data-analysis/008-pandas-datetime/pandas-datetime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with datetime in Pandas DataFrame\n",
    "\n",
    "This is a notebook for the medium article [Working with datetime in Pandas DataFrame](https://towardsdatascience.com/working-with-datetime-in-pandas-dataframe-663f7af6c587)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Convert strings to datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Default arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'date': ['3/10/2000', '3/11/2000', '3/12/2000'],\n",
    "                   'value': [2, 3, 4]})\n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-03-10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-03-11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-03-12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        date  value\n",
       "0 2000-03-10      2\n",
       "1 2000-03-11      3\n",
       "2 2000-03-12      4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Day first format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'date': ['3/10/2000', '3/11/2000', '3/12/2000'],\n",
    "                   'value': [2, 3, 4]})\n",
    "df['date'] = pd.to_datetime(df['date'], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-10-03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-11-03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-12-03</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        date  value\n",
       "0 2000-10-03      2\n",
       "1 2000-11-03      3\n",
       "2 2000-12-03      4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Custom format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'date': ['2016-6-10 20:30:0', \n",
    "                            '2016-7-1 19:45:30', \n",
    "                            '2013-10-12 4:5:1'],\n",
    "                   'value': [2, 3, 4]})\n",
    "df['date'] = pd.to_datetime(df['date'], format=\"%Y-%d-%m %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-06 20:30:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-07 19:45:30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-10 04:05:01</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 date  value\n",
       "0 2016-10-06 20:30:00      2\n",
       "1 2016-01-07 19:45:30      3\n",
       "2 2013-12-10 04:05:01      4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Speedup parsing with infer_datetime_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3/11/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/12/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/13/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/11/2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/12/2000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        date\n",
       "0  3/11/2000\n",
       "1  3/12/2000\n",
       "2  3/13/2000\n",
       "3  3/11/2000\n",
       "4  3/12/2000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'date': ['3/11/2000', '3/12/2000', '3/13/2000'] * 1000 })\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.26 ms ± 36.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.to_datetime(df['date'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2 ms ± 8.49 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.to_datetime(df['date'], infer_datetime_format=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle parsing error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Unknown string format: a/11/2000",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1857\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1858\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1859\u001b[0m             \u001b[0;31m# If tzaware, these values represent unix timestamps, so we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-712eac2d626f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m df = pd.DataFrame({'date': ['3/10/2000', 'a/11/2000', '3/12/2000'],\n\u001b[1;32m      2\u001b[0m                    'value': [2, 3, 4]})\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m    726\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    445\u001b[0m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m             \u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 447\u001b[0;31m             \u001b[0mallow_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    448\u001b[0m         )\n\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1861\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1852\u001b[0m             \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m             \u001b[0myearfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1854\u001b[0;31m             \u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1855\u001b[0m         )\n\u001b[1;32m   1856\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/dateutil/parser/_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[1;32m   1372\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparserinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mDEFAULTPARSER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/dateutil/parser/_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mParserError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown string format: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimestr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    651\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParserError\u001b[0m: Unknown string format: a/11/2000"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'date': ['3/10/2000', 'a/11/2000', '3/12/2000'],\n",
    "                   'value': [2, 3, 4]})\n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3/10/2000</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>a/11/2000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/12/2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  value\n",
       "0  3/10/2000      2\n",
       "1  a/11/2000      3\n",
       "2  3/12/2000      4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], errors='ignore')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-03-10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaT</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-03-12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  value\n",
       "0 2000-03-10      2\n",
       "1        NaT      3\n",
       "2 2000-03-12      4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], errors='coerce')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Assemble a datetime from multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'year': [2015, 2016],\n",
    "                   'month': [2, 3],\n",
    "                   'day': [4, 5]})\n",
    "\n",
    "df['date'] = pd.to_datetime(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-03-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day       date\n",
