Pandas & Datetime Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3cc1df2e",
   "metadata": {},
   "source": [
    "# Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9651be55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c13b9af",
   "metadata": {},
   "source": [
    "## `date`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb2d4793",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = dt.date(2021, 8, 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d03f65d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33882041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99c7155a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48e091f",
   "metadata": {},
   "source": [
    "## `datetime`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "665e9b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = dt.datetime(2021, 8, 22, 17, 45, 51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a091611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-22 17:45:51\n"
     ]
    }
   ],
   "source": [
    "print(today)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "431cc89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44c4908e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c9b4416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.second"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d04ef9",
   "metadata": {},
   "source": [
    "## `Timestamp`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03c3438f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-12-22 00:00:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2021-12-22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "248a1fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-02-13 00:00:00')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2019/02/13')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df9e1884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-04-21 00:00:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2020, 4, 21')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5932efac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-04-21 18:33:12')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2020, 4, 21 18:33:12')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a285f6b1",
   "metadata": {},
   "source": [
    "## `DatetimeIndex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dae83469",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = ['2019/12/22', '2012/8/22', '2015/2/22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f60d761b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-22', '2012-08-22', '2015-02-22'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DatetimeIndex(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c027d505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ec96df99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2012/8/22'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "57ae6f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates2 = [dt.date(2021, 9, 8), dt.date(2021, 9, 9), dt.date(2021, 9, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "15cdb293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.date(2021, 9, 8),\n",
       " datetime.date(2021, 9, 9),\n",
       " datetime.date(2021, 9, 10)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "072f7e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-09-08', '2021-09-09', '2021-09-10'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateIndex = pd.DatetimeIndex(dates2)\n",
    "dateIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bda75f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-09-08    12\n",
       "2021-09-09     7\n",
       "2021-09-10    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = [12, 7, 30]\n",
    "pd.Series(data=values, index=dateIndex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560b963d",
   "metadata": {},
   "source": [
    "## `pd.to_datetime()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "056ec8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-01-05 00:00:00')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('2021, 1, 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ec47ca72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2001-12-12 00:00:00')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(dt.date(2001, 12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "27cf74de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-02-12 18:33:12')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(dt.datetime(2012, 2, 12, 18, 33, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a38ee20b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-22', '2012-08-22', '2015-02-22'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = ['2019/12/22', '2012/8/22', '2015/2/22']\n",
    "pd.to_datetime(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "360ac042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    December 22nd, 1999\n",
       "1           2019, 12, 12\n",
       "2           This is Date\n",
       "3          Jan 5th, 2021\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates3 = pd.Series(\n",
    "    ['December 22nd, 1999', '2019, 12, 12', \"This is Date\", 'Jan 5th, 2021'])\n",
    "dates3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "52fa5519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1999-12-22\n",
       "1   2019-12-12\n",
