Advanced Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container {width:90% !important;}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import pytz\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container {width:90% !important;}</style>\"))\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/happiness_with_continent.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/invoices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Meal Price</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 07:00:00+02:00</td>\n",
       "      <td>['David Bishop']</td>\n",
       "      <td>469.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 20:00:00+02:00</td>\n",
       "      <td>['David Bishop']</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 14:00:00+02:00</td>\n",
       "      <td>['Karen Stansell']</td>\n",
       "      <td>314.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-07 21:00:00+02:00</td>\n",
       "      <td>['Addie Patino']</td>\n",
       "      <td>438.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6YLROQT27B6HRF4E</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2015-07-27 14:00:00+02:00</td>\n",
       "      <td>['Addie Patino' 'Susan Guerrero']</td>\n",
       "      <td>690.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Order Id        Date           Meal Id        Company Id  \\\n",
       "0  839FKFW2LLX4LMBB  2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1  97OX39BGVMHODLJM  2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2  041ORQM5OIHTIU6L  2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "3  YT796QI18WNGZ7ZJ  2014-04-12  C9SDFHF7553BE247  LJKS5NK6788CYMUU   \n",
       "4  6YLROQT27B6HRF4E  2015-07-28  48EQXS6IHYNZDDZ5  LJKS5NK6788CYMUU   \n",
       "\n",
       "                Date of Meal                       Participants  Meal Price  \\\n",
       "0  2016-05-31 07:00:00+02:00                   ['David Bishop']       469.0   \n",
       "1  2018-10-01 20:00:00+02:00                   ['David Bishop']        22.0   \n",
       "2  2014-08-23 14:00:00+02:00                 ['Karen Stansell']       314.0   \n",
       "3  2014-04-07 21:00:00+02:00                   ['Addie Patino']       438.0   \n",
       "4  2015-07-27 14:00:00+02:00  ['Addie Patino' 'Susan Guerrero']       690.0   \n",
       "\n",
       "  Type of Meal  Heroes Adjustment  \n",
       "0    Breakfast              False  \n",
       "1       Dinner              False  \n",
       "2        Lunch              False  \n",
       "3       Dinner              False  \n",
       "4        Lunch              False  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices['Type of Meal'] = invoices['Type of Meal'].astype('category')\n",
    "invoices['Date'] = invoices['Date'].astype('datetime64[ns]')\n",
    "invoices['Meal Price'] = invoices['Meal Price'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1978\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1979\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1980\u001b[0m             \u001b[0;31m# If tzaware, these values represent unix timestamps, so we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Array must be all same time zone",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-204-0d4d497ed08d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minvoices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date of Meal'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'datetime64[ns]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   5880\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5881\u001b[0m             new_data = self._data.astype(\n\u001b[0;32m-> 5882\u001b[0;31m                 \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5883\u001b[0m             )\n\u001b[1;32m   5884\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m    436\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign_copy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_astype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_astype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m                     \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m                     \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m                 \u001b[0;31m# TODO(extension)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    713\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_datetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_timedelta64_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_timedelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    713\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_datetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_timedelta64_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_timedelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    206\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m    792\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_and_box_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_array\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    795\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, box, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    461\u001b[0m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m             \u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m             \u001b[0mallow_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m         )\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1982\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1983\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1984\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1986\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[1;32m   1973\u001b[0m             \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1974\u001b[0m             \u001b[0myearfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1975\u001b[0;31m             \u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1976\u001b[0m         )\n\u001b[1;32m   1977\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True"
     ]
    }
   ],
   "source": [
    "invoices['Date of Meal'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Meal Price</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 07:00:00+02:00</td>\n",
       "      <td>['David Bishop']</td>\n",
       "      <td>469</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 20:00:00+02:00</td>\n",
       "      <td>['David Bishop']</td>\n",
       "      <td>22</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 14:00:00+02:00</td>\n",
       "      <td>['Karen Stansell']</td>\n",
       "      <td>314</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-07 21:00:00+02:00</td>\n",
       "      <td>['Addie Patino']</td>\n",
       "      <td>438</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6YLROQT27B6HRF4E</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2015-07-27 14:00:00+02:00</td>\n",
       "      <td>['Addie Patino' 'Susan Guerrero']</td>\n",
       "      <td>690</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Order Id       Date           Meal Id        Company Id  \\\n",
       "0  839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1  97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2  041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "3  YT796QI18WNGZ7ZJ 2014-04-12  C9SDFHF7553BE247  LJKS5NK6788CYMUU   \n",
