.lower()) or  re.search(r'taco',x.lower()):\n",
+    "        x='Mexican'\n",
+    "    elif re.search(r'sushi',x.lower()) or  re.search(r'poke',x.lower()) or re.search(r'dumpling',x.lower())\\\n",
+    "        or re.search(r'asia',x.lower()) or re.search(r'china?e?',x.lower()) or re.search(r'japan',x.lower())\\\n",
+    "        or re.search(r'korea',x.lower()):\n",
+    "        x='Asian'\n",
+    "    else:\n",
+    "        x='other'\n",
+    "        \n",
+    "    return x  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 26428/26428 [00:00<00:00, 27249.91it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#applying the above function to the data frame using TQDM\n",
+    "tqdm.pandas()\n",
+    "dfrest['type'] = dfrest['name'].progress_apply(typerest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>925 Blossom Hill Road Oakridge Mall</td>\n",
+       "      <td>Buca Di Beppo - San Jose - Oakridge</td>\n",
+       "      <td>95123</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1640 Camino Del Rio N</td>\n",
+       "      <td>Tilted Kilt Mission Valley</td>\n",
+       "      <td>92108</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>1231 Park St</td>\n",
+       "      <td>The Fire Den</td>\n",
+       "      <td>94501</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cuisine                              address  \\\n",
+       "0  Fast Food  925 Blossom Hill Road Oakridge Mall   \n",
+       "1  Fast Food                       555 Soquel Ave   \n",
+       "2  Fast Food                       9822 Libra Ave   \n",
+       "3  Fast Food                1640 Camino Del Rio N   \n",
+       "4  Fast Food                         1231 Park St   \n",
+       "\n",
+       "                                  name postalCode   Tag  ratings     type  \n",
+       "0  Buca Di Beppo - San Jose - Oakridge      95123  Food        4    other  \n",
+       "1                  Ristorante Italiano      95062  Food        4  Italian  \n",
+       "2                        Milanos Pizza      95827  Food        4  Italian  \n",
+       "3           Tilted Kilt Mission Valley      92108  Food        4    other  \n",
+       "4                         The Fire Den      94501  Food        4    other  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "other         22147\n",
+       "Mexican        1165\n",
+       "Asian           929\n",
+       "Italian         833\n",
+       "CoffeeShop      632\n",
+       "Burger          552\n",
+       "Dessert         170\n",
+       "Name: type, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>9822 Libra Ave</td>\n",
+       "      <td>Milanos Pizza</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>5365 Alton Pkwy</td>\n",
+       "      <td>Zpizza</td>\n",
+       "      <td>92604</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>6593 Collins Dr</td>\n",
+       "      <td>Custom Pie</td>\n",
+       "      <td>93021</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Dessert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>820 N Alta Ave</td>\n",
+       "      <td>You-be's U-bake Pizza</td>\n",
+       "      <td>93618</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Italian</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cuisine          address                   name postalCode   Tag  \\\n",
+       "0  Fast Food   555 Soquel Ave    Ristorante Italiano      95062  Food   \n",
+       "1  Fast Food   9822 Libra Ave          Milanos Pizza      95827  Food   \n",
+       "2  Fast Food  5365 Alton Pkwy                 Zpizza      92604  Food   \n",
+       "3  Fast Food  6593 Collins Dr             Custom Pie      93021  Food   \n",
+       "4  Fast Food   820 N Alta Ave  You-be's U-bake Pizza      93618  Food   \n",
+       "\n",
+       "   ratings     type  \n",
+       "0        4  Italian  \n",
+       "1        4  Italian  \n",
+       "2        4  Italian  \n",
+       "3        4  Dessert  \n",
+       "4        4  Italian  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Because of the request number restriction, \n",
+    "#Im deleting every \"other\" type restaurant in database\n",
+    "dfrest = dfrest[dfrest.type != 'other']\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type  \n",
+       "0  Food        4  Mexican  \n",
+       "1  Food        4   Burger  \n",
+       "2  Food        4   Burger  \n",
+       "3  Food        4  Mexican  \n",
+       "4  Food        4   Burger  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#getting a sample of 2000 restaurantes because of api restriction\n",
+    "dfrest = dfrest.sample(2000)\n",
+    "dfrest.reset_index(drop = True, inplace = True)\n",
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2000, 7)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to obtain latitude and longitude of the addresses in DF\n",
+    "def gettingres_coor(x):\n",
+    "    try:\n",
+    "        results = geocoder.geocode(x)\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        return coordinates\n",
+    "    except:\n",
+    "        return np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2000/2000 [39:35<00:00,  1.19s/it]  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#applying function to DF\n",
+    "tqdm.pandas()\n",
+    "dfrest['coordinates'] = dfrest.address.progress_apply(gettingres_coor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#removing nan values\n",
+    "dfrest.dropna(inplace=True)\n",
+    "dfrest.reset_index(drop = True, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting columns\n",
+    "dfrest['latitude'] = dfrest.coordinates.apply(lambda x:x[1])\n",
+    "dfrest['longitude'] = dfrest.coordinates.apply(lambda x:x[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-117.7520488, 34.0684236]</td>\n",
+       "      <td>34.068424</td>\n",
+       "      <td>-117.752049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.3018267, 34.1799755]</td>\n",
+       "      <td>34.179975</td>\n",
+       "      <td>-118.301827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.0734294, 34.0840807]</td>\n",
+       "      <td>34.084081</td>\n",
+       "      <td>-118.073429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-118.5031071, 34.2285216]</td>\n",
+       "      <td>34.228522</td>\n",
+       "      <td>-118.503107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-75.926111, 42.105103]</td>\n",
+       "      <td>42.105103</td>\n",
+       "      <td>-75.926111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type                 coordinates   latitude   longitude  \n",
+       "0  Food        4  Mexican  [-117.7520488, 34.0684236]  34.068424 -117.752049  \n",
+       "1  Food        4   Burger  [-118.3018267, 34.1799755]  34.179975 -118.301827  \n",
+       "2  Food        4   Burger  [-118.0734294, 34.0840807]  34.084081 -118.073429  \n",
+       "3  Food        4  Mexican  [-118.5031071, 34.2285216]  34.228522 -118.503107  \n",
+       "4  Food        4   Burger     [-75.926111, 42.105103]  42.105103  -75.926111  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1995/1995 [00:00<00:00, 4321.81it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#getting a list of the geopoints\n",
+    "lista_pointsres = []\n",
+    "for i in tqdm(range(len(dfrest.latitude))):\n",
+    "    lista_pointsres.append(dict({'type':'Point',\n",
+    "                          'coordinates': [dfrest.longitude[i],\n",
+    "                                         dfrest.latitude[i]]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#adding geopoints to main DF\n",
+    "dfrest['principal'] = pd.Series(lista_pointsres)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>All</td>\n",
+       "      <td>2068 N GAREY AVE</td>\n",
+       "      <td>ANGELES LA MEJOR MEXICAN FOOD</td>\n",
+       "      <td>91767</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-117.7520488, 34.0684236]</td>\n",
+       "      <td>34.068424</td>\n",
+       "      <td>-117.752049</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.7520488...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>All</td>\n",
+       "      <td>8742 GLENOAKS BLVD</td>\n",
+       "      <td>OLD TIME BURGERS</td>\n",
+       "      <td>91352</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.3018267, 34.1799755]</td>\n",
+       "      <td>34.179975</td>\n",
+       "      <td>-118.301827</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.3018267...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>All</td>\n",
+       "      <td>4242 N ROSEMEAD BLVD</td>\n",
+       "      <td>IN-N-OUT BURGER #20</td>\n",
+       "      <td>91770</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-118.0734294, 34.0840807]</td>\n",
+       "      <td>34.084081</td>\n",
+       "      <td>-118.073429</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.0734294...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>All</td>\n",
+       "      <td>15307 PARTHENIA ST</td>\n",
+       "      <td>DANIEL'S TACOS</td>\n",
+       "      <td>91343</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mexican</td>\n",
+       "      <td>[-118.5031071, 34.2285216]</td>\n",
+       "      <td>34.228522</td>\n",
+       "      <td>-118.503107</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-118.5031071...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3748 SLAUSON AVE</td>\n",
+       "      <td>BURGER KING  #11298</td>\n",
+       "      <td>90270</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-75.926111, 42.105103]</td>\n",
+       "      <td>42.105103</td>\n",
+       "      <td>-75.926111</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-75.926111, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Cuisine               address                           name postalCode  \\\n",
+       "0     All      2068 N GAREY AVE  ANGELES LA MEJOR MEXICAN FOOD      91767   \n",
+       "1     All    8742 GLENOAKS BLVD               OLD TIME BURGERS      91352   \n",
+       "2     All  4242 N ROSEMEAD BLVD            IN-N-OUT BURGER #20      91770   \n",
+       "3     All    15307 PARTHENIA ST                 DANIEL'S TACOS      91343   \n",
+       "4     All      3748 SLAUSON AVE            BURGER KING  #11298      90270   \n",
+       "\n",
+       "    Tag  ratings     type                 coordinates   latitude   longitude  \\\n",
+       "0  Food        4  Mexican  [-117.7520488, 34.0684236]  34.068424 -117.752049   \n",
+       "1  Food        4   Burger  [-118.3018267, 34.1799755]  34.179975 -118.301827   \n",
+       "2  Food        4   Burger  [-118.0734294, 34.0840807]  34.084081 -118.073429   \n",
+       "3  Food        4  Mexican  [-118.5031071, 34.2285216]  34.228522 -118.503107   \n",
+       "4  Food        4   Burger     [-75.926111, 42.105103]  42.105103  -75.926111   \n",
+       "\n",
+       "                                           principal  \n",
+       "0  {'type': 'Point', 'coordinates': [-117.7520488...  \n",
+       "1  {'type': 'Point', 'coordinates': [-118.3018267...  \n",
+       "2  {'type': 'Point', 'coordinates': [-118.0734294...  \n",
+       "3  {'type': 'Point', 'coordinates': [-118.5031071...  \n",
+       "4  {'type': 'Point', 'coordinates': [-75.926111, ...  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfrest.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymongo.results.InsertManyResult at 0x124635f40>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#inserting to Compass\n",
+    "db.restaurantes.insert_many(dfrest.to_dict('records')) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'principal_2dsphere'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating 2dsphere index\n",
+    "db.restaurantes.create_index([('principal', '2dsphere')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to find restaurants near clients input\n",
+    "coleres = db.restaurantes\n",
+    "def find_near_res(geopoint, radio=10000):\n",
+    "    \n",
+    "    return coleres.find({'principal':{\n",
+    "        '$near': {'$geometry': geopoint, '$maxDistance':radio}\n",
+    "    }})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? San Diego California\n",
+      "Whats the max distance you would like to evaluate: 2500\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:00<00:00, 411.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount of offices near San Diego California are: 24\n",
+      "Amount of restaurants near San Diego California are: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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+   ],
+   "source": [
+    "#main code to ask client where they want the office and getting all the needed information\n",
+    "\n",
+    "coordenadas = getting_coor() #function discribed above\n",
+    "longlat = coordenadas[0]\n",
+    "latlong = longlat[::-1]\n",
+    "radio = int(coordenadas[1])\n",
+    "pais = coordenadas[4]\n",
+    "localidad = coordenadas[2]\n",
+    "direccion_solicitud = coordenadas[3]\n",
+    "\n",
+    "address={'type': 'Point', 'coordinates':longlat}  # geopoint\n",
+    "\n",
+    "ratio=radio  # radio de busqueda\n",
+    "\n",
+    "#query for offices\n",
+    "n_ofi=find_near(address, ratio).count()\n",
+    "ofi = find_near(address, ratio)\n",
+    "ofi=pd.DataFrame(ofi)\n",
+    "\n",
+    "#query for restaurants\n",
+    "n_res=find_near_res(address, ratio).count()\n",
+    "restaurant = find_near_res(address, ratio)\n",
+    "restaurant = pd.DataFrame(restaurant)\n",
+    "\n",
+    "#creating map object\n",
+    "tooltip = 'Location you chose: {} \\n {}'.format(direccion_solicitud,localidad)\n",
+    "mapa=folium.Map(latlong, zoom_start=11)\n",
+    "datamapa=ofi[['latitude', 'longitude']].values\n",
+    "mapa.add_child(plugins.HeatMap(datamapa, radius=15))\n",
+    "folium.Marker(latlong, tooltip=tooltip, icon=folium.Icon(color='red')).add_to(mapa)\n",
+    "for i in trange(len(restaurant.latitude)):\n",
+    "    folium.Marker([restaurant.latitude[i],restaurant.longitude[i]],\n",
+    "                      tooltip = restaurant.name[i], icon = folium.Icon()).add_to(mapa)\n",
+    "\n",
+    "mapa.save('MapaFinalRestaurantes.html')\n",
+    "print('Amount of offices near {} are: {}'.format(direccion_solicitud,n_ofi))\n",
+    "print('Amount of restaurants near {} are: {}'.format(direccion_solicitud,n_res))\n",
+    "mapa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "      <th>type</th>\n",
+       "      <th>coordinates</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5e9db680e283983c43b35a8e</td>\n",
+       "      <td>All</td>\n",
+       "      <td>740 W BROADWAY</td>\n",
+       "      <td>FRANK'S BURGER</td>\n",
+       "      <td>91204</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Burger</td>\n",
+       "      <td>[-117.1701501, 32.7156373]</td>\n",
+       "      <td>32.715637</td>\n",
+       "      <td>-117.17015</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.1701501...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5e9db680e283983c43b35e67</td>\n",
+       "      <td>All</td>\n",
+       "      <td>3995 24th St</td>\n",
+       "      <td>STARBUCKS COFFEE #545</td>\n",
+       "      <td>94114</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "      <td>CoffeeShop</td>\n",
+       "      <td>[-117.14241, 32.706318]</td>\n",
+       "      <td>32.706318</td>\n",
+       "      <td>-117.14241</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.14241, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id Cuisine         address                   name  \\\n",
+       "0  5e9db680e283983c43b35a8e     All  740 W BROADWAY         FRANK'S BURGER   \n",
+       "1  5e9db680e283983c43b35e67     All    3995 24th St  STARBUCKS COFFEE #545   \n",
+       "\n",
+       "  postalCode   Tag  ratings        type                 coordinates  \\\n",
+       "0      91204  Food        4      Burger  [-117.1701501, 32.7156373]   \n",
+       "1      94114  Food        4  CoffeeShop     [-117.14241, 32.706318]   \n",
+       "\n",
+       "    latitude  longitude                                          principal  \n",
+       "0  32.715637 -117.17015  {'type': 'Point', 'coordinates': [-117.1701501...  \n",
+       "1  32.706318 -117.14241  {'type': 'Point', 'coordinates': [-117.14241, ...  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restaurant.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/README.md b/module-2/python-bi-project/python-bi-project - Esteban/README.md
new file mode 100644
index 0000000..7c58d7b
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/README.md	
@@ -0,0 +1,43 @@
+# Python BI Project
+
+### The following project consists in finding an office space for a Digital Marketing Agency
+
+Main code asks the user for an address or location where they want to search or evaluate if its optimal for their office. 