       "2          NaT\n",
       "3   2021-01-05\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(dates3, errors='coerce')  #invalid parsing will be set as NaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f0610740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1970-02-07 13:16:03', '1970-02-11 00:51:03',\n",
       "               '1970-02-07 12:33:57', '1970-03-27 05:20:36'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unixtime = [3244563, 3545463, 3242037, 7363236]\n",
    "pd.to_datetime(unixtime, unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07def82",
   "metadata": {},
   "source": [
    "## Create Range in the form of Date `pd.date_range()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7d5c6691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',\n",
       "               '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',\n",
       "               '2021-01-09', '2021-01-10',\n",
       "               ...\n",
       "               '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',\n",
       "               '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',\n",
       "               '2021-12-30', '2021-12-31'],\n",
       "              dtype='datetime64[ns]', length=365, freq='D')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='D')\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c13d6ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-12-31', '2022-12-31', '2023-12-31', '2024-12-31',\n",
       "               '2025-12-31', '2026-12-31', '2027-12-31', '2028-12-31',\n",
       "               '2029-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.date_range(start='2021-01-01', end='2029-12-31', freq='A')\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "21aa354a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 05:00:00',\n",
       "               '2021-01-01 10:00:00', '2021-01-01 15:00:00',\n",
       "               '2021-01-01 20:00:00', '2021-01-02 01:00:00',\n",
       "               '2021-01-02 06:00:00', '2021-01-02 11:00:00',\n",
       "               '2021-01-02 16:00:00', '2021-01-02 21:00:00',\n",
       "               ...\n",
       "               '2021-12-29 02:00:00', '2021-12-29 07:00:00',\n",
       "               '2021-12-29 12:00:00', '2021-12-29 17:00:00',\n",
       "               '2021-12-29 22:00:00', '2021-12-30 03:00:00',\n",
       "               '2021-12-30 08:00:00', '2021-12-30 13:00:00',\n",
       "               '2021-12-30 18:00:00', '2021-12-30 23:00:00'],\n",
       "              dtype='datetime64[ns]', length=1748, freq='5H')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='5H')\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5db2d6ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 04:00:00',\n",
       "               '2021-01-01 08:00:00', '2021-01-01 12:00:00',\n",
       "               '2021-01-01 16:00:00', '2021-01-01 20:00:00',\n",
       "               '2021-01-02 00:00:00', '2021-01-02 04:00:00',\n",
       "               '2021-01-02 08:00:00', '2021-01-02 12:00:00',\n",
       "               ...\n",
       "               '2021-12-29 12:00:00', '2021-12-29 16:00:00',\n",
       "               '2021-12-29 20:00:00', '2021-12-30 00:00:00',\n",
       "               '2021-12-30 04:00:00', '2021-12-30 08:00:00',\n",
       "               '2021-12-30 12:00:00', '2021-12-30 16:00:00',\n",
       "               '2021-12-30 20:00:00', '2021-12-31 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=2185, freq='4H')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='4H')\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49d3384c",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='5H')\n",
    "times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6482c17e",
   "metadata": {},
   "source": [
    "## Importing Stock Data using `datareader`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bece31b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "593964d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "08543a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "company = 'MSFT'\n",
    "start = '2020-12-22'\n",
    "end = '2021-06-22'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "61dee028",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = data.DataReader(name=company,\n",
    "                         data_source='yahoo',\n",
    "                         start=start,\n",
    "                         end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "81a64a17",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>217.279999</td>\n",
       "      <td>217.550003</td>\n",
       "      <td>222.589996</td>\n",
       "      <td>37181900.0</td>\n",
       "      <td>221.143524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>225.630005</td>\n",
       "      <td>221.850006</td>\n",
       "      <td>222.690002</td>\n",
       "      <td>223.940002</td>\n",
       "      <td>22612200.0</td>\n",
       "      <td>222.484772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>223.559998</td>\n",
       "      <td>220.800003</td>\n",
       "      <td>223.110001</td>\n",
       "      <td>221.020004</td>\n",
       "      <td>18699600.0</td>\n",
       "      <td>219.583740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>223.610001</td>\n",
       "      <td>221.199997</td>\n",
       "      <td>221.419998</td>\n",
