{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7d963fc",
   "metadata": {},
   "source": [
    "# Working with Data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb6ef0a",
   "metadata": {},
   "source": [
    "## 1. Pandas Library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a080c24c",
   "metadata": {},
   "source": [
    "Pandas library is the one of the most populated used library for manipulating with data. We use the Series and Dataframe data structure extensively as these are much more powerful and useful to manipulate with data when compare with list and dictionary in python.\n",
    "\n",
    "There's another very popular library called Numpy. Pandas bulid on top of it and we usually use pandas directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "122da828",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af8c9270",
   "metadata": {},
   "source": [
    "## 2. Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c82119d",
   "metadata": {},
   "source": [
    "A series is very similar to a list. We can easily convert a list to a simple series. A series also has index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9bfcdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = [\"AAPL\", \"BABA\", \"DIDI\", \"MSFT\", \"AMZN\", \"ADBE\", \"TSLA\", \"MS\", \"V\", \"MA\", \"GS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9026b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_series = pd.Series(stocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66a0bbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     AAPL\n",
       "1     BABA\n",
       "2     DIDI\n",
       "3     MSFT\n",
       "4     AMZN\n",
       "5     ADBE\n",
       "6     TSLA\n",
       "7       MS\n",
       "8        V\n",
       "9       MA\n",
       "10      GS\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f5a6b3",
   "metadata": {},
   "source": [
    "Getting the values using index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db7b2e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    BABA\n",
       "2    DIDI\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_series[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31dd2927",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_series[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "935307a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    DIDI\n",
       "3    MSFT\n",
       "4    AMZN\n",
       "5    ADBE\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_series[2:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c61e98",
   "metadata": {},
   "source": [
    "The difference between list and series is that we can use not use interger as index. Now it looks more like a dictionary. And we can create it from a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59d345f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sales' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sales_series \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(\u001b[43msales\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sales' is not defined"
     ]
    }
   ],
   "source": [
    "sales_series = pd.Series(sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64b9ea46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central Branch    10000\n",
       "TST Branch         2000\n",
       "Mongkok Branch     3000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e8c74c",
   "metadata": {},
   "source": [
    "Getting the number using index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e79920a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_series[\"Central Branch\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbaa8383",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = {'Central Branch' : 10000,\n",
    "         'TST Branch' : 2000,\n",
    "         'Mongkok Branch' : 3000}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388d2ece",
   "metadata": {},
   "source": [
    "## 3 Pandas Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c756f438",
   "metadata": {},
   "source": [
    "You can consider the Series is one column of data on an excel spreadsheet. A dataframe has mulitple series and you can consider that the data of a whole spreadsheet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12aa8a6f",
   "metadata": {},
   "source": [
    "### 3.1 Create dataframe from csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a88f1c9",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'AAPL.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m aapl \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAAPL.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/Note-Vc8kZtnp/lib64/python3.11/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'AAPL.csv'"
     ]
    }
   ],
   "source": [
    "aapl = pd.read_csv(\"AAPL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20064351",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'aapl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maapl\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'aapl' is not defined"
     ]
    }
   ],
   "source": [
    "aapl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6ae2efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_proper_index = pd.read_csv(\"AAPL.csv\", parse_dates=True, index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92625a87",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2019-10-28</th>\n",
       "      <td>61.855000</td>\n",
       "      <td>62.312500</td>\n",
       "      <td>61.680000</td>\n",
       "      <td>62.262501</td>\n",
       "      <td>61.650810</td>\n",
       "      <td>96572800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>62.242500</td>\n",
       "      <td>62.437500</td>\n",
       "      <td>60.642502</td>\n",
       "      <td>60.822498</td>\n",
       "      <td>60.224953</td>\n",
       "      <td>142839600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>61.189999</td>\n",
       "      <td>61.325001</td>\n",
       "      <td>60.302502</td>\n",
       "      <td>60.814999</td>\n",
       "      <td>60.217525</td>\n",
       "      <td>124522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>61.810001</td>\n",
       "      <td>62.292500</td>\n",
       "      <td>59.314999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>61.579021</td>\n",
       "      <td>139162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.982498</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>63.955002</td>\n",
       "      <td>63.326683</td>\n",
       "      <td>151125200</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>2020-10-21</th>\n",
       "      <td>116.669998</td>\n",
       "      <td>118.709999</td>\n",
       "      <td>116.449997</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>89946000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>116.389999</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>114.279999</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>82572600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>115.489998</td>\n",
       "      <td>117.279999</td>\n",
       "      <td>114.540001</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>91927700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-10-28   61.855000   62.312500   61.680000   62.262501   61.650810   \n",
       "2019-10-29   62.242500   62.437500   60.642502   60.822498   60.224953   \n",
       "2019-10-30   61.189999   61.325001   60.302502   60.814999   60.217525   \n",
       "2019-10-31   61.810001   62.292500   59.314999   62.189999   61.579021   \n",
       "2019-11-01   62.384998   63.982498   62.290001   63.955002   63.326683   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-10-21  116.669998  118.709999  116.449997  116.870003  116.870003   \n",
       "2020-10-22  117.449997  118.040001  114.589996  115.750000  115.750000   \n",
       "2020-10-23  116.389999  116.550003  114.279999  115.040001  115.040001   \n",
       "2020-10-26  114.010002  116.550003  112.879997  115.050003  115.050003   \n",
       "2020-10-27  115.489998  117.279999  114.540001  116.599998  116.599998   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2019-10-28   96572800  \n",
       "2019-10-29  142839600  \n",
       "2019-10-30  124522000  \n",
       "2019-10-31  139162000  \n",
       "2019-11-01  151125200  \n",
       "...               ...  \n",
       "2020-10-21   89946000  \n",
       "2020-10-22  101988000  \n",
       "2020-10-23   82572600  \n",
       "2020-10-26  111850700  \n",
       "2020-10-27   91927700  \n",
       "\n",
       "[253 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a3a1988",
   "metadata": {},
   "source": [
    "### 3.2 From Quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da0ee6dc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d0ac03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "quandl.ApiConfig.api_key = 'x9M_pZutNNPnha1WDdjZ'\n",
    "ck = quandl.get('HKEX/00001', start_date='2020-10-20', end_date='2021-10-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebda1d4c",
   "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>Nominal Price</th>\n",
       "      <th>Net Change</th>\n",
       "      <th>Change (%)</th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>P/E(x)</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Previous Close</th>\n",
       "      <th>Share Volume (000)</th>\n",
       "      <th>Turnover (000)</th>\n",
       "      <th>Lot Size</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",
       "      <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>2020-10-20</th>\n",
       "      <td>46.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>46.05</td>\n",
       "      <td>46.10</td>\n",
       "      <td>None</td>\n",
       "      <td>46.35</td>\n",
       "      <td>45.85</td>\n",
       "      <td>46.35</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>192921.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>46.15</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>46.15</td>\n",
       "      <td>46.20</td>\n",
       "      <td>None</td>\n",
       "      <td>46.50</td>\n",
       "      <td>45.95</td>\n",
       "      <td>46.05</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>223077.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>46.10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>46.10</td>\n",
       "      <td>46.15</td>\n",
       "      <td>None</td>\n",
       "      <td>46.35</td>\n",
       "      <td>45.90</td>\n",
       "      <td>46.15</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>226000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>46.40</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>46.40</td>\n",
       "      <td>46.45</td>\n",
       "      <td>None</td>\n",
       "      <td>46.50</td>\n",
       "      <td>45.80</td>\n",
       "      <td>46.10</td>\n",
       "      <td>3815.0</td>\n",
       "      <td>176451.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>46.75</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>46.75</td>\n",
       "      <td>46.80</td>\n",
       "      <td>None</td>\n",
       "      <td>47.15</td>\n",
       "      <td>46.50</td>\n",
       "      <td>46.40</td>\n",
       "      <td>12095.0</td>\n",
       "      <td>566845.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>52.60</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>52.60</td>\n",
       "      <td>52.65</td>\n",
       "      <td>None</td>\n",
       "      <td>53.10</td>\n",
       "      <td>52.35</td>\n",
       "      <td>52.95</td>\n",
       "      <td>2712.0</td>\n",
       "      <td>142802.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>52.50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>52.50</td>\n",
       "      <td>52.55</td>\n",
       "      <td>None</td>\n",
       "      <td>52.95</td>\n",
       "      <td>52.00</td>\n",
       "      <td>52.60</td>\n",
       "      <td>5067.0</td>\n",
       "      <td>266129.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>52.70</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>52.70</td>\n",
       "      <td>52.75</td>\n",
       "      <td>None</td>\n",
       "      <td>53.00</td>\n",
       "      <td>52.30</td>\n",
       "      <td>52.50</td>\n",
       "      <td>4036.0</td>\n",
       "      <td>212393.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>53.30</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>53.25</td>\n",
       "      <td>53.30</td>\n",
       "      <td>None</td>\n",
       "      <td>53.50</td>\n",
       "      <td>52.95</td>\n",
       "      <td>52.70</td>\n",
       "      <td>2484.0</td>\n",
       "      <td>132423.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>53.25</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>53.20</td>\n",
       "      <td>53.25</td>\n",
       "      <td>None</td>\n",
       "      <td>53.40</td>\n",
       "      <td>52.80</td>\n",
       "      <td>53.30</td>\n",
       "      <td>3649.0</td>\n",
       "      <td>193972.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>247 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nominal Price Net Change Change (%)    Bid    Ask P/E(x)   High  \\\n",
       "Date                                                                          \n",
       "2020-10-20          46.05       None       None  46.05  46.10   None  46.35   \n",
       "2020-10-21          46.15       None       None  46.15  46.20   None  46.50   \n",
       "2020-10-22          46.10       None       None  46.10  46.15   None  46.35   \n",
       "2020-10-23          46.40       None       None  46.40  46.45   None  46.50   \n",
       "2020-10-27          46.75       None       None  46.75  46.80   None  47.15   \n",
       "...                   ...        ...        ...    ...    ...    ...    ...   \n",
       "2021-10-12          52.60       None       None  52.60  52.65   None  53.10   \n",
       "2021-10-15          52.50       None       None  52.50  52.55   None  52.95   \n",
       "2021-10-18          52.70       None       None  52.70  52.75   None  53.00   \n",
       "2021-10-19          53.30       None       None  53.25  53.30   None  53.50   \n",
       "2021-10-20          53.25       None       None  53.20  53.25   None  53.40   \n",
       "\n",
       "              Low  Previous Close  Share Volume (000)  Turnover (000) Lot Size  \n",
       "Date                                                                            \n",
       "2020-10-20  45.85           46.35              4193.0        192921.0     None  \n",
       "2020-10-21  45.95           46.05              4830.0        223077.0     None  \n",