       "0  2015      2    4 2015-02-04\n",
       "1  2016      3    5 2016-03-05"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Get year, month and day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'name': ['Tom', 'Andy', 'Lucas'],\n",
    "                 'DoB': ['08-05-1997', '04-28-1996', '12-16-1995']})\n",
    "df['DoB'] = pd.to_datetime(df['DoB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>DoB</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>1997-08-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andy</td>\n",
       "      <td>1996-04-28</td>\n",
       "      <td>1996</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>1995-12-16</td>\n",
       "      <td>1995</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name        DoB  year  month  day\n",
       "0    Tom 1997-08-05  1997      8    5\n",
       "1   Andy 1996-04-28  1996      4   28\n",
       "2  Lucas 1995-12-16  1995     12   16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year']= df['DoB'].dt.year\n",
    "df['month']= df['DoB'].dt.month\n",
    "df['day']= df['DoB'].dt.day\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Get the week of year, the day of week and leap year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>DoB</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_leap_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>1997-08-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andy</td>\n",
       "      <td>1996-04-28</td>\n",
       "      <td>1996</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>1995-12-16</td>\n",
       "      <td>1995</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name        DoB  year  month  day  week_of_year  day_of_week  is_leap_year\n",
       "0    Tom 1997-08-05  1997      8    5            32            1         False\n",
       "1   Andy 1996-04-28  1996      4   28            17            6          True\n",
       "2  Lucas 1995-12-16  1995     12   16            50            5         False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['week_of_year'] = df['DoB'].dt.week\n",
    "df['day_of_week'] = df['DoB'].dt.dayofweek\n",
    "df['is_leap_year'] = df['DoB'].dt.is_leap_year\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>DoB</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_leap_year</th>\n",
       "      <th>day_of_week_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>1997-08-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andy</td>\n",
       "      <td>1996-04-28</td>\n",
       "      <td>1996</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>1995-12-16</td>\n",
       "      <td>1995</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name        DoB  year  month  day  week_of_year  day_of_week  \\\n",
       "0    Tom 1997-08-05  1997      8    5            32            1   \n",
       "1   Andy 1996-04-28  1996      4   28            17            6   \n",
       "2  Lucas 1995-12-16  1995     12   16            50            5   \n",
       "\n",
       "   is_leap_year day_of_week_name  \n",
       "0         False          Tuesday  \n",
       "1          True           Sunday  \n",
       "2         False         Saturday  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dw_mapping={\n",
    "    0: 'Monday', \n",
    "    1: 'Tuesday', \n",
    "    2: 'Wednesday', \n",
    "    3: 'Thursday', \n",
    "    4: 'Friday',\n",
    "    5: 'Saturday', \n",
    "    6: 'Sunday'\n",
    "} \n",
    "df['day_of_week_name']=df['DoB'].dt.weekday.map(dw_mapping)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Get the age from the date of birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>DoB</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_leap_year</th>\n",
       "      <th>day_of_week_name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>1997-08-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andy</td>\n",
       "      <td>1996-04-28</td>\n",
       "      <td>1996</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>1995-12-16</td>\n",
       "      <td>1995</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name        DoB  year  month  day  week_of_year  day_of_week  \\\n",
       "0    Tom 1997-08-05  1997      8    5            32            1   \n",
       "1   Andy 1996-04-28  1996      4   28            17            6   \n",
       "2  Lucas 1995-12-16  1995     12   16            50            5   \n",
       "\n",
       "   is_leap_year day_of_week_name  age  \n",
       "0         False          Tuesday   23  \n",
       "1          True           Sunday   24  \n",
       "2         False         Saturday   25  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today = pd.to_datetime('today')\n",
    "df['age'] = today.year - df['DoB'].dt.year\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>DoB</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_leap_year</th>\n",
       "      <th>day_of_week_name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>1997-08-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andy</td>\n",
       "      <td>1996-04-28</td>\n",
       "      <td>1996</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>1995-12-16</td>\n",
       "      <td>1995</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name        DoB  year  month  day  week_of_year  day_of_week  \\\n",
       "0    Tom 1997-08-05  1997      8    5            32            1   \n",
       "1   Andy 1996-04-28  1996      4   28            17            6   \n",
       "2  Lucas 1995-12-16  1995     12   16            50            5   \n",
       "\n",
       "   is_leap_year day_of_week_name  age  \n",
       "0         False          Tuesday   23  \n",
       "1          True           Sunday   24  \n",
       "2         False         Saturday   24  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Year difference\n",
    "today = pd.to_datetime('today')\n",
    "diff_y = today.year - df['DoB'].dt.year\n",
    "# Haven't had birthday\n",
    "b_md = df['DoB'].apply(lambda x: (x.month,x.day) )\n",
    "no_birthday = b_md > (today.month,today.day)\n",
    "\n",
    "df['age'] = diff_y - no_birthday\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Improve performance by setting date column as the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1795144 entries, 0 to 1795143\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Dtype         \n",
      "---  ------  -----         \n",
      " 0   date    datetime64[ns]\n",
      " 1   num     int64         \n",
      " 2   city    object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 41.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/city_sales.csv',parse_dates=['date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:01:00</th>\n",