       "      <td>222.750000</td>\n",
       "      <td>10550600.0</td>\n",
       "      <td>221.302490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>226.029999</td>\n",
       "      <td>223.020004</td>\n",
       "      <td>224.449997</td>\n",
       "      <td>224.960007</td>\n",
       "      <td>17933500.0</td>\n",
       "      <td>223.498138</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>260.579987</td>\n",
       "      <td>254.419998</td>\n",
       "      <td>259.399994</td>\n",
       "      <td>257.380005</td>\n",
       "      <td>27220000.0</td>\n",
       "      <td>256.888214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-17</th>\n",
       "      <td>261.750000</td>\n",
       "      <td>256.010010</td>\n",
       "      <td>256.070007</td>\n",
       "      <td>260.899994</td>\n",
       "      <td>27565500.0</td>\n",
       "      <td>260.401489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-18</th>\n",
       "      <td>262.299988</td>\n",
       "      <td>258.750000</td>\n",
       "      <td>259.630005</td>\n",
       "      <td>259.429993</td>\n",
       "      <td>37202200.0</td>\n",
       "      <td>258.934296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>263.519989</td>\n",
       "      <td>257.920013</td>\n",
       "      <td>259.820007</td>\n",
       "      <td>262.630005</td>\n",
       "      <td>26696100.0</td>\n",
       "      <td>262.128174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22</th>\n",
       "      <td>265.790009</td>\n",
       "      <td>262.399994</td>\n",
       "      <td>262.720001</td>\n",
       "      <td>265.510010</td>\n",
       "      <td>24694100.0</td>\n",
       "      <td>265.002686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2020-12-21  224.000000  217.279999  217.550003  222.589996  37181900.0   \n",
       "2020-12-22  225.630005  221.850006  222.690002  223.940002  22612200.0   \n",
       "2020-12-23  223.559998  220.800003  223.110001  221.020004  18699600.0   \n",
       "2020-12-24  223.610001  221.199997  221.419998  222.750000  10550600.0   \n",
       "2020-12-28  226.029999  223.020004  224.449997  224.960007  17933500.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-06-16  260.579987  254.419998  259.399994  257.380005  27220000.0   \n",
       "2021-06-17  261.750000  256.010010  256.070007  260.899994  27565500.0   \n",
       "2021-06-18  262.299988  258.750000  259.630005  259.429993  37202200.0   \n",
       "2021-06-21  263.519989  257.920013  259.820007  262.630005  26696100.0   \n",
       "2021-06-22  265.790009  262.399994  262.720001  265.510010  24694100.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2020-12-21  221.143524  \n",
       "2020-12-22  222.484772  \n",
       "2020-12-23  219.583740  \n",
       "2020-12-24  221.302490  \n",
       "2020-12-28  223.498138  \n",
       "...                ...  \n",
       "2021-06-16  256.888214  \n",
       "2021-06-17  260.401489  \n",
       "2021-06-18  258.934296  \n",
       "2021-06-21  262.128174  \n",
       "2021-06-22  265.002686  \n",
       "\n",
       "[126 rows x 6 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c0819ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High         2.617500e+02\n",
       "Low          2.560100e+02\n",
       "Open         2.560700e+02\n",
       "Close        2.609000e+02\n",
       "Volume       2.756550e+07\n",
       "Adj Close    2.604015e+02\n",
       "Name: 2021-06-17 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.loc['2021-06-17']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2470f50a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High         2.492700e+02\n",
       "Low          2.458400e+02\n",
       "Open         2.481300e+02\n",
       "Close        2.473000e+02\n",
       "Volume       1.940670e+07\n",
       "Adj Close    2.468275e+02\n",
       "Name: 2021-06-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.iloc[111]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "84ca2b96",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>223.610001</td>\n",
       "      <td>221.199997</td>\n",
       "      <td>221.419998</td>\n",
       "      <td>222.750000</td>\n",
       "      <td>10550600.0</td>\n",
       "      <td>221.302490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>226.029999</td>\n",
       "      <td>223.020004</td>\n",
       "      <td>224.449997</td>\n",
       "      <td>224.960007</td>\n",
       "      <td>17933500.0</td>\n",
       "      <td>223.498138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>227.179993</td>\n",
       "      <td>223.580002</td>\n",
       "      <td>226.309998</td>\n",
       "      <td>224.149994</td>\n",
       "      <td>17403200.0</td>\n",
       "      <td>222.693390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>225.630005</td>\n",
       "      <td>221.470001</td>\n",
       "      <td>225.229996</td>\n",
       "      <td>221.679993</td>\n",
       "      <td>20272300.0</td>\n",
       "      <td>220.239456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>219.679993</td>\n",
       "      <td>221.699997</td>\n",
       "      <td>222.419998</td>\n",
       "      <td>20942100.0</td>\n",
       "      <td>220.974640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>214.809998</td>\n",
       "      <td>222.529999</td>\n",