       "2020-10-22  45.90           46.15              4902.0        226000.0     None  \n",
       "2020-10-23  45.80           46.10              3815.0        176451.0     None  \n",
       "2020-10-27  46.50           46.40             12095.0        566845.0     None  \n",
       "...           ...             ...                 ...             ...      ...  \n",
       "2021-10-12  52.35           52.95              2712.0        142802.0     None  \n",
       "2021-10-15  52.00           52.60              5067.0        266129.0     None  \n",
       "2021-10-18  52.30           52.50              4036.0        212393.0     None  \n",
       "2021-10-19  52.95           52.70              2484.0        132423.0     None  \n",
       "2021-10-20  52.80           53.30              3649.0        193972.0     None  \n",
       "\n",
       "[247 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ck"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db245a3b",
   "metadata": {},
   "source": [
    "### 3.3 From Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c389d843",
   "metadata": {},
   "outputs": [],
   "source": [
    "costs = {'Central Branch' : 300000,\n",
    "         'TST Branch' : 50000,\n",
    "         'Mongkok Branch' : 20000}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b6936c74",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sales' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m branch_summary \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msales\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[43msales\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcosts\u001b[39m\u001b[38;5;124m\"\u001b[39m: costs})\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sales' is not defined"
     ]
    }
   ],
   "source": [
    "branch_summary = pd.DataFrame({\"sales\": sales, \"costs\": costs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9107a2e3",
   "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>sales</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central Branch</th>\n",
       "      <td>10000</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TST Branch</th>\n",
       "      <td>2000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mongkok Branch</th>\n",
       "      <td>3000</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sales   costs\n",
       "Central Branch  10000  300000\n",
       "TST Branch       2000   50000\n",
       "Mongkok Branch   3000   20000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bab7a42",
   "metadata": {},
   "source": [
    "### 3.4 Getting data from dataframe (getting rows with date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a84288d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'aapl_proper_index' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maapl_proper_index\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2019-10-30\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'aapl_proper_index' is not defined"
     ]
    }
   ],
   "source": [
    "aapl_proper_index.loc[\"2019-10-30\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60c748c1",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2019-10-30</th>\n",
       "      <td>61.189999</td>\n",
       "      <td>61.325001</td>\n",
       "      <td>60.302502</td>\n",
       "      <td>60.814999</td>\n",
       "      <td>60.217525</td>\n",
       "      <td>124522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>61.810001</td>\n",
       "      <td>62.292500</td>\n",
       "      <td>59.314999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>61.579021</td>\n",
       "      <td>139162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.982498</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>63.955002</td>\n",
       "      <td>63.326683</td>\n",
       "      <td>151125200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>64.332497</td>\n",
       "      <td>64.462502</td>\n",
       "      <td>63.845001</td>\n",
       "      <td>64.375000</td>\n",
       "      <td>63.742554</td>\n",
       "      <td>103272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>64.262497</td>\n",
       "      <td>64.547501</td>\n",
       "      <td>64.080002</td>\n",
       "      <td>64.282501</td>\n",
       "      <td>63.650970</td>\n",
       "      <td>79897600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>64.192497</td>\n",
       "      <td>64.372498</td>\n",
       "      <td>63.842499</td>\n",
       "      <td>64.309998</td>\n",
       "      <td>63.678192</td>\n",
       "      <td>75864400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>64.684998</td>\n",
       "      <td>65.087502</td>\n",
       "      <td>64.527496</td>\n",
       "      <td>64.857498</td>\n",
       "      <td>64.413116</td>\n",
       "      <td>94940400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-08</th>\n",
       "      <td>64.672501</td>\n",
       "      <td>65.110001</td>\n",
       "      <td>64.212502</td>\n",
       "      <td>65.035004</td>\n",
       "      <td>64.589409</td>\n",
       "      <td>69986400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-11</th>\n",
       "      <td>64.574997</td>\n",
       "      <td>65.617500</td>\n",
       "      <td>64.570000</td>\n",
       "      <td>65.550003</td>\n",
       "      <td>65.100876</td>\n",
       "      <td>81821200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-12</th>\n",
       "      <td>65.387497</td>\n",
       "      <td>65.697502</td>\n",
       "      <td>65.230003</td>\n",
       "      <td>65.489998</td>\n",
       "      <td>65.041283</td>\n",
       "      <td>87388800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-13</th>\n",
       "      <td>65.282501</td>\n",
       "      <td>66.195000</td>\n",
       "      <td>65.267502</td>\n",
       "      <td>66.117500</td>\n",
       "      <td>65.664490</td>\n",
       "      <td>102734400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-14</th>\n",
       "      <td>65.937500</td>\n",
       "      <td>66.220001</td>\n",
       "      <td>65.525002</td>\n",
       "      <td>65.660004</td>\n",
       "      <td>65.210121</td>\n",
       "      <td>89182800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-15</th>\n",
       "      <td>65.919998</td>\n",
       "      <td>66.445000</td>\n",
       "      <td>65.752502</td>\n",
       "      <td>66.440002</td>\n",
       "      <td>65.984779</td>\n",
       "      <td>100206400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume\n",
       "Date                                                                        \n",
       "2019-10-30  61.189999  61.325001  60.302502  60.814999  60.217525  124522000\n",
       "2019-10-31  61.810001  62.292500  59.314999  62.189999  61.579021  139162000\n",
       "2019-11-01  62.384998  63.982498  62.290001  63.955002  63.326683  151125200\n",
       "2019-11-04  64.332497  64.462502  63.845001  64.375000  63.742554  103272000\n",
       "2019-11-05  64.262497  64.547501  64.080002  64.282501  63.650970   79897600\n",
       "2019-11-06  64.192497  64.372498  63.842499  64.309998  63.678192   75864400\n",
       "2019-11-07  64.684998  65.087502  64.527496  64.857498  64.413116   94940400\n",
       "2019-11-08  64.672501  65.110001  64.212502  65.035004  64.589409   69986400\n",
       "2019-11-11  64.574997  65.617500  64.570000  65.550003  65.100876   81821200\n",
       "2019-11-12  65.387497  65.697502  65.230003  65.489998  65.041283   87388800\n",
       "2019-11-13  65.282501  66.195000  65.267502  66.117500  65.664490  102734400\n",
       "2019-11-14  65.937500  66.220001  65.525002  65.660004  65.210121   89182800\n",
       "2019-11-15  65.919998  66.445000  65.752502  66.440002  65.984779  100206400"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.loc[\"2019-10-30\":\"2019-11-15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0c747d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.982498</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>63.955002</td>\n",
       "      <td>63.326683</td>\n",
       "      <td>151125200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>64.332497</td>\n",
       "      <td>64.462502</td>\n",
       "      <td>63.845001</td>\n",
       "      <td>64.375000</td>\n",
       "      <td>63.742554</td>\n",
       "      <td>103272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>64.262497</td>\n",
       "      <td>64.547501</td>\n",
       "      <td>64.080002</td>\n",
       "      <td>64.282501</td>\n",
       "      <td>63.650970</td>\n",
       "      <td>79897600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>64.192497</td>\n",
       "      <td>64.372498</td>\n",
       "      <td>63.842499</td>\n",
       "      <td>64.309998</td>\n",
       "      <td>63.678192</td>\n",
       "      <td>75864400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>64.684998</td>\n",
       "      <td>65.087502</td>\n",
       "      <td>64.527496</td>\n",
       "      <td>64.857498</td>\n",
       "      <td>64.413116</td>\n",
       "      <td>94940400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-08</th>\n",
       "      <td>64.672501</td>\n",
       "      <td>65.110001</td>\n",
       "      <td>64.212502</td>\n",
       "      <td>65.035004</td>\n",
       "      <td>64.589409</td>\n",
       "      <td>69986400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-11</th>\n",
       "      <td>64.574997</td>\n",
       "      <td>65.617500</td>\n",
       "      <td>64.570000</td>\n",
       "      <td>65.550003</td>\n",
       "      <td>65.100876</td>\n",
       "      <td>81821200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-12</th>\n",
       "      <td>65.387497</td>\n",
       "      <td>65.697502</td>\n",
       "      <td>65.230003</td>\n",
       "      <td>65.489998</td>\n",
       "      <td>65.041283</td>\n",
       "      <td>87388800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-13</th>\n",
       "      <td>65.282501</td>\n",
       "      <td>66.195000</td>\n",
       "      <td>65.267502</td>\n",
       "      <td>66.117500</td>\n",
       "      <td>65.664490</td>\n",
       "      <td>102734400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-14</th>\n",
       "      <td>65.937500</td>\n",
       "      <td>66.220001</td>\n",
       "      <td>65.525002</td>\n",
       "      <td>65.660004</td>\n",
       "      <td>65.210121</td>\n",
       "      <td>89182800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-15</th>\n",
       "      <td>65.919998</td>\n",
       "      <td>66.445000</td>\n",
       "      <td>65.752502</td>\n",
       "      <td>66.440002</td>\n",
       "      <td>65.984779</td>\n",
       "      <td>100206400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-18</th>\n",
       "      <td>66.449997</td>\n",
       "      <td>66.857498</td>\n",
       "      <td>66.057503</td>\n",
       "      <td>66.775002</td>\n",
       "      <td>66.317490</td>\n",
       "      <td>86703200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-19</th>\n",
       "      <td>66.974998</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>66.347504</td>\n",
       "      <td>66.572502</td>\n",
       "      <td>66.116371</td>\n",
       "      <td>76167200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-20</th>\n",
       "      <td>66.385002</td>\n",
       "      <td>66.519997</td>\n",
       "      <td>65.099998</td>\n",
       "      <td>65.797501</td>\n",
       "      <td>65.346687</td>\n",
       "      <td>106234400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-21</th>\n",
       "      <td>65.922501</td>\n",
       "      <td>66.002502</td>\n",
       "      <td>65.294998</td>\n",
       "      <td>65.502502</td>\n",
       "      <td>65.053703</td>\n",
       "      <td>121395200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-22</th>\n",
       "      <td>65.647499</td>\n",
       "      <td>65.794998</td>\n",
       "      <td>65.209999</td>\n",
       "      <td>65.445000</td>\n",
       "      <td>64.996597</td>\n",
       "      <td>65325200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-25</th>\n",
       "      <td>65.677498</td>\n",
       "      <td>66.610001</td>\n",
       "      <td>65.629997</td>\n",
       "      <td>66.592499</td>\n",
       "      <td>66.136230</td>\n",
       "      <td>84020400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-26</th>\n",
       "      <td>66.735001</td>\n",
       "      <td>66.790001</td>\n",
       "      <td>65.625000</td>\n",
       "      <td>66.072502</td>\n",
       "      <td>65.619789</td>\n",
       "      <td>105207600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-27</th>\n",
       "      <td>66.394997</td>\n",
       "      <td>66.995003</td>\n",
       "      <td>66.327499</td>\n",
       "      <td>66.959999</td>\n",
       "      <td>66.501213</td>\n",
       "      <td>65235600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>66.650002</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>66.474998</td>\n",
       "      <td>66.812500</td>\n",
       "      <td>66.354729</td>\n",
       "      <td>46617600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume\n",
       "Date                                                                        \n",
       "2019-11-01  62.384998  63.982498  62.290001  63.955002  63.326683  151125200\n",
       "2019-11-04  64.332497  64.462502  63.845001  64.375000  63.742554  103272000\n",
       "2019-11-05  64.262497  64.547501  64.080002  64.282501  63.650970   79897600\n",
       "2019-11-06  64.192497  64.372498  63.842499  64.309998  63.678192   75864400\n",
       "2019-11-07  64.684998  65.087502  64.527496  64.857498  64.413116   94940400\n",
       "2019-11-08  64.672501  65.110001  64.212502  65.035004  64.589409   69986400\n",
       "2019-11-11  64.574997  65.617500  64.570000  65.550003  65.100876   81821200\n",
       "2019-11-12  65.387497  65.697502  65.230003  65.489998  65.041283   87388800\n",
       "2019-11-13  65.282501  66.195000  65.267502  66.117500  65.664490  102734400\n",
       "2019-11-14  65.937500  66.220001  65.525002  65.660004  65.210121   89182800\n",
       "2019-11-15  65.919998  66.445000  65.752502  66.440002  65.984779  100206400\n",
       "2019-11-18  66.449997  66.857498  66.057503  66.775002  66.317490   86703200\n",
       "2019-11-19  66.974998  67.000000  66.347504  66.572502  66.116371   76167200\n",
       "2019-11-20  66.385002  66.519997  65.099998  65.797501  65.346687  106234400\n",
       "2019-11-21  65.922501  66.002502  65.294998  65.502502  65.053703  121395200\n",