       "4  6YLROQT27B6HRF4E 2015-07-28  48EQXS6IHYNZDDZ5  LJKS5NK6788CYMUU   \n",
       "\n",
       "                Date of Meal                       Participants  Meal Price  \\\n",
       "0  2016-05-31 07:00:00+02:00                   ['David Bishop']         469   \n",
       "1  2018-10-01 20:00:00+02:00                   ['David Bishop']          22   \n",
       "2  2014-08-23 14:00:00+02:00                 ['Karen Stansell']         314   \n",
       "3  2014-04-07 21:00:00+02:00                   ['Addie Patino']         438   \n",
       "4  2015-07-27 14:00:00+02:00  ['Addie Patino' 'Susan Guerrero']         690   \n",
       "\n",
       "  Type of Meal  Heroes Adjustment  \n",
       "0    Breakfast              False  \n",
       "1       Dinner              False  \n",
       "2        Lunch              False  \n",
       "3       Dinner              False  \n",
       "4        Lunch              False  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices.loc[45612,'Meal Price'] = 'I am causing trouble'\n",
    "invoices.loc[35612,'Meal Price'] = 'Me too'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'Me too'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-209-7a45f8f5d74c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minvoices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Meal Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   5880\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5881\u001b[0m             new_data = self._data.astype(\n\u001b[0;32m-> 5882\u001b[0;31m                 \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5883\u001b[0m             )\n\u001b[1;32m   5884\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m    436\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign_copy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_astype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_astype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m                     \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m                     \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m                 \u001b[0;31m# TODO(extension)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Me too'"
     ]
    }
   ],
   "source": [
    "invoices['Meal Price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'int'>    50015\n",
       "<class 'str'>        2\n",
       "Name: Meal Price, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Meal Price'].apply(lambda x: type(x)).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35612                  Me too\n",
       "45612    I am causing trouble\n",
       "Name: Meal Price, dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Meal Price'][invoices['Meal Price'].apply(\n",
    "  lambda x: isinstance(x,str)\n",
    ")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        469.0\n",
       "1         22.0\n",
       "2        314.0\n",
       "3        438.0\n",
       "4        690.0\n",
       "         ...  \n",
       "50012      9.0\n",
       "50013     25.0\n",
       "50014    160.0\n",
       "50015    497.0\n",
       "50016    365.0\n",
       "Name: Meal Price, Length: 50017, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(invoices['Meal Price'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        469\n",
       "1         22\n",
       "2        314\n",
       "3        438\n",
       "4        690\n",
       "        ... \n",
       "50012      9\n",
       "50013     25\n",
       "50014    160\n",
       "50015    497\n",
       "50016    365\n",
       "Name: Meal Price, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Meal Price'] = pd.to_numeric(invoices['Meal Price'],errors='coerce')\n",
    "invoices['Meal Price'] = invoices['Meal Price'].fillna(invoices['Meal Price'].median())\n",
    "invoices['Meal Price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n",
      "2019-08-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(pd.to_datetime('2019-8-1'))\n",
    "print(pd.to_datetime('2019/8/1'))\n",
    "print(pd.to_datetime('8/1/2019'))\n",
    "print(pd.to_datetime('Aug, 1 2019'))\n",
    "print(pd.to_datetime('Aug - 1 2019'))\n",
    "print(pd.to_datetime('August - 1 2019'))\n",
    "print(pd.to_datetime('2019, August - 1'))\n",
    "print(pd.to_datetime('20190108',format='%Y%d%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(pd.to_datetime('yolo 20190108',format='%Y%d%m', exact=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices['Date of Meal'] = pd.to_datetime(invoices['Date of Meal'],utc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## accessor - dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2016-05-31\n",
       "1        2018-10-01\n",
       "            ...    \n",
       "50015    2018-02-01\n",
       "50016    2017-09-09\n",
       "Name: Date of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Tuesday\n",
       "1          Monday\n",
       "           ...   \n",
       "50015    Thursday\n",
       "50016    Saturday\n",
       "Name: Date of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              May\n",
       "1          October\n",
       "           ...    \n",
       "50015     February\n",
       "50016    September\n",
       "Name: Date of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        31\n",
       "1        31\n",
       "         ..\n",
       "50015    28\n",
       "50016    30\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.days_in_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        31\n",
       "1         1\n",
       "         ..\n",
       "50015     1\n",
       "50016     9\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        22\n",
       "1        40\n",
       "         ..\n",
       "50015     5\n",
       "50016    36\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         5\n",
       "1        10\n",
       "         ..\n",
       "50015     2\n",
       "50016     9\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        4\n",
       "        ..\n",
       "50015    1\n",
       "50016    3\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2016\n",
       "1        2018\n",
       "         ... \n",
       "50015    2018\n",
       "50016    2017\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "         ...  \n",
       "50015    False\n",
       "50016    False\n",
       "Name: Date of Meal, Length: 50017, dtype: bool"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.is_month_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Meal Price</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>['David Bishop']</td>\n",
       "      <td>469.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2DDN2LHS7G85GKPQ</td>\n",
       "      <td>2014-04-29</td>\n",
       "      <td>1MKLAKBOE3SP7YUL</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-30 19:00:00+00:00</td>\n",
       "      <td>['Susan Guerrero' 'David Bishop']</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>49896</td>\n",
       "      <td>G3FX5EAE2VCUFELA</td>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>SZ1UUTPDNW3FCIFF</td>\n",
       "      <td>CZTLKWWDEHQ0GW0I</td>\n",