       "      <td>217.690002</td>\n",
       "      <td>37130100.0</td>\n",
       "      <td>216.275375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>218.520004</td>\n",
       "      <td>215.699997</td>\n",
       "      <td>217.259995</td>\n",
       "      <td>217.899994</td>\n",
       "      <td>23823000.0</td>\n",
       "      <td>216.484009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>216.490005</td>\n",
       "      <td>211.940002</td>\n",
       "      <td>212.169998</td>\n",
       "      <td>212.250000</td>\n",
       "      <td>35930700.0</td>\n",
       "      <td>210.870728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>219.339996</td>\n",
       "      <td>213.710007</td>\n",
       "      <td>214.039993</td>\n",
       "      <td>218.289993</td>\n",
       "      <td>27694500.0</td>\n",
       "      <td>216.871460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>220.580002</td>\n",
       "      <td>217.029999</td>\n",
       "      <td>218.679993</td>\n",
       "      <td>219.619995</td>\n",
       "      <td>22956200.0</td>\n",
       "      <td>218.192841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>218.910004</td>\n",
       "      <td>216.729996</td>\n",
       "      <td>218.470001</td>\n",
       "      <td>217.490005</td>\n",
       "      <td>23031300.0</td>\n",
       "      <td>216.076675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-12</th>\n",
       "      <td>217.100006</td>\n",
       "      <td>213.320007</td>\n",
       "      <td>216.500000</td>\n",
       "      <td>214.929993</td>\n",
       "      <td>23249300.0</td>\n",
       "      <td>213.533310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-13</th>\n",
       "      <td>216.759995</td>\n",
       "      <td>213.929993</td>\n",
       "      <td>214.020004</td>\n",
       "      <td>216.339996</td>\n",
       "      <td>20087100.0</td>\n",
       "      <td>214.934143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>217.460007</td>\n",
       "      <td>212.740005</td>\n",
       "      <td>215.910004</td>\n",
       "      <td>213.020004</td>\n",
       "      <td>29480800.0</td>\n",
       "      <td>211.635742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>214.509995</td>\n",
       "      <td>212.029999</td>\n",
       "      <td>213.520004</td>\n",
       "      <td>212.649994</td>\n",
       "      <td>31746500.0</td>\n",
       "      <td>211.268112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2020-12-24  223.610001  221.199997  221.419998  222.750000  10550600.0   \n",
       "2020-12-28  226.029999  223.020004  224.449997  224.960007  17933500.0   \n",
       "2020-12-29  227.179993  223.580002  226.309998  224.149994  17403200.0   \n",
       "2020-12-30  225.630005  221.470001  225.229996  221.679993  20272300.0   \n",
       "2020-12-31  223.000000  219.679993  221.699997  222.419998  20942100.0   \n",
       "2021-01-04  223.000000  214.809998  222.529999  217.690002  37130100.0   \n",
       "2021-01-05  218.520004  215.699997  217.259995  217.899994  23823000.0   \n",
       "2021-01-06  216.490005  211.940002  212.169998  212.250000  35930700.0   \n",
       "2021-01-07  219.339996  213.710007  214.039993  218.289993  27694500.0   \n",
       "2021-01-08  220.580002  217.029999  218.679993  219.619995  22956200.0   \n",
       "2021-01-11  218.910004  216.729996  218.470001  217.490005  23031300.0   \n",
       "2021-01-12  217.100006  213.320007  216.500000  214.929993  23249300.0   \n",
       "2021-01-13  216.759995  213.929993  214.020004  216.339996  20087100.0   \n",
       "2021-01-14  217.460007  212.740005  215.910004  213.020004  29480800.0   \n",
       "2021-01-15  214.509995  212.029999  213.520004  212.649994  31746500.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2020-12-24  221.302490  \n",
       "2020-12-28  223.498138  \n",
       "2020-12-29  222.693390  \n",
       "2020-12-30  220.239456  \n",
       "2020-12-31  220.974640  \n",
       "2021-01-04  216.275375  \n",
       "2021-01-05  216.484009  \n",
       "2021-01-06  210.870728  \n",
       "2021-01-07  216.871460  \n",
       "2021-01-08  218.192841  \n",
       "2021-01-11  216.076675  \n",
       "2021-01-12  213.533310  \n",
       "2021-01-13  214.934143  \n",
       "2021-01-14  211.635742  \n",
       "2021-01-15  211.268112  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.loc['2020-12-24':'2021-01-18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dd19ce99",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>225.630005</td>\n",
       "      <td>221.470001</td>\n",
       "      <td>225.229996</td>\n",
       "      <td>221.679993</td>\n",
       "      <td>20272300.0</td>\n",
       "      <td>220.239456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>219.679993</td>\n",
       "      <td>221.699997</td>\n",
       "      <td>222.419998</td>\n",
       "      <td>20942100.0</td>\n",
       "      <td>220.974640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>214.809998</td>\n",
       "      <td>222.529999</td>\n",
       "      <td>217.690002</td>\n",
       "      <td>37130100.0</td>\n",
       "      <td>216.275375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>218.520004</td>\n",
       "      <td>215.699997</td>\n",
       "      <td>217.259995</td>\n",
       "      <td>217.899994</td>\n",
