python_postgres.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from time import time\n",
    "from sqlalchemy import create_engine\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.base.Connection at 0x7f7d86d8f550>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "engine = create_engine(\"postgresql://{USER}:{PSWD}@localhost:5432/{DB}\".format(**os.environ))\n",
    "engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zones = pd.read_csv('taxi+_zone_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>Yellow Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LocationID        Borough                     Zone service_zone\n",
       "0             1            EWR           Newark Airport          EWR\n",
       "1             2         Queens              Jamaica Bay    Boro Zone\n",
       "2             3          Bronx  Allerton/Pelham Gardens    Boro Zone\n",
       "3             4      Manhattan            Alphabet City  Yellow Zone\n",
       "4             5  Staten Island            Arden Heights    Boro Zone\n",
       "..          ...            ...                      ...          ...\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         None\n",
       "264         265        Unknown                      NaN         None\n",
       "\n",
       "[265 rows x 4 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zones['service_zone'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>Yellow Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>Boro Zone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LocationID        Borough                     Zone service_zone\n",
       "0           1            EWR           Newark Airport          EWR\n",
       "1           2         Queens              Jamaica Bay    Boro Zone\n",
       "2           3          Bronx  Allerton/Pelham Gardens    Boro Zone\n",
       "3           4      Manhattan            Alphabet City  Yellow Zone\n",
       "4           5  Staten Island            Arden Heights    Boro Zone"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones.to_sql(name='zones', con=engine, if_exists='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>service_zone</th>\n",
       "      <th>zone_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EWR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Airports</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yellow Zone</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boro Zone</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_zone  zone_count\n",
       "0          EWR           1\n",
       "1     Airports           2\n",
       "2         None           2\n",
       "3  Yellow Zone          55\n",
       "4    Boro Zone         205"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"SELECT service_zone, COUNT('Zone') AS zone_count FROM zones GROUP BY service_zone ORDER BY zone_count\"\n",
    "df_query = pd.read_sql(query, con=engine)\n",
    "df_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df_query['service_zone'], df_query['zone_count'], color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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