upload_data_green_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cdc22a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be5f8ab4",
   "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>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</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>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 15:17:29</td>\n",
       "      <td>2018-12-21 15:18:57</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:10:16</td>\n",
       "      <td>2019-01-01 00:16:32</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>0.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:27:11</td>\n",
       "      <td>2019-01-01 00:31:38</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:46:20</td>\n",
       "      <td>2019-01-01 01:04:54</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2.68</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:19:06</td>\n",
       "      <td>2019-01-01 00:39:43</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>4.53</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:15:42</td>\n",
       "      <td>2019-01-01 00:41:04</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>7.83</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:21:28</td>\n",
       "      <td>2019-01-01 00:48:15</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>4.49</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:17:10</td>\n",
       "      <td>2019-01-01 00:23:34</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:25:01</td>\n",
       "      <td>2019-01-01 00:43:55</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>232</td>\n",
       "      <td>2</td>\n",
       "      <td>8.74</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:36:56</td>\n",
       "      <td>2019-01-01 00:43:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.32</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0          2  2018-12-21 15:17:29   2018-12-21 15:18:57                  N   \n",
       "1          2  2019-01-01 00:10:16   2019-01-01 00:16:32                  N   \n",
       "2          2  2019-01-01 00:27:11   2019-01-01 00:31:38                  N   \n",
       "3          2  2019-01-01 00:46:20   2019-01-01 01:04:54                  N   \n",
       "4          2  2019-01-01 00:19:06   2019-01-01 00:39:43                  N   \n",
       "..       ...                  ...                   ...                ...   \n",
       "95         2  2019-01-01 00:15:42   2019-01-01 00:41:04                  N   \n",
       "96         2  2019-01-01 00:21:28   2019-01-01 00:48:15                  N   \n",
       "97         2  2019-01-01 00:17:10   2019-01-01 00:23:34                  N   \n",
       "98         2  2019-01-01 00:25:01   2019-01-01 00:43:55                  N   \n",
       "99         2  2019-01-01 00:36:56   2019-01-01 00:43:50                  N   \n",
       "\n",
       "    RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0            1           264           264                5           0.00   \n",
       "1            1            97            49                2           0.86   \n",
       "2            1            49           189                2           0.66   \n",
       "3            1           189            17                2           2.68   \n",
       "4            1            82           258                1           4.53   \n",
       "..         ...           ...           ...              ...            ...   \n",
       "95           1            97           179                1           7.83   \n",
       "96           1             7           230                1           4.49   \n",
       "97           1            42            42                1           1.08   \n",
       "98           1            42           232                2           8.74   \n",
       "99           1           223             7                5           1.32   \n",
       "\n",
       "    fare_amount  extra  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0           3.0    0.5      0.5        0.00           0.0        NaN   \n",
       "1           6.0    0.5      0.5        0.00           0.0        NaN   \n",
       "2           4.5    0.5      0.5        0.00           0.0        NaN   \n",
       "3          13.5    0.5      0.5        2.96           0.0        NaN   \n",
       "4          18.0    0.5      0.5        0.00           0.0        NaN   \n",
       "..          ...    ...      ...         ...           ...        ...   \n",
       "95         26.0    0.5      0.5        5.46           0.0        NaN   \n",
       "96         20.0    0.5      0.5        4.00           0.0        NaN   \n",
       "97          6.5    0.5      0.5        0.00           0.0        NaN   \n",
       "98         25.0    0.5      0.5        0.00           0.0        NaN   \n",
       "99          7.0    0.5      0.5        2.00           0.0        NaN   \n",
       "\n",
       "    improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                     0.3          4.30             2          1   \n",
       "1                     0.3          7.30             2          1   \n",
       "2                     0.3          5.80             1          1   \n",
       "3                     0.3         19.71             1          1   \n",
       "4                     0.3         19.30             2          1   \n",
       "..                    ...           ...           ...        ...   \n",
       "95                    0.3         32.76             1          1   \n",
       "96                    0.3         25.30             1          1   \n",
       "97                    0.3          7.80             2          1   \n",
       "98                    0.3         26.30             2          1   \n",
       "99                    0.3         10.30             1          1   \n",
       "\n",
       "    congestion_surcharge  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  \n",
       "..                   ...  \n",
       "95                   NaN  \n",
