Abc_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33827</li>\n",
       "  <li><b>Dashboard: </b><a href='/user/lemonnn-8-dask-examples-rgt1kimn/proxy/37729/status' target='_blank'>/user/lemonnn-8-dask-examples-rgt1kimn/proxy/37729/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>10</li>\n",
       "  <li><b>Cores: </b>200</li>\n",
       "  <li><b>Memory: </b>54.88 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://127.0.0.1:33827' processes=10 cores=200>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client, LocalCluster\n",
    "cluster = LocalCluster(n_workers=10, threads_per_worker=20, dashboard_address=':0')\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install s3fs"
   ]
  },
  {
   "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>LocationID</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Zone</th>\n",
       "      <th>service_zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>261</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>World Trade Center</td>\n",
       "      <td>Yellow Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Yorkville East</td>\n",
       "      <td>Yellow Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>263</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>Yellow Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>264</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     LocationID    Borough                Zone service_zone\n",
       "260         261  Manhattan  World Trade Center  Yellow Zone\n",
       "261         262  Manhattan      Yorkville East  Yellow Zone\n",
       "262         263  Manhattan      Yorkville West  Yellow Zone\n",
       "263         264    Unknown                  NV          NaN\n",
       "264         265    Unknown                 NaN          NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "s3_df = dd.read_csv('s3://nyc-tlc/misc/taxi _zone_lookup.csv',storage_options={'anon': True})\n",
    "s3_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gcsfs\n",
    "!pip install fastparquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>tpep_pickup_datetime</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_dropoff_datetime</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>store_and_fwd_flag</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",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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></th>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>int64</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: read-parquet, 1 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "              tpep_pickup_datetime VendorID tpep_dropoff_datetime passenger_count trip_distance pickup_longitude pickup_latitude RateCodeID store_and_fwd_flag dropoff_longitude dropoff_latitude payment_type fare_amount    extra  mta_tax tip_amount tolls_amount improvement_surcharge total_amount\n",
       "npartitions=1                                                                                                                                                                                                                                                                                          \n",
       "                    datetime64[ns]    int64        datetime64[ns]           int64       float64          float64         float64      int64             object           float64          float64        int64     float64  float64  float64    float64      float64               float64      float64\n",
       "                               ...      ...                   ...             ...           ...              ...             ...        ...                ...               ...              ...          ...         ...      ...      ...        ...          ...                   ...          ...\n",
       "Dask Name: read-parquet, 1 tasks"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "gcs_df = dd.read_parquet('gcs://anaconda-public-data/nyc-taxi/nyc.parquet/part.0.parquet', engine='fastparquet')\n",
    "gcs_df.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcs_df=gcs_df.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "# username = \"root\"\n",
    "# password = \"root\"\n",
    "# port = 7777\n",
    "# database = \"taxi\"\n",
    "\n",
    "taxi = create_engine('sqlite:///taxi.db', echo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcs_df['tpep_pickup_datetime'] = gcs_df['tpep_pickup_datetime'].dt.hour\n",
    "gcs_df['tpep_dropoff_datetime'] = gcs_df['tpep_dropoff_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "# Create your connection.\n",
    "cnx = sqlite3.connect('taxi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcs_df.to_sql('taxi_record', con=cnx, if_exists='append')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pu = pd.read_sql_query('SELECT tpep_pickup_datetime AS time, count(*) AS PUcount \\\n",
    "                        FROM taxi_record \\\n",
    "                        GROUP BY tpep_pickup_datetime', cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_do = pd.read_sql_query('SELECT tpep_dropoff_datetime AS time, count(*) AS DOcount \\\n",
    "                        FROM taxi_record \\\n",
    "                        GROUP BY tpep_dropoff_datetime', cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q2 = df_pu.merge(df_do, on=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>time</th>\n",
       "      <th>PUcount</th>\n",
       "      <th>DOcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>57167</td>\n",
       "      <td>51182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>54658</td>\n",
       "      <td>56039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>45702</td>\n",
       "      <td>47873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>37813</td>\n",
       "      <td>39491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>26768</td>\n",
       "      <td>29740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  PUcount  DOcount\n",
       "0     0    57167    51182\n",
       "1     1    54658    56039\n",
       "2     2    45702    47873\n",
       "3     3    37813    39491\n",
       "4     4    26768    29740"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as p\n",
    "%matplotlib inline\n",
    "ax = df_q2.plot(x='time', y=['PUcount', 'DOcount'], kind='line', style=\"-o\", figsize=(15,5))\n",
    "ax.set_ylabel(\"count\")\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see peak hour is from 12AM to 1 AM\n",
    "# and ff-peak hour is 5 AM"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}