From 1180cf86cc40708981df3deebe017e873dc87e2f Mon Sep 17 00:00:00 2001
From: aditya desle <50102208+adityadesle@users.noreply.github.com>
Date: Fri, 2 Oct 2020 20:35:46 +0530
Subject: [PATCH 1/2] Update README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index fc61313..c34dbdc 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ Course topics include:
 * Fundamentals of Python and its data types
 * Data analysis packages Numpy and Pandas
 * Plotting packages Matplotlib and Seaborn
+* Example of data analysis : Uber raw data analysis report
 * Statistics
 * Regular expressions
 * Interactive visualization

From d0dde4910a169aa31f1e7f37e259ba1d412930b0 Mon Sep 17 00:00:00 2001
From: aditya desle <50102208+adityadesle@users.noreply.github.com>
Date: Fri, 2 Oct 2020 20:36:12 +0530
Subject: [PATCH 2/2] Add files via upload

---
 Uber_data.ipynb | 1829 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1829 insertions(+)
 create mode 100644 Uber_data.ipynb

diff --git a/Uber_data.ipynb b/Uber_data.ipynb
new file mode 100644
index 0000000..4976042
--- /dev/null
+++ b/Uber_data.ipynb
@@ -0,0 +1,1829 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "import pandas as pd\n",
+    "import seaborn as sb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "data = pd.read_csv('uber-raw-data-apr14.csv')"
+   ]
+  },
+  {
+   "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>Date/Time</th>\n",
+       "      <th>Lat</th>\n",
+       "      <th>Lon</th>\n",
+       "      <th>Base</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4/1/2014 0:11:00</td>\n",
+       "      <td>40.7690</td>\n",
+       "      <td>-73.9549</td>\n",
+       "      <td>B02512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4/1/2014 0:17:00</td>\n",
+       "      <td>40.7267</td>\n",
+       "      <td>-74.0345</td>\n",
+       "      <td>B02512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4/1/2014 0:21:00</td>\n",
+       "      <td>40.7316</td>\n",
+       "      <td>-73.9873</td>\n",
+       "      <td>B02512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4/1/2014 0:28:00</td>\n",
+       "      <td>40.7588</td>\n",
+       "      <td>-73.9776</td>\n",
+       "      <td>B02512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4/1/2014 0:33:00</td>\n",
+       "      <td>40.7594</td>\n",
+       "      <td>-73.9722</td>\n",
+       "      <td>B02512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564511</th>\n",
+       "      <td>4/30/2014 23:22:00</td>\n",
+       "      <td>40.7640</td>\n",
+       "      <td>-73.9744</td>\n",
+       "      <td>B02764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564512</th>\n",
+       "      <td>4/30/2014 23:26:00</td>\n",
+       "      <td>40.7629</td>\n",
+       "      <td>-73.9672</td>\n",
+       "      <td>B02764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564513</th>\n",
+       "      <td>4/30/2014 23:31:00</td>\n",
+       "      <td>40.7443</td>\n",
+       "      <td>-73.9889</td>\n",
+       "      <td>B02764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564514</th>\n",
+       "      <td>4/30/2014 23:32:00</td>\n",
+       "      <td>40.6756</td>\n",
+       "      <td>-73.9405</td>\n",
+       "      <td>B02764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564515</th>\n",
+       "      <td>4/30/2014 23:48:00</td>\n",
+       "      <td>40.6880</td>\n",
+       "      <td>-73.9608</td>\n",
+       "      <td>B02764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>564516 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Date/Time      Lat      Lon    Base\n",
+       "0         4/1/2014 0:11:00  40.7690 -73.9549  B02512\n",
+       "1         4/1/2014 0:17:00  40.7267 -74.0345  B02512\n",
+       "2         4/1/2014 0:21:00  40.7316 -73.9873  B02512\n",
+       "3         4/1/2014 0:28:00  40.7588 -73.9776  B02512\n",
+       "4         4/1/2014 0:33:00  40.7594 -73.9722  B02512\n",
+       "...                    ...      ...      ...     ...\n",
+       "564511  4/30/2014 23:22:00  40.7640 -73.9744  B02764\n",
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+       "564515  4/30/2014 23:48:00  40.6880 -73.9608  B02764\n",
+       "\n",
+       "[564516 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(564516, 4)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "data['Date/Time'] = pd.to_datetime(data['Date/Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tuesday'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Date/Time'][0].day_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0           Tuesday\n",
+       "1           Tuesday\n",
+       "2           Tuesday\n",
+       "3           Tuesday\n",
+       "4           Tuesday\n",
+       "            ...    \n",
+       "564511    Wednesday\n",
+       "564512    Wednesday\n",
+       "564513    Wednesday\n",
+       "564514    Wednesday\n",
+       "564515    Wednesday\n",
+       "Name: Date/Time, Length: 564516, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Date/Time'].dt.day_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['Daysofweek'] = data['Date/Time'].dt.day_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <td>Tuesday</td>\n",
+       "    </tr>\n",