       "      <td>2016-02-29 07:00:00+00:00</td>\n",
       "      <td>['Olga Fortenberry']</td>\n",
       "      <td>288.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49929</td>\n",
       "      <td>HEGJLUD58BTP2CC3</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>C18IITWW7K615G21</td>\n",
       "      <td>DNAC0XNVYCD3J62R</td>\n",
       "      <td>2016-01-31 21:00:00+00:00</td>\n",
       "      <td>['Linda Torros']</td>\n",
       "      <td>331.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1573 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id       Date           Meal Id        Company Id  \\\n",
       "0      839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "6      2DDN2LHS7G85GKPQ 2014-04-29  1MKLAKBOE3SP7YUL  LJKS5NK6788CYMUU   \n",
       "...                 ...        ...               ...               ...   \n",
       "49896  G3FX5EAE2VCUFELA 2016-03-05  SZ1UUTPDNW3FCIFF  CZTLKWWDEHQ0GW0I   \n",
       "49929  HEGJLUD58BTP2CC3 2016-02-04  C18IITWW7K615G21  DNAC0XNVYCD3J62R   \n",
       "\n",
       "                   Date of Meal                       Participants  \\\n",
       "0     2016-05-31 05:00:00+00:00                   ['David Bishop']   \n",
       "6     2014-04-30 19:00:00+00:00  ['Susan Guerrero' 'David Bishop']   \n",
       "...                         ...                                ...   \n",
       "49896 2016-02-29 07:00:00+00:00               ['Olga Fortenberry']   \n",
       "49929 2016-01-31 21:00:00+00:00                   ['Linda Torros']   \n",
       "\n",
       "       Meal Price Type of Meal  Heroes Adjustment  \n",
       "0           469.0    Breakfast              False  \n",
       "6            14.0       Dinner              False  \n",
       "...           ...          ...                ...  \n",
       "49896       288.0    Breakfast              False  \n",
       "49929       331.0       Dinner              False  \n",
       "\n",
       "[1573 rows x 9 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices[invoices['Date of Meal'].dt.is_month_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.datetime(2016, 5, 31, 5, 0, tzinfo=<UTC>),\n",
       "       datetime.datetime(2018, 10, 1, 18, 0, tzinfo=<UTC>),\n",
       "       datetime.datetime(2014, 8, 23, 12, 0, tzinfo=<UTC>), ...,\n",
       "       datetime.datetime(2017, 9, 22, 19, 0, tzinfo=<UTC>),\n",
       "       datetime.datetime(2018, 2, 1, 20, 0, tzinfo=<UTC>),\n",
       "       datetime.datetime(2017, 9, 9, 12, 0, tzinfo=<UTC>)], dtype=object)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fabianbosler/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py:1269: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        2016-05-30/2016-06-05\n",
       "1        2018-10-01/2018-10-07\n",
       "                 ...          \n",
       "50015    2018-01-29/2018-02-04\n",
       "50016    2017-09-04/2017-09-10\n",
       "Name: Date of Meal, Length: 50017, dtype: period[W-SUN]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.to_period('W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "        ..\n",
       "50015    0\n",
       "50016    0\n",
       "Name: Date of Meal, Length: 50017, dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.minute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## accessor - str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        breakfast\n",
       "1           dinner\n",
       "2            lunch\n",
       "3           dinner\n",
       "4            lunch\n",
       "           ...    \n",
       "50012    breakfast\n",
       "50013    breakfast\n",
       "50014       dinner\n",
       "50015       dinner\n",
       "50016        lunch\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        BREAKFAST\n",
       "1           DINNER\n",
       "2            LUNCH\n",
       "3           DINNER\n",
       "4            LUNCH\n",
       "           ...    \n",
       "50012    BREAKFAST\n",
       "50013    BREAKFAST\n",
       "50014       DINNER\n",
       "50015       DINNER\n",
       "50016        LUNCH\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Breakfast      \n",
       "1        Dinner         \n",
       "2        Lunch          \n",
       "3        Dinner         \n",
       "4        Lunch          \n",
       "              ...       \n",
       "50012    Breakfast      \n",
       "50013    Breakfast      \n",
       "50014    Dinner         \n",
       "50015    Dinner         \n",
       "50016    Lunch          \n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.ljust(width=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Breakfast\n",
       "1                 Dinner\n",
       "2                  Lunch\n",
       "3                 Dinner\n",
       "4                  Lunch\n",
       "              ...       \n",
       "50012          Breakfast\n",
       "50013          Breakfast\n",
       "50014             Dinner\n",
       "50015             Dinner\n",
       "50016              Lunch\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.rjust(width=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Breakfast   \n",
       "1             Dinner    \n",
       "2             Lunch     \n",
       "3             Dinner    \n",
       "4             Lunch     \n",
       "              ...       \n",
       "50012       Breakfast   \n",
       "50013       Breakfast   \n",
       "50014         Dinner    \n",
       "50015         Dinner    \n",
       "50016         Lunch     \n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.center(width=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        000000Breakfast\n",
       "1        000000000Dinner\n",
       "2        0000000000Lunch\n",
       "3        000000000Dinner\n",
       "4        0000000000Lunch\n",
       "              ...       \n",
       "50012    000000Breakfast\n",
       "50013    000000Breakfast\n",
       "50014    000000000Dinner\n",
       "50015    000000000Dinner\n",
       "50016    0000000000Lunch\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.zfill(width=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        bREAKFAST\n",
       "1           dINNER\n",
       "2            lUNCH\n",
       "3           dINNER\n",
       "4            lUNCH\n",
       "           ...    \n",
       "50012    bREAKFAST\n",
       "50013    bREAKFAST\n",
       "50014       dINNER\n",
       "50015       dINNER\n",
       "50016        lUNCH\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.swapcase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        BreakfastBreakfast\n",
       "1              DinnerDinner\n",
       "2                LunchLunch\n",
       "3              DinnerDinner\n",
       "4                LunchLunch\n",
       "                ...        \n",
       "50012    BreakfastBreakfast\n",
       "50013    BreakfastBreakfast\n",
       "50014          DinnerDinner\n",
       "50015          DinnerDinner\n",
       "50016            LunchLunch\n",
       "Name: Type of Meal, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.repeat(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "50012     True\n",
       "50013     True\n",
       "50014    False\n",
       "50015    False\n",
       "50016    False\n",