       "      <td>4</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:02:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:03:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:04:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 15:56:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 15:57:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 15:58:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 15:59:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 16:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1795144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2015-01-01 09:00:00    4     London\n",
       "2015-01-01 09:01:00    4     London\n",
       "2015-01-01 09:02:00    3     London\n",
       "2015-01-01 09:03:00    3     London\n",
       "2015-01-01 09:04:00    3     London\n",
       "...                  ...        ...\n",
       "2019-01-31 15:56:00    3  Cambridge\n",
       "2019-01-31 15:57:00    3  Cambridge\n",
       "2019-01-31 15:58:00    3  Cambridge\n",
       "2019-01-31 15:59:00    3  Cambridge\n",
       "2019-01-31 16:00:00    2  Cambridge\n",
       "\n",
       "[1795144 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Select data with a specific year and perform aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:01:00</th>\n",
       "      <td>1</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:02:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:03:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:04:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 15:56:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 15:57:00</th>\n",
       "      <td>2</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 15:58:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 15:59:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 16:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>439524 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2018-01-01 09:00:00    2     London\n",
       "2018-01-01 09:01:00    1     London\n",
       "2018-01-01 09:02:00    3     London\n",
       "2018-01-01 09:03:00    3     London\n",
       "2018-01-01 09:04:00    3     London\n",
       "...                  ...        ...\n",
       "2018-12-31 15:56:00    4  Cambridge\n",
       "2018-12-31 15:57:00    2  Cambridge\n",
       "2018-12-31 15:58:00    3  Cambridge\n",
       "2018-12-31 15:59:00    3  Cambridge\n",
       "2018-12-31 16:00:00    2  Cambridge\n",
       "\n",
       "[439524 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1231190"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018','num'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cambridge</th>\n",
       "      <td>308428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durham</th>\n",
       "      <td>307965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>307431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxford</th>\n",
       "      <td>307366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              num\n",
       "city             \n",
       "Cambridge  308428\n",
       "Durham     307965\n",
       "London     307431\n",
       "Oxford     307366"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['2018'].groupby('city').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Select data with a specific month or a specific day of the month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2018-05-01 09:02:00</th>\n",
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       "      <td>3</td>\n",
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       "      <th>2018-05-31 15:57:00</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-31 15:58:00</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-31 15:59:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 16:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38732 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2018-05-01 09:00:00    1     London\n",
       "2018-05-01 09:01:00    4     London\n",
       "2018-05-01 09:02:00    3     London\n",
       "2018-05-01 09:03:00    2     London\n",
       "2018-05-01 09:04:00    3     London\n",
       "...                  ...        ...\n",
       "2018-05-31 15:56:00    3  Cambridge\n",
       "2018-05-31 15:57:00    4  Cambridge\n",
       "2018-05-31 15:58:00    2  Cambridge\n",
       "2018-05-31 15:59:00    3  Cambridge\n",
       "2018-05-31 16:00:00    3  Cambridge\n",
       "\n",
       "[38732 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018-5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-01 16:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1684 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2018-05-01 09:00:00    1     London\n",
       "2018-05-01 09:01:00    4     London\n",
       "2018-05-01 09:02:00    3     London\n",
       "2018-05-01 09:03:00    2     London\n",
       "2018-05-01 09:04:00    3     London\n",
       "...                  ...        ...\n",
       "2018-05-01 15:56:00    2  Cambridge\n",
       "2018-05-01 15:57:00    3  Cambridge\n",
       "2018-05-01 15:58:00    3  Cambridge\n",
       "2018-05-01 15:59:00    3  Cambridge\n",
       "2018-05-01 16:00:00    1  Cambridge\n",
       "\n",
       "[1684 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-28 15:57:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-28 15:58:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-28 15:59:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-28 16:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136404 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2015-02-02 09:00:00    4     London\n",
       "2015-02-02 09:01:00    3     London\n",
       "2015-02-02 09:02:00    3     London\n",
       "2015-02-02 09:03:00    2     London\n",
       "2015-02-02 09:04:00    1     London\n",
       "...                  ...        ...\n",
       "2018-02-28 15:56:00    4  Cambridge\n",
       "2018-02-28 15:57:00    3  Cambridge\n",
       "2018-02-28 15:58:00    4  Cambridge\n",
       "2018-02-28 15:59:00    4  Cambridge\n",
       "2018-02-28 16:00:00    4  Cambridge\n",
       "\n",
       "[136404 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = df.index.month==2\n",
    "df[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Select data between two dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2018-12-31 15:56:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-31 15:57:00</th>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-31 16:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1316888 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2016-01-01 09:00:00    4     London\n",
       "2016-01-01 09:01:00    3     London\n",
       "2016-01-01 09:02:00    4     London\n",