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+       "      <th>1</th>\n",
+       "      <td>2014-04-01 00:17:00</td>\n",
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+       "      <td>-74.0345</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
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+       "      <td>Tuesday</td>\n",
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+       "    <tr>\n",
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+       "      <td>40.7588</td>\n",
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+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-04-01 00:33:00</td>\n",
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+       "    </tr>\n",
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+       "      <td>...</td>\n",
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+       "      <td>40.7640</td>\n",
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+       "      <td>Wednesday</td>\n",
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+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
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+       "      <td>-73.9889</td>\n",
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+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>2014-04-30 23:32:00</td>\n",
+       "      <td>40.6756</td>\n",
+       "      <td>-73.9405</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564515</th>\n",
+       "      <td>2014-04-30 23:48:00</td>\n",
+       "      <td>40.6880</td>\n",
+       "      <td>-73.9608</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>564516 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Date/Time      Lat      Lon    Base Daysofweek\n",
+       "0      2014-04-01 00:11:00  40.7690 -73.9549  B02512    Tuesday\n",
+       "1      2014-04-01 00:17:00  40.7267 -74.0345  B02512    Tuesday\n",
+       "2      2014-04-01 00:21:00  40.7316 -73.9873  B02512    Tuesday\n",
+       "3      2014-04-01 00:28:00  40.7588 -73.9776  B02512    Tuesday\n",
+       "4      2014-04-01 00:33:00  40.7594 -73.9722  B02512    Tuesday\n",
+       "...                    ...      ...      ...     ...        ...\n",
+       "564511 2014-04-30 23:22:00  40.7640 -73.9744  B02764  Wednesday\n",
+       "564512 2014-04-30 23:26:00  40.7629 -73.9672  B02764  Wednesday\n",
+       "564513 2014-04-30 23:31:00  40.7443 -73.9889  B02764  Wednesday\n",
+       "564514 2014-04-30 23:32:00  40.6756 -73.9405  B02764  Wednesday\n",
+       "564515 2014-04-30 23:48:00  40.6880 -73.9608  B02764  Wednesday\n",
+       "\n",
+       "[564516 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Date/Time'][0].week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        2014-04-01 00:11:00\n",
+       "1        2014-04-01 00:17:00\n",
+       "2        2014-04-01 00:21:00\n",
+       "3        2014-04-01 00:28:00\n",
+       "4        2014-04-01 00:33:00\n",
+       "                 ...        \n",
+       "564511   2014-04-30 23:22:00\n",
+       "564512   2014-04-30 23:26:00\n",
+       "564513   2014-04-30 23:31:00\n",
+       "564514   2014-04-30 23:32:00\n",
+       "564515   2014-04-30 23:48:00\n",
+       "Name: Date/Time, Length: 564516, dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Date/Time']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>40.7640</td>\n",
+       "      <td>-73.9744</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564512</th>\n",
+       "      <td>2014-04-30 23:26:00</td>\n",
+       "      <td>40.7629</td>\n",
+       "      <td>-73.9672</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564513</th>\n",
+       "      <td>2014-04-30 23:31:00</td>\n",
+       "      <td>40.7443</td>\n",
+       "      <td>-73.9889</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564514</th>\n",
+       "      <td>2014-04-30 23:32:00</td>\n",
+       "      <td>40.6756</td>\n",
+       "      <td>-73.9405</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564515</th>\n",
+       "      <td>2014-04-30 23:48:00</td>\n",
+       "      <td>40.6880</td>\n",
+       "      <td>-73.9608</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Date/Time      Lat      Lon    Base Daysofweek\n",
+       "564511 2014-04-30 23:22:00  40.7640 -73.9744  B02764  Wednesday\n",
+       "564512 2014-04-30 23:26:00  40.7629 -73.9672  B02764  Wednesday\n",
+       "564513 2014-04-30 23:31:00  40.7443 -73.9889  B02764  Wednesday\n",
+       "564514 2014-04-30 23:32:00  40.6756 -73.9405  B02764  Wednesday\n",
+       "564515 2014-04-30 23:48:00  40.6880 -73.9608  B02764  Wednesday"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_day(dt):\n",
+    "    return dt.day\n",
+    "\n",
+    "data[\"DoM\"] = data['Date/Time'].map(get_day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_hour(dt):\n",
+    "    return dt.hour\n",
+    "\n",
+    "data['Hour'] = data['Date/Time'].map(get_hour)\n",
+    "\n",
+    "def get_week(dt):\n",
+    "    return dt.weekday()\n",
+    "\n",
+    "data['Weekday'] = data['Date/Time'].map(get_week)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>Date/Time</th>\n",
+       "      <th>Lat</th>\n",
+       "      <th>Lon</th>\n",
+       "      <th>Base</th>\n",
+       "      <th>Daysofweek</th>\n",