       "2019-11-22  65.647499  65.794998  65.209999  65.445000  64.996597   65325200\n",
       "2019-11-25  65.677498  66.610001  65.629997  66.592499  66.136230   84020400\n",
       "2019-11-26  66.735001  66.790001  65.625000  66.072502  65.619789  105207600\n",
       "2019-11-27  66.394997  66.995003  66.327499  66.959999  66.501213   65235600\n",
       "2019-11-29  66.650002  67.000000  66.474998  66.812500  66.354729   46617600"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.loc[\"2019-11\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de65e36f",
   "metadata": {},
   "source": [
    "### 3.5 Getting data from dataframe (get a series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff87318b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-11-01    63.955002\n",
       "2019-11-04    64.375000\n",
       "2019-11-05    64.282501\n",
       "2019-11-06    64.309998\n",
       "2019-11-07    64.857498\n",
       "2019-11-08    65.035004\n",
       "2019-11-11    65.550003\n",
       "2019-11-12    65.489998\n",
       "2019-11-13    66.117500\n",
       "2019-11-14    65.660004\n",
       "2019-11-15    66.440002\n",
       "2019-11-18    66.775002\n",
       "2019-11-19    66.572502\n",
       "2019-11-20    65.797501\n",
       "2019-11-21    65.502502\n",
       "2019-11-22    65.445000\n",
       "2019-11-25    66.592499\n",
       "2019-11-26    66.072502\n",
       "2019-11-27    66.959999\n",
       "2019-11-29    66.812500\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.loc[\"2019-11\"][\"Close\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b48da3d",
   "metadata": {},
   "source": [
    "### 3.6 Getting data from dataframe (get multiple column from a dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a39c7d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.955002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>64.332497</td>\n",
       "      <td>64.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>64.262497</td>\n",
       "      <td>64.282501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>64.192497</td>\n",
       "      <td>64.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>64.684998</td>\n",
       "      <td>64.857498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-08</th>\n",
       "      <td>64.672501</td>\n",
       "      <td>65.035004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-11</th>\n",
       "      <td>64.574997</td>\n",
       "      <td>65.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-12</th>\n",
       "      <td>65.387497</td>\n",
       "      <td>65.489998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-13</th>\n",
       "      <td>65.282501</td>\n",
       "      <td>66.117500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-14</th>\n",
       "      <td>65.937500</td>\n",
       "      <td>65.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-15</th>\n",
       "      <td>65.919998</td>\n",
       "      <td>66.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-18</th>\n",
       "      <td>66.449997</td>\n",
       "      <td>66.775002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-19</th>\n",
       "      <td>66.974998</td>\n",
       "      <td>66.572502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-20</th>\n",
       "      <td>66.385002</td>\n",
       "      <td>65.797501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-21</th>\n",
       "      <td>65.922501</td>\n",
       "      <td>65.502502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-22</th>\n",
       "      <td>65.647499</td>\n",
       "      <td>65.445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-25</th>\n",
       "      <td>65.677498</td>\n",
       "      <td>66.592499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-26</th>\n",
       "      <td>66.735001</td>\n",
       "      <td>66.072502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-27</th>\n",
       "      <td>66.394997</td>\n",
       "      <td>66.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>66.650002</td>\n",
       "      <td>66.812500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open      Close\n",
       "Date                            \n",
       "2019-11-01  62.384998  63.955002\n",
       "2019-11-04  64.332497  64.375000\n",
       "2019-11-05  64.262497  64.282501\n",
       "2019-11-06  64.192497  64.309998\n",
       "2019-11-07  64.684998  64.857498\n",
       "2019-11-08  64.672501  65.035004\n",
       "2019-11-11  64.574997  65.550003\n",
       "2019-11-12  65.387497  65.489998\n",
       "2019-11-13  65.282501  66.117500\n",
       "2019-11-14  65.937500  65.660004\n",
       "2019-11-15  65.919998  66.440002\n",
       "2019-11-18  66.449997  66.775002\n",
       "2019-11-19  66.974998  66.572502\n",
       "2019-11-20  66.385002  65.797501\n",
       "2019-11-21  65.922501  65.502502\n",
       "2019-11-22  65.647499  65.445000\n",
       "2019-11-25  65.677498  66.592499\n",
       "2019-11-26  66.735001  66.072502\n",
       "2019-11-27  66.394997  66.959999\n",
       "2019-11-29  66.650002  66.812500"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.loc[\"2019-11\"][[\"Open\",\"Close\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe98d18",
   "metadata": {},
   "source": [
    "### 3.7 Getting data from dataframe (that's not a date/integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a3e09fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales     10000\n",
       "costs    300000\n",
       "Name: Central Branch, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_summary.loc[\"Central Branch\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "114ba40f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central Branch    10000\n",
       "TST Branch         2000\n",
       "Mongkok Branch     3000\n",
       "Name: sales, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_summary[\"sales\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15222ed8",
   "metadata": {},
   "source": [
    "### 3.8 Getting data from dataframe (using implicit index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "72c1e9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Central Branch', 'TST Branch', 'Mongkok Branch'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_summary.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e34b9495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-10-28', '2019-10-29', '2019-10-30', '2019-10-31',\n",
       "               '2019-11-01', '2019-11-04', '2019-11-05', '2019-11-06',\n",
       "               '2019-11-07', '2019-11-08',\n",
       "               ...\n",
       "               '2020-10-14', '2020-10-15', '2020-10-16', '2020-10-19',\n",
       "               '2020-10-20', '2020-10-21', '2020-10-22', '2020-10-23',\n",
       "               '2020-10-26', '2020-10-27'],\n",
       "              dtype='datetime64[ns]', name='Date', length=253, freq=None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a32d711d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=253, step=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0836e844",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2019-10-28</th>\n",
       "      <td>61.855000</td>\n",
       "      <td>62.312500</td>\n",
       "      <td>61.680000</td>\n",
       "      <td>62.262501</td>\n",
       "      <td>61.650810</td>\n",
       "      <td>96572800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>62.242500</td>\n",
       "      <td>62.437500</td>\n",
       "      <td>60.642502</td>\n",
       "      <td>60.822498</td>\n",
       "      <td>60.224953</td>\n",
       "      <td>142839600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>61.189999</td>\n",
       "      <td>61.325001</td>\n",
       "      <td>60.302502</td>\n",
       "      <td>60.814999</td>\n",
       "      <td>60.217525</td>\n",
       "      <td>124522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>61.810001</td>\n",
       "      <td>62.292500</td>\n",
       "      <td>59.314999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>61.579021</td>\n",
       "      <td>139162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.982498</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>63.955002</td>\n",
       "      <td>63.326683</td>\n",
       "      <td>151125200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>64.332497</td>\n",
       "      <td>64.462502</td>\n",
       "      <td>63.845001</td>\n",
       "      <td>64.375000</td>\n",
       "      <td>63.742554</td>\n",
       "      <td>103272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>64.262497</td>\n",
       "      <td>64.547501</td>\n",
       "      <td>64.080002</td>\n",
       "      <td>64.282501</td>\n",
       "      <td>63.650970</td>\n",
       "      <td>79897600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>64.192497</td>\n",
       "      <td>64.372498</td>\n",
       "      <td>63.842499</td>\n",
       "      <td>64.309998</td>\n",
       "      <td>63.678192</td>\n",
       "      <td>75864400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>64.684998</td>\n",
       "      <td>65.087502</td>\n",
       "      <td>64.527496</td>\n",
       "      <td>64.857498</td>\n",
       "      <td>64.413116</td>\n",
       "      <td>94940400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-08</th>\n",
       "      <td>64.672501</td>\n",
       "      <td>65.110001</td>\n",
       "      <td>64.212502</td>\n",
       "      <td>65.035004</td>\n",
       "      <td>64.589409</td>\n",
       "      <td>69986400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume\n",
       "Date                                                                        \n",
       "2019-10-28  61.855000  62.312500  61.680000  62.262501  61.650810   96572800\n",
       "2019-10-29  62.242500  62.437500  60.642502  60.822498  60.224953  142839600\n",
       "2019-10-30  61.189999  61.325001  60.302502  60.814999  60.217525  124522000\n",
       "2019-10-31  61.810001  62.292500  59.314999  62.189999  61.579021  139162000\n",
       "2019-11-01  62.384998  63.982498  62.290001  63.955002  63.326683  151125200\n",
       "2019-11-04  64.332497  64.462502  63.845001  64.375000  63.742554  103272000\n",
       "2019-11-05  64.262497  64.547501  64.080002  64.282501  63.650970   79897600\n",
       "2019-11-06  64.192497  64.372498  63.842499  64.309998  63.678192   75864400\n",
       "2019-11-07  64.684998  65.087502  64.527496  64.857498  64.413116   94940400\n",
       "2019-11-08  64.672501  65.110001  64.212502  65.035004  64.589409   69986400"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index.iloc[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a78ecf",
   "metadata": {},
   "source": [
    "## 4. Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7706e9dd",
   "metadata": {},
   "source": [
    "### 4.1 Single condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ba29f518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-10-28    False\n",
       "2019-10-29    False\n",
       "2019-10-30    False\n",
       "2019-10-31    False\n",
       "2019-11-01    False\n",
       "              ...  \n",
       "2020-10-21     True\n",
       "2020-10-22     True\n",
       "2020-10-23     True\n",
       "2020-10-26     True\n",
       "2020-10-27     True\n",
       "Name: Open, Length: 253, dtype: bool"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[\"Open\"] > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22ac3ad1",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2020-07-31</th>\n",
       "      <td>102.885002</td>\n",
       "      <td>106.415001</td>\n",
       "      <td>100.824997</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>106.068756</td>\n",
       "      <td>374336800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>111.637497</td>\n",
       "      <td>107.892502</td>\n",
       "      <td>108.937500</td>\n",
       "      <td>108.741440</td>\n",
       "      <td>308151200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>109.132500</td>\n",
       "      <td>110.790001</td>\n",
       "      <td>108.387497</td>\n",
       "      <td>109.665001</td>\n",
       "      <td>109.467628</td>\n",
       "      <td>173071600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>109.377502</td>\n",
       "      <td>110.392502</td>\n",
       "      <td>108.897499</td>\n",
       "      <td>110.062500</td>\n",
       "      <td>109.864410</td>\n",
       "      <td>121992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>110.404999</td>\n",
       "      <td>114.412498</td>\n",
       "      <td>109.797501</td>\n",
       "      <td>113.902496</td>\n",
       "      <td>113.697502</td>\n",
       "      <td>202428800</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>2020-10-21</th>\n",
       "      <td>116.669998</td>\n",
       "      <td>118.709999</td>\n",
       "      <td>116.449997</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>89946000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>116.389999</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>114.279999</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>82572600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>115.489998</td>\n",
       "      <td>117.279999</td>\n",
       "      <td>114.540001</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>91927700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-07-31  102.885002  106.415001  100.824997  106.260002  106.068756   \n",
       "2020-08-03  108.199997  111.637497  107.892502  108.937500  108.741440   \n",
       "2020-08-04  109.132500  110.790001  108.387497  109.665001  109.467628   \n",
       "2020-08-05  109.377502  110.392502  108.897499  110.062500  109.864410   \n",
       "2020-08-06  110.404999  114.412498  109.797501  113.902496  113.697502   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-10-21  116.669998  118.709999  116.449997  116.870003  116.870003   \n",
       "2020-10-22  117.449997  118.040001  114.589996  115.750000  115.750000   \n",
       "2020-10-23  116.389999  116.550003  114.279999  115.040001  115.040001   \n",
       "2020-10-26  114.010002  116.550003  112.879997  115.050003  115.050003   \n",
       "2020-10-27  115.489998  117.279999  114.540001  116.599998  116.599998   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2020-07-31  374336800  \n",
       "2020-08-03  308151200  \n",
       "2020-08-04  173071600  \n",
       "2020-08-05  121992000  \n",
       "2020-08-06  202428800  \n",
       "...               ...  \n",
       "2020-10-21   89946000  \n",