+
+There are some limitations:
+	They want to be sorrounded with Small and Medium Business
+	They want to have a coffee shop nearby
+	They want to be in San Diego California
+
+### For this project I'm using the following libraries:
+
++ Pandas
++ Pymongo
++ TQDM
++ Folium
++ Numpy
++ Warnings
++ Regex
++ Pickle
++ OpenCage (for geocode)
+
+
+### In order to achieve the main result, I'm folowing 6 steps:
+
+#### Step 1
+Define client type: as I mentioned in the README ,they want to be sorrounded with Small and Medium Business, they want to have a coffee shop nearby and they want to be in San Diego California
+
+#### Step 2
+Correctly query the data I want from Mongo. 
+
+#### Step 3
+Transforming data according to client needs.
+
+#### Step 4
+Create a new collection with the final DataFrame in MongoCompass so we can add a 2dsphere index. 
+
+#### Step 5
+Make geoqueries of the new collection with client input  and display them in a heatmap reflecting the concentration of offices in the selected area.
+
+#### Step 6
+Get more information about restaurants in the area and display them with the heatmap mentioned above and marking where the restaurants of coffee shops are. 
\ No newline at end of file
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/draft.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/draft.ipynb
new file mode 100644
index 0000000..be3e1a0
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/draft.ipynb	
@@ -0,0 +1,3121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pymongo\n",
+    "from pymongo import MongoClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cliente=MongoClient('mongodb://localhost:27017')\n",
+    "db = cliente.companies\n",
+    "col = db.companies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>permalink</th>\n",
+       "      <th>crunchbase_url</th>\n",
+       "      <th>homepage_url</th>\n",
+       "      <th>blog_url</th>\n",
+       "      <th>blog_feed_url</th>\n",
+       "      <th>twitter_username</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>...</th>\n",
+       "      <th>offices</th>\n",
+       "      <th>milestones</th>\n",
+       "      <th>video_embeds</th>\n",
+       "      <th>screenshots</th>\n",
+       "      <th>external_links</th>\n",
+       "      <th>partners</th>\n",
+       "      <th>deadpooled_month</th>\n",
+       "      <th>deadpooled_day</th>\n",
+       "      <th>deadpooled_url</th>\n",
+       "      <th>ipo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8a</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>abc2</td>\n",
+       "      <td>http://www.crunchbase.com/company/wetpaint</td>\n",
+       "      <td>http://wetpaint-inc.com</td>\n",
+       "      <td>http://digitalquarters.net/</td>\n",
+       "      <td>http://digitalquarters.net/feed/</td>\n",
+       "      <td>BachelrWetpaint</td>\n",
+       "      <td>web</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': '', 'address1': '710 - 2nd Av...</td>\n",
+       "      <td>[{'id': 5869, 'description': 'Wetpaint named i...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 86], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://www.geekwire.com/201...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8b</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>abc3</td>\n",
+       "      <td>http://www.crunchbase.com/company/adventnet</td>\n",
+       "      <td>http://adventnet.com</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>manageengine</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 94], 'assets/imag...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8c</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>abc4</td>\n",
+       "      <td>http://www.crunchbase.com/company/zoho</td>\n",
+       "      <td>http://zoho.com</td>\n",
+       "      <td>http://blogs.zoho.com/</td>\n",
+       "      <td>http://blogs.zoho.com/feed</td>\n",
+       "      <td>zoho</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 388, 'description': 'Zoho Reaches 2 Mi...</td>\n",
+       "      <td>[{'embed_code': '&lt;object width=\"430\" height=\"2...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'external_url': 'http://www.online-tech-tips...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8d</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>http://www.crunchbase.com/company/digg</td>\n",
+       "      <td>http://www.digg.com</td>\n",
+       "      <td>http://blog.digg.com/</td>\n",
+       "      <td>http://blog.digg.com/?feed=rss2</td>\n",
+       "      <td>digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "      <td>[{'id': 9588, 'description': 'Another Digg Exe...</td>\n",
+       "      <td>[{'embed_code': '&lt;embed src=\"http://blip.tv/pl...</td>\n",
+       "      <td>[{'available_sizes': [[[117, 150], 'assets/ima...</td>\n",
+       "      <td>[{'external_url': 'http://www.sociableblog.com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8e</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>http://www.crunchbase.com/company/facebook</td>\n",
+       "      <td>http://facebook.com</td>\n",
+       "      <td>http://blog.facebook.com</td>\n",
+       "      <td>http://blog.facebook.com/atom.php</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 108, 'description': 'Facebook adds com...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 68], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://latimesblogs.latimes...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>{'valuation_amount': 104000000000, 'valuation_...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id       name permalink  \\\n",
+       "0  52cdef7c4bab8bd675297d8a   Wetpaint      abc2   \n",
+       "1  52cdef7c4bab8bd675297d8b  AdventNet      abc3   \n",
+       "2  52cdef7c4bab8bd675297d8c       Zoho      abc4   \n",
+       "3  52cdef7c4bab8bd675297d8d       Digg      digg   \n",
+       "4  52cdef7c4bab8bd675297d8e   Facebook  facebook   \n",
+       "\n",
+       "                                crunchbase_url             homepage_url  \\\n",
+       "0   http://www.crunchbase.com/company/wetpaint  http://wetpaint-inc.com   \n",
+       "1  http://www.crunchbase.com/company/adventnet     http://adventnet.com   \n",
+       "2       http://www.crunchbase.com/company/zoho          http://zoho.com   \n",
+       "3       http://www.crunchbase.com/company/digg      http://www.digg.com   \n",
+       "4   http://www.crunchbase.com/company/facebook      http://facebook.com   \n",
+       "\n",
+       "                      blog_url                      blog_feed_url  \\\n",
+       "0  http://digitalquarters.net/   http://digitalquarters.net/feed/   \n",
+       "1                                                                   \n",
+       "2       http://blogs.zoho.com/         http://blogs.zoho.com/feed   \n",
+       "3        http://blog.digg.com/    http://blog.digg.com/?feed=rss2   \n",
+       "4     http://blog.facebook.com  http://blog.facebook.com/atom.php   \n",
+       "\n",
+       "  twitter_username category_code  number_of_employees  ...  \\\n",
+       "0  BachelrWetpaint           web                 47.0  ...   \n",
+       "1     manageengine    enterprise                600.0  ...   \n",
+       "2             zoho      software               1600.0  ...   \n",
+       "3             digg          news                 60.0  ...   \n",
+       "4         facebook        social               5299.0  ...   \n",
+       "\n",
+       "                                             offices  \\\n",
+       "0  [{'description': '', 'address1': '710 - 2nd Av...   \n",
+       "1  [{'description': 'Headquarters', 'address1': '...   \n",
+       "2  [{'description': 'Headquarters', 'address1': '...   \n",
+       "3  [{'description': None, 'address1': '135 Missis...   \n",
+       "4  [{'description': 'Headquarters', 'address1': '...   \n",
+       "\n",
+       "                                          milestones  \\\n",
+       "0  [{'id': 5869, 'description': 'Wetpaint named i...   \n",
+       "1                                                 []   \n",
+       "2  [{'id': 388, 'description': 'Zoho Reaches 2 Mi...   \n",
+       "3  [{'id': 9588, 'description': 'Another Digg Exe...   \n",
+       "4  [{'id': 108, 'description': 'Facebook adds com...   \n",
+       "\n",
+       "                                        video_embeds  \\\n",
+       "0                                                 []   \n",
+       "1                                                 []   \n",
+       "2  [{'embed_code': '<object width=\"430\" height=\"2...   \n",
+       "3  [{'embed_code': '<embed src=\"http://blip.tv/pl...   \n",
+       "4                                                 []   \n",
+       "\n",
+       "                                         screenshots  \\\n",
+       "0  [{'available_sizes': [[[150, 86], 'assets/imag...   \n",
+       "1  [{'available_sizes': [[[150, 94], 'assets/imag...   \n",
+       "2                                                 []   \n",
+       "3  [{'available_sizes': [[[117, 150], 'assets/ima...   \n",
+       "4  [{'available_sizes': [[[150, 68], 'assets/imag...   \n",
+       "\n",
+       "                                      external_links partners  \\\n",
+       "0  [{'external_url': 'http://www.geekwire.com/201...       []   \n",
+       "1                                                 []       []   \n",
+       "2  [{'external_url': 'http://www.online-tech-tips...       []   \n",
+       "3  [{'external_url': 'http://www.sociableblog.com...       []   \n",
+       "4  [{'external_url': 'http://latimesblogs.latimes...       []   \n",
+       "\n",
+       "  deadpooled_month deadpooled_day deadpooled_url  \\\n",
+       "0              NaN            NaN            NaN   \n",
+       "1              NaN            NaN            NaN   \n",
+       "2              NaN            NaN            NaN   \n",
+       "3              NaN            NaN           None   \n",
+       "4              NaN            NaN                  \n",
+       "\n",
+       "                                                 ipo  \n",
+       "0                                                NaN  \n",
+       "1                                                NaN  \n",
+       "2                                                NaN  \n",
+       "3                                               None  \n",
+       "4  {'valuation_amount': 104000000000, 'valuation_...  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cole = col.find({})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>permalink</th>\n",
+       "      <th>crunchbase_url</th>\n",
+       "      <th>homepage_url</th>\n",
+       "      <th>blog_url</th>\n",
+       "      <th>blog_feed_url</th>\n",
+       "      <th>twitter_username</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>...</th>\n",
+       "      <th>investments</th>\n",
+       "      <th>acquisition</th>\n",
+       "      <th>acquisitions</th>\n",
+       "      <th>offices</th>\n",
+       "      <th>milestones</th>\n",
+       "      <th>ipo</th>\n",
+       "      <th>video_embeds</th>\n",
+       "      <th>screenshots</th>\n",
+       "      <th>external_links</th>\n",
+       "      <th>partners</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52cdef7c4bab8bd675297d8e</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>http://www.crunchbase.com/company/facebook</td>\n",
+       "      <td>http://facebook.com</td>\n",
+       "      <td>http://blog.facebook.com</td>\n",
+       "      <td>http://blog.facebook.com/atom.php</td>\n",
+       "      <td>facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[{'funding_round': {'round_code': 'seed', 'sou...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'price_amount': None, 'price_currency_code':...</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "      <td>[{'id': 108, 'description': 'Facebook adds com...</td>\n",
+       "      <td>{'valuation_amount': 104000000000, 'valuation_...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'available_sizes': [[[150, 68], 'assets/imag...</td>\n",
+       "      <td>[{'external_url': 'http://latimesblogs.latimes...</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id      name permalink  \\\n",
+       "0  52cdef7c4bab8bd675297d8e  Facebook  facebook   \n",
+       "\n",
+       "                               crunchbase_url         homepage_url  \\\n",
+       "0  http://www.crunchbase.com/company/facebook  http://facebook.com   \n",
+       "\n",
+       "                   blog_url                      blog_feed_url  \\\n",
+       "0  http://blog.facebook.com  http://blog.facebook.com/atom.php   \n",
+       "\n",
+       "  twitter_username category_code  number_of_employees  ...  \\\n",
+       "0         facebook        social                 5299  ...   \n",
+       "\n",
+       "                                         investments  acquisition  \\\n",
+       "0  [{'funding_round': {'round_code': 'seed', 'sou...         None   \n",
+       "\n",
+       "                                        acquisitions  \\\n",
+       "0  [{'price_amount': None, 'price_currency_code':...   \n",
+       "\n",
+       "                                             offices  \\\n",
+       "0  [{'description': 'Headquarters', 'address1': '...   \n",
+       "\n",
+       "                                          milestones  \\\n",
+       "0  [{'id': 108, 'description': 'Facebook adds com...   \n",
+       "\n",
+       "                                                 ipo video_embeds  \\\n",
+       "0  {'valuation_amount': 104000000000, 'valuation_...           []   \n",
+       "\n",
+       "                                         screenshots  \\\n",
+       "0  [{'available_sizes': [[[150, 68], 'assets/imag...   \n",
+       "\n",
+       "                                      external_links partners  \n",
+       "0  [{'external_url': 'http://latimesblogs.latimes...       []  \n",
+       "\n",
+       "[1 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cole = col.find({'name':'Facebook'})\n",
+    "data = pd.DataFrame(cole)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'description': 'Headquarters',\n",
+       "  'address1': '1601 Willow Road',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '94025',\n",
+       "  'city': 'Menlo Park',\n",
+       "  'state_code': 'CA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 37.41605,\n",
+       "  'longitude': -122.151801},\n",
+       " {'description': 'Europe HQ',\n",
+       "  'address1': '',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '',\n",
+       "  'city': 'Dublin',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IRL',\n",
+       "  'latitude': 53.344104,\n",
+       "  'longitude': -6.267494},\n",
+       " {'description': 'New York',\n",
+       "  'address1': '340 Madison Ave',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '10017',\n",
+       "  'city': 'New York',\n",
+       "  'state_code': 'NY',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 40.7557162,\n",
+       "  'longitude': -73.9792469}]"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.offices[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data.offices[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_id                                             52cdef7c4bab8bd675297dba\n",
+       "name                                                              Google\n",
+       "permalink                                                         google\n",
+       "crunchbase_url                  http://www.crunchbase.com/company/google\n",
+       "homepage_url                                           http://google.com\n",
+       "blog_url                                  http://googleblog.blogspot.com\n",
+       "blog_feed_url          http://googleblog.blogspot.com/feeds/posts/def...\n",
+       "twitter_username                                                  google\n",
+       "category_code                                                     search\n",
+       "number_of_employees                                                28000\n",
+       "founded_year                                                        1998\n",
+       "founded_month                                                          9\n",
+       "founded_day                                                            7\n",
+       "deadpooled_year                                                      NaN\n",
+       "tag_list               search, gmail, blogger, find, information, you...\n",
+       "alias_list                                                        Google\n",
+       "email_address                                          google@google.com\n",
+       "phone_number                                                650.253.0000\n",
+       "description                                                             \n",
+       "created_at                                  Sat Jun 30 22:18:12 UTC 2007\n",
+       "updated_at                                  Mon Jan 06 04:52:54 UTC 2014\n",
+       "overview               <p>Google provides search and advertising serv...\n",
+       "image                  {'available_sizes': [[[150, 51], 'assets/image...\n",
+       "products               [{'name': 'Gmail', 'permalink': 'gmail'}, {'na...\n",
+       "relationships          [{'is_past': False, 'title': 'CEO of Google Co...\n",
+       "competitions           [{'competitor': {'name': 'Technorati', 'permal...\n",
+       "providerships          [{'title': 'Public Relations (APAC)', 'is_past...\n",
+       "total_money_raised                                                 $555M\n",
+       "funding_rounds         [{'id': 380, 'round_code': 'angel', 'source_ur...\n",
+       "investments            [{'funding_round': {'round_code': 'a', 'source...\n",
+       "acquisition                                                         None\n",
+       "acquisitions           [{'price_amount': 60000000, 'price_currency_co...\n",
+       "offices                [{'description': 'Google Headquarters', 'addre...\n",
+       "milestones             [{'id': 3, 'description': 'More than 4 Billion...\n",
+       "video_embeds           [{'embed_code': '<embed src=\"http://blip.tv/pl...\n",
+       "screenshots                                                           []\n",
+       "external_links                                                        []\n",
+       "partners               [{'partner_name': 'AngelList', 'homepage_url':...\n",
+       "deadpooled_month                                                     NaN\n",
+       "deadpooled_day                                                       NaN\n",
+       "deadpooled_url                                                      None\n",
+       "ipo                    {'valuation_amount': None, 'valuation_currency...\n",