       "96                   NaN  \n",
       "97                   NaN  \n",
       "98                   NaN  \n",
       "99                   NaN  \n",
       "\n",
       "[100 rows x 20 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('green_tripdata_2019-01.csv.gz', nrows=100)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01b74daf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"green_taxi_data\" (\n",
      "\"VendorID\" INTEGER,\n",
      "  \"lpep_pickup_datetime\" TEXT,\n",
      "  \"lpep_dropoff_datetime\" TEXT,\n",
      "  \"store_and_fwd_flag\" TEXT,\n",
      "  \"RatecodeID\" INTEGER,\n",
      "  \"PULocationID\" INTEGER,\n",
      "  \"DOLocationID\" INTEGER,\n",
      "  \"passenger_count\" INTEGER,\n",
      "  \"trip_distance\" REAL,\n",
      "  \"fare_amount\" REAL,\n",
      "  \"extra\" REAL,\n",
      "  \"mta_tax\" REAL,\n",
      "  \"tip_amount\" REAL,\n",
      "  \"tolls_amount\" REAL,\n",
      "  \"ehail_fee\" REAL,\n",
      "  \"improvement_surcharge\" REAL,\n",
      "  \"total_amount\" REAL,\n",
      "  \"payment_type\" INTEGER,\n",
      "  \"trip_type\" INTEGER,\n",
      "  \"congestion_surcharge\" REAL\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(pd.io.sql.get_schema(df, name='green_taxi_data'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60887a01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"green_taxi_data\" (\n",
      "\"VendorID\" INTEGER,\n",
      "  \"lpep_pickup_datetime\" TIMESTAMP,\n",
      "  \"lpep_dropoff_datetime\" TIMESTAMP,\n",
      "  \"store_and_fwd_flag\" TEXT,\n",
      "  \"RatecodeID\" INTEGER,\n",
      "  \"PULocationID\" INTEGER,\n",
      "  \"DOLocationID\" INTEGER,\n",
      "  \"passenger_count\" INTEGER,\n",
      "  \"trip_distance\" REAL,\n",
      "  \"fare_amount\" REAL,\n",
      "  \"extra\" REAL,\n",
      "  \"mta_tax\" REAL,\n",
      "  \"tip_amount\" REAL,\n",
      "  \"tolls_amount\" REAL,\n",
      "  \"ehail_fee\" REAL,\n",
      "  \"improvement_surcharge\" REAL,\n",
      "  \"total_amount\" REAL,\n",
      "  \"payment_type\" INTEGER,\n",
      "  \"trip_type\" INTEGER,\n",
      "  \"congestion_surcharge\" REAL\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)\n",
    "df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)\n",
    "print(pd.io.sql.get_schema(df, name='green_taxi_data'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "28ce4f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c63b2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgresql://root:root@localhost:5431/ny_taxi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12b3f8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.base.Connection at 0x13cb782e0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c9f13cca",
   "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>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number\n",
       "0       1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT 1 as number;\n",
    "\"\"\"\n",
    "\n",
    "pd.read_sql(query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5c6193a4",
   "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>schemaname</th>\n",
       "      <th>tablename</th>\n",
       "      <th>tableowner</th>\n",
       "      <th>tablespace</th>\n",
       "      <th>hasindexes</th>\n",
       "      <th>hasrules</th>\n",
       "      <th>hastriggers</th>\n",
       "      <th>rowsecurity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>public</td>\n",
       "      <td>yellow_tripdata_trip</td>\n",
       "      <td>root</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>public</td>\n",
       "      <td>yellow_taxi_trips</td>\n",
       "      <td>root</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>public</td>\n",
       "      <td>yellow_taxi_data</td>\n",
       "      <td>root</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>public</td>\n",
       "      <td>zones</td>\n",
       "      <td>root</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  schemaname             tablename tableowner tablespace  hasindexes  \\\n",
       "0     public  yellow_tripdata_trip       root       None       False   \n",
       "1     public     yellow_taxi_trips       root       None        True   \n",
       "2     public      yellow_taxi_data       root       None        True   \n",
       "3     public                 zones       root       None        True   \n",
       "\n",
       "   hasrules  hastriggers  rowsecurity  \n",
       "0     False        False        False  \n",
       "1     False        False        False  \n",
       "2     False        False        False  \n",
       "3     False        False        False  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM pg_catalog.pg_tables\n",
    "WHERE schemaname != 'pg_catalog' AND \n",
    "    schemaname != 'information_schema';\n",
    "\"\"\"\n",
    "\n",
    "pd.read_sql(query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eaa87a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql(name='green_taxi_data', con=engine, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "229b60d6",
   "metadata": {
    "scrolled": true
   },
   "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>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</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>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 15:17:29</td>\n",
       "      <td>2018-12-21 15:18:57</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:10:16</td>\n",
       "      <td>2019-01-01 00:16:32</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>0.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:27:11</td>\n",
       "      <td>2019-01-01 00:31:38</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:46:20</td>\n",
       "      <td>2019-01-01 01:04:54</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2.68</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:19:06</td>\n",
       "      <td>2019-01-01 00:39:43</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>4.53</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:12:35</td>\n",
