Accelerate_and_optimize_the_code_task_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff3591e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math as m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a26dd827",
   "metadata": {},
   "outputs": [],
   "source": [
    "https://stepik.org/media/attachments/lesson/363873/nyc.csv.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b944a2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('https://stepik.org/media/attachments/lesson/363873/nyc.csv.zip', compression='zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbfceac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                1  \n",
       "2                2  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd8f8356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   key                50000 non-null  object \n",
      " 1   fare_amount        50000 non-null  float64\n",
      " 2   pickup_datetime    50000 non-null  object \n",
      " 3   pickup_longitude   50000 non-null  float64\n",
      " 4   pickup_latitude    50000 non-null  float64\n",
      " 5   dropoff_longitude  50000 non-null  float64\n",
      " 6   dropoff_latitude   50000 non-null  float64\n",
      " 7   passenger_count    50000 non-null  int64  \n",
      "dtypes: float64(5), int64(1), object(2)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4298642",
   "metadata": {},
   "source": [
    "Перейдем к практике! Представьте, что вы аналитик в сервисе такси. Ваша задача – написать функцию haversine для подсчета расстояния между пунктами отправления и прибытия и, используя  pd.iterrows(), создать колонку distance с подсчитанным расстоянием. \n",
    "rr — радиус сферы, будем считать его равным 6371.\n",
    "Таким образом, сама функция haversine() должна принимать на вход 5 параметров, один из которых должен иметь значение по умолчанию.\n",
    "\n",
    "def haversine(..., ..., ..., ..., radius=6371):\n",
    "    # ваш код"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f4928675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.450133595805992"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def haversine(lop, lod, lap, lad, r):\n",
    "    lop=np.radians(lop)\n",
    "    lod=np.radians(lod)\n",
    "    lap=np.radians(lap)\n",
    "    lad=np.radians(lad)\n",
    "    d = 2*r*m.asin(m.sqrt((m.sin((lad-lap)/2))**2+m.cos(lap)*m.cos(lad)*m.sin((lod-lop)/2)**2))\n",
    "    return d\n",
    "\n",
    "\n",
    "lop_t=df.pickup_longitude[1]\n",
    "lod_t=df.dropoff_longitude[1]\n",
    "lap_t=df.pickup_latitude[1]\n",
    "lad_t=df.dropoff_latitude[1]\n",
    "\n",
    "haversine(lop_t, lod_t, lap_t, lad_t, 6371)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65e02193",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                           key  fare_amount          pickup_datetime  \\\n",
       "0  2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1  2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b58dd35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4.53 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dists = [ ]\n",
    "for i, row in df.iterrows():\n",
    "    dists.append(haversine(row['pickup_longitude'],row['dropoff_longitude'], row['pickup_latitude'],\n",
    "                           row['dropoff_latitude'], 6371 ))\n",
    "df['distance'] = dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a041981",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4.12 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['distance'] = pd.Series(haversine(row['pickup_longitude'],row['dropoff_longitude'], row['pickup_latitude'],\n",
    "                           row['dropoff_latitude'], 6371) for i, row in df.iterrows())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4616805e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.030764  \n",
       "1                1  8.450134  \n",
       "2                2  1.389525  \n",
       "3                1  2.799270  \n",
       "4                1  1.999157  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "317ff30b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.5\n",
      "16.9\n",
      "5.7\n"
     ]
    }
   ],
   "source": [
    "for row in df[:3].itertuples(name='custom_name'):\n",
    "    print(row[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5011f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df['distance'] = pd.Series(haversine(row['pickup_longitude'],row['dropoff_longitude'], row['pickup_latitude'],\n",
    "                           row['dropoff_latitude'], 6371) for i, row in df.iterrows())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ed9d0fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 531 ms\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math as m\n",
    "\n",
    "%%time\n",
    "\n",
    "def haversine(lop, lod, lap, lad, r=6371):\n",
    "    lop=np.radians(lop)\n",
    "    lod=np.radians(lod)\n",
    "    lap=np.radians(lap)\n",
    "    lad=np.radians(lad)\n",
    "    d = 2*r*m.asin(m.sqrt((m.sin((lad-lap)/2))**2+m.cos(lap)*m.cos(lad)*m.sin((lod-lop)/2)**2))\n",
    "    return d\n",
    "\n",
    "dists = [ ]\n",
    "for row in df[:].itertuples(name='test'):\n",
    "    dists.append(haversine(row[4],row[6], row[5],row[7]))\n",
    "df['distance'] = dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fba3189f",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.030764  \n",
       "1                1  8.450134  \n",
       "2                2  1.389525  \n",
       "3                1  2.799270  \n",
       "4                1  1.999157  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ba25a6c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['distance'] = df[['pickup_longitude','dropoff_longitude', 'pickup_latitude',\n",
    "                           'dropoff_latitude']]\\\n",
    "            .apply(lambda row: haversine(row[0], row[1], row[2], row[3]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "47960f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.030764  \n",