+       "      <th>DoM</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Week</th>\n",
+       "      <th>Weekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-04-01 00:11:00</td>\n",
+       "      <td>40.7690</td>\n",
+       "      <td>-73.9549</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-04-01 00:17:00</td>\n",
+       "      <td>40.7267</td>\n",
+       "      <td>-74.0345</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-04-01 00:21:00</td>\n",
+       "      <td>40.7316</td>\n",
+       "      <td>-73.9873</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-04-01 00:28:00</td>\n",
+       "      <td>40.7588</td>\n",
+       "      <td>-73.9776</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-04-01 00:33:00</td>\n",
+       "      <td>40.7594</td>\n",
+       "      <td>-73.9722</td>\n",
+       "      <td>B02512</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564511</th>\n",
+       "      <td>2014-04-30 23:22:00</td>\n",
+       "      <td>40.7640</td>\n",
+       "      <td>-73.9744</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564512</th>\n",
+       "      <td>2014-04-30 23:26:00</td>\n",
+       "      <td>40.7629</td>\n",
+       "      <td>-73.9672</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564513</th>\n",
+       "      <td>2014-04-30 23:31:00</td>\n",
+       "      <td>40.7443</td>\n",
+       "      <td>-73.9889</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564514</th>\n",
+       "      <td>2014-04-30 23:32:00</td>\n",
+       "      <td>40.6756</td>\n",
+       "      <td>-73.9405</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564515</th>\n",
+       "      <td>2014-04-30 23:48:00</td>\n",
+       "      <td>40.6880</td>\n",
+       "      <td>-73.9608</td>\n",
+       "      <td>B02764</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>564516 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Date/Time      Lat      Lon    Base Daysofweek  DoM  Hour  \\\n",
+       "0      2014-04-01 00:11:00  40.7690 -73.9549  B02512    Tuesday    1     0   \n",
+       "1      2014-04-01 00:17:00  40.7267 -74.0345  B02512    Tuesday    1     0   \n",
+       "2      2014-04-01 00:21:00  40.7316 -73.9873  B02512    Tuesday    1     0   \n",
+       "3      2014-04-01 00:28:00  40.7588 -73.9776  B02512    Tuesday    1     0   \n",
+       "4      2014-04-01 00:33:00  40.7594 -73.9722  B02512    Tuesday    1     0   \n",
+       "...                    ...      ...      ...     ...        ...  ...   ...   \n",
+       "564511 2014-04-30 23:22:00  40.7640 -73.9744  B02764  Wednesday   30    23   \n",
+       "564512 2014-04-30 23:26:00  40.7629 -73.9672  B02764  Wednesday   30    23   \n",
+       "564513 2014-04-30 23:31:00  40.7443 -73.9889  B02764  Wednesday   30    23   \n",
+       "564514 2014-04-30 23:32:00  40.6756 -73.9405  B02764  Wednesday   30    23   \n",
+       "564515 2014-04-30 23:48:00  40.6880 -73.9608  B02764  Wednesday   30    23   \n",
+       "\n",
+       "        Week  Weekday  \n",
+       "0         14        1  \n",
+       "1         14        1  \n",
+       "2         14        1  \n",
+       "3         14        1  \n",
+       "4         14        1  \n",
+       "...      ...      ...  \n",
+       "564511    18        2  \n",
+       "564512    18        2  \n",
+       "564513    18        2  \n",
+       "564514    18        2  \n",
+       "564515    18        2  \n",
+       "\n",
+       "[564516 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Frequency of Uber - Apr - 2014')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(data.DoM , bins = 30 , rwidth = 0.8)\n",
+    "xlabel('Date of the month')\n",
+    "ylabel('frequency')\n",
+    "title('Frequency of Uber - Apr - 2014')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1dc2bf1b730>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(data.DoM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 14546)\n",
+      "(2, 17474)\n",
+      "(3, 20701)\n",
+      "(4, 26714)\n",
+      "(5, 19521)\n",
+      "(6, 13445)\n",
+      "(7, 19550)\n",
+      "(8, 16188)\n",
+      "(9, 16843)\n",
+      "(10, 20041)\n",
+      "(11, 20420)\n",
+      "(12, 18170)\n",
+      "(13, 12112)\n",
+      "(14, 12674)\n",
+      "(15, 20641)\n",
+      "(16, 17717)\n",
+      "(17, 20973)\n",
+      "(18, 18074)\n",
+      "(19, 14602)\n",
+      "(20, 11017)\n",
+      "(21, 13162)\n",
+      "(22, 16975)\n",
+      "(23, 20346)\n",
+      "(24, 23352)\n",
+      "(25, 25095)\n",
+      "(26, 24925)\n",
+      "(27, 14677)\n",
+      "(28, 15475)\n",
+      "(29, 22835)\n",
+      "(30, 36251)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k , row in data.groupby('DoM'):\n",
+    "    print ((k,len(row)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DoM\n",
+       "1     14546\n",
+       "2     17474\n",
+       "3     20701\n",
+       "4     26714\n",
+       "5     19521\n",
+       "6     13445\n",
+       "7     19550\n",
+       "8     16188\n",
+       "9     16843\n",
+       "10    20041\n",
+       "11    20420\n",
+       "12    18170\n",
+       "13    12112\n",
+       "14    12674\n",
+       "15    20641\n",
+       "16    17717\n",
+       "17    20973\n",