       "      <td>2019-01-01 00:19:09</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.05</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:47:55</td>\n",
       "      <td>2019-01-01 01:00:01</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>255</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3.77</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:12:47</td>\n",
       "      <td>2019-01-01 00:30:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:16:23</td>\n",
       "      <td>2019-01-01 00:39:46</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>7.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:58:02</td>\n",
       "      <td>2019-01-01 01:19:02</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>3.68</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0         2  2018-12-21 15:17:29   2018-12-21 15:18:57                  N   \n",
       "1         2  2019-01-01 00:10:16   2019-01-01 00:16:32                  N   \n",
       "2         2  2019-01-01 00:27:11   2019-01-01 00:31:38                  N   \n",
       "3         2  2019-01-01 00:46:20   2019-01-01 01:04:54                  N   \n",
       "4         2  2019-01-01 00:19:06   2019-01-01 00:39:43                  N   \n",
       "5         2  2019-01-01 00:12:35   2019-01-01 00:19:09                  N   \n",
       "6         2  2019-01-01 00:47:55   2019-01-01 01:00:01                  N   \n",
       "7         1  2019-01-01 00:12:47   2019-01-01 00:30:50                  N   \n",
       "8         2  2019-01-01 00:16:23   2019-01-01 00:39:46                  N   \n",
       "9         2  2019-01-01 00:58:02   2019-01-01 01:19:02                  N   \n",
       "\n",
       "   RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0           1           264           264                5           0.00   \n",
       "1           1            97            49                2           0.86   \n",
       "2           1            49           189                2           0.66   \n",
       "3           1           189            17                2           2.68   \n",
       "4           1            82           258                1           4.53   \n",
       "5           1            49            17                1           1.05   \n",
       "6           1           255            33                1           3.77   \n",
       "7           1            76           225                1           4.10   \n",
       "8           1            25            89                1           7.75   \n",
       "9           1            85            39                1           3.68   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount ehail_fee  \\\n",
       "0          3.0    0.5      0.5        0.00           0.0      None   \n",
       "1          6.0    0.5      0.5        0.00           0.0      None   \n",
       "2          4.5    0.5      0.5        0.00           0.0      None   \n",
       "3         13.5    0.5      0.5        2.96           0.0      None   \n",
       "4         18.0    0.5      0.5        0.00           0.0      None   \n",
       "5          6.5    0.5      0.5        0.00           0.0      None   \n",
       "6         13.5    0.5      0.5        0.00           0.0      None   \n",
       "7         16.0    0.5      0.5        0.00           0.0      None   \n",
       "8         25.5    0.5      0.5        0.00           0.0      None   \n",
       "9         15.5    0.5      0.5        0.00           0.0      None   \n",
       "\n",
       "   improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                    0.3          4.30             2          1   \n",
       "1                    0.3          7.30             2          1   \n",
       "2                    0.3          5.80             1          1   \n",
       "3                    0.3         19.71             1          1   \n",
       "4                    0.3         19.30             2          1   \n",
       "5                    0.3          7.80             1          1   \n",
       "6                    0.3         14.80             1          1   \n",
       "7                    0.3         17.30             1          1   \n",
       "8                    0.3         26.80             1          1   \n",
       "9                    0.3         16.80             1          1   \n",
       "\n",
       "  congestion_surcharge  \n",
       "0                 None  \n",
       "1                 None  \n",
       "2                 None  \n",
       "3                 None  \n",
       "4                 None  \n",
       "5                 None  \n",
       "6                 None  \n",
       "7                 None  \n",
       "8                 None  \n",
       "9                 None  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT * FROM green_taxi_data LIMIT 10\n",
    "\"\"\"\n",
    "\n",
    "pd.read_sql(query, con=engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4164196a",
   "metadata": {},
   "source": [
    "SELECT *\n",
    "FROM pg_catalog.pg_tables\n",
    "WHERE schemaname != 'pg_catalog' AND \n",
    "    schemaname != 'information_schema';\n",
    "Source: https://www.postgresqltutorial.com/postgresql-show-tables/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0aa966fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iter = pd.read_csv('green_tripdata_2019-01.csv.gz', iterator=True, chunksize=100000, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f709f0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = next(df_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da0c32e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6fdbf0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)\n",
    "df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "af81d127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=0).to_sql(name='green_taxi_data', con=engine, if_exists='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e7512e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.48 s, sys: 1.9 s, total: 7.38 s\n",