       "      <td>23823000.0</td>\n",
       "      <td>216.484009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>216.490005</td>\n",
       "      <td>211.940002</td>\n",
       "      <td>212.169998</td>\n",
       "      <td>212.250000</td>\n",
       "      <td>35930700.0</td>\n",
       "      <td>210.870728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>219.339996</td>\n",
       "      <td>213.710007</td>\n",
       "      <td>214.039993</td>\n",
       "      <td>218.289993</td>\n",
       "      <td>27694500.0</td>\n",
       "      <td>216.871460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>220.580002</td>\n",
       "      <td>217.029999</td>\n",
       "      <td>218.679993</td>\n",
       "      <td>219.619995</td>\n",
       "      <td>22956200.0</td>\n",
       "      <td>218.192841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>218.910004</td>\n",
       "      <td>216.729996</td>\n",
       "      <td>218.470001</td>\n",
       "      <td>217.490005</td>\n",
       "      <td>23031300.0</td>\n",
       "      <td>216.076675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2020-12-30  225.630005  221.470001  225.229996  221.679993  20272300.0   \n",
       "2020-12-31  223.000000  219.679993  221.699997  222.419998  20942100.0   \n",
       "2021-01-04  223.000000  214.809998  222.529999  217.690002  37130100.0   \n",
       "2021-01-05  218.520004  215.699997  217.259995  217.899994  23823000.0   \n",
       "2021-01-06  216.490005  211.940002  212.169998  212.250000  35930700.0   \n",
       "2021-01-07  219.339996  213.710007  214.039993  218.289993  27694500.0   \n",
       "2021-01-08  220.580002  217.029999  218.679993  219.619995  22956200.0   \n",
       "2021-01-11  218.910004  216.729996  218.470001  217.490005  23031300.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2020-12-30  220.239456  \n",
       "2020-12-31  220.974640  \n",
       "2021-01-04  216.275375  \n",
       "2021-01-05  216.484009  \n",
       "2021-01-06  210.870728  \n",
       "2021-01-07  216.871460  \n",
       "2021-01-08  218.192841  \n",
       "2021-01-11  216.076675  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.truncate(before='2020-12-30', after='2021-01-11')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6b0220",
   "metadata": {},
   "source": [
    "## `Timedelta`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f372b303",
   "metadata": {},
   "outputs": [],
   "source": [
    "timeA = pd.Timestamp('2021-01-01 10:26:00')\n",
    "timeB = pd.Timestamp('2020-01-15 10:33:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "84a5004e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('351 days 23:53:00')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeA - timeB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c831c431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('79 days 00:12:00')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timedelta(weeks=10, days=9, minutes=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9b36f576",
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>continent_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state/province</th>\n",
       "      <th>population</th>\n",
       "      <th>city/town</th>\n",
       "      <th>distance</th>\n",
       "      <th>location_description</th>\n",
       "      <th>...</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>hazard_type</th>\n",
       "      <th>landslide_type</th>\n",
       "      <th>landslide_size</th>\n",
       "      <th>trigger</th>\n",
       "      <th>storm_name</th>\n",
       "      <th>injuries</th>\n",
       "      <th>fatalities</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_link</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3/2/07</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>16000</td>\n",
       "      <td>Cherry Hill</td>\n",
       "      <td>3.40765</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>(38.600900000000003, -77.268199999999993)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NBC 4 news</td>\n",
       "      <td>http://www.nbc4.com/news/11186871/detail.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3/22/07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>17288</td>\n",
       "      <td>New Philadelphia</td>\n",
       "      <td>3.33522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(40.517499999999998, -81.430499999999995)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canton Rep.com</td>\n",
       "      <td>http://www.cantonrep.com/index.php?ID=345054&amp;C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4/6/07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15930</td>\n",
       "      <td>Wilkinsburg</td>\n",
       "      <td>2.91977</td>\n",
       "      <td>Urban area</td>\n",
       "      <td>...</td>\n",
       "      <td>(40.4377, -79.915999999999997)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Pittsburgh Channel.com</td>\n",
       "      <td>https://web.archive.org/web/20080423132842/htt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4/14/07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>42786</td>\n",
       "      <td>Châteauguay</td>\n",
       "      <td>2.98682</td>\n",
       "      <td>Above river</td>\n",
       "      <td>...</td>\n",