+       "Name: 48, dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[48]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def longitudoficinas(x):\n",
+    "    return lista_longitudes.append(len(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        None\n",
+       "1        None\n",
+       "2        None\n",
+       "3        None\n",
+       "4        None\n",
+       "         ... \n",
+       "18796    None\n",
+       "18797    None\n",
+       "18798    None\n",
+       "18799    None\n",
+       "18800    None\n",
+       "Name: offices, Length: 18801, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lista_longitudes = []\n",
+    "\n",
+    "data.offices.apply(longitudoficinas).sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'description': 'Google Headquarters',\n",
+       "  'address1': '1600 Amphitheatre Parkway',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '',\n",
+       "  'city': 'Mountain View',\n",
+       "  'state_code': 'CA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 37.421972,\n",
+       "  'longitude': -122.084143},\n",
+       " {'description': 'Google Ann Arbor',\n",
+       "  'address1': '112 S. Main St.',\n",
+       "  'address2': '2nd Floor',\n",
+       "  'zip_code': '48104',\n",
+       "  'city': 'Ann Arbor',\n",
+       "  'state_code': 'MI',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 42.280988,\n",
+       "  'longitude': -83.748882},\n",
+       " {'description': 'Google Atlanta',\n",
+       "  'address1': '10 10th Street NE',\n",
+       "  'address2': 'Suite 600',\n",
+       "  'zip_code': '30309',\n",
+       "  'city': 'Atlanta',\n",
+       "  'state_code': 'GA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 33.781466,\n",
+       "  'longitude': -84.387519},\n",
+       " {'description': 'Google Austin',\n",
+       "  'address1': 'Plaza 7000',\n",
+       "  'address2': '7000 North MoPac Expressway, 2nd Floor',\n",
+       "  'zip_code': '78731',\n",
+       "  'city': 'Austin',\n",
+       "  'state_code': 'TX',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 30.351416,\n",
+       "  'longitude': -97.751382},\n",
+       " {'description': 'Google Boulder',\n",
+       "  'address1': '2590 Pearl Street',\n",
+       "  'address2': 'Suite 100',\n",
+       "  'zip_code': '80302',\n",
+       "  'city': 'Boulder',\n",
+       "  'state_code': 'CO',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 40.021416,\n",
+       "  'longitude': -105.260931},\n",
+       " {'description': 'Google Cambridge',\n",
+       "  'address1': '5 Cambridge Center',\n",
+       "  'address2': 'Floors 3-6',\n",
+       "  'zip_code': '02142',\n",
+       "  'city': 'Cambridge',\n",
+       "  'state_code': 'MA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 42.36305,\n",
+       "  'longitude': -71.087478},\n",
+       " {'description': 'Google Chicago',\n",
+       "  'address1': '20 West Kinzie St.',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '60610',\n",
+       "  'city': 'Chicago',\n",
+       "  'state_code': 'IL',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 41.889474,\n",
+       "  'longitude': -87.628912},\n",
+       " {'description': 'Google Coppell',\n",
+       "  'address1': '701 Canyon Drive',\n",
+       "  'address2': 'Suite 120',\n",
+       "  'zip_code': '75019',\n",
+       "  'city': 'Coppell',\n",
+       "  'state_code': 'TX',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 32.981465,\n",
+       "  'longitude': -97.018196},\n",
+       " {'description': 'Google Dallas',\n",
+       "  'address1': '3102 Oak Lawn',\n",
+       "  'address2': 'Suite 200',\n",
+       "  'zip_code': '75219',\n",
+       "  'city': 'Dallas',\n",
+       "  'state_code': 'TX',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 32.809161,\n",
+       "  'longitude': -96.807693},\n",
+       " {'description': 'Google Denver',\n",
+       "  'address1': '4600 S. Syracuse St.',\n",
+       "  'address2': '8th Floor',\n",
+       "  'zip_code': '80237',\n",
+       "  'city': 'Denver',\n",
+       "  'state_code': 'CO',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 39.62919,\n",
+       "  'longitude': -104.898419},\n",
+       " {'description': 'Google Detroit',\n",
+       "  'address1': '114 Willits Street',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '48009',\n",
+       "  'city': 'Birmingham',\n",
+       "  'state_code': 'MI',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 42.547807,\n",
+       "  'longitude': -83.215498},\n",
+       " {'description': 'Google Irvine',\n",
+       "  'address1': '19540 Jamboree Road',\n",
+       "  'address2': '2nd Floor',\n",
+       "  'zip_code': '92612',\n",
+       "  'city': 'Irvine',\n",
+       "  'state_code': 'CA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 33.658331,\n",
+       "  'longitude': -117.861817},\n",
+       " {'description': 'Google Kirkland',\n",
+       "  'address1': 'Central Way Plaza',\n",
+       "  'address2': '720 4th Avenue, Ste 400',\n",
+       "  'zip_code': '98033',\n",
+       "  'city': 'Kirkland',\n",
+       "  'state_code': 'WA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google New York',\n",
+       "  'address1': '76 Ninth Avenue',\n",
+       "  'address2': '4th Floor',\n",
+       "  'zip_code': '10011',\n",
+       "  'city': 'New York',\n",
+       "  'state_code': 'NY',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 40.74222,\n",
+       "  'longitude': -74.004489},\n",
+       " {'description': 'Google Phoenix',\n",
+       "  'address1': '1551 S. Rural Road',\n",
+       "  'address2': '2nd floor',\n",
+       "  'zip_code': '85281',\n",
+       "  'city': 'Tempe',\n",
+       "  'state_code': 'AZ',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 33.411898,\n",
+       "  'longitude': -111.926263},\n",
+       " {'description': 'Google Pittsburgh',\n",
+       "  'address1': '4720 Forbes Avenue',\n",
+       "  'address2': 'Lower Level',\n",
+       "  'zip_code': '15213',\n",
+       "  'city': 'Pittsburgh',\n",
+       "  'state_code': 'PA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 40.444528,\n",
+       "  'longitude': -79.946269},\n",
+       " {'description': 'Google San Francisco',\n",
+       "  'address1': '345 Spear Street',\n",
+       "  'address2': '4th Floor',\n",
+       "  'zip_code': '94105',\n",
+       "  'city': 'San Francisco',\n",
+       "  'state_code': 'CA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 37.789803,\n",
+       "  'longitude': -122.389757},\n",
+       " {'description': 'Google Santa Monica',\n",
+       "  'address1': '604 Arizona Avenue',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '90401',\n",
+       "  'city': 'Santa Monica',\n",
+       "  'state_code': 'CA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 34.019282,\n",
+       "  'longitude': -118.494288},\n",
+       " {'description': 'Google Seattle',\n",
+       "  'address1': 'Evanston Building',\n",
+       "  'address2': '501 N. 34th Street, Suite 300',\n",
+       "  'zip_code': '98103',\n",
+       "  'city': 'Seattle',\n",
+       "  'state_code': 'WA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 47.675326,\n",
+       "  'longitude': -122.351397},\n",
+       " {'description': 'Google Seattle',\n",
+       "  'address1': '651 N. 34th St.',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '98103',\n",
+       "  'city': 'Seattle',\n",
+       "  'state_code': 'WA',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 47.649701,\n",
+       "  'longitude': -122.350592},\n",
+       " {'description': 'Google Washington DC',\n",
+       "  'address1': '1101 New York Avenue, N.W.',\n",
+       "  'address2': 'Second Floor',\n",
+       "  'zip_code': '20005',\n",
+       "  'city': 'Washington',\n",
+       "  'state_code': 'DC',\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': 38.901113,\n",
+       "  'longitude': -77.027101},\n",
+       " {'description': 'Google Melbourne',\n",
+       "  'address1': 'Rialto South Tower, Level 27',\n",
+       "  'address2': '525 Collins Street',\n",
+       "  'zip_code': '3000',\n",
+       "  'city': 'Melbourne',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'AUS',\n",
+       "  'latitude': -37.879234,\n",
+       "  'longitude': 145.073608},\n",
+       " {'description': 'Google Sydney',\n",
+       "  'address1': 'Levels 11 & 18, Tower 1, Darling Park',\n",
+       "  'address2': '201 Sussex St.',\n",
+       "  'zip_code': '2000',\n",
+       "  'city': 'Sydney',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'AUS',\n",
+       "  'latitude': -34.822723,\n",
+       "  'longitude': 138.612396},\n",
+       " {'description': 'Google Beijing',\n",
+       "  'address1': 'Tsinghua Science Park Bldg 6',\n",
+       "  'address2': 'No. 1 Zhongguancun East Road',\n",
+       "  'zip_code': '100084',\n",
+       "  'city': 'Beijing',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CHN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Guangzhou',\n",
+       "  'address1': 'Tower A, 23/F, Center Plaza',\n",
+       "  'address2': '161 Lin he Xi Lu, Tian He District',\n",
+       "  'zip_code': '510620',\n",
+       "  'city': 'Guangzhou',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CHN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Shanghai',\n",
+       "  'address1': '16F, Raffles City',\n",
+       "  'address2': 'No.268 Xizang Zhong Road, Huangpu District',\n",
+       "  'zip_code': '200001',\n",
+       "  'city': 'Shanghai',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CHN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Hong Kong',\n",
+       "  'address1': 'One IFC - 18/F',\n",
+       "  'address2': '1 Harbour View Street',\n",
+       "  'zip_code': '',\n",
+       "  'city': 'Central',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'HKG',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Bangalore',\n",
+       "  'address1': 'Prestige Sigma, No. 3, 1st Floor',\n",
+       "  'address2': 'Vittal Mallya Road (Grant Road)',\n",
+       "  'zip_code': '560 001',\n",
+       "  'city': 'Bangalore',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IND',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Delhi',\n",
+       "  'address1': 'Tower C, Level 9',\n",
+       "  'address2': 'Building No. 8',\n",
+       "  'zip_code': '122002',\n",
+       "  'city': 'Gurgaon',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Delhi (Honeybee)',\n",
+       "  'address1': 'No. B1/A6, First Floor',\n",
+       "  'address2': 'Mohan Co-operative Industrial Estate Mathura Road',\n",
+       "  'zip_code': '110044',\n",
+       "  'city': 'New Delhi',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IND',\n",
+       "  'latitude': 28.86999,\n",
+       "  'longitude': 77.122261},\n",
+       " {'description': 'Google Hyderabad',\n",
+       "  'address1': 'BLOCK A, Fourth Floor',\n",
+       "  'address2': 'Plot No. 14, Road NO 2',\n",
+       "  'zip_code': '500 081',\n",
+       "  'city': 'Hyderabad',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IND',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Mumbai',\n",
+       "  'address1': '264-265 Vasvani Chambers',\n",
+       "  'address2': 'Dr Annie Besant Road',\n",
+       "  'zip_code': '400 025',\n",
+       "  'city': 'Mumbai',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IND',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Japan Headquarters',\n",
+       "  'address1': '6F Cerulean Tower',\n",
+       "  'address2': '26-1 Sakuragaoka-cho',\n",
+       "  'zip_code': '150-8512',\n",
+       "  'city': 'Tokyo',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'JPN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Osaka',\n",
+       "  'address1': '9F, Edobori Center Building',\n",
+       "  'address2': '2-1-1 Edobori',\n",
+       "  'zip_code': '550-0002',\n",
+       "  'city': 'Osaka',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'USA',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Seoul',\n",
+       "  'address1': '22nd Floor, Gangnam Finance Center',\n",
+       "  'address2': '737 Yeoksam-dong',\n",
+       "  'zip_code': '135 984',\n",
+       "  'city': 'Seoul',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'PRK',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Singapore',\n",
+       "  'address1': 'Level 25 North Tower',\n",
+       "  'address2': 'One Raffles Quay',\n",
+       "  'zip_code': '048583',\n",
+       "  'city': 'Singapore',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'SGP',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Taipei',\n",
+       "  'address1': 'Level 73, Taipei 101 Tower',\n",
+       "  'address2': '7 Xinyi Road, Sec. 5,',\n",
+       "  'zip_code': '110',\n",
+       "  'city': 'Taipei',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'TWN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Aarhus',\n",
+       "  'address1': 'Aabogade 15',\n",
+       "  'address2': '8200 Aarhus N',\n",
+       "  'zip_code': '',\n",
+       "  'city': '',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'DNK',\n",
+       "  'latitude': 56.171882,\n",
+       "  'longitude': 10.187799},\n",
+       " {'description': 'Google Copenhagen',\n",
+       "  'address1': 'Larsbjornsstraede 3',\n",
+       "  'address2': '1454 Copenhagen K.',\n",
+       "  'zip_code': '',\n",
+       "  'city': '',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'DNK',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Helsinki',\n",
+       "  'address1': 'Luna House',\n",
+       "  'address2': 'Mannerheimintie 12 B',\n",
+       "  'zip_code': '',\n",
+       "  'city': 'Helsinki',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'FIN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Paris',\n",
+       "  'address1': \"38 avenue de l'Opéra\",\n",
+       "  'address2': '',\n",
+       "  'zip_code': '75002',\n",
+       "  'city': 'Paris',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'FRA',\n",
+       "  'latitude': 48.869606,\n",
+       "  'longitude': 2.332749},\n",
+       " {'description': 'Google Hamburg',\n",
+       "  'address1': 'ABC-Strasse 19',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '20354',\n",
+       "  'city': 'Hamburg',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'DEU',\n",
+       "  'latitude': 53.554052,\n",
+       "  'longitude': 9.985266},\n",
+       " {'description': 'Google Dublin',\n",
+       "  'address1': 'Gordon House',\n",
+       "  'address2': 'Barrow Street',\n",
+       "  'zip_code': '4',\n",
+       "  'city': 'Dublin',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'IRL',\n",
+       "  'latitude': 53.339914,\n",
+       "  'longitude': -6.234424},\n",
+       " {'description': 'Google Italy',\n",
+       "  'address1': 'Corso Europa 2',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '20122',\n",
+       "  'city': 'Milan',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ITA',\n",
+       "  'latitude': 45.46381,\n",
+       "  'longitude': 9.195978},\n",
+       " {'description': 'Google Amsterdam',\n",
+       "  'address1': 'Claude Debussylaan 34',\n",
+       "  'address2': 'Vinoly Mahler 4',\n",
+       "  'zip_code': '1082',\n",
+       "  'city': 'Amsterdam',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'NLD',\n",
+       "  'latitude': 52.337495,\n",
+       "  'longitude': 4.871464},\n",
+       " {'description': 'Google Oslo',\n",
+       "  'address1': 'C.J. Hambros plass 2C',\n",
+       "  'address2': '',\n",
+       "  'zip_code': 'N-0164',\n",
+       "  'city': 'Oslo',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'NOR',\n",
+       "  'latitude': 59.915848,\n",
+       "  'longitude': 10.741047},\n",
+       " {'description': 'Google Trondheim',\n",
+       "  'address1': 'Beddingen 10',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '7014',\n",
+       "  'city': 'Trondheim',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'NOR',\n",
+       "  'latitude': 63.435324,\n",
+       "  'longitude': 10.412428},\n",
+       " {'description': 'Google Kraków',\n",
+       "  'address1': 'ul. Krupnicza 16',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '31-123',\n",
+       "  'city': 'Kraków',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'POL',\n",
+       "  'latitude': 50.063281,\n",
+       "  'longitude': 19.929806},\n",
+       " {'description': 'Google Wrocław',\n",
+       "  'address1': 'Bema Plaza, V pietro',\n",
+       "  'address2': 'Plac Gen. Jozefa Bema nr 2',\n",
+       "  'zip_code': '50-265',\n",
+       "  'city': 'Wroclaw',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'POL',\n",
+       "  'latitude': 51.116574,\n",
+       "  'longitude': 17.041942},\n",
+       " {'description': 'Google Moscow',\n",
+       "  'address1': '7 Balchug st.',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '115035',\n",
+       "  'city': 'Moscow',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'RUS',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google St. Petersburg',\n",
+       "  'address1': 'Nevsky 25',\n",
+       "  'address2': 'Nevsky Prospect 25',\n",
+       "  'zip_code': '191186',\n",
+       "  'city': 'St. Petersburg',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'RUS',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Madrid',\n",
+       "  'address1': 'Torre Picasso',\n",
+       "  'address2': 'Plaza Pablo Ruiz Picasso 1',\n",
+       "  'zip_code': '28020',\n",
+       "  'city': 'Madrid',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ESP',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Luleå Sweden',\n",
+       "  'address1': 'Aurorum 2',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '977 75',\n",
+       "  'city': 'Luleå',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'SWE',\n",
+       "  'latitude': 65.617491,\n",
+       "  'longitude': 22.126255},\n",
+       " {'description': 'Google Stockholm',\n",
+       "  'address1': 'Mäster Samuelsgatan 60',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '111 21',\n",
+       "  'city': 'Stockholm',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'SWE',\n",
+       "  'latitude': 59.332344,\n",
+       "  'longitude': 18.060162},\n",
+       " {'description': 'Google Zürich',\n",
+       "  'address1': 'Brandschenkestrasse 110',\n",
+       "  'address2': '',\n",
+       "  'zip_code': '8002',\n",
+       "  'city': 'Zürich',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CHE',\n",
+       "  'latitude': 47.36554,\n",
+       "  'longitude': 8.524864},\n",
+       " {'description': 'Google London',\n",
+       "  'address1': 'Belgrave House',\n",
+       "  'address2': '76 Buckingham Palace Road',\n",
+       "  'zip_code': 'SW1W 9TQ',\n",