      "Wall time: 43.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time df.to_sql(name='green_taxi_data', con=engine, if_exists='append')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c016b9f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8b915d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inserted another chuck..., took  23.995 second\n",
      "inserted another chuck..., took  39.374 second\n",
      "inserted another chuck..., took  53.098 second\n",
      "inserted another chuck..., took  25.218 second\n",
      "inserted another chuck..., took  6.566 second\n"
     ]
    },
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Input \u001b[0;32mIn [53]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m      2\u001b[0m     t_start \u001b[38;5;241m=\u001b[39m time()\n\u001b[0;32m----> 3\u001b[0m     df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdf_iter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     df\u001b[38;5;241m.\u001b[39mlpep_pickup_datetime \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df\u001b[38;5;241m.\u001b[39mlpep_pickup_datetime)\n\u001b[1;32m      5\u001b[0m     df\u001b[38;5;241m.\u001b[39mlpep_dropoff_datetime \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df\u001b[38;5;241m.\u001b[39mlpep_dropoff_datetime)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1188\u001b[0m, in \u001b[0;36mTextFileReader.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__next__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1187\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1188\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1189\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m   1190\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1285\u001b[0m, in \u001b[0;36mTextFileReader.get_chunk\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m   1283\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n\u001b[1;32m   1284\u001b[0m     size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(size, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnrows \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_currow)\n\u001b[0;32m-> 1285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1255\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1253\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1254\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1255\u001b[0m     index, columns, col_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py:230\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    227\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_first_chunk:\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:787\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:879\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    t_start = time()\n",
    "    df = next(df_iter)\n",
    "    df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)\n",
    "    df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)\n",
    "    \n",
    "    df.to_sql(name='green_taxi_data', con=engine, if_exists='append')\n",
    "    \n",
    "    t_end = time()\n",
    "    print('inserted another chuck..., took % .3f second' %(t_end - t_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "533d43d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data = pd.read_csv('https://github.com/DataTalksClub/nyc-tlc-data/releases/download/green/green_tripdata_2019-01.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "866b17e3",
   "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>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</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>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 15:17:29</td>\n",
       "      <td>2018-12-21 15:18:57</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:10:16</td>\n",
       "      <td>2019-01-01 00:16:32</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>0.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:27:11</td>\n",
       "      <td>2019-01-01 00:31:38</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:46:20</td>\n",
       "      <td>2019-01-01 01:04:54</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2.68</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:19:06</td>\n",
       "      <td>2019-01-01 00:39:43</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>4.53</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:12:35</td>\n",
       "      <td>2019-01-01 00:19:09</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.05</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:47:55</td>\n",
       "      <td>2019-01-01 01:00:01</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>255</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3.77</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:12:47</td>\n",
       "      <td>2019-01-01 00:30:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:16:23</td>\n",
       "      <td>2019-01-01 00:39:46</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>7.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:58:02</td>\n",
       "      <td>2019-01-01 01:19:02</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>3.68</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:37:00</td>\n",
       "      <td>2019-01-01 00:56:42</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>6.84</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>37.06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:13:48</td>\n",
       "      <td>2019-01-01 00:21:00</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:19:59</td>\n",
       "      <td>2019-01-01 00:45:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0.49</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:57:57</td>\n",
       "      <td>2019-01-01 01:20:10</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "      <td>3.61</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0          2  2018-12-21 15:17:29   2018-12-21 15:18:57                  N   \n",