       "2020-10-22  101988000  \n",
       "2020-10-23   82572600  \n",
       "2020-10-26  111850700  \n",
       "2020-10-27   91927700  \n",
       "\n",
       "[62 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[aapl_proper_index[\"Open\"] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ff9063",
   "metadata": {},
   "source": [
    "## 4.2 multiple condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c5574e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-10-28    False\n",
       "2019-10-29    False\n",
       "2019-10-30    False\n",
       "2019-10-31    False\n",
       "2019-11-01    False\n",
       "              ...  \n",
       "2020-10-21    False\n",
       "2020-10-22     True\n",
       "2020-10-23    False\n",
       "2020-10-26     True\n",
       "2020-10-27    False\n",
       "Length: 253, dtype: bool"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(aapl_proper_index[\"Open\"] > 100) & (aapl_proper_index[\"Volume\"] > 100000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ab72ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (aapl_proper_index[\"Open\"] > 100) & (aapl_proper_index[\"Volume\"] > 100000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfe5d612",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2020-07-31</th>\n",
       "      <td>102.885002</td>\n",
       "      <td>106.415001</td>\n",
       "      <td>100.824997</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>106.068756</td>\n",
       "      <td>374336800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>111.637497</td>\n",
       "      <td>107.892502</td>\n",
       "      <td>108.937500</td>\n",
       "      <td>108.741440</td>\n",
       "      <td>308151200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>109.132500</td>\n",
       "      <td>110.790001</td>\n",
       "      <td>108.387497</td>\n",
       "      <td>109.665001</td>\n",
       "      <td>109.467628</td>\n",
       "      <td>173071600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>109.377502</td>\n",
       "      <td>110.392502</td>\n",
       "      <td>108.897499</td>\n",
       "      <td>110.062500</td>\n",
       "      <td>109.864410</td>\n",
       "      <td>121992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>110.404999</td>\n",
       "      <td>114.412498</td>\n",
       "      <td>109.797501</td>\n",
       "      <td>113.902496</td>\n",
       "      <td>113.697502</td>\n",
       "      <td>202428800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-07</th>\n",
       "      <td>113.205002</td>\n",
       "      <td>113.675003</td>\n",
       "      <td>110.292503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>198045600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-10</th>\n",
       "      <td>112.599998</td>\n",
       "      <td>113.775002</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>212403600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-11</th>\n",
       "      <td>111.970001</td>\n",
       "      <td>112.482498</td>\n",
       "      <td>109.107498</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>187902400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-12</th>\n",
       "      <td>110.497498</td>\n",
       "      <td>113.275002</td>\n",
       "      <td>110.297501</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>165944800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-13</th>\n",
       "      <td>114.430000</td>\n",
       "      <td>116.042503</td>\n",
       "      <td>113.927498</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>210082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-14</th>\n",
       "      <td>114.830002</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>113.044998</td>\n",
       "      <td>114.907501</td>\n",
       "      <td>114.907501</td>\n",
       "      <td>165565200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>116.062500</td>\n",
       "      <td>116.087502</td>\n",
       "      <td>113.962502</td>\n",
       "      <td>114.607498</td>\n",
       "      <td>114.607498</td>\n",
       "      <td>119561600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>114.352501</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>114.007500</td>\n",
       "      <td>115.562500</td>\n",
       "      <td>115.562500</td>\n",
       "      <td>105633600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>115.982498</td>\n",
       "      <td>117.162498</td>\n",
       "      <td>115.610001</td>\n",
       "      <td>115.707497</td>\n",
       "      <td>115.707497</td>\n",
       "      <td>145538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>115.750000</td>\n",
       "      <td>118.392502</td>\n",
       "      <td>115.732498</td>\n",
       "      <td>118.275002</td>\n",
       "      <td>118.275002</td>\n",
       "      <td>126907200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>119.262497</td>\n",
       "      <td>124.867500</td>\n",
       "      <td>119.250000</td>\n",
       "      <td>124.370003</td>\n",
       "      <td>124.370003</td>\n",
       "      <td>338054800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>128.697495</td>\n",
       "      <td>128.785004</td>\n",
       "      <td>123.937500</td>\n",
       "      <td>125.857498</td>\n",
       "      <td>125.857498</td>\n",
       "      <td>345937600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>124.697502</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>123.052498</td>\n",
       "      <td>124.824997</td>\n",
       "      <td>124.824997</td>\n",
       "      <td>211495600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>126.180000</td>\n",
       "      <td>126.992500</td>\n",
       "      <td>125.082497</td>\n",
       "      <td>126.522499</td>\n",
       "      <td>126.522499</td>\n",
       "      <td>163022400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>127.142502</td>\n",
       "      <td>127.485001</td>\n",
       "      <td>123.832497</td>\n",
       "      <td>125.010002</td>\n",
       "      <td>125.010002</td>\n",
       "      <td>155552400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>126.012497</td>\n",
       "      <td>126.442497</td>\n",
       "      <td>124.577499</td>\n",
       "      <td>124.807503</td>\n",
       "      <td>124.807503</td>\n",
       "      <td>187630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>127.580002</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>129.039993</td>\n",
       "      <td>129.039993</td>\n",
       "      <td>225702700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>132.759995</td>\n",
       "      <td>134.800003</td>\n",
       "      <td>130.529999</td>\n",
       "      <td>134.179993</td>\n",
       "      <td>134.179993</td>\n",
       "      <td>152470100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-02</th>\n",
       "      <td>137.589996</td>\n",
       "      <td>137.979996</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>131.399994</td>\n",
       "      <td>131.399994</td>\n",
       "      <td>200119000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-03</th>\n",
       "      <td>126.910004</td>\n",
       "      <td>128.839996</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>120.879997</td>\n",
       "      <td>120.879997</td>\n",
       "      <td>257599600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04</th>\n",
       "      <td>120.070000</td>\n",
       "      <td>123.699997</td>\n",
       "      <td>110.889999</td>\n",
       "      <td>120.959999</td>\n",
       "      <td>120.959999</td>\n",
       "      <td>332607200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-08</th>\n",
       "      <td>113.949997</td>\n",
       "      <td>118.989998</td>\n",
       "      <td>112.680000</td>\n",
       "      <td>112.820000</td>\n",
       "      <td>112.820000</td>\n",
       "      <td>231366600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-09</th>\n",
       "      <td>117.260002</td>\n",
       "      <td>119.139999</td>\n",
       "      <td>115.260002</td>\n",
       "      <td>117.320000</td>\n",
       "      <td>117.320000</td>\n",
       "      <td>176940500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-10</th>\n",
       "      <td>120.360001</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>112.500000</td>\n",
       "      <td>113.489998</td>\n",
       "      <td>113.489998</td>\n",
       "      <td>182274400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-11</th>\n",
       "      <td>114.570000</td>\n",
       "      <td>115.230003</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>180860300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-14</th>\n",
       "      <td>114.720001</td>\n",
       "      <td>115.930000</td>\n",
       "      <td>112.800003</td>\n",
       "      <td>115.360001</td>\n",
       "      <td>115.360001</td>\n",
       "      <td>140150100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15</th>\n",
       "      <td>118.330002</td>\n",
       "      <td>118.830002</td>\n",
       "      <td>113.610001</td>\n",
       "      <td>115.540001</td>\n",
       "      <td>115.540001</td>\n",
       "      <td>184642000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-16</th>\n",
       "      <td>115.230003</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>112.040001</td>\n",
       "      <td>112.129997</td>\n",
       "      <td>112.129997</td>\n",
       "      <td>154679000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-17</th>\n",
       "      <td>109.720001</td>\n",
       "      <td>112.199997</td>\n",
       "      <td>108.709999</td>\n",
       "      <td>110.339996</td>\n",
       "      <td>110.339996</td>\n",
       "      <td>178011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-18</th>\n",
       "      <td>110.400002</td>\n",
       "      <td>110.879997</td>\n",
       "      <td>106.089996</td>\n",
       "      <td>106.839996</td>\n",
       "      <td>106.839996</td>\n",
       "      <td>287104900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-21</th>\n",
       "      <td>104.540001</td>\n",
       "      <td>110.190002</td>\n",
       "      <td>103.099998</td>\n",
       "      <td>110.080002</td>\n",
       "      <td>110.080002</td>\n",
       "      <td>195713800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-22</th>\n",
       "      <td>112.680000</td>\n",
       "      <td>112.860001</td>\n",
       "      <td>109.160004</td>\n",
       "      <td>111.809998</td>\n",
       "      <td>111.809998</td>\n",
       "      <td>183055400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-23</th>\n",
       "      <td>111.620003</td>\n",
       "      <td>112.110001</td>\n",
       "      <td>106.769997</td>\n",
       "      <td>107.120003</td>\n",
       "      <td>107.120003</td>\n",
       "      <td>150718700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-24</th>\n",
       "      <td>105.169998</td>\n",
       "      <td>110.250000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>108.220001</td>\n",
       "      <td>108.220001</td>\n",
       "      <td>167743300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>108.430000</td>\n",
       "      <td>112.440002</td>\n",
       "      <td>107.669998</td>\n",
       "      <td>112.279999</td>\n",
       "      <td>112.279999</td>\n",
       "      <td>149981400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>115.010002</td>\n",
       "      <td>115.320000</td>\n",
       "      <td>112.779999</td>\n",
       "      <td>114.959999</td>\n",
       "      <td>114.959999</td>\n",
       "      <td>137672400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>113.790001</td>\n",
       "      <td>117.260002</td>\n",
       "      <td>113.620003</td>\n",
       "      <td>115.809998</td>\n",
       "      <td>115.809998</td>\n",
       "      <td>142675200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>117.639999</td>\n",
       "      <td>117.720001</td>\n",
       "      <td>115.830002</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>116120400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>112.889999</td>\n",
       "      <td>115.370003</td>\n",
       "      <td>112.220001</td>\n",
       "      <td>113.019997</td>\n",
       "      <td>113.019997</td>\n",
       "      <td>144712000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>113.910004</td>\n",
       "      <td>116.650002</td>\n",
       "      <td>113.550003</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>106243800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>115.699997</td>\n",
       "      <td>116.120003</td>\n",
       "      <td>112.250000</td>\n",
       "      <td>113.160004</td>\n",
       "      <td>113.160004</td>\n",
       "      <td>161498200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>115.279999</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>114.919998</td>\n",
       "      <td>116.970001</td>\n",
       "      <td>116.970001</td>\n",
       "      <td>100506900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>120.059998</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>119.279999</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>240226800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>125.269997</td>\n",
       "      <td>125.389999</td>\n",
       "      <td>119.650002</td>\n",
       "      <td>121.099998</td>\n",
       "      <td>121.099998</td>\n",
       "      <td>262330500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>123.029999</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>121.190002</td>\n",
       "      <td>121.190002</td>\n",
       "      <td>151062300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>118.720001</td>\n",
       "      <td>121.199997</td>\n",
       "      <td>118.150002</td>\n",
       "      <td>120.709999</td>\n",
       "      <td>120.709999</td>\n",
       "      <td>112559200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>121.279999</td>\n",
       "      <td>121.550003</td>\n",
       "      <td>118.809998</td>\n",
       "      <td>119.019997</td>\n",
       "      <td>119.019997</td>\n",
       "      <td>115393800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>119.959999</td>\n",
       "      <td>120.419998</td>\n",
       "      <td>115.660004</td>\n",
       "      <td>115.980003</td>\n",
       "      <td>115.980003</td>\n",
       "      <td>120639300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>116.199997</td>\n",
       "      <td>118.980003</td>\n",
       "      <td>115.629997</td>\n",
       "      <td>117.510002</td>\n",