+       "  'city': 'London',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'GBR',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Manchester',\n",
+       "  'address1': 'Peter House',\n",
+       "  'address2': 'Oxford Street',\n",
+       "  'zip_code': 'M1 5AN',\n",
+       "  'city': 'Manchester',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'GBR',\n",
+       "  'latitude': 53.473606,\n",
+       "  'longitude': -2.250166},\n",
+       " {'description': 'Google Montreal',\n",
+       "  'address1': '1000 rue de la Gauchetiere Ouest',\n",
+       "  'address2': 'Suite 2449',\n",
+       "  'zip_code': 'H3B4W5',\n",
+       "  'city': 'Montreal',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CAN',\n",
+       "  'latitude': 45.498588,\n",
+       "  'longitude': -73.567044},\n",
+       " {'description': 'Google Toronto',\n",
+       "  'address1': 'BCE Place, TD Canada Trust Tower',\n",
+       "  'address2': '161 Bay Street, 27th Floor',\n",
+       "  'zip_code': 'M5J 2S1',\n",
+       "  'city': 'Toronto',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CAN',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Waterloo',\n",
+       "  'address1': '340 Hagey Blvd',\n",
+       "  'address2': '2nd Floor',\n",
+       "  'zip_code': 'N2L 6R6',\n",
+       "  'city': 'Waterloo',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'CAN',\n",
+       "  'latitude': 43.474704,\n",
+       "  'longitude': -80.546381},\n",
+       " {'description': 'Google Buenos Aires',\n",
+       "  'address1': 'Alicia M. De Justo 350, 2º',\n",
+       "  'address2': 'Puerto Madero',\n",
+       "  'zip_code': 'C1107AAH',\n",
+       "  'city': 'Buenos Aires',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ARG',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Sao Paulo',\n",
+       "  'address1': 'Av. Brigadeiro Faria Lima',\n",
+       "  'address2': 'nº 3900 5th floor, Itaim',\n",
+       "  'zip_code': '04538-132',\n",
+       "  'city': 'Sao Paulo',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'BRA',\n",
+       "  'latitude': -23.590463,\n",
+       "  'longitude': -46.681494},\n",
+       " {'description': 'Google Mexico',\n",
+       "  'address1': 'Blvd. Manuel Avila Camacho #36',\n",
+       "  'address2': 'Torre Esmeralda 2, Piso 12, Lomas de Chapultepec',\n",
+       "  'zip_code': '1100',\n",
+       "  'city': 'Mexico',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'MEX',\n",
+       "  'latitude': 19.507519,\n",
+       "  'longitude': -99.030156},\n",
+       " {'description': 'Google Haifa',\n",
+       "  'address1': 'Building 30, MATAM, Advanced Technology Center',\n",
+       "  'address2': 'PO Box 15096',\n",
+       "  'zip_code': '31905',\n",
+       "  'city': 'Haifa',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ISR',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Tel Aviv',\n",
+       "  'address1': 'Levinstein Tower',\n",
+       "  'address2': '23 Menachem Begin Road',\n",
+       "  'zip_code': '66183',\n",
+       "  'city': 'Tel-Aviv',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ISR',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Istanbul',\n",
+       "  'address1': 'Kanyon Ofis Binasi Kat. 6',\n",
+       "  'address2': 'Buyukdere Cad. No. 185',\n",
+       "  'zip_code': '34394',\n",
+       "  'city': 'Istanbul',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'TUR',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None},\n",
+       " {'description': 'Google Dubai',\n",
+       "  'address1': 'First Floor, Building No 17',\n",
+       "  'address2': 'Dubai Internet City',\n",
+       "  'zip_code': '1',\n",
+       "  'city': 'Dubai',\n",
+       "  'state_code': None,\n",
+       "  'country_code': 'ARE',\n",
+       "  'latitude': None,\n",
+       "  'longitude': None}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[48].offices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def principal(x):\n",
+    "    principal = {'type':'Point',\n",
+    "                 'coordinates': [x.longitude[0],\n",
+    "                                 x.latitude[0]]}\n",
+    "    \n",
+    "    return {'totalOffices': len(x),\n",
+    "            'principal': principal}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lista_x = []\n",
+    "for i in range(len(test1.longitude)):\n",
+    "    \n",
+    "    lista_x.append(str({'type':'Point',\n",
+    "                          'coordinates': [test1.longitude[i],\n",
+    "                                         test1.latitude[i]]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_number_of_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Google Headquarters</td>\n",
+       "      <td>1600 Amphitheatre Parkway</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Mountain View</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.421972</td>\n",
+       "      <td>-122.084143</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.084143,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Google Ann Arbor</td>\n",
+       "      <td>112 S. Main St.</td>\n",
+       "      <td>2nd Floor</td>\n",
+       "      <td>48104</td>\n",
+       "      <td>Ann Arbor</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.280988</td>\n",
+       "      <td>-83.748882</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-83.748882, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Google Atlanta</td>\n",
+       "      <td>10 10th Street NE</td>\n",
+       "      <td>Suite 600</td>\n",
+       "      <td>30309</td>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.781466</td>\n",
+       "      <td>-84.387519</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-84.387519, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Google Austin</td>\n",
+       "      <td>Plaza 7000</td>\n",
+       "      <td>7000 North MoPac Expressway, 2nd Floor</td>\n",
+       "      <td>78731</td>\n",
+       "      <td>Austin</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>30.351416</td>\n",
+       "      <td>-97.751382</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-97.751382, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Google Boulder</td>\n",
+       "      <td>2590 Pearl Street</td>\n",
+       "      <td>Suite 100</td>\n",
+       "      <td>80302</td>\n",
+       "      <td>Boulder</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.021416</td>\n",
+       "      <td>-105.260931</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-105.260931,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           description                   address1  \\\n",
+       "0  Google Headquarters  1600 Amphitheatre Parkway   \n",
+       "1     Google Ann Arbor            112 S. Main St.   \n",
+       "2       Google Atlanta          10 10th Street NE   \n",
+       "3        Google Austin                 Plaza 7000   \n",
+       "4       Google Boulder          2590 Pearl Street   \n",
+       "\n",
+       "                                 address2 zip_code           city state_code  \\\n",
+       "0                                                   Mountain View         CA   \n",
+       "1                               2nd Floor    48104      Ann Arbor         MI   \n",
+       "2                               Suite 600    30309        Atlanta         GA   \n",
+       "3  7000 North MoPac Expressway, 2nd Floor    78731         Austin         TX   \n",
+       "4                               Suite 100    80302        Boulder         CO   \n",
+       "\n",
+       "  country_code   latitude   longitude    name  total_number_of_employees  \\\n",
+       "0          USA  37.421972 -122.084143  Google                    28000.0   \n",
+       "1          USA  42.280988  -83.748882  Google                    28000.0   \n",
+       "2          USA  33.781466  -84.387519  Google                    28000.0   \n",
+       "3          USA  30.351416  -97.751382  Google                    28000.0   \n",
+       "4          USA  40.021416 -105.260931  Google                    28000.0   \n",
+       "\n",
+       "  category                                          principal  \n",
+       "0   search  {'type': 'Point', 'coordinates': [-122.084143,...  \n",
+       "1   search  {'type': 'Point', 'coordinates': [-83.748882, ...  \n",
+       "2   search  {'type': 'Point', 'coordinates': [-84.387519, ...  \n",
+       "3   search  {'type': 'Point', 'coordinates': [-97.751382, ...  \n",
+       "4   search  {'type': 'Point', 'coordinates': [-105.260931,...  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test1 = pd.DataFrame(data.iloc[48].offices)\n",
+    "test1['name'] = str(data.loc[48,['name'][0]])\n",
+    "test1['total_number_of_employees'] = data.iloc[48].number_of_employees\n",
+    "test1['category'] = str(data.loc[48,['category_code'][0]])\n",
+    "test1['principal'] = pd.Series(lista_x)\n",
+    "test1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"{'type': 'Point', 'coordinates': [-122.084143, 37.421972]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-83.748882, 42.280988]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-84.387519, 33.781466]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-97.751382, 30.351416]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-105.260931, 40.021416]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-71.087478, 42.36305]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-87.628912, 41.889474]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-97.018196, 32.981465]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-96.807693, 32.809161]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-104.898419, 39.62919]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-83.215498, 42.547807]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-117.861817, 33.658331]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-74.004489, 40.74222]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-111.926263, 33.411898]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-79.946269, 40.444528]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-122.389757, 37.789803]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-118.494288, 34.019282]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-122.351397, 47.675326]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-122.350592, 47.649701]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-77.027101, 38.901113]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [145.073608, -37.879234]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [138.612396, -34.822723]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [77.122261, 28.86999]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [10.187799, 56.171882]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [2.332749, 48.869606]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [9.985266, 53.554052]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-6.234424, 53.339914]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [9.195978, 45.46381]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [4.871464, 52.337495]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [10.741047, 59.915848]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [10.412428, 63.435324]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [19.929806, 50.063281]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [17.041942, 51.116574]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [22.126255, 65.617491]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [18.060162, 59.332344]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [8.524864, 47.36554]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-2.250166, 53.473606]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-73.567044, 45.498588]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-80.546381, 43.474704]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-46.681494, -23.590463]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [-99.030156, 19.507519]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\",\n",
+       " \"{'type': 'Point', 'coordinates': [nan, nan]}\"]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lista_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    -122.084143\n",
+       "1     -83.748882\n",
+       "2     -84.387519\n",
+       "3     -97.751382\n",
+       "4    -105.260931\n",
+       "         ...    \n",
+       "62    -99.030156\n",
+       "63           NaN\n",
+       "64           NaN\n",
+       "65           NaN\n",
+       "66           NaN\n",
+       "Name: longitude, Length: 67, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test1.longitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "principal = {'type':'Point',\n",
+    "             'coordinates': [test1.longitude[0],\n",
+    "                             test1.latitude[0]]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'Point', 'coordinates': [-122.084143, 37.421972]}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "principal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def principal(x):\n",
+    "    principal = {'type':'Point',\n",
+    "                 'coordinates': [x.longitude,\n",
+    "                                 x.latitude]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'search'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(data.loc[48,['category_code'][0]])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "al_menos_1_ofi=db.companies.find({'offices': {'$not': {'$size':0}}},\n",
+    "                                 {'name':1, '_id':0, 'offices':1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['_id', 'name', 'permalink', 'crunchbase_url', 'homepage_url',\n",
+       "       'blog_url', 'blog_feed_url', 'twitter_username', 'category_code',\n",
+       "       'number_of_employees', 'founded_year', 'founded_month', 'founded_day',\n",
+       "       'deadpooled_year', 'tag_list', 'alias_list', 'email_address',\n",
+       "       'phone_number', 'description', 'created_at', 'updated_at', 'overview',\n",
+       "       'image', 'products', 'relationships', 'competitions', 'providerships',\n",
+       "       'total_money_raised', 'funding_rounds', 'investments', 'acquisition',\n",
+       "       'acquisitions', 'offices', 'milestones', 'video_embeds', 'screenshots',\n",
+       "       'external_links', 'partners', 'deadpooled_month', 'deadpooled_day',\n",
+       "       'deadpooled_url', 'ipo'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 13744 entries, 0 to 13743\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   name                 13744 non-null  object \n",
+      " 1   category_code        13092 non-null  object \n",
+      " 2   number_of_employees  7467 non-null   float64\n",
+      " 3   offices              13744 non-null  object \n",
+      "dtypes: float64(1), object(3)\n",
+      "memory usage: 429.6+ KB\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'head'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-224-5a406ae9659b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'head'"
+     ]
+    }
+   ],
+   "source": [
+    "data.info().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "web            2812\n",
+       "software       2387\n",
+       "games_video     839\n",
+       "mobile          821\n",
+       "advertising     794\n",
+       "Name: category_code, dtype: int64"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.category_code.value_counts().head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_number_of_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Google Headquarters</td>\n",
+       "      <td>1600 Amphitheatre Parkway</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Mountain View</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.421972</td>\n",
+       "      <td>-122.084143</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.084143,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Google Ann Arbor</td>\n",
+       "      <td>112 S. Main St.</td>\n",
+       "      <td>2nd Floor</td>\n",
+       "      <td>48104</td>\n",
+       "      <td>Ann Arbor</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.280988</td>\n",
+       "      <td>-83.748882</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-83.748882, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Google Atlanta</td>\n",
+       "      <td>10 10th Street NE</td>\n",
+       "      <td>Suite 600</td>\n",
+       "      <td>30309</td>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.781466</td>\n",
+       "      <td>-84.387519</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-84.387519, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Google Austin</td>\n",
+       "      <td>Plaza 7000</td>\n",
+       "      <td>7000 North MoPac Expressway, 2nd Floor</td>\n",
+       "      <td>78731</td>\n",
+       "      <td>Austin</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>30.351416</td>\n",
+       "      <td>-97.751382</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-97.751382, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Google Boulder</td>\n",
+       "      <td>2590 Pearl Street</td>\n",
+       "      <td>Suite 100</td>\n",
+       "      <td>80302</td>\n",
+       "      <td>Boulder</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.021416</td>\n",
+       "      <td>-105.260931</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>28000.0</td>\n",
+       "      <td>search</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-105.260931,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           description                   address1  \\\n",
+       "0  Google Headquarters  1600 Amphitheatre Parkway   \n",
+       "1     Google Ann Arbor            112 S. Main St.   \n",
+       "2       Google Atlanta          10 10th Street NE   \n",
+       "3        Google Austin                 Plaza 7000   \n",
+       "4       Google Boulder          2590 Pearl Street   \n",
+       "\n",
+       "                                 address2 zip_code           city state_code  \\\n",
+       "0                                                   Mountain View         CA   \n",
+       "1                               2nd Floor    48104      Ann Arbor         MI   \n",
+       "2                               Suite 600    30309        Atlanta         GA   \n",
+       "3  7000 North MoPac Expressway, 2nd Floor    78731         Austin         TX   \n",
+       "4                               Suite 100    80302        Boulder         CO   \n",
+       "\n",
+       "  country_code   latitude   longitude    name  total_number_of_employees  \\\n",
+       "0          USA  37.421972 -122.084143  Google                    28000.0   \n",
+       "1          USA  42.280988  -83.748882  Google                    28000.0   \n",
+       "2          USA  33.781466  -84.387519  Google                    28000.0   \n",