       "1                1  8.450134  \n",
       "2                2  1.389525  \n",
       "3                1  2.799270  \n",
       "4                1  1.999157  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cd35be59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8667.818812217454"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longest= df.distance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3b9fc2da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def haversine(lop, lod, lap, lad, r=6371):\n",
    "    lop, lod, lap, lad = [np.radians(x) for x in (lop, lod, lap, lad)]\n",
    "    d = 2*r*np.arcsin(np.sqrt((np.sin((lad-lap)/2))**2+np.cos(lap)*np.cos(lad)*np.sin((lod-lop)/2)**2))\n",
    "    return d\n",
    "\n",
    "df['distance'] = haversine(df['pickup_longitude'], df['dropoff_longitude'], \n",
    "                           df['pickup_latitude'], df['dropoff_latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5e1a31b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.030764  \n",
       "1                1  8.450134  \n",
       "2                2  1.389525  \n",
       "3                1  2.799270  \n",
       "4                1  1.999157  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d79b5dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 16.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def haversine(lop, lod, lap, lad, r=6371):\n",
    "    lop, lod, lap, lad = [np.radians(x) for x in (lop, lod, lap, lad)]\n",
    "    d = 2*r*np.arcsin(np.sqrt((np.sin((lad-lap)/2))**2+np.cos(lap)*np.cos(lad)*np.sin((lod-lop)/2)**2))\n",
    "    return d\n",
    "\n",
    "df['distance'] = haversine(df['pickup_longitude'].values, df['dropoff_longitude'].values, \n",
    "                           df['pickup_latitude'].values, df['dropoff_latitude'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f4d02e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.030764  \n",
       "1                1  8.450134  \n",
       "2                2  1.389525  \n",
       "3                1  2.799270  \n",
       "4                1  1.999157  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f558d875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>1.030764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.450134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.799270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999157</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>49995</th>\n",
       "      <td>2013-06-12 23:25:15.0000004</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2013-06-12 23:25:15 UTC</td>\n",
       "      <td>-73.999973</td>\n",
       "      <td>40.748531</td>\n",
       "      <td>-74.016899</td>\n",
       "      <td>40.705993</td>\n",
       "      <td>1</td>\n",
       "      <td>4.940374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>2015-06-22 17:19:18.0000007</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015-06-22 17:19:18 UTC</td>\n",
       "      <td>-73.984756</td>\n",
       "      <td>40.768211</td>\n",
       "      <td>-73.987366</td>\n",
       "      <td>40.760597</td>\n",
       "      <td>1</td>\n",
       "      <td>0.874706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>2011-01-30 04:53:00.00000063</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2011-01-30 04:53:00 UTC</td>\n",
       "      <td>-74.002698</td>\n",
       "      <td>40.739428</td>\n",
       "      <td>-73.998108</td>\n",
       "      <td>40.759483</td>\n",
       "      <td>1</td>\n",
       "      <td>2.263286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>2012-11-06 07:09:00.00000069</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2012-11-06 07:09:00 UTC</td>\n",
       "      <td>-73.946062</td>\n",
       "      <td>40.777567</td>\n",
       "      <td>-73.953450</td>\n",
       "      <td>40.779687</td>\n",
       "      <td>2</td>\n",
       "      <td>0.665245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>2010-01-13 08:13:14.0000007</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2010-01-13 08:13:14 UTC</td>\n",
       "      <td>-73.932603</td>\n",
       "      <td>40.763805</td>\n",
       "      <td>-73.932603</td>\n",
       "      <td>40.763805</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49045 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 key  fare_amount          pickup_datetime  \\\n",
       "0        2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1        2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2       2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3        2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4      2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "...                              ...          ...                      ...   \n",
       "49995    2013-06-12 23:25:15.0000004         15.0  2013-06-12 23:25:15 UTC   \n",
       "49996    2015-06-22 17:19:18.0000007          7.5  2015-06-22 17:19:18 UTC   \n",
       "49997   2011-01-30 04:53:00.00000063          6.9  2011-01-30 04:53:00 UTC   \n",
       "49998   2012-11-06 07:09:00.00000069          4.5  2012-11-06 07:09:00 UTC   \n",
       "49999    2010-01-13 08:13:14.0000007         10.9  2010-01-13 08:13:14 UTC   \n",
       "\n",
       "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0            -73.844311        40.721319         -73.841610         40.712278   \n",
       "1            -74.016048        40.711303         -73.979268         40.782004   \n",
       "2            -73.982738        40.761270         -73.991242         40.750562   \n",
       "3            -73.987130        40.733143         -73.991567         40.758092   \n",
       "4            -73.968095        40.768008         -73.956655         40.783762   \n",
       "...                 ...              ...                ...               ...   \n",
       "49995        -73.999973        40.748531         -74.016899         40.705993   \n",
       "49996        -73.984756        40.768211         -73.987366         40.760597   \n",