       "      <td>117.510002</td>\n",
       "      <td>124423700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-07-31  102.885002  106.415001  100.824997  106.260002  106.068756   \n",
       "2020-08-03  108.199997  111.637497  107.892502  108.937500  108.741440   \n",
       "2020-08-04  109.132500  110.790001  108.387497  109.665001  109.467628   \n",
       "2020-08-05  109.377502  110.392502  108.897499  110.062500  109.864410   \n",
       "2020-08-06  110.404999  114.412498  109.797501  113.902496  113.697502   \n",
       "2020-08-07  113.205002  113.675003  110.292503  111.112503  111.112503   \n",
       "2020-08-10  112.599998  113.775002  110.000000  112.727501  112.727501   \n",
       "2020-08-11  111.970001  112.482498  109.107498  109.375000  109.375000   \n",
       "2020-08-12  110.497498  113.275002  110.297501  113.010002  113.010002   \n",
       "2020-08-13  114.430000  116.042503  113.927498  115.010002  115.010002   \n",
       "2020-08-14  114.830002  115.000000  113.044998  114.907501  114.907501   \n",
       "2020-08-17  116.062500  116.087502  113.962502  114.607498  114.607498   \n",
       "2020-08-18  114.352501  116.000000  114.007500  115.562500  115.562500   \n",
       "2020-08-19  115.982498  117.162498  115.610001  115.707497  115.707497   \n",
       "2020-08-20  115.750000  118.392502  115.732498  118.275002  118.275002   \n",
       "2020-08-21  119.262497  124.867500  119.250000  124.370003  124.370003   \n",
       "2020-08-24  128.697495  128.785004  123.937500  125.857498  125.857498   \n",
       "2020-08-25  124.697502  125.180000  123.052498  124.824997  124.824997   \n",
       "2020-08-26  126.180000  126.992500  125.082497  126.522499  126.522499   \n",
       "2020-08-27  127.142502  127.485001  123.832497  125.010002  125.010002   \n",
       "2020-08-28  126.012497  126.442497  124.577499  124.807503  124.807503   \n",
       "2020-08-31  127.580002  131.000000  126.000000  129.039993  129.039993   \n",
       "2020-09-01  132.759995  134.800003  130.529999  134.179993  134.179993   \n",
       "2020-09-02  137.589996  137.979996  127.000000  131.399994  131.399994   \n",
       "2020-09-03  126.910004  128.839996  120.500000  120.879997  120.879997   \n",
       "2020-09-04  120.070000  123.699997  110.889999  120.959999  120.959999   \n",
       "2020-09-08  113.949997  118.989998  112.680000  112.820000  112.820000   \n",
       "2020-09-09  117.260002  119.139999  115.260002  117.320000  117.320000   \n",
       "2020-09-10  120.360001  120.500000  112.500000  113.489998  113.489998   \n",
       "2020-09-11  114.570000  115.230003  110.000000  112.000000  112.000000   \n",
       "2020-09-14  114.720001  115.930000  112.800003  115.360001  115.360001   \n",
       "2020-09-15  118.330002  118.830002  113.610001  115.540001  115.540001   \n",
       "2020-09-16  115.230003  116.000000  112.040001  112.129997  112.129997   \n",
       "2020-09-17  109.720001  112.199997  108.709999  110.339996  110.339996   \n",
       "2020-09-18  110.400002  110.879997  106.089996  106.839996  106.839996   \n",
       "2020-09-21  104.540001  110.190002  103.099998  110.080002  110.080002   \n",
       "2020-09-22  112.680000  112.860001  109.160004  111.809998  111.809998   \n",
       "2020-09-23  111.620003  112.110001  106.769997  107.120003  107.120003   \n",
       "2020-09-24  105.169998  110.250000  105.000000  108.220001  108.220001   \n",
       "2020-09-25  108.430000  112.440002  107.669998  112.279999  112.279999   \n",
       "2020-09-28  115.010002  115.320000  112.779999  114.959999  114.959999   \n",
       "2020-09-30  113.790001  117.260002  113.620003  115.809998  115.809998   \n",
       "2020-10-01  117.639999  117.720001  115.830002  116.790001  116.790001   \n",
       "2020-10-02  112.889999  115.370003  112.220001  113.019997  113.019997   \n",
       "2020-10-05  113.910004  116.650002  113.550003  116.500000  116.500000   \n",
       "2020-10-06  115.699997  116.120003  112.250000  113.160004  113.160004   \n",
       "2020-10-09  115.279999  117.000000  114.919998  116.970001  116.970001   \n",
       "2020-10-12  120.059998  125.180000  119.279999  124.400002  124.400002   \n",
       "2020-10-13  125.269997  125.389999  119.650002  121.099998  121.099998   \n",
       "2020-10-14  121.000000  123.029999  119.620003  121.190002  121.190002   \n",
       "2020-10-15  118.720001  121.199997  118.150002  120.709999  120.709999   \n",
       "2020-10-16  121.279999  121.550003  118.809998  119.019997  119.019997   \n",
       "2020-10-19  119.959999  120.419998  115.660004  115.980003  115.980003   \n",
       "2020-10-20  116.199997  118.980003  115.629997  117.510002  117.510002   \n",
       "2020-10-22  117.449997  118.040001  114.589996  115.750000  115.750000   \n",
       "2020-10-26  114.010002  116.550003  112.879997  115.050003  115.050003   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2020-07-31  374336800  \n",
       "2020-08-03  308151200  \n",
       "2020-08-04  173071600  \n",
       "2020-08-05  121992000  \n",
       "2020-08-06  202428800  \n",
       "2020-08-07  198045600  \n",
       "2020-08-10  212403600  \n",
       "2020-08-11  187902400  \n",
       "2020-08-12  165944800  \n",
       "2020-08-13  210082000  \n",
       "2020-08-14  165565200  \n",
       "2020-08-17  119561600  \n",
       "2020-08-18  105633600  \n",
       "2020-08-19  145538000  \n",
       "2020-08-20  126907200  \n",
       "2020-08-21  338054800  \n",
       "2020-08-24  345937600  \n",
       "2020-08-25  211495600  \n",
       "2020-08-26  163022400  \n",
       "2020-08-27  155552400  \n",
       "2020-08-28  187630000  \n",
       "2020-08-31  225702700  \n",
       "2020-09-01  152470100  \n",
       "2020-09-02  200119000  \n",
       "2020-09-03  257599600  \n",
       "2020-09-04  332607200  \n",
       "2020-09-08  231366600  \n",
       "2020-09-09  176940500  \n",
       "2020-09-10  182274400  \n",
       "2020-09-11  180860300  \n",
       "2020-09-14  140150100  \n",
       "2020-09-15  184642000  \n",
       "2020-09-16  154679000  \n",
       "2020-09-17  178011000  \n",
       "2020-09-18  287104900  \n",
       "2020-09-21  195713800  \n",
       "2020-09-22  183055400  \n",
       "2020-09-23  150718700  \n",
       "2020-09-24  167743300  \n",
       "2020-09-25  149981400  \n",
       "2020-09-28  137672400  \n",
       "2020-09-30  142675200  \n",
       "2020-10-01  116120400  \n",
       "2020-10-02  144712000  \n",
       "2020-10-05  106243800  \n",
       "2020-10-06  161498200  \n",
       "2020-10-09  100506900  \n",
       "2020-10-12  240226800  \n",
       "2020-10-13  262330500  \n",
       "2020-10-14  151062300  \n",
       "2020-10-15  112559200  \n",
       "2020-10-16  115393800  \n",
       "2020-10-19  120639300  \n",
       "2020-10-20  124423700  \n",
       "2020-10-22  101988000  \n",
       "2020-10-26  111850700  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649b6f86",
   "metadata": {},
   "source": [
    "#### Side notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599252b7",
   "metadata": {},
   "source": [
    "Showing the top "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3b0d0293",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2020-07-31</th>\n",
       "      <td>102.885002</td>\n",
       "      <td>106.415001</td>\n",
       "      <td>100.824997</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>106.068756</td>\n",
       "      <td>374336800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>111.637497</td>\n",
       "      <td>107.892502</td>\n",
       "      <td>108.937500</td>\n",
       "      <td>108.741440</td>\n",
       "      <td>308151200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>109.132500</td>\n",
       "      <td>110.790001</td>\n",
       "      <td>108.387497</td>\n",
       "      <td>109.665001</td>\n",
       "      <td>109.467628</td>\n",
       "      <td>173071600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>109.377502</td>\n",
       "      <td>110.392502</td>\n",
       "      <td>108.897499</td>\n",
       "      <td>110.062500</td>\n",
       "      <td>109.864410</td>\n",
       "      <td>121992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>110.404999</td>\n",
       "      <td>114.412498</td>\n",
       "      <td>109.797501</td>\n",
       "      <td>113.902496</td>\n",
       "      <td>113.697502</td>\n",
       "      <td>202428800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-07</th>\n",
       "      <td>113.205002</td>\n",
       "      <td>113.675003</td>\n",
       "      <td>110.292503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>198045600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-10</th>\n",
       "      <td>112.599998</td>\n",
       "      <td>113.775002</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>212403600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-11</th>\n",
       "      <td>111.970001</td>\n",
       "      <td>112.482498</td>\n",
       "      <td>109.107498</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>187902400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-12</th>\n",
       "      <td>110.497498</td>\n",
       "      <td>113.275002</td>\n",
       "      <td>110.297501</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>165944800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-13</th>\n",
       "      <td>114.430000</td>\n",
       "      <td>116.042503</td>\n",
       "      <td>113.927498</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>210082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-14</th>\n",
       "      <td>114.830002</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>113.044998</td>\n",
       "      <td>114.907501</td>\n",
       "      <td>114.907501</td>\n",
       "      <td>165565200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>116.062500</td>\n",
       "      <td>116.087502</td>\n",
       "      <td>113.962502</td>\n",
       "      <td>114.607498</td>\n",
       "      <td>114.607498</td>\n",
       "      <td>119561600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>114.352501</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>114.007500</td>\n",
       "      <td>115.562500</td>\n",
       "      <td>115.562500</td>\n",
       "      <td>105633600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>115.982498</td>\n",
       "      <td>117.162498</td>\n",
       "      <td>115.610001</td>\n",
       "      <td>115.707497</td>\n",
       "      <td>115.707497</td>\n",
       "      <td>145538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>115.750000</td>\n",
       "      <td>118.392502</td>\n",
       "      <td>115.732498</td>\n",
       "      <td>118.275002</td>\n",
       "      <td>118.275002</td>\n",
       "      <td>126907200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>119.262497</td>\n",
       "      <td>124.867500</td>\n",
       "      <td>119.250000</td>\n",
       "      <td>124.370003</td>\n",
       "      <td>124.370003</td>\n",
       "      <td>338054800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>128.697495</td>\n",
       "      <td>128.785004</td>\n",
       "      <td>123.937500</td>\n",
       "      <td>125.857498</td>\n",
       "      <td>125.857498</td>\n",
       "      <td>345937600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>124.697502</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>123.052498</td>\n",
       "      <td>124.824997</td>\n",
       "      <td>124.824997</td>\n",
       "      <td>211495600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>126.180000</td>\n",
       "      <td>126.992500</td>\n",
       "      <td>125.082497</td>\n",
       "      <td>126.522499</td>\n",
       "      <td>126.522499</td>\n",
       "      <td>163022400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>127.142502</td>\n",
       "      <td>127.485001</td>\n",
       "      <td>123.832497</td>\n",
       "      <td>125.010002</td>\n",
       "      <td>125.010002</td>\n",
       "      <td>155552400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-07-31  102.885002  106.415001  100.824997  106.260002  106.068756   \n",
       "2020-08-03  108.199997  111.637497  107.892502  108.937500  108.741440   \n",
       "2020-08-04  109.132500  110.790001  108.387497  109.665001  109.467628   \n",
       "2020-08-05  109.377502  110.392502  108.897499  110.062500  109.864410   \n",
       "2020-08-06  110.404999  114.412498  109.797501  113.902496  113.697502   \n",
       "2020-08-07  113.205002  113.675003  110.292503  111.112503  111.112503   \n",
       "2020-08-10  112.599998  113.775002  110.000000  112.727501  112.727501   \n",
       "2020-08-11  111.970001  112.482498  109.107498  109.375000  109.375000   \n",
       "2020-08-12  110.497498  113.275002  110.297501  113.010002  113.010002   \n",
       "2020-08-13  114.430000  116.042503  113.927498  115.010002  115.010002   \n",
       "2020-08-14  114.830002  115.000000  113.044998  114.907501  114.907501   \n",
       "2020-08-17  116.062500  116.087502  113.962502  114.607498  114.607498   \n",
       "2020-08-18  114.352501  116.000000  114.007500  115.562500  115.562500   \n",
       "2020-08-19  115.982498  117.162498  115.610001  115.707497  115.707497   \n",
       "2020-08-20  115.750000  118.392502  115.732498  118.275002  118.275002   \n",
       "2020-08-21  119.262497  124.867500  119.250000  124.370003  124.370003   \n",
       "2020-08-24  128.697495  128.785004  123.937500  125.857498  125.857498   \n",
       "2020-08-25  124.697502  125.180000  123.052498  124.824997  124.824997   \n",
       "2020-08-26  126.180000  126.992500  125.082497  126.522499  126.522499   \n",
       "2020-08-27  127.142502  127.485001  123.832497  125.010002  125.010002   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2020-07-31  374336800  \n",
       "2020-08-03  308151200  \n",
       "2020-08-04  173071600  \n",
       "2020-08-05  121992000  \n",
       "2020-08-06  202428800  \n",
       "2020-08-07  198045600  \n",
       "2020-08-10  212403600  \n",
       "2020-08-11  187902400  \n",
       "2020-08-12  165944800  \n",
       "2020-08-13  210082000  \n",
       "2020-08-14  165565200  \n",
       "2020-08-17  119561600  \n",
       "2020-08-18  105633600  \n",
       "2020-08-19  145538000  \n",
       "2020-08-20  126907200  \n",
       "2020-08-21  338054800  \n",
       "2020-08-24  345937600  \n",
       "2020-08-25  211495600  \n",
       "2020-08-26  163022400  \n",