+       "3          USA  30.351416  -97.751382  Google                    28000.0   \n",
+       "4          USA  40.021416 -105.260931  Google                    28000.0   \n",
+       "\n",
+       "  category                                          principal  \n",
+       "0   search  {'type': 'Point', 'coordinates': [-122.084143,...  \n",
+       "1   search  {'type': 'Point', 'coordinates': [-83.748882, ...  \n",
+       "2   search  {'type': 'Point', 'coordinates': [-84.387519, ...  \n",
+       "3   search  {'type': 'Point', 'coordinates': [-97.751382, ...  \n",
+       "4   search  {'type': 'Point', 'coordinates': [-105.260931,...  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test1 = pd.DataFrame(data.iloc[48].offices)\n",
+    "test1['name'] = str(data.loc[48,['name'][0]])\n",
+    "test1['total_number_of_employees'] = data.iloc[48].number_of_employees\n",
+    "test1['category'] = str(data.loc[48,['category_code'][0]])\n",
+    "test1['principal'] = pd.Series(lista_x)\n",
+    "test1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "al_menos_1_ofi=db.companies.find({'offices': {'$not': {'$size':0}}},\n",
+    "                                 {'name':1,'number_of_employees':1, '_id':0, 'offices':1,'category_code':1\n",
+    "                                  })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>category_code</th>\n",
+       "      <th>number_of_employees</th>\n",
+       "      <th>offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>web</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>[{'description': '', 'address1': '710 - 2nd Av...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Digg</td>\n",
+       "      <td>news</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>[{'description': None, 'address1': '135 Missis...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>social</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>[{'description': 'Headquarters', 'address1': '...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        name category_code  number_of_employees  \\\n",
+       "0   Wetpaint           web                 47.0   \n",
+       "1  AdventNet    enterprise                600.0   \n",
+       "2       Zoho      software               1600.0   \n",
+       "3       Digg          news                 60.0   \n",
+       "4   Facebook        social               5299.0   \n",
+       "\n",
+       "                                             offices  \n",
+       "0  [{'description': '', 'address1': '710 - 2nd Av...  \n",
+       "1  [{'description': 'Headquarters', 'address1': '...  \n",
+       "2  [{'description': 'Headquarters', 'address1': '...  \n",
+       "3  [{'description': None, 'address1': '135 Missis...  \n",
+       "4  [{'description': 'Headquarters', 'address1': '...  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame(al_menos_1_ofi)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13744, 4)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 13744/13744 [01:11<00:00, 192.60it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista_df=[] #ver de utilizar el coso de barras \n",
+    "\n",
+    "for i in tqdm(range(len(data.offices))):\n",
+    "    test1 = pd.DataFrame(data.iloc[i].offices)\n",
+    "    test1['name'] = str(data.loc[i,['name'][0]])\n",
+    "    test1['total_employees'] = data.iloc[i].number_of_employees\n",
+    "    test1['category'] = str(data.loc[i,['category_code'][0]])\n",
+    "    test1['total_offices'] = len(data.iloc[i].offices)\n",
+    "    lista_df.append(test1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td>710 - 2nd Avenue</td>\n",
+       "      <td>Suite 1100</td>\n",
+       "      <td>98104</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>47.6031</td>\n",
+       "      <td>-122.333</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td></td>\n",
+       "      <td>270 Lafayette Street</td>\n",
+       "      <td>Suite 505</td>\n",
+       "      <td>10012</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7237</td>\n",
+       "      <td>-73.9964</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd.</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.6929</td>\n",
+       "      <td>-121.905</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.6929</td>\n",
+       "      <td>-121.905</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.7647</td>\n",
+       "      <td>-122.395</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    description              address1    address2 zip_code           city  \\\n",
+       "0                    710 - 2nd Avenue  Suite 1100    98104        Seattle   \n",
+       "1                270 Lafayette Street   Suite 505    10012       New York   \n",
+       "2  Headquarters      4900 Hopyard Rd.   Suite 310    94588     Pleasanton   \n",
+       "3  Headquarters       4900 Hopyard Rd   Suite 310    94588     Pleasanton   \n",
+       "4          None    135 Mississippi St                94107  San Francisco   \n",
+       "\n",
+       "  state_code country_code latitude longitude       name  total_employees  \\\n",
+       "0         WA          USA  47.6031  -122.333   Wetpaint             47.0   \n",
+       "1         NY          USA  40.7237  -73.9964   Wetpaint             47.0   \n",
+       "2         CA          USA  37.6929  -121.905  AdventNet            600.0   \n",
+       "3         CA          USA  37.6929  -121.905       Zoho           1600.0   \n",
+       "4         CA          USA  37.7647  -122.395       Digg             60.0   \n",
+       "\n",
+       "     category  total_offices  \n",
+       "0         web              2  \n",
+       "1         web              2  \n",
+       "2  enterprise              1  \n",
+       "3    software              1  \n",
+       "4        news              1  "
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final = pd.concat(lista_df).reset_index(drop=True)\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country_code\n",
+       "USA    10229\n",
+       "GBR     1385\n",
+       "CAN      621\n",
+       "IND      519\n",
+       "DEU      407\n",
+       "FRA      375\n",
+       "AUS      272\n",
+       "ESP      240\n",
+       "ISR      239\n",
+       "CHN      209\n",
+       "Name: name, dtype: int64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.groupby('country_code')['name'].count().sort_values(ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 16705/16705 [00:01<00:00, 13950.65it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista_x = []\n",
+    "for i in tqdm(range(len(final.longitude))):\n",
+    "    \n",
+    "    lista_x.append(dict({'type':'Point',\n",
+    "                          'coordinates': [final.longitude[i],\n",
+    "                                         final.latitude[i]]}))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(lista_x[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'type': 'Point', 'coordinates': [-122.333253, 47.603122]}\n",
+      "{'type': 'Point', 'coordinates': [-73.9964312, 40.7237306]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(lista_x[0])):\n",
+    "    print(lista_x[i])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(lista_x[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values(['Point', [-122.333253, 47.603122]])"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lista_x[0].values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final['principal'] = pd.Series(lista_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'Point', 'coordinates': [-122.173887, 37.480999]}"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16705, 14)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td>710 - 2nd Avenue</td>\n",
+       "      <td>Suite 1100</td>\n",
+       "      <td>98104</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>47.6031</td>\n",
+       "      <td>-122.333</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.333253,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td></td>\n",
+       "      <td>270 Lafayette Street</td>\n",
+       "      <td>Suite 505</td>\n",
+       "      <td>10012</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.7237</td>\n",
+       "      <td>-73.9964</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-73.9964312,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd.</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.6929</td>\n",
+       "      <td>-121.905</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-121.904945,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.6929</td>\n",
+       "      <td>-121.905</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-121.904945,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>1601 Willow Road</td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.416</td>\n",
+       "      <td>-122.152</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>5299.0</td>\n",
+       "      <td>social</td>\n",
+       "      <td>3</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.151801,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16677</th>\n",
+       "      <td>Corporate Headquarters</td>\n",
+       "      <td>2 Waters Park Drive, #250</td>\n",
+       "      <td></td>\n",
+       "      <td>94403</td>\n",
+       "      <td>San Mateo</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.5531</td>\n",
+       "      <td>-122.302</td>\n",
+       "      <td>NextLabs</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.301534,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16687</th>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>89511</td>\n",
+       "      <td>Reno</td>\n",
+       "      <td>NV</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>39.3946</td>\n",
+       "      <td>-119.812</td>\n",
+       "      <td>MyChances</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>consulting</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-119.8120795...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16688</th>\n",
+       "      <td>Menlo Park Office</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>94025</td>\n",
+       "      <td>Menlo Park</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.4485</td>\n",
+       "      <td>-122.18</td>\n",
+       "      <td>MyChances</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>consulting</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.1802812...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16697</th>\n",
+       "      <td></td>\n",
+       "      <td>2401 East Katella Avenue</td>\n",
+       "      <td>Suite 300</td>\n",
+       "      <td>92806-5909</td>\n",
+       "      <td>Anaheim</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.8065</td>\n",
+       "      <td>-117.882</td>\n",
+       "      <td>Willdan Group</td>\n",
+       "      <td>385.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-117.882314,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16699</th>\n",
+       "      <td></td>\n",
+       "      <td>400 Royal Palm Way</td>\n",
+       "      <td></td>\n",
+       "      <td>33480</td>\n",
+       "      <td>Palm Beach</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>26.7053</td>\n",
+       "      <td>-80.0414</td>\n",
+       "      <td>QSGI</td>\n",
+       "      <td>164.0</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-80.041395, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3711 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  description                   address1    address2  \\\n",
+       "0                                       710 - 2nd Avenue  Suite 1100   \n",
+       "1                                   270 Lafayette Street   Suite 505   \n",
+       "2                Headquarters           4900 Hopyard Rd.   Suite 310   \n",
+       "3                Headquarters            4900 Hopyard Rd   Suite 310   \n",
+       "5                Headquarters           1601 Willow Road               \n",
+       "...                       ...                        ...         ...   \n",
+       "16677  Corporate Headquarters  2 Waters Park Drive, #250               \n",
+       "16687            Headquarters                                          \n",
+       "16688       Menlo Park Office                                          \n",
+       "16697                           2401 East Katella Avenue   Suite 300   \n",
+       "16699                                 400 Royal Palm Way               \n",
+       "\n",
+       "         zip_code        city state_code country_code latitude longitude  \\\n",
+       "0           98104     Seattle         WA          USA  47.6031  -122.333   \n",
+       "1           10012    New York         NY          USA  40.7237  -73.9964   \n",
+       "2           94588  Pleasanton         CA          USA  37.6929  -121.905   \n",
+       "3           94588  Pleasanton         CA          USA  37.6929  -121.905   \n",
+       "5           94025  Menlo Park         CA          USA   37.416  -122.152   \n",
+       "...           ...         ...        ...          ...      ...       ...   \n",
+       "16677       94403   San Mateo         CA          USA  37.5531  -122.302   \n",
+       "16687       89511        Reno         NV          USA  39.3946  -119.812   \n",
+       "16688       94025  Menlo Park         CA          USA  37.4485   -122.18   \n",
+       "16697  92806-5909     Anaheim         CA          USA  33.8065  -117.882   \n",
+       "16699       33480  Palm Beach         FL          USA  26.7053  -80.0414   \n",
+       "\n",
+       "                name  total_employees    category  total_offices  \\\n",
+       "0           Wetpaint             47.0         web              2   \n",
+       "1           Wetpaint             47.0         web              2   \n",
+       "2          AdventNet            600.0  enterprise              1   \n",
+       "3               Zoho           1600.0    software              1   \n",
+       "5           Facebook           5299.0      social              3   \n",
+       "...              ...              ...         ...            ...   \n",
+       "16677       NextLabs            100.0  enterprise              1   \n",
+       "16687      MyChances              2.0  consulting              2   \n",
+       "16688      MyChances              2.0  consulting              2   \n",
+       "16697  Willdan Group            385.0       other              1   \n",
+       "16699           QSGI            164.0    software              1   \n",
+       "\n",
+       "                                               principal  \n",
+       "0      {'type': 'Point', 'coordinates': [-122.333253,...  \n",
+       "1      {'type': 'Point', 'coordinates': [-73.9964312,...  \n",
+       "2      {'type': 'Point', 'coordinates': [-121.904945,...  \n",
+       "3      {'type': 'Point', 'coordinates': [-121.904945,...  \n",
+       "5      {'type': 'Point', 'coordinates': [-122.151801,...  \n",
+       "...                                                  ...  \n",
+       "16677  {'type': 'Point', 'coordinates': [-122.301534,...  \n",
+       "16687  {'type': 'Point', 'coordinates': [-119.8120795...  \n",
+       "16688  {'type': 'Point', 'coordinates': [-122.1802812...  \n",
+       "16697  {'type': 'Point', 'coordinates': [-117.882314,...  \n",
+       "16699  {'type': 'Point', 'coordinates': [-80.041395, ...  \n",
+       "\n",
+       "[3711 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final.to_json('oficinas.json') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/EstebanCardona/Documents/IronHack/datamex0320/module-2/python-bi-project/python-bi-project - Esteban'"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pymongo.results.InsertManyResult at 0x1400f6c80>"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.first_officetest.insert_many(final.to_dict('records')) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OperationFailure",
+     "evalue": "Can't extract geo keys: { _id: ObjectId('5e9b71b554d244e37a3cc54a'), description: \"\", address1: \"Suite 200\", address2: \"654 High Street\", zip_code: \"94301\", city: \"Palo Alto\", state_code: \"CA\", country_code: \"ISR\", latitude: null, longitude: null, name: \"Omnidrive\", total_employees: nan.0, category: \"network_hosting\", total_offices: 1, principal: { type: \"Point\", coordinates: [ null, null ] } }  Point must only contain numeric elements",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOperationFailure\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-222-00e165964913>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst_officetest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'principal'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'2dsphere'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36mcreate_index\u001b[0;34m(self, keys, session, **kwargs)\u001b[0m\n\u001b[1;32m   1993\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"maxTimeMS\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1994\u001b[0m             \u001b[0mcmd_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"maxTimeMS\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"maxTimeMS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1995\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__create_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcmd_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1996\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36m__create_index\u001b[0;34m(self, keys, index_options, session, **kwargs)\u001b[0m\n\u001b[1;32m   1888\u001b[0m             \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSON\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'createIndexes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'indexes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1889\u001b[0m             \u001b[0mcmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1890\u001b[0;31m             self._command(\n\u001b[0m\u001b[1;32m   1891\u001b[0m                 \u001b[0msock_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_preference\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mReadPreference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPRIMARY\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1892\u001b[0m                 \u001b[0mcodec_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_UNICODE_REPLACE_CODEC_OPTIONS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36m_command\u001b[0;34m(self, sock_info, command, slave_ok, read_preference, codec_options, check, allowable_errors, read_concern, write_concern, collation, session, retryable_write, user_fields)\u001b[0m\n\u001b[1;32m    233\u001b[0m         \"\"\"\n\u001b[1;32m    234\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__database\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tmp_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             return sock_info.command(\n\u001b[0m\u001b[1;32m    236\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__database\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/pool.py\u001b[0m in \u001b[0;36mcommand\u001b[0;34m(self, dbname, spec, slave_ok, read_preference, codec_options, check, allowable_errors, check_keys, read_concern, write_concern, parse_write_concern_error, collation, session, client, retryable_write, publish_events, user_fields)\u001b[0m\n\u001b[1;32m    601\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_not_writable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munacknowledged\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m             return command(self.sock, dbname, spec, slave_ok,\n\u001b[0m\u001b[1;32m    604\u001b[0m                            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_mongos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_preference\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodec_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m                            \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowable_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/network.py\u001b[0m in \u001b[0;36mcommand\u001b[0;34m(sock, dbname, spec, slave_ok, is_mongos, read_preference, codec_options, session, client, check, allowable_errors, address, check_keys, listeners, max_bson_size, read_concern, parse_write_concern_error, collation, compression_ctx, use_op_msg, unacknowledged, user_fields)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_doc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 helpers._check_command_response(\n\u001b[0m\u001b[1;32m    166\u001b[0m                     \u001b[0mresponse_doc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowable_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                     parse_write_concern_error=parse_write_concern_error)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/helpers.py\u001b[0m in \u001b[0;36m_check_command_response\u001b[0;34m(response, msg, allowable_errors, parse_write_concern_error)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"%s\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mOperationFailure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationFailure\u001b[0m: Can't extract geo keys: { _id: ObjectId('5e9b71b554d244e37a3cc54a'), description: \"\", address1: \"Suite 200\", address2: \"654 High Street\", zip_code: \"94301\", city: \"Palo Alto\", state_code: \"CA\", country_code: \"ISR\", latitude: null, longitude: null, name: \"Omnidrive\", total_employees: nan.0, category: \"network_hosting\", total_offices: 1, principal: { type: \"Point\", coordinates: [ null, null ] } }  Point must only contain numeric elements"