       "Name: Type of Meal, Length: 50017, dtype: bool"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].str.endswith('ast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          [\"David Bishop\"]\n",
       "1                          [\"David Bishop\"]\n",
       "2                        [\"Karen Stansell\"]\n",
       "3                          [\"Addie Patino\"]\n",
       "4         [\"Addie Patino\",\"Susan Guerrero\"]\n",
       "                        ...                \n",
       "50012                    [\"Regina Shirley\"]\n",
       "50013    [\"Robin Ramos\",\"Chester Mortimer\"]\n",
       "50014                       [\"Robin Ramos\"]\n",
       "50015    [\"Chester Mortimer\",\"Robin Ramos\"]\n",
       "50016    [\"Chester Mortimer\",\"Robin Ramos\"]\n",
       "Name: Participants, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Participants'].str.lower().str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          [\"David Bishop\"]\n",
       "1                          [\"David Bishop\"]\n",
       "2                        [\"Karen Stansell\"]\n",
       "3                          [\"Addie Patino\"]\n",
       "4         [\"Addie Patino\",\"Susan Guerrero\"]\n",
       "                        ...                \n",
       "50012                    [\"Regina Shirley\"]\n",
       "50013    [\"Robin Ramos\",\"Chester Mortimer\"]\n",
       "50014                       [\"Robin Ramos\"]\n",
       "50015    [\"Chester Mortimer\",\"Robin Ramos\"]\n",
       "50016    [\"Chester Mortimer\",\"Robin Ramos\"]\n",
       "Name: Participants, Length: 50017, dtype: object"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Participants'].str.lower().str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Meal Price</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>PSQXKK7KIDIRKDSG</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>GG9MXLE8MGY8VGPD</td>\n",
       "      <td>MR6NETSKD2PSN54L</td>\n",
       "      <td>2018-12-09 06:00:00+00:00</td>\n",
       "      <td>[\"Jane Bruce\"]</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>215</td>\n",
       "      <td>Z8PYUN4L85MEW5W4</td>\n",
       "      <td>2016-07-26</td>\n",
       "      <td>MG7LI3RM8K3UKR34</td>\n",
       "      <td>MR6NETSKD2PSN54L</td>\n",
       "      <td>2016-07-30 19:00:00+00:00</td>\n",
       "      <td>[\"Jane Bruce\",\"Jennifer Lee\",\"Rosa Parramore\"]</td>\n",
       "      <td>503.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216</td>\n",
       "      <td>8XWFE5AX3D9LX5AY</td>\n",
       "      <td>2017-09-18</td>\n",
       "      <td>99NEQYAVFP9W1IHK</td>\n",
       "      <td>MR6NETSKD2PSN54L</td>\n",
       "      <td>2017-09-22 06:00:00+00:00</td>\n",
       "      <td>[\"Jane Bruce\"]</td>\n",
       "      <td>137.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>X546I8JFNVJFE7FH</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>G2UDNGRTBYGIS90Z</td>\n",
       "      <td>MR6NETSKD2PSN54L</td>\n",
       "      <td>2017-09-26 05:00:00+00:00</td>\n",
       "      <td>[\"Martin Riley\",\"Jane Bruce\",\"Rosa Parramore\"]</td>\n",
       "      <td>409.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>GCB5ULGM1W8A8HPR</td>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>XI16TWCYLA0F7NLZ</td>\n",
       "      <td>MR6NETSKD2PSN54L</td>\n",
       "      <td>2017-12-15 08:00:00+00:00</td>\n",
       "      <td>[\"Earl Sorrentino\",\"Jane Bruce\"]</td>\n",
       "      <td>339.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>48761</td>\n",
       "      <td>W7X0DMISLLY4X9I6</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>EDVP4TD6YCHF8N00</td>\n",
       "      <td>TXTJD46IGQWLD75D</td>\n",
       "      <td>2017-09-02 05:00:00+00:00</td>\n",
       "      <td>[\"John Leo\",\"Judy Dammann\",\"Todd Bradshaw\",\"Co...</td>\n",
       "      <td>465.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48764</td>\n",
       "      <td>K69T43KA9WCW9GOI</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>16I4B1HW4T60CO8H</td>\n",
       "      <td>TXTJD46IGQWLD75D</td>\n",
       "      <td>2016-05-29 11:00:00+00:00</td>\n",
       "      <td>[\"Judy Dammann\",\"John Leo\",\"Courtney Shaw\",\"Br...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48767</td>\n",
       "      <td>66DNKN5WXDS73VUA</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>5HLCRT0G3S9D20SQ</td>\n",
       "      <td>TXTJD46IGQWLD75D</td>\n",
       "      <td>2018-05-23 06:00:00+00:00</td>\n",
       "      <td>[\"Lydia Muske\",\"Todd Bradshaw\",\"Judy Dammann\",...</td>\n",
       "      <td>898.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48770</td>\n",
       "      <td>RYN3CZCDK3TQLX5O</td>\n",
       "      <td>2015-08-11</td>\n",
       "      <td>TE9PC2623UV8XL4L</td>\n",
       "      <td>TXTJD46IGQWLD75D</td>\n",
       "      <td>2015-08-11 07:00:00+00:00</td>\n",
       "      <td>[\"Courtney Shaw\",\"Lydia Muske\",\"Bruce Duenas\",...</td>\n",
       "      <td>280.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48773</td>\n",
       "      <td>YUBXH8YTFTTGT7CQ</td>\n",
       "      <td>2015-10-13</td>\n",
       "      <td>7YKRRY4LA72E03MR</td>\n",
       "      <td>TXTJD46IGQWLD75D</td>\n",
       "      <td>2015-10-11 05:00:00+00:00</td>\n",
       "      <td>[\"Bruce Duenas\",\"Courtney Shaw\",\"Todd Bradshaw...</td>\n",
       "      <td>299.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id       Date           Meal Id        Company Id  \\\n",
       "214    PSQXKK7KIDIRKDSG 2018-12-07  GG9MXLE8MGY8VGPD  MR6NETSKD2PSN54L   \n",
       "215    Z8PYUN4L85MEW5W4 2016-07-26  MG7LI3RM8K3UKR34  MR6NETSKD2PSN54L   \n",
       "216    8XWFE5AX3D9LX5AY 2017-09-18  99NEQYAVFP9W1IHK  MR6NETSKD2PSN54L   \n",
       "217    X546I8JFNVJFE7FH 2017-10-01  G2UDNGRTBYGIS90Z  MR6NETSKD2PSN54L   \n",
       "219    GCB5ULGM1W8A8HPR 2017-12-14  XI16TWCYLA0F7NLZ  MR6NETSKD2PSN54L   \n",
       "...                 ...        ...               ...               ...   \n",
       "48761  W7X0DMISLLY4X9I6 2017-08-29  EDVP4TD6YCHF8N00  TXTJD46IGQWLD75D   \n",
       "48764  K69T43KA9WCW9GOI 2016-06-03  16I4B1HW4T60CO8H  TXTJD46IGQWLD75D   \n",
       "48767  66DNKN5WXDS73VUA 2018-05-20  5HLCRT0G3S9D20SQ  TXTJD46IGQWLD75D   \n",
       "48770  RYN3CZCDK3TQLX5O 2015-08-11  TE9PC2623UV8XL4L  TXTJD46IGQWLD75D   \n",
       "48773  YUBXH8YTFTTGT7CQ 2015-10-13  7YKRRY4LA72E03MR  TXTJD46IGQWLD75D   \n",
       "\n",
       "                   Date of Meal  \\\n",
       "214   2018-12-09 06:00:00+00:00   \n",
       "215   2016-07-30 19:00:00+00:00   \n",
       "216   2017-09-22 06:00:00+00:00   \n",
       "217   2017-09-26 05:00:00+00:00   \n",
       "219   2017-12-15 08:00:00+00:00   \n",
       "...                         ...   \n",
       "48761 2017-09-02 05:00:00+00:00   \n",
       "48764 2016-05-29 11:00:00+00:00   \n",
       "48767 2018-05-23 06:00:00+00:00   \n",
       "48770 2015-08-11 07:00:00+00:00   \n",
       "48773 2015-10-11 05:00:00+00:00   \n",
       "\n",
       "                                            Participants  Meal Price  \\\n",
       "214                                       [\"Jane Bruce\"]       400.0   \n",
       "215       [\"Jane Bruce\",\"Jennifer Lee\",\"Rosa Parramore\"]       503.0   \n",