       "2020-08-27  155552400  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[cond].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d4bd5fc0",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2020-10-16</th>\n",
       "      <td>121.279999</td>\n",
       "      <td>121.550003</td>\n",
       "      <td>118.809998</td>\n",
       "      <td>119.019997</td>\n",
       "      <td>119.019997</td>\n",
       "      <td>115393800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>119.959999</td>\n",
       "      <td>120.419998</td>\n",
       "      <td>115.660004</td>\n",
       "      <td>115.980003</td>\n",
       "      <td>115.980003</td>\n",
       "      <td>120639300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>116.199997</td>\n",
       "      <td>118.980003</td>\n",
       "      <td>115.629997</td>\n",
       "      <td>117.510002</td>\n",
       "      <td>117.510002</td>\n",
       "      <td>124423700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-10-16  121.279999  121.550003  118.809998  119.019997  119.019997   \n",
       "2020-10-19  119.959999  120.419998  115.660004  115.980003  115.980003   \n",
       "2020-10-20  116.199997  118.980003  115.629997  117.510002  117.510002   \n",
       "2020-10-22  117.449997  118.040001  114.589996  115.750000  115.750000   \n",
       "2020-10-26  114.010002  116.550003  112.879997  115.050003  115.050003   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2020-10-16  115393800  \n",
       "2020-10-19  120639300  \n",
       "2020-10-20  124423700  \n",
       "2020-10-22  101988000  \n",
       "2020-10-26  111850700  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[cond].tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fcccd2",
   "metadata": {},
   "source": [
    "## 4.3 query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f866b899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2020-07-31</th>\n",
       "      <td>102.885002</td>\n",
       "      <td>106.415001</td>\n",
       "      <td>100.824997</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>106.068756</td>\n",
       "      <td>374336800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>111.637497</td>\n",
       "      <td>107.892502</td>\n",
       "      <td>108.937500</td>\n",
       "      <td>108.741440</td>\n",
       "      <td>308151200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>109.132500</td>\n",
       "      <td>110.790001</td>\n",
       "      <td>108.387497</td>\n",
       "      <td>109.665001</td>\n",
       "      <td>109.467628</td>\n",
       "      <td>173071600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>109.377502</td>\n",
       "      <td>110.392502</td>\n",
       "      <td>108.897499</td>\n",
       "      <td>110.062500</td>\n",
       "      <td>109.864410</td>\n",
       "      <td>121992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>110.404999</td>\n",
       "      <td>114.412498</td>\n",
       "      <td>109.797501</td>\n",
       "      <td>113.902496</td>\n",
       "      <td>113.697502</td>\n",
       "      <td>202428800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-07</th>\n",
       "      <td>113.205002</td>\n",
       "      <td>113.675003</td>\n",
       "      <td>110.292503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>111.112503</td>\n",
       "      <td>198045600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-10</th>\n",
       "      <td>112.599998</td>\n",
       "      <td>113.775002</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>112.727501</td>\n",
       "      <td>212403600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-11</th>\n",
       "      <td>111.970001</td>\n",
       "      <td>112.482498</td>\n",
       "      <td>109.107498</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>109.375000</td>\n",
       "      <td>187902400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-12</th>\n",
       "      <td>110.497498</td>\n",
       "      <td>113.275002</td>\n",
       "      <td>110.297501</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>113.010002</td>\n",
       "      <td>165944800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-13</th>\n",
       "      <td>114.430000</td>\n",
       "      <td>116.042503</td>\n",
       "      <td>113.927498</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>210082000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-07-31  102.885002  106.415001  100.824997  106.260002  106.068756   \n",
       "2020-08-03  108.199997  111.637497  107.892502  108.937500  108.741440   \n",
       "2020-08-04  109.132500  110.790001  108.387497  109.665001  109.467628   \n",
       "2020-08-05  109.377502  110.392502  108.897499  110.062500  109.864410   \n",
       "2020-08-06  110.404999  114.412498  109.797501  113.902496  113.697502   \n",
       "2020-08-07  113.205002  113.675003  110.292503  111.112503  111.112503   \n",
       "2020-08-10  112.599998  113.775002  110.000000  112.727501  112.727501   \n",
       "2020-08-11  111.970001  112.482498  109.107498  109.375000  109.375000   \n",
       "2020-08-12  110.497498  113.275002  110.297501  113.010002  113.010002   \n",
       "2020-08-13  114.430000  116.042503  113.927498  115.010002  115.010002   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "2020-07-31  374336800  \n",
       "2020-08-03  308151200  \n",
       "2020-08-04  173071600  \n",
       "2020-08-05  121992000  \n",
       "2020-08-06  202428800  \n",
       "2020-08-07  198045600  \n",
       "2020-08-10  212403600  \n",
       "2020-08-11  187902400  \n",
       "2020-08-12  165944800  \n",
       "2020-08-13  210082000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[cond].query(\"Open > 100 and Volume > 110000000\").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932bccc7",
   "metadata": {},
   "source": [
    "# 5. New columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023d022d",
   "metadata": {},
   "source": [
    "## 5.1 Density Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "83ebf03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>149995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  149995"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "population = pd.Series({'California': 38332521,\n",
    "                   'Texas': 26448193,\n",
    "                   'New York': 19651127,\n",
    "                   'Florida': 19552860,\n",
    "                   'Illinois': 12882135}\n",
    ")\n",
    "\n",
    "area = pd.Series({'California': 423967, \n",
    "             'Texas': 695662, \n",
    "             'New York': 141297,\n",
    "             'Florida': 170312, \n",
    "             'Illinois': 149995})\n",
    "\n",
    "states = pd.DataFrame(  {'population': population,'area': area} )\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "278f4672",
   "metadata": {},
   "outputs": [],
   "source": [
    "states[\"density\"] = states[\"population\"] / states[\"area\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "005b3ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>149995</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area     density\n",
       "California    38332521  423967   90.413926\n",
       "Texas         26448193  695662   38.018740\n",
       "New York      19651127  141297  139.076746\n",
       "Florida       19552860  170312  114.806121\n",
       "Illinois      12882135  149995   85.883763"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8526db19",
   "metadata": {},
   "source": [
    "## 5.2 Stocks example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "59008d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_proper_index[\"Percent Changes\"] = aapl_proper_index[\"Close\"].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3115a2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Percent Changes</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-28</th>\n",
       "      <td>61.855000</td>\n",
       "      <td>62.312500</td>\n",
       "      <td>61.680000</td>\n",
       "      <td>62.262501</td>\n",
       "      <td>61.650810</td>\n",
       "      <td>96572800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>62.242500</td>\n",
       "      <td>62.437500</td>\n",
       "      <td>60.642502</td>\n",
       "      <td>60.822498</td>\n",
       "      <td>60.224953</td>\n",
       "      <td>142839600</td>\n",
       "      <td>-0.023128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>61.189999</td>\n",
       "      <td>61.325001</td>\n",
       "      <td>60.302502</td>\n",
       "      <td>60.814999</td>\n",
       "      <td>60.217525</td>\n",
       "      <td>124522000</td>\n",
       "      <td>-0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>61.810001</td>\n",
       "      <td>62.292500</td>\n",
       "      <td>59.314999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>61.579021</td>\n",
       "      <td>139162000</td>\n",
       "      <td>0.022610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62.384998</td>\n",
       "      <td>63.982498</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>63.955002</td>\n",
       "      <td>63.326683</td>\n",
       "      <td>151125200</td>\n",
       "      <td>0.028381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>116.669998</td>\n",
       "      <td>118.709999</td>\n",
       "      <td>116.449997</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>89946000</td>\n",
       "      <td>-0.005446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000</td>\n",
       "      <td>-0.009583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>116.389999</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>114.279999</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>82572600</td>\n",
       "      <td>-0.006134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>115.489998</td>\n",
       "      <td>117.279999</td>\n",
       "      <td>114.540001</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>91927700</td>\n",
       "      <td>0.013472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-10-28   61.855000   62.312500   61.680000   62.262501   61.650810   \n",
       "2019-10-29   62.242500   62.437500   60.642502   60.822498   60.224953   \n",
       "2019-10-30   61.189999   61.325001   60.302502   60.814999   60.217525   \n",
       "2019-10-31   61.810001   62.292500   59.314999   62.189999   61.579021   \n",
       "2019-11-01   62.384998   63.982498   62.290001   63.955002   63.326683   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-10-21  116.669998  118.709999  116.449997  116.870003  116.870003   \n",
       "2020-10-22  117.449997  118.040001  114.589996  115.750000  115.750000   \n",
       "2020-10-23  116.389999  116.550003  114.279999  115.040001  115.040001   \n",
       "2020-10-26  114.010002  116.550003  112.879997  115.050003  115.050003   \n",
       "2020-10-27  115.489998  117.279999  114.540001  116.599998  116.599998   \n",
       "\n",
       "               Volume  Percent Changes  \n",
       "Date                                    \n",
       "2019-10-28   96572800              NaN  \n",
       "2019-10-29  142839600        -0.023128  \n",
       "2019-10-30  124522000        -0.000123  \n",
       "2019-10-31  139162000         0.022610  \n",
       "2019-11-01  151125200         0.028381  \n",
       "...               ...              ...  \n",
       "2020-10-21   89946000        -0.005446  \n",
       "2020-10-22  101988000        -0.009583  \n",
       "2020-10-23   82572600        -0.006134  \n",
       "2020-10-26  111850700         0.000087  \n",
       "2020-10-27   91927700         0.013472  \n",
       "\n",
       "[253 rows x 7 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3279df01",
   "metadata": {},
   "source": [
    "# 6. Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dec0caa",
   "metadata": {},
   "source": [
    "## 6.1 Basic operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "876e6b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0028956964634767705"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[\"Percent Changes\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "27c153e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11980826040056836"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[\"Percent Changes\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "23e031ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12864694751232164"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[\"Percent Changes\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "92849318",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0024045071214521263"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[\"Percent Changes\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e6f50326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01985402460478214"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[aapl_proper_index[\"Percent Changes\"] > 0][\"Percent Changes\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a3d0e96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.01881547236798305"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_proper_index[aapl_proper_index[\"Percent Changes\"] < 0][\"Percent Changes\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7781d1",
   "metadata": {},
   "source": [
    "## 6.2 Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ec7f60",
   "metadata": {},
   "source": [
    "We use planets discovery data as an example for grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "26b8b374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 6)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "planets = sns.load_dataset('planets')\n",
    "planets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3c8426a5",
   "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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300000</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774000</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</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>1030</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.941507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.00</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.191524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.125083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.187757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1035 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  number  orbital_period   mass  distance  year\n",
       "0     Radial Velocity       1      269.300000   7.10     77.40  2006\n",
       "1     Radial Velocity       1      874.774000   2.21     56.95  2008\n",