+     ]
+    }
+   ],
+   "source": [
+    "db.first_officetest.create_index([('principal', '2dsphere')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cole = db.first_office"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>description</th>\n",
+       "      <th>address1</th>\n",
+       "      <th>address2</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state_code</th>\n",
+       "      <th>country_code</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>name</th>\n",
+       "      <th>total_employees</th>\n",
+       "      <th>category</th>\n",
+       "      <th>total_offices</th>\n",
+       "      <th>principal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5e9b6c4d54d244e37a3c42c0</td>\n",
+       "      <td></td>\n",
+       "      <td>710 - 2nd Avenue</td>\n",
+       "      <td>Suite 1100</td>\n",
+       "      <td>98104</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>47.603122</td>\n",
+       "      <td>-122.333253</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.333253,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5e9b6c4d54d244e37a3c42c1</td>\n",
+       "      <td></td>\n",
+       "      <td>270 Lafayette Street</td>\n",
+       "      <td>Suite 505</td>\n",
+       "      <td>10012</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.723731</td>\n",
+       "      <td>-73.996431</td>\n",
+       "      <td>Wetpaint</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>web</td>\n",
+       "      <td>2</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-73.9964312,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5e9b6c4d54d244e37a3c42c2</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd.</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.692934</td>\n",
+       "      <td>-121.904945</td>\n",
+       "      <td>AdventNet</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>enterprise</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-121.904945,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5e9b6c4d54d244e37a3c42c3</td>\n",
+       "      <td>Headquarters</td>\n",
+       "      <td>4900 Hopyard Rd</td>\n",
+       "      <td>Suite 310</td>\n",
+       "      <td>94588</td>\n",
+       "      <td>Pleasanton</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.692934</td>\n",
+       "      <td>-121.904945</td>\n",
+       "      <td>Zoho</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>software</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-121.904945,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5e9b6c4d54d244e37a3c42c4</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135 Mississippi St</td>\n",
+       "      <td></td>\n",
+       "      <td>94107</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.764726</td>\n",
+       "      <td>-122.394523</td>\n",
+       "      <td>Digg</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>news</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'type': 'Point', 'coordinates': [-122.394523,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        _id   description              address1    address2  \\\n",
+       "0  5e9b6c4d54d244e37a3c42c0                    710 - 2nd Avenue  Suite 1100   \n",
+       "1  5e9b6c4d54d244e37a3c42c1                270 Lafayette Street   Suite 505   \n",
+       "2  5e9b6c4d54d244e37a3c42c2  Headquarters      4900 Hopyard Rd.   Suite 310   \n",
+       "3  5e9b6c4d54d244e37a3c42c3  Headquarters       4900 Hopyard Rd   Suite 310   \n",
+       "4  5e9b6c4d54d244e37a3c42c4          None    135 Mississippi St               \n",
+       "\n",
+       "  zip_code           city state_code country_code   latitude   longitude  \\\n",
+       "0    98104        Seattle         WA          USA  47.603122 -122.333253   \n",
+       "1    10012       New York         NY          USA  40.723731  -73.996431   \n",
+       "2    94588     Pleasanton         CA          USA  37.692934 -121.904945   \n",
+       "3    94588     Pleasanton         CA          USA  37.692934 -121.904945   \n",
+       "4    94107  San Francisco         CA          USA  37.764726 -122.394523   \n",
+       "\n",
+       "        name  total_employees    category  total_offices  \\\n",
+       "0   Wetpaint             47.0         web              2   \n",
+       "1   Wetpaint             47.0         web              2   \n",
+       "2  AdventNet            600.0  enterprise              1   \n",
+       "3       Zoho           1600.0    software              1   \n",
+       "4       Digg             60.0        news              1   \n",
+       "\n",
+       "                                           principal  \n",
+       "0  {'type': 'Point', 'coordinates': [-122.333253,...  \n",
+       "1  {'type': 'Point', 'coordinates': [-73.9964312,...  \n",
+       "2  {'type': 'Point', 'coordinates': [-121.904945,...  \n",
+       "3  {'type': 'Point', 'coordinates': [-121.904945,...  \n",
+       "4  {'type': 'Point', 'coordinates': [-122.394523,...  "
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(cole.find({})).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_near(geopoint, radio=1000):\n",
+    "    \n",
+    "    return cole.find({'principal':{\n",
+    "        '$near': {'$geometry': geopoint, '$maxDistance':radio}\n",
+    "    }})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-219-1f5f90edae0c>:5: DeprecationWarning: count is deprecated. Use Collection.count_documents instead.\n",
+      "  n_ofi=find_near(park_avenue, radio).count()\n"
+     ]
+    },
+    {
+     "ename": "OperationFailure",
+     "evalue": "error processing query: ns=companies.first_officeTree: GEONEAR  field=principal maxdist=1000 isNearSphere=0\nSort: {}\nProj: {}\n planner returned error :: caused by :: unable to find index for $geoNear query",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOperationFailure\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-219-1f5f90edae0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mradio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m  \u001b[0;31m# radio de busqueda\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mn_ofi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_near\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpark_avenue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mn_ofi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/cursor.py\u001b[0m in \u001b[0;36mcount\u001b[0;34m(self, with_limit_and_skip)\u001b[0m\n\u001b[1;32m    784\u001b[0m                 \u001b[0mcmd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"skip\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__skip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m         return self.__collection._count(\n\u001b[0m\u001b[1;32m    787\u001b[0m             cmd, self.__collation, session=self.__session)\n\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36m_count\u001b[0;34m(self, cmd, collation, session)\u001b[0m\n\u001b[1;32m   1597\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"n\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m         return self.__database.client._retryable_read(\n\u001b[0m\u001b[1;32m   1600\u001b[0m             _cmd, self._read_preference_for(session), session)\n\u001b[1;32m   1601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/mongo_client.py\u001b[0m in \u001b[0;36m_retryable_read\u001b[0;34m(self, func, read_pref, session, address, retryable, exhaust)\u001b[0m\n\u001b[1;32m   1462\u001b[0m                         \u001b[0;31m# not support retryable reads, raise the last error.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1463\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mlast_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1464\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msock_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslave_ok\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1465\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mServerSelectionTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1466\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mretrying\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36m_cmd\u001b[0;34m(session, server, sock_info, slave_ok)\u001b[0m\n\u001b[1;32m   1584\u001b[0m         \u001b[0;34m\"\"\"Internal count helper.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1585\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_cmd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msock_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslave_ok\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1586\u001b[0;31m             res = self._command(\n\u001b[0m\u001b[1;32m   1587\u001b[0m                 \u001b[0msock_info\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m                 \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/collection.py\u001b[0m in \u001b[0;36m_command\u001b[0;34m(self, sock_info, command, slave_ok, read_preference, codec_options, check, allowable_errors, read_concern, write_concern, collation, session, retryable_write, user_fields)\u001b[0m\n\u001b[1;32m    233\u001b[0m         \"\"\"\n\u001b[1;32m    234\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__database\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tmp_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             return sock_info.command(\n\u001b[0m\u001b[1;32m    236\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__database\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/pool.py\u001b[0m in \u001b[0;36mcommand\u001b[0;34m(self, dbname, spec, slave_ok, read_preference, codec_options, check, allowable_errors, check_keys, read_concern, write_concern, parse_write_concern_error, collation, session, client, retryable_write, publish_events, user_fields)\u001b[0m\n\u001b[1;32m    601\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_not_writable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munacknowledged\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m             return command(self.sock, dbname, spec, slave_ok,\n\u001b[0m\u001b[1;32m    604\u001b[0m                            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_mongos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread_preference\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodec_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m                            \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowable_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/network.py\u001b[0m in \u001b[0;36mcommand\u001b[0;34m(sock, dbname, spec, slave_ok, is_mongos, read_preference, codec_options, session, client, check, allowable_errors, address, check_keys, listeners, max_bson_size, read_concern, parse_write_concern_error, collation, compression_ctx, use_op_msg, unacknowledged, user_fields)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_doc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 helpers._check_command_response(\n\u001b[0m\u001b[1;32m    166\u001b[0m                     \u001b[0mresponse_doc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowable_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                     parse_write_concern_error=parse_write_concern_error)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pymongo/helpers.py\u001b[0m in \u001b[0;36m_check_command_response\u001b[0;34m(response, msg, allowable_errors, parse_write_concern_error)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"%s\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mOperationFailure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationFailure\u001b[0m: error processing query: ns=companies.first_officeTree: GEONEAR  field=principal maxdist=1000 isNearSphere=0\nSort: {}\nProj: {}\n planner returned error :: caused by :: unable to find index for $geoNear query"
+     ]
+    }
+   ],
+   "source": [
+    "park_avenue={'type': 'Point', 'coordinates':[-73.9964312, 40.7237306]}  # geopoint\n",
+    "\n",
+    "radio=1000  # radio de busqueda\n",
+    "\n",
+    "n_ofi=find_near(park_avenue, radio).count()\n",
+    "\n",
+    "n_ofi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "GeocoderServiceError",
+     "evalue": "[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mSSLCertVerificationError\u001b[0m                  Traceback (most recent call last)",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1316\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1317\u001b[0;31m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m   1318\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1230\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1275\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1276\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1224\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1225\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    943\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    945\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m             self.sock = self._context.wrap_socket(self.sock,\n\u001b[0m\u001b[1;32m   1400\u001b[0m                                                   server_hostname=server_hostname)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# ctx._wrap_socket()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m         return self.sslsocket_class._create(\n\u001b[0m\u001b[1;32m    501\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36m_create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1039\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1308\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mSSLCertVerificationError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[0;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequester\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'urllib.Request'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m    543\u001b[0m                                   '_open', req)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1359\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1360\u001b[0;31m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m   1361\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1319\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1321\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mURLError\u001b[0m: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)>",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mGeocoderServiceError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-09c5e8c778a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mgeolocator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNominatim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'teburgr_app'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mlocation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeolocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"175 5th Avenue NYC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/osm.py\u001b[0m in \u001b[0;36mgeocode\u001b[0;34m(self, query, exactly_one, timeout, limit, addressdetails, language, geometry, extratags, country_codes, viewbox, bounded, featuretype, namedetails)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         return self._parse_json(\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_geocoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexactly_one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         )\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[0;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"timed out\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mGeocoderTimedOut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Service timed out'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mGeocoderServiceError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'getcode'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mGeocoderServiceError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)"
+     ]
+    }
+   ],
+   "source": [
+    "from geopy.geocoders import Nominatim\n",
+    "import certifi\n",
+    "\n",
+    "geolocator = Nominatim(user_agent='teburgr_app')\n",