       "      <td>(45.322600000000001, -73.777100000000004)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Riverbank collapse</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Le Soleil</td>\n",
       "      <td>http://www.hebdos.net/lsc/edition162007/articl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>4/15/07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>6903</td>\n",
       "      <td>Pikeville</td>\n",
       "      <td>5.66542</td>\n",
       "      <td>Below road</td>\n",
       "      <td>...</td>\n",
       "      <td>(37.432499999999997, -82.493099999999998)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Downpour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Matthew Crawford (KGS)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   time continent_code   country_name country_code state/province  \\\n",
       "id                                                                             \n",
       "34   3/2/07  Night            NaN  United States           US       Virginia   \n",
       "42  3/22/07    NaN            NaN  United States           US           Ohio   \n",
       "56   4/6/07    NaN            NaN  United States           US   Pennsylvania   \n",
       "59  4/14/07    NaN            NaN         Canada           CA         Quebec   \n",
       "61  4/15/07    NaN            NaN  United States           US       Kentucky   \n",
       "\n",
       "    population         city/town  distance location_description  ...  \\\n",
       "id                                                               ...   \n",
       "34       16000       Cherry Hill   3.40765              Unknown  ...   \n",
       "42       17288  New Philadelphia   3.33522                  NaN  ...   \n",
       "56       15930       Wilkinsburg   2.91977           Urban area  ...   \n",
       "59       42786       Châteauguay   2.98682          Above river  ...   \n",
       "61        6903         Pikeville   5.66542           Below road  ...   \n",
       "\n",
       "                                  geolocation  hazard_type  \\\n",
       "id                                                           \n",
       "34  (38.600900000000003, -77.268199999999993)    Landslide   \n",
       "42  (40.517499999999998, -81.430499999999995)    Landslide   \n",
       "56             (40.4377, -79.915999999999997)    Landslide   \n",
       "59  (45.322600000000001, -73.777100000000004)    Landslide   \n",
       "61  (37.432499999999997, -82.493099999999998)    Landslide   \n",
       "\n",
       "        landslide_type landslide_size   trigger storm_name injuries  \\\n",
       "id                                                                    \n",
       "34           Landslide          Small      Rain        NaN      NaN   \n",
       "42           Landslide          Small      Rain        NaN      NaN   \n",
       "56           Landslide          Small      Rain        NaN      NaN   \n",
       "59  Riverbank collapse          Small      Rain        NaN      NaN   \n",
       "61           Landslide          Small  Downpour        NaN      NaN   \n",
       "\n",
       "   fatalities                 source_name  \\\n",
       "id                                          \n",
       "34        NaN                  NBC 4 news   \n",
       "42        NaN              Canton Rep.com   \n",
       "56        NaN  The Pittsburgh Channel.com   \n",
       "59        NaN                   Le Soleil   \n",
       "61        0.0      Matthew Crawford (KGS)   \n",
       "\n",
       "                                          source_link  \n",
       "id                                                     \n",
       "34      http://www.nbc4.com/news/11186871/detail.html  \n",
       "42  http://www.cantonrep.com/index.php?ID=345054&C...  \n",
       "56  https://web.archive.org/web/20080423132842/htt...  \n",
       "59  http://www.hebdos.net/lsc/edition162007/articl...  \n",
       "61                                                NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parsing/converting dates into datetime from csv files\n",
    "df = pd.read_csv('catalog.csv', index_col='id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4e65053d",
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>continent_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state/province</th>\n",
       "      <th>population</th>\n",
       "      <th>city/town</th>\n",
       "      <th>distance</th>\n",
       "      <th>location_description</th>\n",
       "      <th>...</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>hazard_type</th>\n",
       "      <th>landslide_type</th>\n",
       "      <th>landslide_size</th>\n",
       "      <th>trigger</th>\n",
       "      <th>storm_name</th>\n",
       "      <th>injuries</th>\n",
       "      <th>fatalities</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_link</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2007-03-02</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>16000</td>\n",
       "      <td>Cherry Hill</td>\n",
       "      <td>3.40765</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>(38.600900000000003, -77.268199999999993)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NBC 4 news</td>\n",
       "      <td>http://www.nbc4.com/news/11186871/detail.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2007-03-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>17288</td>\n",