       "2     Radial Velocity       1      763.000000   2.60     19.84  2011\n",
       "3     Radial Velocity       1      326.030000  19.40    110.62  2007\n",
       "4     Radial Velocity       1      516.220000  10.50    119.47  2009\n",
       "...               ...     ...             ...    ...       ...   ...\n",
       "1030          Transit       1        3.941507    NaN    172.00  2006\n",
       "1031          Transit       1        2.615864    NaN    148.00  2007\n",
       "1032          Transit       1        3.191524    NaN    174.00  2007\n",
       "1033          Transit       1        4.125083    NaN    293.00  2008\n",
       "1034          Transit       1        4.187757    NaN    260.00  2008\n",
       "\n",
       "[1035 rows x 6 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9872086c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         631.180000\n",
       "Eclipse Timing Variations         4343.500000\n",
       "Imaging                          27500.000000\n",
       "Microlensing                      3300.000000\n",
       "Orbital Brightness Modulation        0.342887\n",
       "Pulsar Timing                       66.541900\n",
       "Pulsation Timing Variations       1170.000000\n",
       "Radial Velocity                    360.200000\n",
       "Transit                              5.714932\n",
       "Transit Timing Variations           57.011000\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8399a771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</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>Astrometry</th>\n",
       "      <td>2.0</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>544.217663</td>\n",
       "      <td>246.360000</td>\n",
       "      <td>438.770000</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>823.590000</td>\n",
       "      <td>1016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4751.644444</td>\n",
       "      <td>2499.130945</td>\n",
       "      <td>1916.250000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>4343.500000</td>\n",
       "      <td>5767.000000</td>\n",
       "      <td>10220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>12.0</td>\n",
       "      <td>118247.737500</td>\n",
       "      <td>213978.177277</td>\n",
       "      <td>4639.150000</td>\n",
       "      <td>8343.900000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>94250.000000</td>\n",
       "      <td>730000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3153.571429</td>\n",
       "      <td>1113.166333</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>0.725493</td>\n",
       "      <td>0.240104</td>\n",
       "      <td>0.291496</td>\n",
       "      <td>0.342887</td>\n",
       "      <td>0.943908</td>\n",
       "      <td>1.544929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7343.021201</td>\n",
       "      <td>16313.265573</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>25.262000</td>\n",
       "      <td>66.541900</td>\n",
       "      <td>98.211400</td>\n",
       "      <td>36525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>553.0</td>\n",
       "      <td>823.354680</td>\n",
       "      <td>1454.926210</td>\n",
       "      <td>0.736540</td>\n",
       "      <td>38.021000</td>\n",
       "      <td>360.200000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>17337.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>397.0</td>\n",
       "      <td>21.102073</td>\n",
       "      <td>46.185893</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>3.160630</td>\n",
       "      <td>5.714932</td>\n",
       "      <td>16.145700</td>\n",
       "      <td>331.600590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>3.0</td>\n",
       "      <td>79.783500</td>\n",
       "      <td>71.599884</td>\n",
       "      <td>22.339500</td>\n",
       "      <td>39.675250</td>\n",
       "      <td>57.011000</td>\n",
       "      <td>108.505500</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               count           mean            std  \\\n",
       "method                                                               \n",
       "Astrometry                       2.0     631.180000     544.217663   \n",
       "Eclipse Timing Variations        9.0    4751.644444    2499.130945   \n",
       "Imaging                         12.0  118247.737500  213978.177277   \n",
       "Microlensing                     7.0    3153.571429    1113.166333   \n",
       "Orbital Brightness Modulation    3.0       0.709307       0.725493   \n",
       "Pulsar Timing                    5.0    7343.021201   16313.265573   \n",
       "Pulsation Timing Variations      1.0    1170.000000            NaN   \n",
       "Radial Velocity                553.0     823.354680    1454.926210   \n",
       "Transit                        397.0      21.102073      46.185893   \n",
       "Transit Timing Variations        3.0      79.783500      71.599884   \n",
       "\n",
       "                                       min          25%           50%  \\\n",
       "method                                                                  \n",
       "Astrometry                      246.360000   438.770000    631.180000   \n",
       "Eclipse Timing Variations      1916.250000  2900.000000   4343.500000   \n",
       "Imaging                        4639.150000  8343.900000  27500.000000   \n",
       "Microlensing                   1825.000000  2375.000000   3300.000000   \n",
       "Orbital Brightness Modulation     0.240104     0.291496      0.342887   \n",
       "Pulsar Timing                     0.090706    25.262000     66.541900   \n",
       "Pulsation Timing Variations    1170.000000  1170.000000   1170.000000   \n",
       "Radial Velocity                   0.736540    38.021000    360.200000   \n",
       "Transit                           0.355000     3.160630      5.714932   \n",
       "Transit Timing Variations        22.339500    39.675250     57.011000   \n",
       "\n",
       "                                        75%            max  \n",
       "method                                                      \n",
       "Astrometry                       823.590000    1016.000000  \n",
       "Eclipse Timing Variations       5767.000000   10220.000000  \n",
       "Imaging                        94250.000000  730000.000000  \n",
       "Microlensing                    3550.000000    5100.000000  \n",
       "Orbital Brightness Modulation      0.943908       1.544929  \n",
       "Pulsar Timing                     98.211400   36525.000000  \n",
       "Pulsation Timing Variations     1170.000000    1170.000000  \n",
       "Radial Velocity                  982.000000   17337.500000  \n",
       "Transit                           16.145700     331.600590  \n",
       "Transit Timing Variations        108.505500     160.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4cd98382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         2\n",
       "Eclipse Timing Variations          9\n",
       "Imaging                           38\n",
       "Microlensing                      23\n",
       "Orbital Brightness Modulation      3\n",
       "Pulsar Timing                      5\n",
       "Pulsation Timing Variations        1\n",
       "Radial Velocity                  553\n",
       "Transit                          397\n",
       "Transit Timing Variations          4\n",
       "Name: number, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')[\"number\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1430d995",
   "metadata": {},
   "source": [
    "# 7. Joining Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3abca64",
   "metadata": {},
   "source": [
    "## 7.1 Merge (or join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "de4cab14",
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "department = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
    "\n",
    "department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6301c1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hire_date = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_date': [2004, 2008, 2012, 2014]})\n",
    "\n",
    "hire_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7a4f923b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = pd.merge(department, hire_date)\n",
    "employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0bab79da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = pd.merge(department, hire_date, on=\"employee\")\n",
    "employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "29d1d5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  salary\n",
       "0   Bob   70000\n",
       "1  Jake   80000\n",
       "2  Lisa  120000\n",
       "3   Sue   90000"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'salary': [70000, 80000, 120000, 90000]})\n",
    "salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "200d57d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  name  salary\n",
       "0      Bob   Accounting   Bob   70000\n",
       "1     Jake  Engineering  Jake   80000\n",
       "2     Lisa  Engineering  Lisa  120000\n",
       "3      Sue           HR   Sue   90000"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = pd.merge(department, salary, left_on=\"employee\", right_on=\"name\")\n",
    "employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cd98a441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
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       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  salary\n",
       "0      Bob   Accounting   70000\n",
       "1     Jake  Engineering   80000\n",
       "2     Lisa  Engineering  120000\n",
       "3      Sue           HR   90000"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = employee.drop('name',axis=1)\n",
    "employee"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3762163c",
   "metadata": {},
   "source": [
    "## 7.2 one to many merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b1aa1315",
   "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>group</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HR</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group supervisor\n",
       "0   Accounting      Carly\n",
       "1  Engineering      Guido\n",
       "2           HR      Steve"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supervisor = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n",
    "                    'supervisor': ['Carly', 'Guido', 'Steve']})\n",
    "supervisor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b01c9052",
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>salary</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>70000</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  salary supervisor\n",
       "0      Bob   Accounting   70000      Carly\n",
       "1     Jake  Engineering   80000      Guido\n",
       "2     Lisa  Engineering  120000      Guido\n",
       "3      Sue           HR   90000      Steve"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(employee,supervisor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211fc106",
   "metadata": {},
   "source": [
    "# 7.3 Many to Many merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "87fb2052",
   "metadata": {},
   "outputs": [],
   "source": [
    "skills = pd.DataFrame({'group': ['Accounting', 'Accounting','Engineering', \n",
    "                              'Engineering', 'HR', 'HR'],\n",
    "                    'skills': ['math', 'spreadsheets', 'coding', \n",
    "                               'linux','spreadsheets', 'organization']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e1d7ebc4",
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>salary</th>\n",
       "      <th>skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>70000</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>70000</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "      <td>organization</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  salary        skills\n",
       "0      Bob   Accounting   70000          math\n",
       "1      Bob   Accounting   70000  spreadsheets\n",
       "2     Jake  Engineering   80000        coding\n",
       "3     Jake  Engineering   80000         linux\n",
       "4     Lisa  Engineering  120000        coding\n",
       "5     Lisa  Engineering  120000         linux\n",
       "6      Sue           HR   90000  spreadsheets\n",
       "7      Sue           HR   90000  organization"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(employee,skills)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec89a9e8",
   "metadata": {},
   "source": [
    "It's a very strange set of data. Make sure you know how to use it for many-to-many merging"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d976a18",
   "metadata": {},
   "source": [
    "## 7.4 Inner Join / Outer Join / Left Join / Right Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1c7edc09",
   "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>name</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name   food\n",
       "0  Peter   fish\n",
       "1   Paul  beans\n",
       "2   Mary  bread"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_food = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n",
    "                    'food': ['fish', 'beans', 'bread']},\n",
    "                   columns=['name', 'food'])\n",
    "fav_food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "74aa124b",
   "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>name</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name drink\n",
       "0    Mary  wine\n",
       "1  Joseph  beer"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_drink = pd.DataFrame({'name': ['Mary', 'Joseph'],\n",
    "                    'drink': ['wine', 'beer']},\n",
    "                   columns=['name', 'drink'])\n",
    "fav_drink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "48886a2f",
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name   food drink\n",
       "0  Mary  bread  wine"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(fav_food, fav_drink)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d5e4c0e9",
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name   food drink\n",
       "0   Peter   fish   NaN\n",
       "1    Paul  beans   NaN\n",
       "2    Mary  bread  wine\n",
       "3  Joseph    NaN  beer"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(fav_food, fav_drink, how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "84ab9843",
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name   food drink\n",
       "0  Peter   fish   NaN\n",
