dask_updates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dask.array as da\n",
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No problem handling 100000000 integer numbers.\n"
     ]
    }
   ],
   "source": [
    "images = np.ones((100, 1000, 1000))\n",
    "print(f'No problem handling {np.prod(images.shape)} integer numbers.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to allocate an array with shape (10000, 1000, 1000)\n"
     ]
    }
   ],
   "source": [
    "try: \n",
    "    images = np.ones((10000, 1000, 1000))\n",
    "    print('Impresive! No need for dask arrays.')\n",
    "except:\n",
    "    print('Unable to allocate an array with shape (10000, 1000, 1000)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dask arrays save the day!\n"
     ]
    },
    {
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       "dask.array<ones, shape=(10000, 1000, 1000), dtype=float64, chunksize=(250, 250, 250), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: \n",
    "    images = da.ones((10000, 1000, 1000))\n",
    "    print('Dask arrays save the day!')\n",
    "except:\n",
    "    print('Dask arrays cannot to allocate an array with shape (10000, 1000, 1000)')\n",
    "\n",
    "images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\martin\\Anaconda3\\envs\\uber\\lib\\site-packages\\google\\auth\\_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
     ]
    }
   ],
   "source": [
    "df = dd.read_csv(\n",
    "    'gcs://anaconda-public-data/nyc-taxi/csv/2015/yellow_*.csv'\n",
    ").persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>VendorID</th>\n",
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       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
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       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
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      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         2  2015-01-15 19:05:39   2015-01-15 19:23:42                1   \n",
       "1         1  2015-01-10 20:33:38   2015-01-10 20:53:28                1   \n",
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       "3         1  2015-01-10 20:33:39   2015-01-10 20:35:31                1   \n",
       "4         1  2015-01-10 20:33:39   2015-01-10 20:52:58                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  RateCodeID  \\\n",
       "0           1.59        -73.993896        40.750111           1   \n",
       "1           3.30        -74.001648        40.724243           1   \n",
       "2           1.80        -73.963341        40.802788           1   \n",
       "3           0.50        -74.009087        40.713818           1   \n",
       "4           3.00        -73.971176        40.762428           1   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type  \\\n",
       "0                  N         -73.974785         40.750618             1   \n",
       "1                  N         -73.994415         40.759109             1   \n",
       "2                  N         -73.951820         40.824413             2   \n",
       "3                  N         -74.004326         40.719986             2   \n",
       "4                  N         -74.004181         40.742653             2   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0         12.0    1.0      0.5        3.25           0.0   \n",
       "1         14.5    0.5      0.5        2.00           0.0   \n",
       "2          9.5    0.5      0.5        0.00           0.0   \n",
       "3          3.5    0.5      0.5        0.00           0.0   \n",
       "4         15.0    0.5      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3         17.05  \n",
       "1                    0.3         17.80  \n",
       "2                    0.3         10.80  \n",
       "3                    0.3          4.80  \n",
       "4                    0.3         16.30  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'compute'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-7869d5bb3e7f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'compute'"
     ]
    }
   ],
   "source": [
    "df.shape.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "      <td>1.461130e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.524653e+00</td>\n",
       "      <td>1.680663e+00</td>\n",
       "      <td>1.313727e+01</td>\n",
       "      <td>-7.280689e+01</td>\n",
       "      <td>4.010771e+01</td>\n",
       "      <td>1.042062e+00</td>\n",
       "      <td>-7.285360e+01</td>\n",
       "      <td>4.013439e+01</td>\n",
       "      <td>1.379040e+00</td>\n",
       "      <td>1.293878e+01</td>\n",
       "      <td>3.142661e-01</td>\n",
       "      <td>4.975636e-01</td>\n",
       "      <td>1.728442e+00</td>\n",
       "      <td>3.077738e-01</td>\n",
       "      <td>2.983088e-01</td>\n",
       "      <td>1.609625e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.993919e-01</td>\n",
       "      <td>1.333365e+00</td>\n",
       "      <td>1.987510e+04</td>\n",
       "      <td>9.220263e+00</td>\n",
       "      <td>5.078433e+00</td>\n",
       "      <td>6.680777e-01</td>\n",
       "      <td>9.039230e+00</td>\n",
       "      <td>4.979399e+00</td>\n",
       "      <td>4.992620e-01</td>\n",
       "      <td>1.245994e+02</td>\n",
       "      <td>5.314586e-01</td>\n",
       "      <td>5.007977e-02</td>\n",
       "      <td>3.268367e+02</td>\n",
       "      <td>1.664408e+00</td>\n",
       "      <td>2.606618e-02</td>\n",
       "      <td>3.569936e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-4.084012e+07</td>\n",