       "      <td>New Philadelphia</td>\n",
       "      <td>3.33522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(40.517499999999998, -81.430499999999995)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canton Rep.com</td>\n",
       "      <td>http://www.cantonrep.com/index.php?ID=345054&amp;C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2007-04-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15930</td>\n",
       "      <td>Wilkinsburg</td>\n",
       "      <td>2.91977</td>\n",
       "      <td>Urban area</td>\n",
       "      <td>...</td>\n",
       "      <td>(40.4377, -79.915999999999997)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Pittsburgh Channel.com</td>\n",
       "      <td>https://web.archive.org/web/20080423132842/htt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2007-04-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>42786</td>\n",
       "      <td>Châteauguay</td>\n",
       "      <td>2.98682</td>\n",
       "      <td>Above river</td>\n",
       "      <td>...</td>\n",
       "      <td>(45.322600000000001, -73.777100000000004)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Riverbank collapse</td>\n",
       "      <td>Small</td>\n",
       "      <td>Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Le Soleil</td>\n",
       "      <td>http://www.hebdos.net/lsc/edition162007/articl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>6903</td>\n",
       "      <td>Pikeville</td>\n",
       "      <td>5.66542</td>\n",
       "      <td>Below road</td>\n",
       "      <td>...</td>\n",
       "      <td>(37.432499999999997, -82.493099999999998)</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Landslide</td>\n",
       "      <td>Small</td>\n",
       "      <td>Downpour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Matthew Crawford (KGS)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   time continent_code   country_name country_code  \\\n",
       "id                                                                \n",
       "34 2007-03-02  Night            NaN  United States           US   \n",
       "42 2007-03-22    NaN            NaN  United States           US   \n",
       "56 2007-04-06    NaN            NaN  United States           US   \n",
       "59 2007-04-14    NaN            NaN         Canada           CA   \n",
       "61 2007-04-15    NaN            NaN  United States           US   \n",
       "\n",
       "   state/province  population         city/town  distance  \\\n",
       "id                                                          \n",
       "34       Virginia       16000       Cherry Hill   3.40765   \n",
       "42           Ohio       17288  New Philadelphia   3.33522   \n",
       "56   Pennsylvania       15930       Wilkinsburg   2.91977   \n",
       "59         Quebec       42786       Châteauguay   2.98682   \n",
       "61       Kentucky        6903         Pikeville   5.66542   \n",
       "\n",
       "   location_description  ...                                geolocation  \\\n",
       "id                       ...                                              \n",
       "34              Unknown  ...  (38.600900000000003, -77.268199999999993)   \n",
       "42                  NaN  ...  (40.517499999999998, -81.430499999999995)   \n",
       "56           Urban area  ...             (40.4377, -79.915999999999997)   \n",
       "59          Above river  ...  (45.322600000000001, -73.777100000000004)   \n",
       "61           Below road  ...  (37.432499999999997, -82.493099999999998)   \n",
       "\n",
       "    hazard_type      landslide_type landslide_size   trigger storm_name  \\\n",
       "id                                                                        \n",
       "34    Landslide           Landslide          Small      Rain        NaN   \n",
       "42    Landslide           Landslide          Small      Rain        NaN   \n",
       "56    Landslide           Landslide          Small      Rain        NaN   \n",
       "59    Landslide  Riverbank collapse          Small      Rain        NaN   \n",
       "61    Landslide           Landslide          Small  Downpour        NaN   \n",
       "\n",
       "   injuries fatalities                 source_name  \\\n",
       "id                                                   \n",
       "34      NaN        NaN                  NBC 4 news   \n",
       "42      NaN        NaN              Canton Rep.com   \n",
       "56      NaN        NaN  The Pittsburgh Channel.com   \n",
       "59      NaN        NaN                   Le Soleil   \n",
       "61      NaN        0.0      Matthew Crawford (KGS)   \n",
       "\n",
       "                                          source_link  \n",
       "id                                                     \n",
       "34      http://www.nbc4.com/news/11186871/detail.html  \n",
       "42  http://www.cantonrep.com/index.php?ID=345054&C...  \n",
       "56  https://web.archive.org/web/20080423132842/htt...  \n",
       "59  http://www.hebdos.net/lsc/edition162007/articl...  \n",
       "61                                                NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('catalog.csv', index_col='id', parse_dates=['date'])\n",
    "df.head()"
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}