       "1          2  2019-01-01 00:10:16   2019-01-01 00:16:32                  N   \n",
       "2          2  2019-01-01 00:27:11   2019-01-01 00:31:38                  N   \n",
       "3          2  2019-01-01 00:46:20   2019-01-01 01:04:54                  N   \n",
       "4          2  2019-01-01 00:19:06   2019-01-01 00:39:43                  N   \n",
       "5          2  2019-01-01 00:12:35   2019-01-01 00:19:09                  N   \n",
       "6          2  2019-01-01 00:47:55   2019-01-01 01:00:01                  N   \n",
       "7          1  2019-01-01 00:12:47   2019-01-01 00:30:50                  N   \n",
       "8          2  2019-01-01 00:16:23   2019-01-01 00:39:46                  N   \n",
       "9          2  2019-01-01 00:58:02   2019-01-01 01:19:02                  N   \n",
       "10         2  2019-01-01 00:37:00   2019-01-01 00:56:42                  N   \n",
       "11         2  2019-01-01 00:13:48   2019-01-01 00:21:00                  N   \n",
       "12         2  2019-01-01 00:19:59   2019-01-01 00:45:50                  N   \n",
       "13         2  2019-01-01 00:57:57   2019-01-01 01:20:10                  N   \n",
       "\n",
       "    RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0            1           264           264                5           0.00   \n",
       "1            1            97            49                2           0.86   \n",
       "2            1            49           189                2           0.66   \n",
       "3            1           189            17                2           2.68   \n",
       "4            1            82           258                1           4.53   \n",
       "5            1            49            17                1           1.05   \n",
       "6            1           255            33                1           3.77   \n",
       "7            1            76           225                1           4.10   \n",
       "8            1            25            89                1           7.75   \n",
       "9            1            85            39                1           3.68   \n",
       "10           1           223           238                1           6.84   \n",
       "11           1           129           129                2           1.15   \n",
       "12           1            71            71                1           0.49   \n",
       "13           1            85           177                1           3.61   \n",
       "\n",
       "    fare_amount  extra  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0           3.0    0.5      0.5        0.00          0.00        NaN   \n",
       "1           6.0    0.5      0.5        0.00          0.00        NaN   \n",
       "2           4.5    0.5      0.5        0.00          0.00        NaN   \n",
       "3          13.5    0.5      0.5        2.96          0.00        NaN   \n",
       "4          18.0    0.5      0.5        0.00          0.00        NaN   \n",
       "5           6.5    0.5      0.5        0.00          0.00        NaN   \n",
       "6          13.5    0.5      0.5        0.00          0.00        NaN   \n",
       "7          16.0    0.5      0.5        0.00          0.00        NaN   \n",
       "8          25.5    0.5      0.5        0.00          0.00        NaN   \n",
       "9          15.5    0.5      0.5        0.00          0.00        NaN   \n",
       "10         22.0    0.5      0.5        8.00          5.76        NaN   \n",
       "11          6.5    0.5      0.5        1.56          0.00        NaN   \n",
       "12         15.5    0.5      0.5        0.00          0.00        NaN   \n",
       "13         17.0    0.5      0.5        0.00          0.00        NaN   \n",
       "\n",
       "    improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                     0.3          4.30             2          1   \n",
       "1                     0.3          7.30             2          1   \n",
       "2                     0.3          5.80             1          1   \n",
       "3                     0.3         19.71             1          1   \n",
       "4                     0.3         19.30             2          1   \n",
       "5                     0.3          7.80             1          1   \n",
       "6                     0.3         14.80             1          1   \n",
       "7                     0.3         17.30             1          1   \n",
       "8                     0.3         26.80             1          1   \n",
       "9                     0.3         16.80             1          1   \n",
       "10                    0.3         37.06             1          1   \n",
       "11                    0.3          9.36             1          1   \n",
       "12                    0.3         16.80             1          1   \n",
       "13                    0.3         18.30             1          1   \n",
       "\n",
       "    congestion_surcharge  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  \n",
       "5                    NaN  \n",
       "6                    NaN  \n",
       "7                    NaN  \n",
       "8                    NaN  \n",
       "9                    NaN  \n",
       "10                   NaN  \n",
       "11                   NaN  \n",
       "12                   NaN  \n",
       "13                   NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cca48cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://s3.amazonaws.com/nyc-tlc/misc/taxi+_zone_lookup.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9e4c9be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zones = pd.read_csv('taxi+_zone_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c3e561be",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "344eb13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones.to_sql(name='zones', con=engine, if_exists=\"replace\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b09adf6e",
   "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}