       "1   Paul  beans   NaN\n",
       "2   Mary  bread  wine"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(fav_food, fav_drink, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3d11be90",
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name   food drink\n",
       "0    Mary  bread  wine\n",
       "1  Joseph    NaN  beer"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(fav_food, fav_drink, how=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e16b5b",
   "metadata": {},
   "source": [
    "# 8. Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e782f9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "hibor = pd.read_csv(\"hibor.csv\", parse_dates=True, index_col='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "04401df8",
   "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>overnight</th>\n",
       "      <th>1 week</th>\n",
       "      <th>2 weeks</th>\n",
       "      <th>1 months</th>\n",
       "      <th>2 months</th>\n",
       "      <th>3 months</th>\n",
       "      <th>6 months</th>\n",
       "      <th>12 months</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.29929</td>\n",
       "      <td>0.68929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.66857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight   1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                    \n",
       "2010-01-01        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-02        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-03        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-04       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-05       0.03  0.04971  0.05000   0.07964   0.11000   0.15000   \n",
       "2010-01-06       0.03  0.04900  0.04971   0.08000   0.11000   0.14000   \n",
       "2010-01-07       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-08       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-09        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-10        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-11       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-01       NaN        NaN  \n",
       "2010-01-02       NaN        NaN  \n",
       "2010-01-03       NaN        NaN  \n",
       "2010-01-04   0.31571    0.71429  \n",
       "2010-01-05   0.29929    0.68929  \n",
       "2010-01-06   0.28000    0.66857  \n",
       "2010-01-07   0.26000    0.62857  \n",
       "2010-01-08   0.26000    0.62857  \n",
       "2010-01-09       NaN        NaN  \n",
       "2010-01-10       NaN        NaN  \n",
       "2010-01-11   0.24000    0.57000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a880cff4",
   "metadata": {},
   "source": [
    "## 8.1 Check missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6fc86cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>overnight</th>\n",
       "      <th>1 week</th>\n",
       "      <th>2 weeks</th>\n",
       "      <th>1 months</th>\n",
       "      <th>2 months</th>\n",
       "      <th>3 months</th>\n",
       "      <th>6 months</th>\n",
       "      <th>12 months</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight  1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                   \n",
       "2010-01-01       True    True     True      True      True      True   \n",
       "2010-01-02       True    True     True      True      True      True   \n",
       "2010-01-03       True    True     True      True      True      True   \n",
       "2010-01-04      False   False    False     False     False     False   \n",
       "2010-01-05      False   False    False     False     False     False   \n",
       "2010-01-06      False   False    False     False     False     False   \n",
       "2010-01-07      False   False    False     False     False     False   \n",
       "2010-01-08      False   False    False     False     False     False   \n",
       "2010-01-09       True    True     True      True      True      True   \n",
       "2010-01-10       True    True     True      True      True      True   \n",
       "2010-01-11      False   False    False     False     False     False   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-01      True       True  \n",
       "2010-01-02      True       True  \n",
       "2010-01-03      True       True  \n",
       "2010-01-04     False      False  \n",
       "2010-01-05     False      False  \n",
       "2010-01-06     False      False  \n",
       "2010-01-07     False      False  \n",
       "2010-01-08     False      False  \n",
       "2010-01-09      True       True  \n",
       "2010-01-10      True       True  \n",
       "2010-01-11     False      False  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "84d0ac6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fc5a7223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor[\"overnight\"].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "87d7e12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    6\n",
       "True     5\n",
       "Name: overnight, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor[\"overnight\"].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d3acd78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-01-01   NaN\n",
       "2010-01-02   NaN\n",
       "2010-01-03   NaN\n",
       "2010-01-09   NaN\n",
       "2010-01-10   NaN\n",
       "Name: overnight, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor[\"overnight\"][hibor[\"overnight\"].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27237840",
   "metadata": {},
   "source": [
    "## 8.2 Drop Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f550b706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overnight</th>\n",
       "      <th>1 week</th>\n",
       "      <th>2 weeks</th>\n",
       "      <th>1 months</th>\n",
       "      <th>2 months</th>\n",
       "      <th>3 months</th>\n",
       "      <th>6 months</th>\n",
       "      <th>12 months</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.29929</td>\n",
       "      <td>0.68929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.66857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight   1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                    \n",
       "2010-01-04       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-05       0.03  0.04971  0.05000   0.07964   0.11000   0.15000   \n",
       "2010-01-06       0.03  0.04900  0.04971   0.08000   0.11000   0.14000   \n",
       "2010-01-07       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-08       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-11       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-04   0.31571    0.71429  \n",
       "2010-01-05   0.29929    0.68929  \n",
       "2010-01-06   0.28000    0.66857  \n",
       "2010-01-07   0.26000    0.62857  \n",
       "2010-01-08   0.26000    0.62857  \n",
       "2010-01-11   0.24000    0.57000  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0d7ba1",
   "metadata": {},
   "source": [
    "## 8.3 Fill with specific values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4033679b",
   "metadata": {},
   "source": [
    "Notes: Just show as an example. Does not make sense in this scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0ac6a858",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overnight</th>\n",
       "      <th>1 week</th>\n",
       "      <th>2 weeks</th>\n",
       "      <th>1 months</th>\n",
       "      <th>2 months</th>\n",
       "      <th>3 months</th>\n",
       "      <th>6 months</th>\n",
       "      <th>12 months</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.29929</td>\n",
       "      <td>0.68929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.66857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight   1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                    \n",
       "2010-01-01       0.00  0.00000  0.00000   0.00000   0.00000   0.00000   \n",
       "2010-01-02       0.00  0.00000  0.00000   0.00000   0.00000   0.00000   \n",
       "2010-01-03       0.00  0.00000  0.00000   0.00000   0.00000   0.00000   \n",
       "2010-01-04       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-05       0.03  0.04971  0.05000   0.07964   0.11000   0.15000   \n",
       "2010-01-06       0.03  0.04900  0.04971   0.08000   0.11000   0.14000   \n",
       "2010-01-07       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-08       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-09       0.00  0.00000  0.00000   0.00000   0.00000   0.00000   \n",
       "2010-01-10       0.00  0.00000  0.00000   0.00000   0.00000   0.00000   \n",
       "2010-01-11       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-01   0.00000    0.00000  \n",
       "2010-01-02   0.00000    0.00000  \n",
       "2010-01-03   0.00000    0.00000  \n",
       "2010-01-04   0.31571    0.71429  \n",
       "2010-01-05   0.29929    0.68929  \n",
       "2010-01-06   0.28000    0.66857  \n",
       "2010-01-07   0.26000    0.62857  \n",
       "2010-01-08   0.26000    0.62857  \n",
       "2010-01-09   0.00000    0.00000  \n",
       "2010-01-10   0.00000    0.00000  \n",
       "2010-01-11   0.24000    0.57000  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03e428d",
   "metadata": {},
   "source": [
    "## 8.4 Fill with previous values (i.e. forward fill)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0576a4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overnight</th>\n",
       "      <th>1 week</th>\n",
       "      <th>2 weeks</th>\n",
       "      <th>1 months</th>\n",
       "      <th>2 months</th>\n",
       "      <th>3 months</th>\n",
       "      <th>6 months</th>\n",
       "      <th>12 months</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",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.29929</td>\n",
       "      <td>0.68929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.66857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight   1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                    \n",
       "2010-01-01        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-02        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-03        NaN      NaN      NaN       NaN       NaN       NaN   \n",
       "2010-01-04       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-05       0.03  0.04971  0.05000   0.07964   0.11000   0.15000   \n",
       "2010-01-06       0.03  0.04900  0.04971   0.08000   0.11000   0.14000   \n",
       "2010-01-07       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-08       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-09       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-10       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-11       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-01       NaN        NaN  \n",
       "2010-01-02       NaN        NaN  \n",
       "2010-01-03       NaN        NaN  \n",
       "2010-01-04   0.31571    0.71429  \n",
       "2010-01-05   0.29929    0.68929  \n",
       "2010-01-06   0.28000    0.66857  \n",
       "2010-01-07   0.26000    0.62857  \n",
       "2010-01-08   0.26000    0.62857  \n",
       "2010-01-09   0.26000    0.62857  \n",
       "2010-01-10   0.26000    0.62857  \n",
       "2010-01-11   0.24000    0.57000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d916df12",
   "metadata": {},
   "source": [
    "## 8.5 Fill with next values (i.e. back fill)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff4fd55",
   "metadata": {},
   "source": [
    "Remark: may not make sense in this example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "19fdac25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>3 months</th>\n",
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       "    <tr>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11893</td>\n",
       "      <td>0.15679</td>\n",
       "      <td>0.31571</td>\n",
       "      <td>0.71429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07964</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.29929</td>\n",
       "      <td>0.68929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.66857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06964</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.62857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.06036</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.57000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            overnight   1 week  2 weeks  1 months  2 months  3 months  \\\n",
       "date                                                                    \n",
       "2010-01-01       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-02       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-03       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-04       0.03  0.04971  0.05000   0.07964   0.11893   0.15679   \n",
       "2010-01-05       0.03  0.04971  0.05000   0.07964   0.11000   0.15000   \n",
       "2010-01-06       0.03  0.04900  0.04971   0.08000   0.11000   0.14000   \n",
       "2010-01-07       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-08       0.03  0.04971  0.04971   0.06964   0.10000   0.13000   \n",
       "2010-01-09       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "2010-01-10       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "2010-01-11       0.03  0.04971  0.04971   0.06036   0.09000   0.12000   \n",
       "\n",
       "            6 months  12 months  \n",
       "date                             \n",
       "2010-01-01   0.31571    0.71429  \n",
       "2010-01-02   0.31571    0.71429  \n",
       "2010-01-03   0.31571    0.71429  \n",
       "2010-01-04   0.31571    0.71429  \n",
       "2010-01-05   0.29929    0.68929  \n",
       "2010-01-06   0.28000    0.66857  \n",
       "2010-01-07   0.26000    0.62857  \n",
       "2010-01-08   0.26000    0.62857  \n",
       "2010-01-09   0.24000    0.57000  \n",
       "2010-01-10   0.24000    0.57000  \n",
       "2010-01-11   0.24000    0.57000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hibor.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785c9ec4",
   "metadata": {},
   "source": [
    "# 9. Export CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd978fcf",
   "metadata": {},
   "source": [
    "Export dataframe to a csv. Remember don't override the original file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3e53c244",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_proper_index.to_csv(\"AAPL_new.csv\")"
   ]
  }
 ],
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