+       "18    18074\n",
+       "19    14602\n",
+       "20    11017\n",
+       "21    13162\n",
+       "22    16975\n",
+       "23    20346\n",
+       "24    23352\n",
+       "25    25095\n",
+       "26    24925\n",
+       "27    14677\n",
+       "28    15475\n",
+       "29    22835\n",
+       "30    36251\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def count(row):\n",
+    "    return(len(row))\n",
+    "\n",
+    "by_date  = data.groupby('DoM').apply(count)\n",
+    "by_date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1dc2c5983d0>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(by_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hour  Week\n",
+       "0     14      3030\n",
+       "      15      2665\n",
+       "      16      2316\n",
+       "      17      3343\n",
+       "      18       556\n",
+       "              ... \n",
+       "23    14      4753\n",
+       "      15      4273\n",
+       "      16      4195\n",
+       "      17      5772\n",
+       "      18      1656\n",
+       "Length: 120, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " data.groupby('Hour Week'.split()).apply(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "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>Weekday</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hour</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>518</td>\n",
+       "      <td>765</td>\n",
+       "      <td>899</td>\n",
+       "      <td>792</td>\n",
+       "      <td>1367</td>\n",
+       "      <td>3027</td>\n",
+       "      <td>4542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>261</td>\n",
+       "      <td>367</td>\n",
+       "      <td>507</td>\n",
+       "      <td>459</td>\n",
+       "      <td>760</td>\n",
+       "      <td>2479</td>\n",
+       "      <td>2936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>238</td>\n",
+       "      <td>304</td>\n",
+       "      <td>371</td>\n",
+       "      <td>342</td>\n",
+       "      <td>513</td>\n",
+       "      <td>1577</td>\n",
+       "      <td>1590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>571</td>\n",
+       "      <td>516</td>\n",
+       "      <td>585</td>\n",
+       "      <td>567</td>\n",
+       "      <td>736</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>1052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1021</td>\n",
+       "      <td>887</td>\n",
+       "      <td>1003</td>\n",
+       "      <td>861</td>\n",
+       "      <td>932</td>\n",
+       "      <td>706</td>\n",
+       "      <td>685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1619</td>\n",
+       "      <td>1734</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1454</td>\n",
+       "      <td>1382</td>\n",
+       "      <td>704</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2974</td>\n",
+       "      <td>3766</td>\n",
+       "      <td>4230</td>\n",
+       "      <td>3179</td>\n",
+       "      <td>2836</td>\n",
+       "      <td>844</td>\n",
+       "      <td>669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3888</td>\n",
+       "      <td>5304</td>\n",
+       "      <td>5647</td>\n",
+       "      <td>4159</td>\n",
+       "      <td>3943</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3138</td>\n",
+       "      <td>4594</td>\n",
+       "      <td>5242</td>\n",
+       "      <td>3616</td>\n",
+       "      <td>3648</td>\n",
+       "      <td>1372</td>\n",
+       "      <td>1233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2211</td>\n",
+       "      <td>2962</td>\n",
+       "      <td>3846</td>\n",
+       "      <td>2654</td>\n",
+       "      <td>2732</td>\n",
+       "      <td>1764</td>\n",
+       "      <td>1770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1953</td>\n",
+       "      <td>2900</td>\n",
+       "      <td>3844</td>\n",
+       "      <td>2370</td>\n",
+       "      <td>2599</td>\n",
+       "      <td>2086</td>\n",
+       "      <td>2113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1929</td>\n",
+       "      <td>2949</td>\n",
+       "      <td>3889</td>\n",
+       "      <td>2516</td>\n",
+       "      <td>2816</td>\n",
+       "      <td>2315</td>\n",
+       "      <td>2360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1945</td>\n",
+       "      <td>2819</td>\n",
+       "      <td>3988</td>\n",
+       "      <td>2657</td>\n",
+       "      <td>2978</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>2478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2294</td>\n",
+       "      <td>3556</td>\n",
+       "      <td>4469</td>\n",
+       "      <td>3301</td>\n",
+       "      <td>3535</td>\n",
+       "      <td>2685</td>\n",
+       "      <td>2763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>3117</td>\n",
+       "      <td>4489</td>\n",
+       "      <td>5438</td>\n",
+       "      <td>4083</td>\n",
+       "      <td>4087</td>\n",
+       "      <td>3042</td>\n",
+       "      <td>2934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3818</td>\n",
+       "      <td>6042</td>\n",
+       "      <td>7071</td>\n",
+       "      <td>5182</td>\n",
+       "      <td>5354</td>\n",