       "2016-01-01 09:03:00    4     London\n",
       "2016-01-01 09:04:00    2     London\n",
       "...                  ...        ...\n",
       "2018-12-31 15:56:00    4  Cambridge\n",
       "2018-12-31 15:57:00    2  Cambridge\n",
       "2018-12-31 15:58:00    3  Cambridge\n",
       "2018-12-31 15:59:00    3  Cambridge\n",
       "2018-12-31 16:00:00    2  Cambridge\n",
       "\n",
       "[1316888 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016' : '2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2018-05-02 11:55:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 11:56:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 11:57:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 11:58:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 11:59:00</th>\n",
       "      <td>1</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2018-05-02 10:00:00    2     London\n",
       "2018-05-02 10:01:00    3     London\n",
       "2018-05-02 10:02:00    4     London\n",
       "2018-05-02 10:03:00    4     London\n",
       "2018-05-02 10:04:00    4     London\n",
       "...                  ...        ...\n",
       "2018-05-02 11:55:00    3  Cambridge\n",
       "2018-05-02 11:56:00    3  Cambridge\n",
       "2018-05-02 11:57:00    4  Cambridge\n",
       "2018-05-02 11:58:00    4  Cambridge\n",
       "2018-05-02 11:59:00    1  Cambridge\n",
       "\n",
       "[480 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018-5-2 10' : '2018-5-2 11' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-02 10:31:00</th>\n",
       "      <td>3</td>\n",
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       "      <th>2018-05-02 10:32:00</th>\n",
       "      <td>1</td>\n",
       "      <td>London</td>\n",
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       "      <th>2018-05-02 10:33:00</th>\n",
       "      <td>3</td>\n",
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       "      <th>2018-05-02 10:34:00</th>\n",
       "      <td>3</td>\n",
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       "      <th>2018-05-02 10:41:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-02 10:42:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 10:43:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-02 10:44:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02 10:45:00</th>\n",
       "      <td>1</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2018-05-02 10:30:00    3     London\n",
       "2018-05-02 10:31:00    3     London\n",
       "2018-05-02 10:32:00    1     London\n",
       "2018-05-02 10:33:00    3     London\n",
       "2018-05-02 10:34:00    3     London\n",
       "...                  ...        ...\n",
       "2018-05-02 10:41:00    3  Cambridge\n",
       "2018-05-02 10:42:00    3  Cambridge\n",
       "2018-05-02 10:43:00    3  Cambridge\n",
       "2018-05-02 10:44:00    3  Cambridge\n",
       "2018-05-02 10:45:00    1  Cambridge\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018-5-2 10:30' : '2018-5-2 10:45' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-01-01 10:33:00</th>\n",
       "      <td>3</td>\n",
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       "      <th>2019-01-31 10:41:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:42:00</th>\n",
       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "      <td>3</td>\n",
       "      <td>Cambridge</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-31 10:45:00</th>\n",
       "      <td>4</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68224 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num       city\n",
       "date                               \n",
       "2015-01-01 10:30:00    4     London\n",
       "2015-01-01 10:31:00    3     London\n",
       "2015-01-01 10:32:00    3     London\n",
       "2015-01-01 10:33:00    3     London\n",
       "2015-01-01 10:34:00    4     London\n",
       "...                  ...        ...\n",
       "2019-01-31 10:41:00    3  Cambridge\n",
       "2019-01-31 10:42:00    3  Cambridge\n",
       "2019-01-31 10:43:00    1  Cambridge\n",
       "2019-01-31 10:44:00    3  Cambridge\n",
       "2019-01-31 10:45:00    4  Cambridge\n",
       "\n",
       "[68224 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.between_time('10:30','10:45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Handle missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>rolling_sum</th>\n",
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       "      <th></th>\n",
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       "      <th>2015-01-01 09:00:00</th>\n",
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       "      <th>2015-01-01 09:02:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>2015-01-01 09:03:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:04:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num    city  rolling_sum\n",
       "date                                         \n",
       "2015-01-01 09:00:00    4  London          NaN\n",
       "2015-01-01 09:01:00    4  London          NaN\n",
       "2015-01-01 09:02:00    3  London         11.0\n",
       "2015-01-01 09:03:00    3  London         10.0\n",
       "2015-01-01 09:04:00    3  London          9.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rolling_sum'] = df.rolling(3).sum()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2015-01-01 09:01:00</th>\n",
       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-01 09:02:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:03:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 09:04:00</th>\n",
       "      <td>3</td>\n",
       "      <td>London</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num    city  rolling_sum  rolling_sum_backfilled\n",
       "date                                                                 \n",
       "2015-01-01 09:00:00    4  London          NaN                    11.0\n",
       "2015-01-01 09:01:00    4  London          NaN                    11.0\n",
       "2015-01-01 09:02:00    3  London         11.0                    11.0\n",
       "2015-01-01 09:03:00    3  London         10.0                    10.0\n",
       "2015-01-01 09:04:00    3  London          9.0                     9.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rolling_sum_backfilled'] = df['rolling_sum'].fillna(method='backfill')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### That's it\n",
    "This is a notebook for the medium article [Working with datetime in Pandas DataFrame](https://towardsdatascience.com/working-with-datetime-in-pandas-dataframe-663f7af6c587)\n",
    "\n",
    "Please check out article for instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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