{
 "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": [
    {
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       "      <th>2020-12-24</th>\n",
       "      <td>223.610001</td>\n",
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       "      <td>221.419998</td>\n",
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       "      <th>2020-12-28</th>\n",
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       "      <th>2020-12-29</th>\n",
       "      <td>227.179993</td>\n",
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       "      <td>226.309998</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>220.974640</td>\n",
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       "      <th>2021-01-04</th>\n",
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       "      <th>2021-01-05</th>\n",
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       "      <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",
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       "    <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",
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       "      <th>2021-01-08</th>\n",
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       "      <th>2021-01-11</th>\n",
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       "      <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",
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       "      <th>2021-01-13</th>\n",
       "      <td>216.759995</td>\n",
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       "                  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",
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     "execution_count": 88,
     "metadata": {},
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   "source": [
    "stocks.loc['2020-12-24':'2021-01-18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dd19ce99",
   "metadata": {},
   "outputs": [
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       "      <th>2021-01-06</th>\n",
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       "      <th>2021-01-08</th>\n",
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       "      <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": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": {
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       "      <th>id</th>\n",
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       "      <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",
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       "  <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()"
   ]
  }
 ],
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