       "216                                       [\"Jane Bruce\"]       137.0   \n",
       "217       [\"Martin Riley\",\"Jane Bruce\",\"Rosa Parramore\"]       409.0   \n",
       "219                     [\"Earl Sorrentino\",\"Jane Bruce\"]       339.0   \n",
       "...                                                  ...         ...   \n",
       "48761  [\"John Leo\",\"Judy Dammann\",\"Todd Bradshaw\",\"Co...       465.0   \n",
       "48764  [\"Judy Dammann\",\"John Leo\",\"Courtney Shaw\",\"Br...       112.0   \n",
       "48767  [\"Lydia Muske\",\"Todd Bradshaw\",\"Judy Dammann\",...       898.0   \n",
       "48770  [\"Courtney Shaw\",\"Lydia Muske\",\"Bruce Duenas\",...       280.0   \n",
       "48773  [\"Bruce Duenas\",\"Courtney Shaw\",\"Todd Bradshaw...       299.0   \n",
       "\n",
       "      Type of Meal  Heroes Adjustment  \n",
       "214      Breakfast              False  \n",
       "215         Dinner              False  \n",
       "216      Breakfast              False  \n",
       "217      Breakfast              False  \n",
       "219      Breakfast              False  \n",
       "...            ...                ...  \n",
       "48761    Breakfast               True  \n",
       "48764        Lunch               True  \n",
       "48767    Breakfast               True  \n",
       "48770    Breakfast               True  \n",
       "48773    Breakfast               True  \n",
       "\n",
       "[183 rows x 9 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices[invoices['Participants'].str.contains('Bruce')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## accessor - cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].cat.ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Breakfast', 'Dinner', 'Lunch'], dtype='object')"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        2\n",
       "3        1\n",
       "4        2\n",
       "        ..\n",
       "50012    0\n",
       "50013    0\n",
       "50014    1\n",
       "50015    1\n",
       "50016    2\n",
       "Length: 50017, dtype: int8"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Breakfast\n",
       "1           Dinner\n",
       "2            Lunch\n",
       "3           Dinner\n",
       "4            Lunch\n",
       "           ...    \n",
       "50012    Breakfast\n",
       "50013    Breakfast\n",
       "50014       Dinner\n",
       "50015       Dinner\n",
       "50016        Lunch\n",
       "Name: Type of Meal, Length: 50017, dtype: category\n",
       "Categories (3, object): [Lunch, Breakfast, Dinner]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Type of Meal'].cat.reorder_categories(['Lunch','Breakfast','Dinner'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013       34\n",
       "2014    10051\n",
       "2015    10036\n",
       "2016     9862\n",
       "2017     9921\n",
       "2018    10095\n",
       "2019       18\n",
       "Name: Date of Meal, dtype: int64"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices['Date of Meal'].dt.year.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_2013 = invoices[invoices['Date of Meal'].dt.year == 2013].copy()\n",
    "y_2014 = invoices[invoices['Date of Meal'].dt.year == 2014].copy()\n",
    "y_2015 = invoices[invoices['Date of Meal'].dt.year == 2015].copy()\n",
    "y_2016 = invoices[invoices['Date of Meal'].dt.year == 2016].copy()\n",
    "y_2017 = invoices[invoices['Date of Meal'].dt.year == 2017].copy()\n",
    "y_2018 = invoices[invoices['Date of Meal'].dt.year == 2018].copy()\n",
    "y_2019 = invoices[invoices['Date of Meal'].dt.year == 2019].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>469.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>FESGKOQN2OZZWXY3</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>US0NQYNNHS1SQJ4S</td>\n",
       "      <td>36MFTZOYMTAJP1RK</td>\n",
       "      <td>2016-01-14 21:00:00+00:00</td>\n",
       "      <td>[\"Glenn Gould\",\"Amanda Knowles\",\"Ginger Hoagla...</td>\n",
       "      <td>451.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>YITOTLOF0MWZ0VYX</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>RGYX8772307H78ON</td>\n",
       "      <td>36MFTZOYMTAJP1RK</td>\n",
       "      <td>2016-10-01 20:00:00+00:00</td>\n",
       "      <td>[\"Ginger Hoagland\",\"Amanda Knowles\",\"Michael W...</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>TH60C9D8TPYS7DGG</td>\n",
       "      <td>2016-12-15</td>\n",
       "      <td>KDSMP2VJ22HNEPYF</td>\n",
       "      <td>36MFTZOYMTAJP1RK</td>\n",
       "      <td>2016-12-13 07:00:00+00:00</td>\n",
       "      <td>[\"Cheryl Feaster\",\"Bret Adams\",\"Ginger Hoagland\"]</td>\n",
       "      <td>755.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <td>50005</td>\n",
       "      <td>WQ9O30HM7Z20CX11</td>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>R3CY4BU2QN5ZVMC0</td>\n",
       "      <td>FC1LJXHNH2883N3Y</td>\n",
       "      <td>2016-08-27 07:00:00+00:00</td>\n",
       "      <td>[\"Jerry Nelson\",\"Alice Allen\"]</td>\n",
       "      <td>342.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50008</td>\n",
       "      <td>UHIE76EGIC2KFP34</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>BFLY4LHHUI5OIREM</td>\n",
       "      <td>FC1LJXHNH2883N3Y</td>\n",
       "      <td>2016-08-08 06:00:00+00:00</td>\n",
       "      <td>[\"Jerry Nelson\"]</td>\n",
       "      <td>164.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>50009</td>\n",
       "      <td>FWH3N8N5ARW53BE6</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>WHVI4J3J388I8I4B</td>\n",
       "      <td>FC1LJXHNH2883N3Y</td>\n",
       "      <td>2016-03-12 08:00:00+00:00</td>\n",
       "      <td>[\"Jon Ponce\",\"Alice Allen\"]</td>\n",
       "      <td>523.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50010</td>\n",
       "      <td>C4MSVKV2UFVGROTR</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>UR2WMW1QHJU3PUVS</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2016-05-15 20:00:00+00:00</td>\n",
       "      <td>[\"Regina Shirley\"]</td>\n",
       "      <td>539.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9862 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id       Date           Meal Id        Company Id  \\\n",
       "0      839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "9      FESGKOQN2OZZWXY3 2016-01-10  US0NQYNNHS1SQJ4S  36MFTZOYMTAJP1RK   \n",
       "10     YITOTLOF0MWZ0VYX 2016-10-03  RGYX8772307H78ON  36MFTZOYMTAJP1RK   \n",
       "12     TH60C9D8TPYS7DGG 2016-12-15  KDSMP2VJ22HNEPYF  36MFTZOYMTAJP1RK   \n",
       "...                 ...        ...               ...               ...   \n",
       "50005  WQ9O30HM7Z20CX11 2016-08-26  R3CY4BU2QN5ZVMC0  FC1LJXHNH2883N3Y   \n",
       "50008  UHIE76EGIC2KFP34 2016-08-06  BFLY4LHHUI5OIREM  FC1LJXHNH2883N3Y   \n",
       "50009  FWH3N8N5ARW53BE6 2016-03-17  WHVI4J3J388I8I4B  FC1LJXHNH2883N3Y   \n",
       "50010  C4MSVKV2UFVGROTR 2016-05-12  UR2WMW1QHJU3PUVS  E4K99D4JR9E40VE1   \n",
       "\n",
       "                   Date of Meal  \\\n",
       "0     2016-05-31 05:00:00+00:00   \n",
       "9     2016-01-14 21:00:00+00:00   \n",
       "10    2016-10-01 20:00:00+00:00   \n",
       "12    2016-12-13 07:00:00+00:00   \n",