+       "      <td>4457</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>4962</td>\n",
+       "      <td>7521</td>\n",
+       "      <td>8213</td>\n",
+       "      <td>6149</td>\n",
+       "      <td>6259</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>3489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>5574</td>\n",
+       "      <td>8297</td>\n",
+       "      <td>9151</td>\n",
+       "      <td>6951</td>\n",
+       "      <td>6790</td>\n",
+       "      <td>5558</td>\n",
+       "      <td>3154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>4725</td>\n",
+       "      <td>7089</td>\n",
+       "      <td>8334</td>\n",
+       "      <td>6637</td>\n",
+       "      <td>7258</td>\n",
+       "      <td>6165</td>\n",
+       "      <td>2795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>4386</td>\n",
+       "      <td>6459</td>\n",
+       "      <td>7794</td>\n",
+       "      <td>5929</td>\n",
+       "      <td>6247</td>\n",
+       "      <td>5529</td>\n",
+       "      <td>2579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3573</td>\n",
+       "      <td>6310</td>\n",
+       "      <td>7783</td>\n",
+       "      <td>6345</td>\n",
+       "      <td>5165</td>\n",
+       "      <td>4792</td>\n",
+       "      <td>2276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3079</td>\n",
+       "      <td>5993</td>\n",
+       "      <td>6921</td>\n",
+       "      <td>6585</td>\n",
+       "      <td>6265</td>\n",
+       "      <td>5811</td>\n",
+       "      <td>2310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1976</td>\n",
+       "      <td>3614</td>\n",
+       "      <td>4845</td>\n",
+       "      <td>5370</td>\n",
+       "      <td>6708</td>\n",
+       "      <td>6493</td>\n",
+       "      <td>1639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1091</td>\n",
+       "      <td>1948</td>\n",
+       "      <td>2571</td>\n",
+       "      <td>2909</td>\n",
+       "      <td>5393</td>\n",
+       "      <td>5719</td>\n",
+       "      <td>1018</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Weekday     0     1     2     3     4     5     6\n",
+       "Hour                                             \n",
+       "0         518   765   899   792  1367  3027  4542\n",
+       "1         261   367   507   459   760  2479  2936\n",
+       "2         238   304   371   342   513  1577  1590\n",
+       "3         571   516   585   567   736  1013  1052\n",
+       "4        1021   887  1003   861   932   706   685\n",
+       "5        1619  1734  1990  1454  1382   704   593\n",
+       "6        2974  3766  4230  3179  2836   844   669\n",
+       "7        3888  5304  5647  4159  3943  1110   873\n",
+       "8        3138  4594  5242  3616  3648  1372  1233\n",
+       "9        2211  2962  3846  2654  2732  1764  1770\n",
+       "10       1953  2900  3844  2370  2599  2086  2113\n",
+       "11       1929  2949  3889  2516  2816  2315  2360\n",
+       "12       1945  2819  3988  2657  2978  2560  2478\n",
+       "13       2294  3556  4469  3301  3535  2685  2763\n",
+       "14       3117  4489  5438  4083  4087  3042  2934\n",
+       "15       3818  6042  7071  5182  5354  4457  3400\n",
+       "16       4962  7521  8213  6149  6259  5410  3489\n",
+       "17       5574  8297  9151  6951  6790  5558  3154\n",
+       "18       4725  7089  8334  6637  7258  6165  2795\n",
+       "19       4386  6459  7794  5929  6247  5529  2579\n",
+       "20       3573  6310  7783  6345  5165  4792  2276\n",
+       "21       3079  5993  6921  6585  6265  5811  2310\n",
+       "22       1976  3614  4845  5370  6708  6493  1639\n",
+       "23       1091  1948  2571  2909  5393  5719  1018"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_cross = data.groupby('Hour Weekday'.split()).apply(count).unstack()\n",
+    "by_cross"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1dc2c515dc0>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UFlWj9ABJl6RK2s+mbV1qG9/ivEWS1khas/GljUWEZmbWUPSo7a3KiiqSsRx4HjglIiZGxETgXantumYn5auJHDb2sIJCMzN7o4j2tyorKqkfHhGXpgogQFYNJCIuBZytzaxy3FNv7UlJF0ia3NsgabKkC4GnCrqmmdlui1DbW38kPSHpQUn3S1qT2iZIWinpsfTzwNzxF0taL+lRSafl2memz1kv6bJU1q6lfpO6pCGSHur3T/Hr/gSYCNwm6TlJzwG3AhNIJZzMzKqku1ttb216V0QcHxG9FZAuAlZFxAxgVXqNpGOAecCxwBzgcum1J+OuABaR1S2dkd5vqd/ZLxHRI+kBSYdFRFt3LyPieeDCtP0aSR8Frm7jM9q51KCr6hevqsY1UkV9GdxzY6jmE6WjVc1JaXUvZzcIDx/NBU5J+0vJOroXpvZlEbED2CBpPTBb0hPAuIi4EyDVej6TfuqUtvuvZwqwVtJq4OXexog4o83z8z5JG0ndzGwwdXisPIDvSwrgXyNiCTA5IjYDRMRmSZPSsVOBu3LndqW2nWm/b3tL7Sb1T7Z5HACSftrsLWByk/fMzEozkMEBSYvIhkV6LUmJu9dJEbEpJe6Vkh5p9XGNwmnR3lJbST0ibmvnuJzJwGlkUxjzBPx4gJ9lZla4gfTUUwJf0uL9TennNkk3ArOBrZKmpF76FGBbOrwLODR3+jRgU2qf1qC9pbYGPCVtl/Ri2n4lqVvSiy1O+TYwNiKe7LM9QTaOZGZWKd09Q9reWpE0RtL+vfvAHwIPASuABemwBcBNaX8FME/SSEnTyW6Irk5DNdslnZBmvczPndNUuz31/fsEfSbZb55mxy9s8d6ftnNNM7PB1MG5GZOBG9Psw2HAVyPiFkn3AMslLQQ2kmYCRsRaScuBh4FdwDkR0Z0+62zgGmA02Q3SljdJey84YBHxTUkX7c65ZmZV1NOh2S8R8Tjw9gbtzwKnNjlnMbC4Qfsa4LiBXL+tpC7pA7mXQ4BZtDFgb2a2t9jX1lN/f25/F/AE2dxKM7NaqOijMQPW7pj6R4sOpK82noYtxc6KfkEZUdF/kSMKW4liz42saGxjKvrwUXdF/+13SqeGX8rW7uyXaZJulLRN0lZJN0ia1v+ZZmZ7h07Nfilbu9FdTTbt5hCyJ5q+hZ8KNbMaiQFsVdZuUj84Iq6OiF1puwY4uMC4zMwGVU+o7a3K2k3qz0j6iKShafsI8GyRgZmZDaZOLr1bpnaT+l8CHwK2AJuBD6a2hiSNk/SPkv6fpD/t897lLc5zOTszK0XPALYqayupR8TGiDgjIg6OiEkRcWZEPNnilKvJ1nm5gezx1xskjUzvndDiOi5nZ2alCNT2VmUt505J+gIt7gtExHlN3joyIv5T2v+mpE8AP5C0O0v1mpkVblfFh1Xa1d+E2DW5/U8C/9Dm546UNCQieiB7BFZSF/AjYOzAwzQzK1bVe+DtapnUI2Jp776kj+df9+NbwLuBf8t/lqStwBfa+YDxr43WVMtbuqv5YMjk2FF2CA0dFtX87wgwtqKDo9uHVnMe9NCaJL1mKvrPYcAGkqHanp4ZERc0ab9F0qcHcE0zs0FRl556GV2CAVVRMjMbDHWZ/dLfjdLtvN5D3y9XGENARMS4Jue5nJ2Z7VW6a9JT729Mff9W77fgcnZmtlfpbN1pkDSUbLLJ0xFxuqQJwNeBw8lWuv1QRDyfjr0YWAh0A+dFxPdS+0xeL5LxXeD8iNar9xU1/OJydma2V+lBbW9tOh9Yl3t9EbAqImYAq9JrJB0DzAOOBeYAl6dfCABXkBW4npG2Of1dtJCkHhELI+L2Ju+5nJ2ZVU4nF/RKq9i+D7gy1zwX6J1BuBQ4M9e+LCJ2RMQGYD0wOxWnHhcRd6be+bW5c5qq5vw8M7NB1uEboJ8DLgDyQ9iTUzFpImKzpEmpfSpwV+64rtS2M+33bW+pmhNizcwGWY/U9pZfpypti3o/R9LpwLaIuLfNSzcaz4kW7S1Vtqd+FPuVHUJDb9v5q7JDaOiI6c+VHUJDz214U9kh7HW2DB3a/0ElqGrVr07pHsCxEbEEWNLk7ZOAMyT9ETAKGCfpy8BWSVNSL30KsC0d3wUcmjt/GrAptU9r0N6Se+pmZmSzX9rdWomIiyNiWkQcTnYD9AcR8RGyQkML0mELgJvS/gqyhQ9HSppOdkN0dRqq2S7pBGX1Pefnzmmqsj11M7PBNIBZLbvrEmC5pIXARuAsgIhYK2k58DCwCzgnInq/OJzN61Mab05bS07qZmYUU6YuIm4lTeOOiGeBU5sctxhY3KB9DXDcQK45aEld0qSI2Nb/kWZmg6/TDx+VpZAxdUkT+mwTgdWSDkxPVTU777U7yve+tL6I0MzMGton1n7ZA88AfSsjTQXuI/uWc0Sjk/J3lP/Xm/+s3rfazaxSumvSUy8qqV8AvAf424h4EEDShoiYXtD1zMz2SNV74O0qJKlHxD9LWgZ8VtJTZBWT3PM2s8pyUu9HRHQBZ0l6P7ASBvY00RG7qvld6G3verbsEBoa8TtHlR1CQ7951YayQ2hq2MiBPG4yeCZ0Nb3tVKp7RlS3ilUn1KREafEPH0XEt4B3kQ3HIOmjRV/TzGyg6nKjdFCeKI2IX0bEQ+mlKx+ZWeV0D2CrskKGX1z5yMz2NnWZp17UmLorH5nZXqXqwyrtKiqp91Y+ur/vG5JuLeiaZma7zUm9hYhY2OI9Vz4ys8qpy5xrL+hlZobH1M3MaqXqs1raVdmkPmtU33us1TDy9N8tO4SGdPjRZYfQ0MSfVHdhNo2oZoWhI4ZW8wG3BzYdUnYIheqpyQBMZZO6mdlgqsuNUpezMzMju1Ha7taKpFGSVkt6QNJaSZ9M7RMkrZT0WPp5YO6ciyWtl/SopNNy7TMlPZjeuyyVtWvJSd3MjI4uE7ADeHdEvB04Hpgj6QTgImBVRMwAVqXXSDqGrJbpscAc4HJJvWODVwCLyOqWzkjvtzRoST0VyjAzq6Rdira3ViLzUno5PG0BzAWWpvalwJlpfy6wLCJ2RMQGYD0wW9IUYFxE3BkRAVybO6epoiofXSLpoLQ/S9LjwN2SnpT0+y3Oe63y0fIXNxYRmplZQwMZfsnnqrQtyn+WpKGS7ge2ASsj4m5gckRsBkg/J6XDpwJP5U7vSm1T037f9paKulH6voi4KO3/b+BPIuIeSUcBXwVmNTopX/no4SPfV49b0Wa2VxjIjdJ8rmryfjdwvKTxwI2SWhWPbjROHi3aWypq+GW4pN5fGKMj4h6AiPgPoN6LMpvZXqmHaHtrV0S8ANxKNha+NQ2pkH5uS4d1AYfmTpsGbErt0xq0t1RUUv+/wHclvRu4RdLnJJ2c7gK/YT0YM7OydXD2y8Gph46k0WS1JB4BVgAL0mELgJvS/gpgnqSRkqaT3RBdnYZotks6Ic16mZ87p6mi1n75gqQHgbOBo9J1jgK+CXyqnc+YeOjLRYS2x3Rkq29R5Rn6lt8uO4SGhh/zb2WH0FTseLXsEBoa9ujTZYfQ0PND6j0i2sF56lOApWkGyxBgeUR8W9KdwHJJC4GNwFkAEbFW0nLgYWAXcE4avoEsh14DjAZuTltLRZazu5Xsa8evSZWPri7qumZmu6O7Q0+URsRPgd9q0P4scGqTcxYDixu0rwEG1JMsY566Kx+ZWeXUpZydKx+ZmQHhtV9acuUjM9urVL0H3i5XPjIzw6s0tuTKR2a2t6lHSvfSu2ZmAOyqSVp3UjczwzdKC/fU+vFlh9DQ/nf9oOwQGhtSzVWUe154sewQmhry5mn9H1SC0UduLTuEhp7fWJeCb435RqmZWY24p25mViPuqZuZ1Uh3uKduZlYbdZmnXlTlo1mSfijpy5IOTUVWfyHpHklvWOgmd95r1US+8fITRYRmZtZQDOB/VVbUlInLgX8CvkO2LMC/RsQBZIVWL292UkQsiYhZETHrA2MOLyg0M7M3qsuCXoVVPoqImyPia2R1WK8n21kFjCrommZmu62IykdlKCqp/0rSH0o6CwhJZwKkotP1nuxqZnulTg2/pCHnH0paJ2mtpPNT+4Q0FP1Y+nlg7pyLJa2X9Kik03LtMyU9mN67LFVAaqmoG6X/lWz4pYdstcazJV0DPA18rJ0PeHHniIJC2zPdT/RbIrAUOuLJskNoaOd/PFt2CE0NfWZ72SE09Mpj1ez3vFLz/lgHZ7/sAv46Iu6TtD9wr6SVwF8AqyLiEkkXkQ1HXyjpGGAecCxwCPBvko5K1Y+uABYBdwHfJat12rL6USE99Yh4ICJOi4j3RsQjEXF+RIyPiGOBtxZxTTOzPdGp4ZeI2BwR96X97cA6YCowF1iaDlsKnJn25wLLImJHRGwA1gOzU3HqcRFxZ0QEcG3unKZc+cjMjGJulEo6nKy03d3A5FRMmvRzUjpsKvBU7rSu1DY17fdtb8mVj8zMGNgyAZIWkQ2L9FoSEUv6HDMWuAH4eES82GI4vNEb0aK9JVc+MjNjYA8fpQS+pNn7koaTJfSvRMQ3UvNWSVMiYnMaWtmW2ruAQ3OnTwM2pfZpDdpbKmr4pbfy0ZN9tieAWwu6ppnZbouItrdW0gyVq4B1EfGZ3FsrgAVpfwFwU659nqSRkqYDM4DVaYhmu6QT0mfOz53TlCsfmZkB3Z2bf34S8OfAg5J6S3r+HXAJsFzSQmAjcBZARKyVtBx4mGzmzDlp5gvA2cA1wGiyWS8tZ76A134xMwM6t/ZLRNxO4/FwgFObnLMYWNygfQ1w3ECu76RuZgb9DqvsLSqb1B8ZUc2Hj377VxWt5BPVXJFi2OTRZYfQ1LYfVfPv7MUXx5UdQkMvamfZIRSq6o//t6uySd3MbDBVffXFdjmpm5nhIhlmZrVSl+GXoopkHCDpEkmPSHo2betS2/girmlmtie89G5ry8meJj0lIiZGxETgXantumYn5Ssf3f7SYwWFZmb2Rp16+KhsRSX1wyPi0ojY0tsQEVsi4lLgsGYn5Ssf/e7YGQWFZmb2Ru6pt/akpAskvbZ4l6TJki7k11cjMzOrBNcobe1PgInAbZKel/Qc2ZovE4APFXRNM7Pd1h09bW9VVtTaL89LuhpYCdwVES/1vidpDnBLf5+xZWg1fxt2P7+j7BAaGvLw2rJDaEj7V/fho4NPernsEBoav+mFskNoSD+dUHYIhar6WHm7ipr9ch7ZamJ/BTwkaW7u7U8XcU0zsz1RlzH1ouapfwyYGREvpcof10s6PCI+T/OFbszMSlP1sfJ2FZXUh/YOuUTEE5JOIUvsb8ZJ3cwqqMfDLy1tkXR874uU4E8HDgLeVtA1zcx2m2e/tDYf2JJviIhdETEfOLmga5qZ7bZOzn6R9CVJ2yQ9lGubIGmlpMfSzwNz710sab2kRyWdlmufKenB9N5lalHotFchST0iuvIPHvV5744irmlmtid6Itre2nANMKdP20XAqoiYAaxKr5F0DDAPODadc7mkoemcK8gKXM9IW9/PfIOieupmZnuVTg6/RMSPgOf6NM8Flqb9pcCZufZlEbEjIjYA64HZqTj1uIi4M7L5ltfmzmnKqzSamTEoN0onp2LSRMRmSZNS+1TgrtxxXaltZ9rv295SZZP6mp7nyw6hoWU/7vfvtBQn3t23U1AdBx78StkhNDRhzsSyQ2hoyJiRZYfQ0FP3VbTqV4cM5AaopEVkwyK9lkTEkt28dKNx8mjR3lJlk7rVQ1UTullf3dHd9rEpgQ80iW+VNCX10qcA21J7F3Bo7rhpwKbUPq1Be0seUzczY1CW3l0BLEj7C8ieuu9tnydppKTpZDdEV6ehmu2STkizXubnzmnKPXUzMzpb+UjS14BTgIMkdQH/AFwCLJe0ENgInAUQEWslLQceBnYB50S89rXhbLKZNKOBm9PWUiFJXdI44GKyrws3R8RXc+9dHhH/rYjrmpntrk4u6BURH27y1qlNjl8MLG7QvgY4biDXLmr45WqyQf4byL5W3CCp9+7PCc1Oylc+2vjSxoJCMzN7ow7PUy9NUUn9yIi4KCK+GRFnAPcBP5DUcrpBvvLRYWObFkgyM+u4uiwTUNSY+khJQyKy52kjYnEaV/oRMLaga5qZ7baqF43kjScAAAaoSURBVL9oV1E99W8B7843RMRS4K+BVwu6ppnZbqtL4emiKh9dIOloSacCd+eW4b0lFdDo12O/3FpEaHtsRUUL+fyEMWWH0NjPR3FARSdZnff9av4bGzW+/fnSg+npHfV++KjqY+XtKqry0blk8ynP5Y2Vj95wh9fqq6oJ3awv99RbW4QrH5nZXqTqZera5cpHZma48HR/XPnIzPYqnSySUaaieurzyR53fU1E7ALmS/rXgq5pZrbb6nKjtKjZL10t3nPlIzOrnLoMv3hqgpkZA1tPvcqc1M3McE/dzKxW6jKmrrr8dmpF0qI9KDVVqKrG5rgGpqpxQXVjq2pce7t9pfLRov4PKU1VY3NcA1PVuKC6sVU1rr3avpLUzcz2CU7qZmY1sq8k9SqP21U1Nsc1MFWNC6obW1Xj2