+    "\n",
+    "location = geolocator.geocode(\"175 5th Avenue NYC\")\n",
+    "\n",
+    "location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "GeocoderServiceError",
+     "evalue": "[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mSSLCertVerificationError\u001b[0m                  Traceback (most recent call last)",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1316\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1317\u001b[0;31m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m   1318\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1230\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1275\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1276\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1224\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1225\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    943\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    945\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m             self.sock = self._context.wrap_socket(self.sock,\n\u001b[0m\u001b[1;32m   1400\u001b[0m                                                   server_hostname=server_hostname)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# ctx._wrap_socket()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m         return self.sslsocket_class._create(\n\u001b[0m\u001b[1;32m    501\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36m_create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1039\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1308\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mSSLCertVerificationError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[0;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequester\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'urllib.Request'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m    543\u001b[0m                                   '_open', req)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1359\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1360\u001b[0;31m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m   1361\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1319\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1321\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mURLError\u001b[0m: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)>",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mGeocoderServiceError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-67d0c48332d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlocation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeolocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"175 5th Avenue NYC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/osm.py\u001b[0m in \u001b[0;36mgeocode\u001b[0;34m(self, query, exactly_one, timeout, limit, addressdetails, language, geometry, extratags, country_codes, viewbox, bounded, featuretype, namedetails)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         return self._parse_json(\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_geocoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexactly_one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         )\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopy/geocoders/base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[0;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"timed out\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mGeocoderTimedOut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Service timed out'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mGeocoderServiceError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'getcode'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mGeocoderServiceError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)"
+     ]
+    }
+   ],
+   "source": [
+    "location = geolocator.geocode(\"175 5th Avenue NYC\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/geocoder testing.ipynb b/module-2/python-bi-project/python-bi-project - Esteban/geocoder testing.ipynb
new file mode 100644
index 0000000..e656f10
--- /dev/null
+++ b/module-2/python-bi-project/python-bi-project - Esteban/geocoder testing.ipynb	
@@ -0,0 +1,1136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygeocoder import Geocoder\n",
+    "import pandas as pd\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "GeocoderError",
+     "evalue": "Error REQUEST_DENIED\nQuery: https://maps.google.com/maps/api/geocode/json?address=4207+N+Washington+Ave%2C+Douglas%2C+AZ+85607&sensor=false&bounds=&region=&language=&components=",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mGeocoderError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-d3bbfc4bacdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mGeocoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4207 N Washington Ave, Douglas, AZ 85607\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_address\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pygeocoder.py\u001b[0m in \u001b[0;36mgeocode\u001b[0;34m(self, address, sensor, bounds, region, language, components)\u001b[0m\n\u001b[1;32m    127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mGeocoderResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mGeocoderResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGeocoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0momnimethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pygeocoder.py\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresponse_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mGeocoderError\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mG_GEO_OK\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mGeocoderError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'results'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mGeocoderError\u001b[0m: Error REQUEST_DENIED\nQuery: https://maps.google.com/maps/api/geocode/json?address=4207+N+Washington+Ave%2C+Douglas%2C+AZ+85607&sensor=false&bounds=&region=&language=&components="
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting opencage\n",
+      "  Downloading opencage-1.2-py3-none-any.whl (5.6 kB)\n",
+      "Requirement already satisfied: six>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from opencage) (1.14.0)\n",
+      "Requirement already satisfied: Requests>=2.2.0 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from opencage) (2.23.0)\n",
+      "Requirement already satisfied: chardet<4,>=3.0.2 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from Requests>=2.2.0->opencage) (3.0.4)\n",
+      "Requirement already satisfied: idna<3,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from Requests>=2.2.0->opencage) (2.9)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from Requests>=2.2.0->opencage) (2020.4.5.1)\n",
+      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (from Requests>=2.2.0->opencage) (1.25.8)\n",
+      "Installing collected packages: opencage\n",
+      "Successfully installed opencage-1.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip3 install opencage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from opencage.geocoder import OpenCageGeocode\n",
+    "\n",
+    "\n",
+    "key = 'd8314354b31d46a298e0f21ffb920a34'\n",
+    "geocoder = OpenCageGeocode(key)\n",
+    "\n",
+    "results = geocoder.reverse_geocode(44.8303087, -0.5761911)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['annotations', 'bounds', 'components', 'confidence', 'formatted', 'geometry'])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'annotations': {'DMS': {'lat': \"44° 49' 49.11132'' N\",\n",
+       "    'lng': \"0° 34' 34.28796'' E\"},\n",
+       "   'MGRS': '30TXQ9159666958',\n",
+       "   'Maidenhead': 'IN94rt09ug',\n",
+       "   'Mercator': {'x': -64141.3, 'y': 5564710.789},\n",
+       "   'OSM': {'edit_url': 'https://www.openstreetmap.org/edit?node=2754853042#map=16/44.83031/-0.57619',\n",
+       "    'note_url': 'https://www.openstreetmap.org/note/new#map=16/44.83031/-0.57619&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=44.83031&mlon=-0.57619#map=16/44.83031/-0.57619'},\n",
+       "   'UN_M49': {'regions': {'EUROPE': '150',\n",
+       "     'FR': '250',\n",
+       "     'WESTERN_EUROPE': '155',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 33,\n",
+       "   'currency': {'alternate_symbols': [],\n",
+       "    'decimal_mark': ',',\n",
+       "    'html_entity': '&#x20AC;',\n",
+       "    'iso_code': 'EUR',\n",
+       "    'iso_numeric': '978',\n",
+       "    'name': 'Euro',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '€',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': '.'},\n",
+       "   'flag': '🇫🇷',\n",
+       "   'geohash': 'ezzx4ch7mz0zbyftcpdf',\n",
+       "   'qibla': 111.75,\n",
+       "   'roadinfo': {'drive_on': 'right',\n",
+       "    'road': 'Rue Sauteyron',\n",
+       "    'speed_in': 'km/h'},\n",
+       "   'sun': {'rise': {'apparent': 1587273120,\n",
+       "     'astronomical': 1587266580,\n",
+       "     'civil': 1587271260,\n",
+       "     'nautical': 1587268980},\n",
+       "    'set': {'apparent': 1587322320,\n",
+       "     'astronomical': 1587328860,\n",
+       "     'civil': 1587324180,\n",
+       "     'nautical': 1587326460}},\n",
+       "   'timezone': {'name': 'Europe/Paris',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': 7200,\n",
+       "    'offset_string': '+0200',\n",
+       "    'short_name': 'CEST'},\n",
+       "   'what3words': {'words': 'advice.quiet.surveyed'}},\n",
+       "  'bounds': {'northeast': {'lat': 44.8304087, 'lng': -0.5760911},\n",
+       "   'southwest': {'lat': 44.8302087, 'lng': -0.5762911}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'FR',\n",
+       "   'ISO_3166-1_alpha-3': 'FRA',\n",
+       "   '_category': 'building',\n",
+       "   '_type': 'building',\n",
+       "   'city': 'Bordeaux',\n",
+       "   'continent': 'Europe',\n",
+       "   'country': 'France',\n",
+       "   'country_code': 'fr',\n",
+       "   'county': 'Bordeaux',\n",
+       "   'house_number': '11',\n",
+       "   'political_union': 'European Union',\n",
+       "   'postcode': '33000',\n",
+       "   'road': 'Rue Sauteyron',\n",
+       "   'state': 'New Aquitaine',\n",
+       "   'state_district': 'Gironde',\n",
+       "   'suburb': 'Bordeaux Sud'},\n",
+       "  'confidence': 10,\n",
+       "  'formatted': '11 Rue Sauteyron, 33000 Bordeaux, France',\n",
+       "  'geometry': {'lat': 44.8303087, 'lng': -0.5761911}}]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'OpenCageGeocode' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-5e06e9daa870>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeocoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'175 5th Avenue NYC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'OpenCageGeocode' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "results = geocoder('175 5th Avenue NYC')\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'annotations': {'DMS': {'lat': \"40° 44' 27.81312'' N\",\n",
+       "    'lng': \"73° 59' 22.70976'' W\"},\n",
+       "   'FIPS': {'county': '36061', 'state': '36'},\n",
+       "   'MGRS': '18TWL8530510503',\n",
+       "   'Maidenhead': 'FN30ar17fu',\n",
+       "   'Mercator': {'x': -8236489.229, 'y': 4946329.848},\n",
+       "   'OSM': {'edit_url': 'https://www.openstreetmap.org/edit?way=264768896#map=16/40.74106/-73.98964',\n",
+       "    'note_url': 'https://www.openstreetmap.org/note/new#map=16/40.74106/-73.98964&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=40.74106&mlon=-73.98964#map=16/40.74106/-73.98964'},\n",
+       "   'UN_M49': {'regions': {'AMERICAS': '019',\n",
+       "     'NORTHERN_AMERICA': '021',\n",
+       "     'US': '840',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 1,\n",
+       "   'currency': {'alternate_symbols': ['US$'],\n",
+       "    'decimal_mark': '.',\n",
+       "    'disambiguate_symbol': 'US$',\n",
+       "    'html_entity': '$',\n",
+       "    'iso_code': 'USD',\n",
+       "    'iso_numeric': '840',\n",
+       "    'name': 'United States Dollar',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '$',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': ','},\n",
+       "   'flag': '🇺🇸',\n",
+       "   'geohash': 'dr5ru2dmu7ukx328qsw0',\n",
+       "   'qibla': 58.49,\n",
+       "   'roadinfo': {'drive_on': 'right', 'road': '5th Avenue', 'speed_in': 'mph'},\n",
+       "   'sun': {'rise': {'apparent': 1587291060,\n",
+       "     'astronomical': 1587285120,\n",
+       "     'civil': 1587289380,\n",
+       "     'nautical': 1587287280},\n",
+       "    'set': {'apparent': 1587339540,\n",
+       "     'astronomical': 1587259140,\n",
+       "     'civil': 1587254880,\n",
+       "     'nautical': 1587256980}},\n",
+       "   'timezone': {'name': 'America/New_York',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': -14400,\n",
+       "    'offset_string': '-0400',\n",
+       "    'short_name': 'EDT'},\n",
+       "   'what3words': {'words': 'news.lung.bossy'},\n",
+       "   'wikidata': 'Q220728'},\n",
+       "  'bounds': {'northeast': {'lat': 40.7413004, 'lng': -73.9895014},\n",
+       "   'southwest': {'lat': 40.7407597, 'lng': -73.9898715}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'US',\n",
+       "   'ISO_3166-1_alpha-3': 'USA',\n",
+       "   '_category': 'travel/tourism',\n",
+       "   '_type': 'attraction',\n",
+       "   'attraction': 'Flatiron Building',\n",
+       "   'city': 'New York',\n",
+       "   'continent': 'North America',\n",
+       "   'country': 'United States of America',\n",
+       "   'country_code': 'us',\n",
+       "   'county': 'New York County',\n",
+       "   'house_number': '175',\n",
+       "   'postcode': '10010',\n",
+       "   'road': '5th Avenue',\n",
+       "   'state': 'New York',\n",
+       "   'state_code': 'NY',\n",
+       "   'suburb': 'Manhattan'},\n",
+       "  'confidence': 9,\n",
+       "  'formatted': 'Flatiron Building, 175 5th Avenue, New York, NY 10010, United States of America',\n",
+       "  'geometry': {'lat': 40.7410592, 'lng': -73.9896416}},\n",
+       " {'annotations': {'DMS': {'lat': \"40° 44' 27.85992'' N\",\n",
+       "    'lng': \"73° 59' 22.75224'' W\"},\n",
+       "   'FIPS': {'county': '36061', 'state': '36'},\n",
+       "   'MGRS': '18TWL8530410505',\n",
+       "   'Maidenhead': 'FN30ar17fu',\n",
+       "   'Mercator': {'x': -8236490.54, 'y': 4946331.751},\n",
+       "   'OSM': {'edit_url': 'https://www.openstreetmap.org/edit?node=2517056822#map=16/40.74107/-73.98965',\n",
+       "    'note_url': 'https://www.openstreetmap.org/note/new#map=16/40.74107/-73.98965&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=40.74107&mlon=-73.98965#map=16/40.74107/-73.98965'},\n",
+       "   'UN_M49': {'regions': {'AMERICAS': '019',\n",
+       "     'NORTHERN_AMERICA': '021',\n",
+       "     'US': '840',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 1,\n",
+       "   'currency': {'alternate_symbols': ['US$'],\n",
+       "    'decimal_mark': '.',\n",
+       "    'disambiguate_symbol': 'US$',\n",
+       "    'html_entity': '$',\n",
+       "    'iso_code': 'USD',\n",
+       "    'iso_numeric': '840',\n",
+       "    'name': 'United States Dollar',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '$',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': ','},\n",
+       "   'flag': '🇺🇸',\n",
+       "   'geohash': 'dr5ru2dmun7dn4zctf59',\n",
+       "   'qibla': 58.49,\n",
+       "   'roadinfo': {'drive_on': 'right', 'road': '5th Avenue', 'speed_in': 'mph'},\n",
+       "   'sun': {'rise': {'apparent': 1587291060,\n",
+       "     'astronomical': 1587285120,\n",
+       "     'civil': 1587289380,\n",
+       "     'nautical': 1587287280},\n",
+       "    'set': {'apparent': 1587339540,\n",
+       "     'astronomical': 1587259140,\n",
+       "     'civil': 1587254880,\n",
+       "     'nautical': 1587256980}},\n",
+       "   'timezone': {'name': 'America/New_York',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': -14400,\n",
+       "    'offset_string': '-0400',\n",
+       "    'short_name': 'EDT'},\n",
+       "   'what3words': {'words': 'mute.liver.spoke'},\n",
+       "   'wikidata': 'Q1427377'},\n",
+       "  'bounds': {'northeast': {'lat': 40.7411222, 'lng': -73.9896034},\n",
+       "   'southwest': {'lat': 40.7410222, 'lng': -73.9897034}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'US',\n",
+       "   'ISO_3166-1_alpha-3': 'USA',\n",
+       "   '_category': 'travel/tourism',\n",
+       "   '_type': 'attraction',\n",
+       "   'attraction': 'Flatiron District',\n",
+       "   'city': 'New York',\n",
+       "   'continent': 'North America',\n",
+       "   'country': 'United States of America',\n",
+       "   'country_code': 'us',\n",
+       "   'county': 'New York County',\n",
+       "   'house_number': '175',\n",
+       "   'postcode': '10010',\n",
+       "   'road': '5th Avenue',\n",
+       "   'state': 'New York',\n",
+       "   'state_code': 'NY',\n",
+       "   'suburb': 'Manhattan'},\n",
+       "  'confidence': 9,\n",
+       "  'formatted': 'Flatiron District, 175 5th Avenue, New York, NY 10010, United States of America',\n",
+       "  'geometry': {'lat': 40.7410722, 'lng': -73.9896534}},\n",
+       " {'annotations': {'DMS': {'lat': \"40° 40' 38.10144'' N\",\n",
+       "    'lng': \"73° 58' 46.65180'' W\"},\n",
+       "   'FIPS': {'county': '36047', 'state': '36'},\n",
+       "   'MGRS': '18TWL8623203430',\n",
+       "   'Maidenhead': 'FN30aq22km',\n",
+       "   'Mercator': {'x': -8235374.234, 'y': 4936995.433},\n",
+       "   'OSM': {'edit_url': 'https://www.openstreetmap.org/edit?way=250014491#map=16/40.67725/-73.97963',\n",
+       "    'note_url': 'https://www.openstreetmap.org/note/new#map=16/40.67725/-73.97963&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=40.67725&mlon=-73.97963#map=16/40.67725/-73.97963'},\n",
+       "   'UN_M49': {'regions': {'AMERICAS': '019',\n",
+       "     'NORTHERN_AMERICA': '021',\n",
+       "     'US': '840',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 1,\n",
+       "   'currency': {'alternate_symbols': ['US$'],\n",
+       "    'decimal_mark': '.',\n",
+       "    'disambiguate_symbol': 'US$',\n",
+       "    'html_entity': '$',\n",
+       "    'iso_code': 'USD',\n",
+       "    'iso_numeric': '840',\n",
+       "    'name': 'United States Dollar',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '$',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': ','},\n",
+       "   'flag': '🇺🇸',\n",
+       "   'geohash': 'dr5rkt1djv11fmw94014',\n",
+       "   'qibla': 58.5,\n",
+       "   'roadinfo': {'drive_on': 'right', 'road': '5th Avenue', 'speed_in': 'mph'},\n",
+       "   'sun': {'rise': {'apparent': 1587291060,\n",