       "...                         ...   \n",
       "50005 2016-08-27 07:00:00+00:00   \n",
       "50008 2016-08-08 06:00:00+00:00   \n",
       "50009 2016-03-12 08:00:00+00:00   \n",
       "50010 2016-05-15 20:00:00+00:00   \n",
       "\n",
       "                                            Participants  Meal Price  \\\n",
       "0                                       [\"David Bishop\"]       469.0   \n",
       "9      [\"Glenn Gould\",\"Amanda Knowles\",\"Ginger Hoagla...       451.0   \n",
       "10     [\"Ginger Hoagland\",\"Amanda Knowles\",\"Michael W...       263.0   \n",
       "12     [\"Cheryl Feaster\",\"Bret Adams\",\"Ginger Hoagland\"]       755.0   \n",
       "...                                                  ...         ...   \n",
       "50005                     [\"Jerry Nelson\",\"Alice Allen\"]       342.0   \n",
       "50008                                   [\"Jerry Nelson\"]       164.0   \n",
       "50009                        [\"Jon Ponce\",\"Alice Allen\"]       523.0   \n",
       "50010                                 [\"Regina Shirley\"]       539.0   \n",
       "\n",
       "      Type of Meal  Heroes Adjustment  \n",
       "0        Breakfast              False  \n",
       "9           Dinner              False  \n",
       "10          Dinner              False  \n",
       "12       Breakfast              False  \n",
       "...            ...                ...  \n",
       "50005    Breakfast              False  \n",
       "50008    Breakfast              False  \n",
       "50009    Breakfast              False  \n",
       "50010       Dinner              False  \n",
       "\n",
       "[9862 rows x 9 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
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       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>original_index</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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">13</td>\n",
       "      <td>1449</td>\n",
       "      <td>HH1CUKNKIIK87D4L</td>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>M4QG9N01E25U3A53</td>\n",
       "      <td>N4BN0GG5XKZV113C</td>\n",
       "      <td>2013-12-31 21:00:00+00:00</td>\n",
       "      <td>[\"Jason Cuevas\"]</td>\n",
       "      <td>546.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4256</td>\n",
       "      <td>KFXTYDP0YG9C6AZF</td>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>RUMIZCB9KM3N57BN</td>\n",
       "      <td>9FOCJR50GUHXT2S0</td>\n",
       "      <td>2013-12-31 12:00:00+00:00</td>\n",
       "      <td>[\"Robert Hairfield\",\"Ray Dowty\",\"Adam Bowers\",...</td>\n",
       "      <td>586.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4747</td>\n",
       "      <td>D5NR6EP8NQZ0TGW6</td>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>5GDXSLG0F4WA3JEZ</td>\n",
       "      <td>RJ1NLTCTP1COK3YS</td>\n",
       "      <td>2013-12-31 21:00:00+00:00</td>\n",
       "      <td>[\"Leta Stevens\"]</td>\n",
       "      <td>549.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5097</td>\n",
       "      <td>2T71YISMHMPBKE3Z</td>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>0C7DIKEPG5KXNRFR</td>\n",
       "      <td>R835HX2AVB1KJJL0</td>\n",
       "      <td>2013-12-30 19:00:00+00:00</td>\n",
       "      <td>[\"Hunter Marquez\"]</td>\n",
       "      <td>338.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td rowspan=\"4\" valign=\"top\">19</td>\n",
       "      <td>35406</td>\n",
       "      <td>IASPC12JU67ANTA5</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>0MJH7DMFHIPI2T81</td>\n",
       "      <td>90KJEJUQ6MQO6NEW</td>\n",
       "      <td>2019-01-02 11:00:00+00:00</td>\n",
       "      <td>[\"Ted Pickering\"]</td>\n",
       "      <td>285.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36958</td>\n",
       "      <td>0GNFQ2XTTI56QE1E</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>9UPI7YU9WQDSBR1P</td>\n",
       "      <td>9IPX1GTNSJJOULSB</td>\n",
       "      <td>2019-01-03 11:00:00+00:00</td>\n",
       "      <td>[\"John Hightower\",\"Adam Hill\",\"Betty Sagen\"]</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43534</td>\n",
       "      <td>8YHA5UTWAT7BOC24</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>041RWJS7KUFPFVV9</td>\n",
       "      <td>Z19C6Y8F5Q22DMFK</td>\n",
       "      <td>2019-01-03 07:00:00+00:00</td>\n",
       "      <td>[\"Marva Cormier\",\"Amy Boothe\"]</td>\n",
       "      <td>377.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48207</td>\n",
       "      <td>J5FBQ0JWGIKWFSCO</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>V8HPYOK6H6W5C5WQ</td>\n",
       "      <td>5AI9YF2ROZIV8QPJ</td>\n",
       "      <td>2019-01-01 06:00:00+00:00</td>\n",
       "      <td>[\"Pamela Davis\"]</td>\n",
       "      <td>536.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50017 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Order Id       Date           Meal Id  \\\n",
       "year original_index                                                  \n",
       "13   1449            HH1CUKNKIIK87D4L 2014-01-03  M4QG9N01E25U3A53   \n",
       "     4256            KFXTYDP0YG9C6AZF 2014-01-03  RUMIZCB9KM3N57BN   \n",
       "     4747            D5NR6EP8NQZ0TGW6 2014-01-05  5GDXSLG0F4WA3JEZ   \n",
       "     5097            2T71YISMHMPBKE3Z 2014-01-03  0C7DIKEPG5KXNRFR   \n",
       "...                               ...        ...               ...   \n",
       "19   35406           IASPC12JU67ANTA5 2018-12-29  0MJH7DMFHIPI2T81   \n",
       "     36958           0GNFQ2XTTI56QE1E 2018-12-30  9UPI7YU9WQDSBR1P   \n",
       "     43534           8YHA5UTWAT7BOC24 2018-12-31  041RWJS7KUFPFVV9   \n",
       "     48207           J5FBQ0JWGIKWFSCO 2018-12-30  V8HPYOK6H6W5C5WQ   \n",
       "\n",
       "                           Company Id              Date of Meal  \\\n",
       "year original_index                                               \n",
       "13   1449            N4BN0GG5XKZV113C 2013-12-31 21:00:00+00:00   \n",
       "     4256            9FOCJR50GUHXT2S0 2013-12-31 12:00:00+00:00   \n",
       "     4747            RJ1NLTCTP1COK3YS 2013-12-31 21:00:00+00:00   \n",
       "     5097            R835HX2AVB1KJJL0 2013-12-30 19:00:00+00:00   \n",
       "...                               ...                       ...   \n",
       "19   35406           90KJEJUQ6MQO6NEW 2019-01-02 11:00:00+00:00   \n",
       "     36958           9IPX1GTNSJJOULSB 2019-01-03 11:00:00+00:00   \n",
       "     43534           Z19C6Y8F5Q22DMFK 2019-01-03 07:00:00+00:00   \n",
       "     48207           5AI9YF2ROZIV8QPJ 2019-01-01 06:00:00+00:00   \n",
       "\n",
       "                                                          Participants  \\\n",
       "year original_index                                                      \n",
       "13   1449                                             [\"Jason Cuevas\"]   \n",
       "     4256            [\"Robert Hairfield\",\"Ray Dowty\",\"Adam Bowers\",...   \n",
       "     4747                                             [\"Leta Stevens\"]   \n",
       "     5097                                           [\"Hunter Marquez\"]   \n",
       "...                                                                ...   \n",