       "      <td>-8.740026e+02</td>\n",
       "      <td>-7.819468e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.818333e+02</td>\n",
       "      <td>-7.819468e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-4.960000e+02</td>\n",
       "      <td>-7.900000e+01</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>-4.400000e+02</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-3.000000e-01</td>\n",
       "      <td>-4.963000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>-7.399049e+01</td>\n",
       "      <td>4.074049e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.399023e+01</td>\n",
       "      <td>4.074017e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>9.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.110000e+00</td>\n",
       "      <td>-7.397923e+01</td>\n",
       "      <td>4.075641e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.397843e+01</td>\n",
       "      <td>4.075641e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.050000e+01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>1.330000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.960000e+00</td>\n",
       "      <td>-7.396299e+01</td>\n",
       "      <td>4.077013e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.395604e+01</td>\n",
       "      <td>4.077245e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.650000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>2.660000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>2.016000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.986230e+08</td>\n",
       "      <td>1.726000e+02</td>\n",
       "      <td>4.048667e+02</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>1.726000e+02</td>\n",
       "      <td>4.834500e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>8.259986e+05</td>\n",
       "      <td>9.999900e+02</td>\n",
       "      <td>9.100000e+01</td>\n",
       "      <td>3.950589e+06</td>\n",
       "      <td>1.901400e+03</td>\n",
       "      <td>1.376300e+02</td>\n",
       "      <td>3.950612e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VendorID  passenger_count  trip_distance  pickup_longitude  \\\n",
       "count  1.461130e+08     1.461130e+08   1.461130e+08      1.461130e+08   \n",
       "mean   1.524653e+00     1.680663e+00   1.313727e+01     -7.280689e+01   \n",
       "std    4.993919e-01     1.333365e+00   1.987510e+04      9.220263e+00   \n",
       "min    1.000000e+00     0.000000e+00  -4.084012e+07     -8.740026e+02   \n",
       "25%    1.000000e+00     1.000000e+00   1.200000e+00     -7.399049e+01   \n",
       "50%    2.000000e+00     1.000000e+00   2.110000e+00     -7.397923e+01   \n",
       "75%    2.000000e+00     2.000000e+00   3.960000e+00     -7.396299e+01   \n",
       "max    2.000000e+00     9.000000e+00   1.986230e+08      1.726000e+02   \n",
       "\n",
       "       pickup_latitude    RateCodeID  dropoff_longitude  dropoff_latitude  \\\n",
       "count     1.461130e+08  1.461130e+08       1.461130e+08      1.461130e+08   \n",
       "mean      4.010771e+01  1.042062e+00      -7.285360e+01      4.013439e+01   \n",
       "std       5.078433e+00  6.680777e-01       9.039230e+00      4.979399e+00   \n",
       "min      -7.819468e+01  1.000000e+00      -7.818333e+02     -7.819468e+01   \n",
       "25%       4.074049e+01  1.000000e+00      -7.399023e+01      4.074017e+01   \n",
       "50%       4.075641e+01  1.000000e+00      -7.397843e+01      4.075641e+01   \n",
       "75%       4.077013e+01  1.000000e+00      -7.395604e+01      4.077245e+01   \n",
       "max       4.048667e+02  9.900000e+01       1.726000e+02      4.834500e+02   \n",
       "\n",
       "       payment_type   fare_amount         extra       mta_tax    tip_amount  \\\n",
       "count  1.461130e+08  1.461130e+08  1.461130e+08  1.461130e+08  1.461130e+08   \n",
       "mean   1.379040e+00  1.293878e+01  3.142661e-01  4.975636e-01  1.728442e+00   \n",
       "std    4.992620e-01  1.245994e+02  5.314586e-01  5.007977e-02  3.268367e+02   \n",
       "min    1.000000e+00 -4.960000e+02 -7.900000e+01 -3.000000e+00 -4.400000e+02   \n",
       "25%    1.000000e+00  7.000000e+00  0.000000e+00  5.000000e-01  0.000000e+00   \n",
       "50%    1.000000e+00  1.050000e+01  5.000000e-01  5.000000e-01  1.500000e+00   \n",
       "75%    2.000000e+00  1.650000e+01  1.000000e+00  5.000000e-01  2.660000e+00   \n",
       "max    5.000000e+00  8.259986e+05  9.999900e+02  9.100000e+01  3.950589e+06   \n",
       "\n",
       "       tolls_amount  improvement_surcharge  total_amount  \n",
       "count  1.461130e+08           1.461130e+08  1.461130e+08  \n",
       "mean   3.077738e-01           2.983088e-01  1.609625e+01  \n",
       "std    1.664408e+00           2.606618e-02  3.569936e+02  \n",
       "min   -9.900000e+01          -3.000000e-01 -4.963000e+02  \n",
       "25%    0.000000e+00           3.000000e-01  9.300000e+00  \n",
       "50%    0.000000e+00           3.000000e-01  1.330000e+01  \n",
       "75%    0.000000e+00           3.000000e-01  2.016000e+01  \n",
       "max    1.901400e+03           1.376300e+02  3.950612e+06  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dask.dataframe.core.DataFrame'>\n",
      "Columns: 19 entries, VendorID to total_amount\n",
      "dtypes: object(3), float64(12), int64(4)"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    102991045\n",
       "2     20901372\n",
       "5      7939001\n",
       "3      6135107\n",
       "6      5123951\n",
       "4      2981071\n",
       "0        40853\n",
       "7          239\n",
       "8          181\n",