qvtEzdKzcz2FftKT93MbJ9Q+6QuaY6kRyWtl3RR2fH0kvQlSdskPVR2LL0kHSrph5LWSVor6fyyY+olaZSk1ZIeSLF9suyY8iQNlfQTSd8uO5Zekp6Q9KCk+yWtKTueXpLGS7pe0iPp39o7y46pTmo9/CJpKPAfwB8AXcA9wIcj4uFSAwMknQy8BFwbEceVHQ+ApCnAlIi4T9L+wL3AmRX5+xIwJq3RPxy4HTg/Iu4qOTQAJP0PYBYwLiJOLzseyJI6MCsinik7ljxJS4F/j4grJY0A9ouIF8qOqy7q3lOfDayPiMcj4lVgGTC3n3MGRUT8CHiu7DjyImJzRNyX9rcD64Cp5UaVicxL6eXwtFWiRyJpGvA+4MqyY6k6SeOAk4GrACLiVSf0zqp7Up8KPJV73UVFklTVpYpVvwXcXW4kr0tDHPcD24CVEVGV2D4HXABUbaHtAL4v6V5JVXnQ5wjg58DVabjqSkkVLbC7d6p7Um9UZakSvbsqkzQWuAH4eERUptpwRHRHxPHANGC2pNKHrSSdDmyLiHvLjqWBkyLiHcB7gXPSkF/ZhgHvAK6IiN8CXgYqc6+rDuqe1LuAQ3OvpwGbSoplr5DGq28AvhIR3yg7nkbS1/VbgTklhwJwEnBGGr9eBrxb0pfLDSkTEZvSz23AjWTDkWXrArpy37KuJ0vy1iF1T+r3ADMkTU83ZOYBK0qOqbLSzcirgHUR8Zmy48mTdLCk8Wl/NPAe4JFyo4KIuDgipkXE4WT/vn4QER8pOSwkjUk3u0nDG38IlD7TKiK2AE9JemtqOhUo/UZ8ndR66d2I2CXpr4DvAUOBL0XE2pLDAkDS14BTgIMkdQH/EBFXlRsVJwF/DjyYxq4B/i4ivltiTL2mAEvTjKYhwPKIqMz0wQqaDNyY/Z5mGPDViLil3JBecy7wldTRehz4aMnx1EqtpzSame1r6j78Yma2T3FSNzOrESd1M7MacVI3M6sRJ3UzsxpxUrdCSPqspI/nXn9P0pW51/+SFsEayGdeI+mD/RxzSpVWSjQbbE7qVpQfAycCSBoCHAQcm3v/ROCOEuIyqzUndSvKHaSkTpbMHwK2SzpQ0kjgNwAk3ZYWnPpeWvoXSUdKuiW1/7uko/t+uKRPpZ77kLRm/iOSbgc+kDtmtqQfp4Wjftz7FGP6zONzx90h6TeL+oswG0xO6laItO7ILkmHkSX3O8lWfHwn2brj64DPAh+MiJnAl4DF6fQlwLmp/W+Ay/OfLemfgElkTyKOAL4IvB/4PeBNuUMfAU5OC0f9T+DTqf1K4C/SZx0FjIyIn3bqz25WplovE2Cl6+2tnwh8hmzZ4xOBXwBPk61HsjI9yj4U2JxWiDwRuC61A4zMfebfA3dHxCKA1IvfEBGPpddfBnqXmT2AbGmBGWSrcw5P7dcBfy/pb4G/BK7p6J/arERO6lak3nH1t5ENvzwF/DXwIvADYGpE/Fops1RE4YW0xG4j9wAzJU2IiN4iI83WuvgU8MOI+OO0PvytABHxiqSVZAVTPkT2zcGsFjz8YkW6AzgdeC6thf4cMJ5sCObrwMG99SklDZd0bFq/fYOks1K7JL0995m3AJcA30mrED4CTJd0ZHr/w7ljDyD7RgBpuCXnSuAy4J7cLwezvZ6TuhXpQbJZL3f1aftFWuP7g8Clkh4A7uf1G6t/BixM7WvpU4IwIq4jG0dfQVYIZRFZkr8deDJ36D8B/yjpDrLhnfxn3Ev2jeHqDvw5zSrDqzTaPknSIWTDMUdHRNXK0JntNvfUbZ8jaT7ZTJxPOKFb3binbmZWI+6pm5nViJO6mVmNOKmbmdWIk7qZWY04qZuZ1YiTuplZjfx/fIHgK2tuOjYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn\n",
+    "seaborn.heatmap(by_cross)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>Div</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>HomeTeam</th>\n",
+       "      <th>AwayTeam</th>\n",
+       "      <th>FTHG</th>\n",
+       "      <th>FTAG</th>\n",
+       "      <th>FTR</th>\n",
+       "      <th>HTHG</th>\n",
+       "      <th>HTAG</th>\n",
+       "      <th>HTR</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BbAv&lt;2.5</th>\n",
+       "      <th>BbAH</th>\n",
+       "      <th>BbAHh</th>\n",
+       "      <th>BbMxAHH</th>\n",
+       "      <th>BbAvAHH</th>\n",
+       "      <th>BbMxAHA</th>\n",
+       "      <th>BbAvAHA</th>\n",
+       "      <th>PSCH</th>\n",
+       "      <th>PSCD</th>\n",
+       "      <th>PSCA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>17/08/2018</td>\n",
+       "      <td>Betis</td>\n",
+       "      <td>Levante</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.06</td>\n",
+       "      <td>20</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>1.89</td>\n",
+       "      <td>1.85</td>\n",
+       "      <td>2.07</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>4.42</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>17/08/2018</td>\n",
+       "      <td>Girona</td>\n",
+       "      <td>Valladolid</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>D</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>20</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>2.06</td>\n",
+       "      <td>2.01</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>1.85</td>\n",
+       "      <td>1.76</td>\n",
+       "      <td>3.57</td>\n",
+       "      <td>5.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/08/2018</td>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>Alaves</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.18</td>\n",
+       "      <td>19</td>\n",
+       "      <td>-2.50</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>11.85</td>\n",
+       "      <td>32.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/08/2018</td>\n",
+       "      <td>Celta</td>\n",
+       "      <td>Espanol</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.76</td>\n",
+       "      <td>18</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>3.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/08/2018</td>\n",
+       "      <td>Villarreal</td>\n",
+       "      <td>Sociedad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.81</td>\n",
+       "      <td>18</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>1.76</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>2.23</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>2.32</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>3.53</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",
+       "      <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>375</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/05/2019</td>\n",
+       "      <td>Levante</td>\n",
+       "      <td>Ath Madrid</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>D</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.23</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>1.87</td>\n",
+       "      <td>2.04</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>4.34</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/05/2019</td>\n",