       "19   35406                                           [\"Ted Pickering\"]   \n",
       "     36958                [\"John Hightower\",\"Adam Hill\",\"Betty Sagen\"]   \n",
       "     43534                              [\"Marva Cormier\",\"Amy Boothe\"]   \n",
       "     48207                                            [\"Pamela Davis\"]   \n",
       "\n",
       "                     Meal Price Type of Meal  Heroes Adjustment  \n",
       "year original_index                                              \n",
       "13   1449                 546.0       Dinner              False  \n",
       "     4256                 586.0        Lunch              False  \n",
       "     4747                 549.0       Dinner              False  \n",
       "     5097                 338.0       Dinner              False  \n",
       "...                         ...          ...                ...  \n",
       "19   35406                285.0        Lunch              False  \n",
       "     36958                600.0        Lunch              False  \n",
       "     43534                377.0    Breakfast              False  \n",
       "     48207                536.0    Breakfast              False  \n",
       "\n",
       "[50017 rows x 9 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [y_2013,y_2014,y_2015,y_2016,y_2017,y_2018,y_2019],\n",
    "    keys=[13,14,15,16,17,18,19],\n",
    "    names=['year','original_index']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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",
       "\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>observations_A</th>\n",
       "      <th>observations_B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-01-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            observations_A  observations_B\n",
       "2019-01-02             3.0             NaN\n",
       "2019-01-03             6.0             NaN\n",
       "2019-01-04             5.0             NaN\n",
       "2019-01-05             3.0             4.0\n",
       "...                    ...             ...\n",
       "2019-01-09             NaN             7.0\n",
       "2019-01-10             NaN             9.0\n",
       "2019-01-11             NaN             6.0\n",
       "2019-01-12             NaN             3.0\n",
       "\n",
       "[11 rows x 2 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_a = pd.date_range(\n",
    "    datetime.datetime(2019,1,2),\n",
    "    datetime.datetime(2019,1,8)\n",
    ")\n",
    "df_a = pd.DataFrame(\n",
    "    index=range_a, \n",
    "    data=np.random.randint(2,10,size=len(range_a)),\n",
    "    columns=['observations_A']\n",
    ")\n",
    "\n",
    "range_b = pd.date_range(\n",
    "    datetime.datetime(2019,1,5),\n",
    "    datetime.datetime(2019,1,12)\n",
    ")\n",
    "\n",
    "df_b = pd.DataFrame(\n",
    "    index=range_b, \n",
    "    data=np.random.randint(2,10,size=len(range_b)),\n",
    "    columns=['observations_B']\n",
    ")\n",
    "\n",
    "pd.concat([df_a,df_b],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_data = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/order_leads.csv',parse_dates=[3])\n",
    "sales_team = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/sales_team.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Meal Price</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>469.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>314.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Order Id       Date           Meal Id        Company Id  \\\n",
       "0  839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1  97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2  041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "\n",
       "               Date of Meal        Participants  Meal Price Type of Meal  \\\n",
       "0 2016-05-31 05:00:00+00:00    [\"David Bishop\"]       469.0    Breakfast   \n",
       "1 2018-10-01 18:00:00+00:00    [\"David Bishop\"]        22.0       Dinner   \n",
       "2 2014-08-23 12:00:00+00:00  [\"Karen Stansell\"]       314.0        Lunch   \n",
       "\n",
       "   Heroes Adjustment  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
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       "           Order Id        Company Id            Company Name       Date  \\\n",
       "0  80EYLOKP9E762WKG  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2017-02-18   \n",
       "1  TLEXR1HZWTUTBHPB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2015-07-30   \n",
       "2  839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "\n",
       "   Order Value  Converted  \n",
       "0         4875          1  \n",
       "1         8425          0  \n",
       "2         4837          0  "
      ]
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     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "order_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
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       "           Sales Rep      Sales Rep Id            Company Name  \\\n",
       "0  Jessie Mcallister  97UNNAT790E0WM4N  Chimera-Chasing Casbah   \n",
       "1  Jessie Mcallister  97UNNAT790E0WM4N        Tangential Sheds   \n",
       "2  Jessie Mcallister  97UNNAT790E0WM4N           Two-Mile Grab   \n",
       "\n",
       "         Company Id  \n",
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       "1  36MFTZOYMTAJP1RK  \n",
       "2  H3JRC7XX7WJAD4ZO  "
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     "metadata": {},
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   ],
   "source": [
    "sales_team.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging-how"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
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       "      <td>314.0</td>\n",
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       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>2228</td>\n",
       "      <td>1</td>\n",
       "      <td>LMX18PNGWCIMG1QW</td>\n",
       "      <td>2017-09-22 19:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\"]</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>50015</td>\n",
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       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>8869</td>\n",
       "      <td>0</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
       "      <td>2018-02-01 20:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>497.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>50016</td>\n",
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       "      <td>Fierce Productivity</td>\n",
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       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "1      97OX39BGVMHODLJM  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2018-09-27   \n",
       "2      041ORQM5OIHTIU6L  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2014-08-24   \n",
       "...                 ...               ...                     ...        ...   \n",
       "50014  STJ6QJC30WPRM93H  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-21   \n",
       "50015  QHEUIYNC0XQX7GDR  E4K99D4JR9E40VE1     Fierce Productivity 2018-01-28   \n",