       "9          169\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.passenger_count.value_counts().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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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",
       "      <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_fares_url = 'E:\\\\datasets\\\\new-york-city-taxi-fare-prediction\\\\train.csv'\n",
    "df_taxi_fare = dd.read_csv(\n",
    "    taxi_fares_url\n",
    ").persist()\n",
    "\n",
    "df_taxi_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      38337524\n",
       "2       8175243\n",
       "5       3929346\n",
       "3       2432712\n",
       "4       1178852\n",
       "6       1174647\n",
       "0        195416\n",
       "208          64\n",
       "9            23\n",
       "7            15\n",
       "8             9\n",
       "129           2\n",
       "51            1\n",
       "49            1\n",
       "34            1\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taxi_fare.passenger_count.value_counts().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
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       "      <td>2011-08-18 00:35:00.00000049</td>\n",
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       "      <td>-73.982738</td>\n",
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       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
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       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>55423851</th>\n",
       "      <td>2014-03-15 03:28:00.00000070</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2014-03-15 03:28:00 UTC</td>\n",
       "      <td>-74.005272</td>\n",
       "      <td>40.740027</td>\n",
       "      <td>-73.963280</td>\n",
       "      <td>40.762555</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55423852</th>\n",
       "      <td>2009-03-24 20:46:20.0000002</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2009-03-24 20:46:20 UTC</td>\n",
       "      <td>-73.957784</td>\n",
       "      <td>40.765530</td>\n",
       "      <td>-73.951640</td>\n",
       "      <td>40.773959</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>55423853</th>\n",
       "      <td>2011-04-02 22:04:24.0000004</td>\n",
       "      <td>14.1</td>\n",
       "      <td>2011-04-02 22:04:24 UTC</td>\n",
       "      <td>-73.970505</td>\n",
       "      <td>40.752325</td>\n",
       "      <td>-73.960537</td>\n",
       "      <td>40.797342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55423854</th>\n",
       "      <td>2011-10-26 05:57:51.0000002</td>\n",
       "      <td>28.9</td>\n",
       "      <td>2011-10-26 05:57:51 UTC</td>\n",
       "      <td>-73.980901</td>\n",
       "      <td>40.764629</td>\n",
       "      <td>-73.870605</td>\n",
       "      <td>40.773963</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>55423855</th>\n",
       "      <td>2014-12-12 11:33:00.00000015</td>\n",
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       "      <td>2014-12-12 11:33:00 UTC</td>\n",
       "      <td>-73.969722</td>\n",
       "      <td>40.797668</td>\n",
       "      <td>-73.970885</td>\n",
       "      <td>40.783313</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55423856 rows × 8 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",
       "55423851   2014-03-15 03:28:00.00000070         14.0  2014-03-15 03:28:00 UTC   \n",
       "55423852    2009-03-24 20:46:20.0000002          4.2  2009-03-24 20:46:20 UTC   \n",
       "55423853    2011-04-02 22:04:24.0000004         14.1  2011-04-02 22:04:24 UTC   \n",
       "55423854    2011-10-26 05:57:51.0000002         28.9  2011-10-26 05:57:51 UTC   \n",
       "55423855   2014-12-12 11:33:00.00000015          7.5  2014-12-12 11:33:00 UTC   \n",
       "\n",
       "          pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0               -73.844311        40.721319         -73.841610   \n",
       "1               -74.016048        40.711303         -73.979268   \n",
       "2               -73.982738        40.761270         -73.991242   \n",
       "3               -73.987130        40.733143         -73.991567   \n",
       "4               -73.968095        40.768008         -73.956655   \n",
       "...                    ...              ...                ...   \n",
       "55423851        -74.005272        40.740027         -73.963280   \n",
       "55423852        -73.957784        40.765530         -73.951640   \n",
       "55423853        -73.970505        40.752325         -73.960537   \n",
       "55423854        -73.980901        40.764629         -73.870605   \n",
       "55423855        -73.969722        40.797668         -73.970885   \n",
       "\n",
       "          dropoff_latitude  passenger_count  \n",
       "0                40.712278                1  \n",
       "1                40.782004                1  \n",
       "2                40.750562                2  \n",
       "3                40.758092                1  \n",
       "4                40.783762                1  \n",
       "...                    ...              ...  \n",
       "55423851         40.762555                1  \n",
       "55423852         40.773959                1  \n",
       "55423853         40.797342                1  \n",
       "55423854         40.773963                1  \n",
       "55423855         40.783313                1  \n",
       "\n",
       "[55423856 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(taxi_fares_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ".passenger_count.value_counts()"
   ]
  }
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