+       "      <td>Sevilla</td>\n",
+       "      <td>Ath Bilbao</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.08</td>\n",
+       "      <td>19</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>1.53</td>\n",
+       "      <td>2.17</td>\n",
+       "      <td>3.08</td>\n",
+       "      <td>4.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>18/05/2019</td>\n",
+       "      <td>Valladolid</td>\n",
+       "      <td>Valencia</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>2.13</td>\n",
+       "      <td>8.01</td>\n",
+       "      <td>5.13</td>\n",
+       "      <td>1.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>19/05/2019</td>\n",
+       "      <td>Eibar</td>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>D</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>1.98</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>1.88</td>\n",
+       "      <td>4.96</td>\n",
+       "      <td>4.55</td>\n",
+       "      <td>1.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>SP1</td>\n",
+       "      <td>19/05/2019</td>\n",
+       "      <td>Real Madrid</td>\n",
+       "      <td>Betis</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>D</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>23</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>1.94</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>2.01</td>\n",
+       "      <td>1.98</td>\n",
+       "      <td>1.33</td>\n",
+       "      <td>6.38</td>\n",
+       "      <td>8.09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>380 rows × 61 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Div        Date     HomeTeam    AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  \\\n",
+       "0    SP1  17/08/2018        Betis     Levante     0     3   A     0     1   A   \n",
+       "1    SP1  17/08/2018       Girona  Valladolid     0     0   D     0     0   D   \n",
+       "2    SP1  18/08/2018    Barcelona      Alaves     3     0   H     0     0   D   \n",
+       "3    SP1  18/08/2018        Celta     Espanol     1     1   D     0     1   A   \n",
+       "4    SP1  18/08/2018   Villarreal    Sociedad     1     2   A     1     1   D   \n",
+       "..   ...         ...          ...         ...   ...   ...  ..   ...   ...  ..   \n",
+       "375  SP1  18/05/2019      Levante  Ath Madrid     2     2   D     2     0   H   \n",
+       "376  SP1  18/05/2019      Sevilla  Ath Bilbao     2     0   H     1     0   H   \n",
+       "377  SP1  18/05/2019   Valladolid    Valencia     0     2   A     0     1   A   \n",
+       "378  SP1  19/05/2019        Eibar   Barcelona     2     2   D     2     2   D   \n",
+       "379  SP1  19/05/2019  Real Madrid       Betis     0     2   A     0     0   D   \n",
+       "\n",
+       "     ...  BbAv<2.5  BbAH  BbAHh  BbMxAHH  BbAvAHH  BbMxAHA  BbAvAHA  PSCH  \\\n",
+       "0    ...      2.06    20  -0.75     1.89     1.85     2.07     2.00  1.59   \n",
+       "1    ...      1.71    20  -0.75     2.06     2.01     1.90     1.85  1.76   \n",
+       "2    ...      3.18    19  -2.50     1.95     1.91     2.00     1.95  1.10   \n",
+       "3    ...      1.76    18  -0.75     2.26     2.18     1.74     1.71  2.18   \n",
+       "4    ...      1.81    18  -0.25     1.76     1.74     2.23     2.14  2.32   \n",
+       "..   ...       ...   ...    ...      ...      ...      ...      ...   ...   \n",
+       "375  ...      2.23    18   0.50     1.91     1.87     2.04     1.99  4.34   \n",
+       "376  ...      2.08    19  -1.00     2.70     2.60     1.60     1.53  2.17   \n",
+       "377  ...      2.25    20   1.50     1.78     1.74     2.20     2.13  8.01   \n",
+       "378  ...      2.65    19   0.50     2.03     1.98     1.92     1.88  4.96   \n",
+       "379  ...      3.42    23  -1.00     1.94     1.91     2.01     1.98  1.33   \n",
+       "\n",
+       "      PSCD   PSCA  \n",
+       "0     4.42   5.89  \n",
+       "1     3.57   5.62  \n",
+       "2    11.85  32.17  \n",
+       "3     3.26   3.85  \n",
+       "4     3.21   3.53  \n",
+       "..     ...    ...  \n",
+       "375   4.10   1.81  \n",
+       "376   3.08   4.15  \n",
+       "377   5.13   1.40  \n",
+       "378   4.55   1.65  \n",
+       "379   6.38   8.09  \n",
+       "\n",
+       "[380 rows x 61 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "season = pd.read_csv('season-1819_csv.csv')\n",
+    "season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Div', 'Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG',\n",
+       "       'HTAG', 'HTR', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC', 'AC', 'HY',\n",
+       "       'AY', 'HR', 'AR', 'B365H', 'B365D', 'B365A', 'BWH', 'BWD', 'BWA', 'IWH',\n",
+       "       'IWD', 'IWA', 'PSH', 'PSD', 'PSA', 'WHH', 'WHD', 'WHA', 'VCH', 'VCD',\n",
+       "       'VCA', 'Bb1X2', 'BbMxH', 'BbAvH', 'BbMxD', 'BbAvD', 'BbMxA', 'BbAvA',\n",
+       "       'BbOU', 'BbMx>2.5', 'BbAv>2.5', 'BbMx<2.5', 'BbAv<2.5', 'BbAH', 'BbAHh',\n",
+       "       'BbMxAHH', 'BbAvAHH', 'BbMxAHA', 'BbAvAHA', 'PSCH', 'PSCD', 'PSCA'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "season.columns"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}