+       "     'astronomical': 1587285120,\n",
+       "     'civil': 1587289380,\n",
+       "     'nautical': 1587287280},\n",
+       "    'set': {'apparent': 1587339540,\n",
+       "     'astronomical': 1587259140,\n",
+       "     'civil': 1587254880,\n",
+       "     'nautical': 1587256920}},\n",
+       "   'timezone': {'name': 'America/New_York',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': -14400,\n",
+       "    'offset_string': '-0400',\n",
+       "    'short_name': 'EDT'},\n",
+       "   'what3words': {'words': 'values.bills.part'}},\n",
+       "  'bounds': {'northeast': {'lat': 40.6773242, 'lng': -73.979504},\n",
+       "   'southwest': {'lat': 40.6771765, 'lng': -73.9797469}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'US',\n",
+       "   'ISO_3166-1_alpha-3': 'USA',\n",
+       "   '_category': 'place_of_worship',\n",
+       "   '_type': 'place_of_worship',\n",
+       "   'city': 'New York',\n",
+       "   'continent': 'North America',\n",
+       "   'country': 'United States of America',\n",
+       "   'country_code': 'us',\n",
+       "   'county': 'Kings County',\n",
+       "   'house_number': '175',\n",
+       "   'neighbourhood': 'Park Slope',\n",
+       "   'place_of_worship': 'Iglesia Pentecostal De Jesu Cristo',\n",
+       "   'postcode': '11217',\n",
+       "   'road': '5th Avenue',\n",
+       "   'state': 'New York',\n",
+       "   'state_code': 'NY',\n",
+       "   'suburb': 'Brooklyn'},\n",
+       "  'confidence': 9,\n",
+       "  'formatted': 'Iglesia Pentecostal De Jesu Cristo, 175 5th Avenue, New York, NY 11217, United States of America',\n",
+       "  'geometry': {'lat': 40.6772504, 'lng': -73.9796255}},\n",
+       " {'annotations': {'DMS': {'lat': \"40° 44' 27.95208'' N\",\n",
+       "    'lng': \"73° 59' 23.00856'' W\"},\n",
+       "   'FIPS': {'county': '36061', 'state': '36'},\n",
+       "   'MGRS': '18TWL8529710507',\n",
+       "   'Maidenhead': 'FN30ar17fu',\n",
+       "   'Mercator': {'x': -8236498.466, 'y': 4946335.498},\n",
+       "   'OSM': {'edit_url': 'https://www.openstreetmap.org/edit?node=7139825363#map=16/40.74110/-73.98972',\n",
+       "    'note_url': 'https://www.openstreetmap.org/note/new#map=16/40.74110/-73.98972&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=40.74110&mlon=-73.98972#map=16/40.74110/-73.98972'},\n",
+       "   'UN_M49': {'regions': {'AMERICAS': '019',\n",
+       "     'NORTHERN_AMERICA': '021',\n",
+       "     'US': '840',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 1,\n",
+       "   'currency': {'alternate_symbols': ['US$'],\n",
+       "    'decimal_mark': '.',\n",
+       "    'disambiguate_symbol': 'US$',\n",
+       "    'html_entity': '$',\n",
+       "    'iso_code': 'USD',\n",
+       "    'iso_numeric': '840',\n",
+       "    'name': 'United States Dollar',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '$',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': ','},\n",
+       "   'flag': '🇺🇸',\n",
+       "   'geohash': 'dr5ru2dq47ndg3e0w1sk',\n",
+       "   'qibla': 58.49,\n",
+       "   'roadinfo': {'drive_on': 'right', 'road': '5th Avenue', 'speed_in': 'mph'},\n",
+       "   'sun': {'rise': {'apparent': 1587291060,\n",
+       "     'astronomical': 1587285120,\n",
+       "     'civil': 1587289380,\n",
+       "     'nautical': 1587287280},\n",
+       "    'set': {'apparent': 1587339540,\n",
+       "     'astronomical': 1587259140,\n",
+       "     'civil': 1587254880,\n",
+       "     'nautical': 1587256980}},\n",
+       "   'timezone': {'name': 'America/New_York',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': -14400,\n",
+       "    'offset_string': '-0400',\n",
+       "    'short_name': 'EDT'},\n",
+       "   'what3words': {'words': 'fits.lime.trying'}},\n",
+       "  'bounds': {'northeast': {'lat': 40.7411478, 'lng': -73.9896746},\n",
+       "   'southwest': {'lat': 40.7410478, 'lng': -73.9897746}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'US',\n",
+       "   'ISO_3166-1_alpha-3': 'USA',\n",
+       "   '_category': 'building',\n",
+       "   '_type': 'building',\n",
+       "   'city': 'New York',\n",
+       "   'continent': 'North America',\n",
+       "   'country': 'United States of America',\n",
+       "   'country_code': 'us',\n",
+       "   'county': 'New York County',\n",
+       "   'house_number': '175',\n",
+       "   'postcode': '10035',\n",
+       "   'road': '5th Avenue',\n",
+       "   'state': 'New York',\n",
+       "   'state_code': 'NY',\n",
+       "   'suburb': 'Manhattan'},\n",
+       "  'confidence': 10,\n",
+       "  'formatted': '175 5th Avenue, New York, NY 10035, United States of America',\n",
+       "  'geometry': {'lat': 40.7410978, 'lng': -73.9897246}},\n",
+       " {'annotations': {'DMS': {'lat': \"40° 46' 17.29200'' N\",\n",
+       "    'lng': \"73° 58' 3.79200'' W\"},\n",
+       "   'FIPS': {'county': '36061', 'state': '36'},\n",
+       "   'MGRS': '18TWL8711613901',\n",
+       "   'Maidenhead': 'FN30as35ud',\n",
+       "   'Mercator': {'x': -8234048.926, 'y': 4950781.733},\n",
+       "   'OSM': {'note_url': 'https://www.openstreetmap.org/note/new#map=16/40.77147/-73.96772&layers=N',\n",
+       "    'url': 'https://www.openstreetmap.org/?mlat=40.77147&mlon=-73.96772#map=16/40.77147/-73.96772'},\n",
+       "   'UN_M49': {'regions': {'AMERICAS': '019',\n",
+       "     'NORTHERN_AMERICA': '021',\n",
+       "     'US': '840',\n",
+       "     'WORLD': '001'},\n",
+       "    'statistical_groupings': ['MEDC']},\n",
+       "   'callingcode': 1,\n",
+       "   'currency': {'alternate_symbols': ['US$'],\n",
+       "    'decimal_mark': '.',\n",
+       "    'disambiguate_symbol': 'US$',\n",
+       "    'html_entity': '$',\n",
+       "    'iso_code': 'USD',\n",
+       "    'iso_numeric': '840',\n",
+       "    'name': 'United States Dollar',\n",
+       "    'smallest_denomination': 1,\n",
+       "    'subunit': 'Cent',\n",
+       "    'subunit_to_unit': 100,\n",
+       "    'symbol': '$',\n",
+       "    'symbol_first': 1,\n",
+       "    'thousands_separator': ','},\n",
+       "   'flag': '🇺🇸',\n",
+       "   'geohash': 'dr5ruy4r50ykj8cu4xjv',\n",
+       "   'qibla': 58.51,\n",
+       "   'roadinfo': {'drive_on': 'right', 'speed_in': 'mph'},\n",
+       "   'sun': {'rise': {'apparent': 1587291060,\n",
+       "     'astronomical': 1587285060,\n",
+       "     'civil': 1587289320,\n",
+       "     'nautical': 1587287280},\n",
+       "    'set': {'apparent': 1587339540,\n",
+       "     'astronomical': 1587259140,\n",
+       "     'civil': 1587254880,\n",
+       "     'nautical': 1587256980}},\n",
+       "   'timezone': {'name': 'America/New_York',\n",
+       "    'now_in_dst': 1,\n",
+       "    'offset_sec': -14400,\n",
+       "    'offset_string': '-0400',\n",
+       "    'short_name': 'EDT'},\n",
+       "   'what3words': {'words': 'reader.packet.pitch'}},\n",
+       "  'components': {'ISO_3166-1_alpha-2': 'US',\n",
+       "   'ISO_3166-1_alpha-3': 'USA',\n",
+       "   '_category': 'place',\n",
+       "   '_type': 'county',\n",
+       "   'continent': 'North America',\n",
+       "   'country': 'United States of America',\n",
+       "   'country_code': 'us',\n",
+       "   'county': 'New York',\n",
+       "   'state': 'New York',\n",
+       "   'state_code': 'NY'},\n",
+       "  'confidence': 9,\n",
+       "  'formatted': 'New York, NY, United States of America',\n",
+       "  'geometry': {'lat': 40.77147, 'lng': -73.96772}}]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = '175 5th Avenue NYC'\n",
+    "results = geocoder.geocode(query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40.7410592 -73.9896416\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results[0]['geometry']['lat'], results[0]['geometry']['lng'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'🇺🇸'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['annotations']['flag']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getting_coor():\n",
+    "    try:\n",
+    "        x = input('Where would you like your office? ')\n",
+    "        results = geocoder.geocode(x)\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['annotations']['flag'])\n",
+    "        return coordinates\n",
+    "    except:\n",
+    "        return 'Not a valid address'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getting_coor():\n",
+    "    try:\n",
+    "        x = input('Where would you like your office? ')\n",
+    "        results = geocoder.geocode(x)\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        country = results[0]['annotations']['flag']\n",
+    "        return list(coordinates,country)\n",
+    "    except:\n",
+    "        return 'Not a valid address'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? avenida sonora 113 roma norte\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Not a valid address'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getting_coor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getting_coor():\n",
+    "    try:\n",
+    "        x = input('Where would you like your office? ')\n",
+    "        results = geocoder.geocode(x)\n",
+    "        fcoordinates=[]\n",
+    "        coordinates=[]\n",
+    "        coordinates.append(results[0]['geometry']['lng'])\n",
+    "        coordinates.append(results[0]['geometry']['lat']) \n",
+    "        country = results[0]['annotations']['flag']\n",
+    "        fcoordinates.append(coordinates)\n",
+    "        fcoordinates.append(country)\n",
+    "        return fcoordinates\n",
+    "    except:\n",
+    "        return 'Not a valid address'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? avenida sonora 113\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[-99.1711985, 19.4173531]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getting_coor()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = geocoder.geocode(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['annotations', 'bounds', 'components', 'confidence', 'formatted', 'geometry'])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ISO_3166-1_alpha-2': 'MX',\n",
+       " 'ISO_3166-1_alpha-3': 'MEX',\n",
+       " '_category': 'road',\n",
+       " '_type': 'road',\n",
+       " 'city': 'Mexico City',\n",
+       " 'continent': 'North America',\n",
+       " 'country': 'Mexico',\n",
+       " 'country_code': 'mx',\n",
+       " 'county': 'Cuauhtémoc',\n",
+       " 'neighbourhood': 'Condesa',\n",
+       " 'postcode': '06140',\n",
+       " 'road': 'Avenida Sonora',\n",
+       " 'road_type': 'tertiary',\n",
+       " 'state': 'Mexico City'}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['components']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'neighbourhood'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-74-88b48998b555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'city'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcounty\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'county'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnei\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'neighbourhood'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mlocalidad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountry\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcity\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcounty\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnei\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'neighbourhood'"
+     ]
+    }
+   ],
+   "source": [
+    "country = results[0]['components']['country']\n",
+    "city=results[0]['components']['city']\n",
+    "county = results[0]['components']['county']\n",
+    "nei = results[0]['components']['neighbourhood']\n",
+    "\n",
+    "localidad = country+', '+city+', '+county+', '+nei\n",
+    "localidad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ISO_3166-1_alpha-2': 'US',\n",
+       " 'ISO_3166-1_alpha-3': 'USA',\n",
+       " '_category': 'outdoors/recreation',\n",
+       " '_type': 'park',\n",
+       " 'city': 'New York',\n",
+       " 'continent': 'North America',\n",
+       " 'country': 'United States of America',\n",
+       " 'country_code': 'us',\n",
+       " 'county': 'New York County',\n",
+       " 'park': 'Central Park',\n",
+       " 'state': 'New York',\n",
+       " 'state_code': 'NY',\n",
+       " 'suburb': 'Manhattan'}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nei = results[0]['components']\n",
+    "nei"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where would you like your office? central park\n",
+      "Whats the max distance you would like to evaluate: 4000\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'neighbourhood'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-73-ff8ef520b0e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mcity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'city'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mcounty\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'county'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mnei\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'components'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'neighbourhood'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mlocalidad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountry\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcity\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcounty\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnei\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mfcoordinates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'neighbourhood'"
+     ]
+    }
+   ],
+   "source": [
+    "x = input('Where would you like your office? ')\n",
+    "m = input('Whats the max distance you would like to evaluate: ')\n",
+    "results = geocoder.geocode(x)\n",
+    "fcoordinates=[]\n",
+    "coordinates=[]\n",
+    "coordinates.append(results[0]['geometry']['lng'])\n",
+    "coordinates.append(results[0]['geometry']['lat']) \n",
+    "bandera = results[0]['annotations']['flag']\n",
+    "country = results[0]['components']['country']\n",
+    "city=results[0]['components']['city']\n",
+    "county = results[0]['components']['county']\n",
+    "nei = results[0]['components']['neighbourhood']\n",
+    "localidad = country+', '+city+', '+county+', '+nei\n",
+    "fcoordinates.append(coordinates)\n",
+    "fcoordinates.append(m)\n",
+    "fcoordinates.append(localidad)\n",
+    "fcoordinates.append(x)\n",
+    "fcoordinates.append(bandera)\n",
+    "fcoordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('rest_data_ca.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cuisine</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>925 Blossom Hill Road Oakridge Mall</td>\n",
+       "      <td>Buca Di Beppo - San Jose - Oakridge</td>\n",
+       "      <td>95123</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>26900 Newport Rd</td>\n",
+       "      <td>Giovanni's Pizza and Pasta</td>\n",
+       "      <td>92584</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>3060 Naglee Rd</td>\n",
+       "      <td>Applebee's Tracy</td>\n",
+       "      <td>95304</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>50a</td>\n",
+       "      <td>Patrick Molloy's Sports Pub</td>\n",
+       "      <td>90254</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Fast Food</td>\n",
+       "      <td>555 Soquel Ave</td>\n",
+       "      <td>Ristorante Italiano</td>\n",
+       "      <td>95062</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56153</th>\n",
+       "      <td>All</td>\n",
+       "      <td>3470 E 1ST ST</td>\n",
+       "      <td>LA BUFADORA BAR</td>\n",
+       "      <td>90063</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56154</th>\n",
+       "      <td>All</td>\n",
+       "      <td>10250 SANTA MONICA BLVD # 1025</td>\n",
+       "      <td>SAINT MARC</td>\n",
+       "      <td>90067</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56155</th>\n",
+       "      <td>All</td>\n",
+       "      <td>217 W TEMPLE AVE</td>\n",
+       "      <td>HALL OF JUSTICE</td>\n",
+       "      <td>90012</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56156</th>\n",
+       "      <td>All</td>\n",
+       "      <td>5750 MELROSE AVE</td>\n",
+       "      <td>FIN-MELROSE</td>\n",
+       "      <td>90038</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56157</th>\n",
+       "      <td>All</td>\n",
+       "      <td>6741 HOLLYWOOD BLVD</td>\n",
+       "      <td>JUICY WINGZ</td>\n",
+       "      <td>90028</td>\n",
+       "      <td>Food</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>52477 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Cuisine                              address  \\\n",
+       "0      Fast Food  925 Blossom Hill Road Oakridge Mall   \n",
+       "1      Fast Food                     26900 Newport Rd   \n",
+       "2      Fast Food                       3060 Naglee Rd   \n",
+       "3      Fast Food                                  50a   \n",
+       "4      Fast Food                       555 Soquel Ave   \n",
+       "...          ...                                  ...   \n",
+       "56153        All                        3470 E 1ST ST   \n",
+       "56154        All       10250 SANTA MONICA BLVD # 1025   \n",
+       "56155        All                     217 W TEMPLE AVE   \n",
+       "56156        All                     5750 MELROSE AVE   \n",
+       "56157        All                  6741 HOLLYWOOD BLVD   \n",
+       "\n",
+       "                                      name postalCode   Tag  ratings  \n",
+       "0      Buca Di Beppo - San Jose - Oakridge      95123  Food        4  \n",
+       "1               Giovanni's Pizza and Pasta      92584  Food        3  \n",
+       "2                         Applebee's Tracy      95304  Food        3  \n",
+       "3              Patrick Molloy's Sports Pub      90254  Food        3  \n",
+       "4                      Ristorante Italiano      95062  Food        4  \n",
+       "...                                    ...        ...   ...      ...  \n",
+       "56153                      LA BUFADORA BAR      90063  Food        3  \n",
+       "56154                           SAINT MARC      90067  Food        3  \n",
+       "56155                      HALL OF JUSTICE      90012  Food        4  \n",
+       "56156                          FIN-MELROSE      90038  Food        3  \n",
+       "56157                          JUICY WINGZ      90028  Food        4  \n",
+       "\n",
+       "[52477 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module-2/python-bi-project/python-bi-project - Esteban/rest_data_ca.pkl b/module-2/python-bi-project/python-bi-project - Esteban/rest_data_ca.pkl
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