       "49997        -74.002698        40.739428         -73.998108         40.759483   \n",
       "49998        -73.946062        40.777567         -73.953450         40.779687   \n",
       "49999        -73.932603        40.763805         -73.932603         40.763805   \n",
       "\n",
       "       passenger_count  distance  \n",
       "0                    1  1.030764  \n",
       "1                    1  8.450134  \n",
       "2                    2  1.389525  \n",
       "3                    1  2.799270  \n",
       "4                    1  1.999157  \n",
       "...                ...       ...  \n",
       "49995                1  4.940374  \n",
       "49996                1  0.874706  \n",
       "49997                1  2.263286  \n",
       "49998                2  0.665245  \n",
       "49999                1  0.000000  \n",
       "\n",
       "[49045 rows x 9 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_right=df.query(\"dropoff_longitude!=0 | dropoff_latitude!=0\")\n",
    "df_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b33bcb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "33db5b37",
   "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>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49045.000000</td>\n",
       "      <td>49045.000000</td>\n",
       "      <td>49045.000000</td>\n",
       "      <td>49045.000000</td>\n",
       "      <td>49045.000000</td>\n",
       "      <td>49045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.362722</td>\n",
       "      <td>-73.864325</td>\n",
       "      <td>40.677284</td>\n",
       "      <td>-73.916420</td>\n",
       "      <td>40.703692</td>\n",
       "      <td>1.669039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.657274</td>\n",
       "      <td>3.161950</td>\n",
       "      <td>3.038180</td>\n",
       "      <td>2.463555</td>\n",
       "      <td>2.288267</td>\n",
       "      <td>1.290029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.000000</td>\n",
       "      <td>-75.423848</td>\n",
       "      <td>-74.006893</td>\n",
       "      <td>-84.654241</td>\n",
       "      <td>-74.006377</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992288</td>\n",
       "      <td>40.736407</td>\n",
       "      <td>-73.991327</td>\n",
       "      <td>40.735842</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.982105</td>\n",
       "      <td>40.753368</td>\n",
       "      <td>-73.980472</td>\n",
       "      <td>40.754063</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.968292</td>\n",
       "      <td>40.767731</td>\n",
       "      <td>-73.965260</td>\n",
       "      <td>40.768475</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>40.783472</td>\n",
       "      <td>401.083332</td>\n",
       "      <td>40.851027</td>\n",
       "      <td>43.415190</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  49045.000000      49045.000000     49045.000000       49045.000000   \n",
       "mean      11.362722        -73.864325        40.677284         -73.916420   \n",
       "std        9.657274          3.161950         3.038180           2.463555   \n",
       "min       -5.000000        -75.423848       -74.006893         -84.654241   \n",
       "25%        6.000000        -73.992288        40.736407         -73.991327   \n",
       "50%        8.500000        -73.982105        40.753368         -73.980472   \n",
       "75%       12.500000        -73.968292        40.767731         -73.965260   \n",
       "max      200.000000         40.783472       401.083332          40.851027   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count      49045.000000     49045.000000  \n",
       "mean          40.703692         1.669039  \n",
       "std            2.288267         1.290029  \n",
       "min          -74.006377         0.000000  \n",
       "25%           40.735842         1.000000  \n",
       "50%           40.754063         1.000000  \n",
       "75%           40.768475         2.000000  \n",
       "max           43.415190         6.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_right.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fded0e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'pickup_longitude'}>,\n",
       "        <AxesSubplot:title={'center':'pickup_latitude'}>],\n",
       "       [<AxesSubplot:title={'center':'dropoff_longitude'}>,\n",
       "        <AxesSubplot:title={'center':'dropoff_latitude'}>]], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_right[['pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude']].hist(figsize=[12,8], bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "68311cc4",
   "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>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33502</th>\n",
       "      <td>2014-02-22 02:44:00.00000052</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2014-02-22 02:44:00 UTC</td>\n",
       "      <td>-73.98954</td>\n",
       "      <td>40.757032</td>\n",
       "      <td>-73.86058</td>\n",
       "      <td>41.147487</td>\n",
       "      <td>1</td>\n",
       "      <td>44.746988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                key  fare_amount          pickup_datetime  \\\n",
       "33502  2014-02-22 02:44:00.00000052        136.0  2014-02-22 02:44:00 UTC   \n",
       "\n",
       "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "33502         -73.98954        40.757032          -73.86058         41.147487   \n",
       "\n",
       "       passenger_count   distance  \n",
       "33502                1  44.746988  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_right[df_right.distance==df_right.query('10<pickup_latitude<120 & pickup_longitude<-60 & 10<dropoff_latitude \\\n",
    "               & dropoff_longitude<-60 & distance<=50').distance.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f562a36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65cd125b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3cac13e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}