       "50016  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "\n",
       "       Order Value  Converted           Meal Id              Date of Meal  \\\n",
       "0             4837          0  INBUX904GIHI8YBD 2016-05-31 05:00:00+00:00   \n",
       "1              343          0  J0MMOOPP709DIDIE 2018-10-01 18:00:00+00:00   \n",
       "2             4185          0  E4UJLQNCI16UX5CS 2014-08-23 12:00:00+00:00   \n",
       "...            ...        ...               ...                       ...   \n",
       "50014         2228          1  LMX18PNGWCIMG1QW 2017-09-22 19:00:00+00:00   \n",
       "50015         8869          0  4U0VH2TGQL30X23X 2018-02-01 20:00:00+00:00   \n",
       "50016         5138          0  ORWFRT5TUSYGNYG7 2017-09-09 12:00:00+00:00   \n",
       "\n",
       "                             Participants  Meal Price Type of Meal  \\\n",
       "0                        [\"David Bishop\"]       469.0    Breakfast   \n",
       "1                        [\"David Bishop\"]        22.0       Dinner   \n",
       "2                      [\"Karen Stansell\"]       314.0        Lunch   \n",
       "...                                   ...         ...          ...   \n",
       "50014                     [\"Robin Ramos\"]       160.0       Dinner   \n",
       "50015  [\"Chester Mortimer\",\"Robin Ramos\"]       497.0       Dinner   \n",
       "50016  [\"Chester Mortimer\",\"Robin Ramos\"]       365.0        Lunch   \n",
       "\n",
       "       Heroes Adjustment  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "...                  ...  \n",
       "50014              False  \n",
       "50015              False  \n",
       "50016              False  \n",
       "\n",
       "[50017 rows x 12 columns]"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(order_data,invoices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2015-10-29</td>\n",
       "      <td>2036</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>EHN2QR78K2RX0FQ9</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
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       "      <td>2015-03-07</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      80EYLOKP9E762WKG  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2017-02-18   \n",
       "1      TLEXR1HZWTUTBHPB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2015-07-30   \n",
       "2      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "...                 ...               ...                     ...        ...   \n",
       "99997  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "99998  OF79M3H9C44UM6PO  E4K99D4JR9E40VE1     Fierce Productivity 2015-10-29   \n",
       "99999  EHN2QR78K2RX0FQ9  E4K99D4JR9E40VE1     Fierce Productivity 2015-03-07   \n",
       "\n",
       "       Order Value  Converted           Meal Id              Date of Meal  \\\n",
       "0             4875          1               NaN                       NaT   \n",
       "1             8425          0               NaN                       NaT   \n",
       "2             4837          0  INBUX904GIHI8YBD 2016-05-31 05:00:00+00:00   \n",
       "...            ...        ...               ...                       ...   \n",
       "99997         5138          0  ORWFRT5TUSYGNYG7 2017-09-09 12:00:00+00:00   \n",
       "99998         2036          0               NaN                       NaT   \n",
       "99999         7183          0               NaN                       NaT   \n",
       "\n",
       "                             Participants  Meal Price Type of Meal  \\\n",
       "0                                     NaN         NaN          NaN   \n",
       "1                                     NaN         NaN          NaN   \n",
       "2                        [\"David Bishop\"]       469.0    Breakfast   \n",
       "...                                   ...         ...          ...   \n",
       "99997  [\"Chester Mortimer\",\"Robin Ramos\"]       365.0        Lunch   \n",
       "99998                                 NaN         NaN          NaN   \n",
       "99999                                 NaN         NaN          NaN   \n",
       "\n",
       "      Heroes Adjustment  \n",
       "0                   NaN  \n",
       "1                   NaN  \n",
       "2                 False  \n",
       "...                 ...  \n",
       "99997             False  \n",
       "99998               NaN  \n",
       "99999               NaN  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(order_data,invoices,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
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       "      <td>343</td>\n",
       "      <td>0</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
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       "      <td>4185</td>\n",
       "      <td>0</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>314.0</td>\n",
       "      <td>Lunch</td>\n",
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       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>2228</td>\n",
       "      <td>1</td>\n",
       "      <td>LMX18PNGWCIMG1QW</td>\n",
       "      <td>2017-09-22 19:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\"]</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>50015</td>\n",
       "      <td>QHEUIYNC0XQX7GDR</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>8869</td>\n",
       "      <td>0</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
       "      <td>2018-02-01 20:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>497.0</td>\n",
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       "      <td>2017-09-06</td>\n",
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       "      <td>0</td>\n",
       "      <td>ORWFRT5TUSYGNYG7</td>\n",
       "      <td>2017-09-09 12:00:00+00:00</td>\n",
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       "      <td>365.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50017 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "1      97OX39BGVMHODLJM  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2018-09-27   \n",
       "2      041ORQM5OIHTIU6L  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2014-08-24   \n",
       "...                 ...               ...                     ...        ...   \n",
       "50014  STJ6QJC30WPRM93H  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-21   \n",
       "50015  QHEUIYNC0XQX7GDR  E4K99D4JR9E40VE1     Fierce Productivity 2018-01-28   \n",
       "50016  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "\n",
       "       Order Value  Converted           Meal Id              Date of Meal  \\\n",
       "0             4837          0  INBUX904GIHI8YBD 2016-05-31 05:00:00+00:00   \n",
       "1              343          0  J0MMOOPP709DIDIE 2018-10-01 18:00:00+00:00   \n",
       "2             4185          0  E4UJLQNCI16UX5CS 2014-08-23 12:00:00+00:00   \n",
       "...            ...        ...               ...                       ...   \n",
       "50014         2228          1  LMX18PNGWCIMG1QW 2017-09-22 19:00:00+00:00   \n",
       "50015         8869          0  4U0VH2TGQL30X23X 2018-02-01 20:00:00+00:00   \n",
       "50016         5138          0  ORWFRT5TUSYGNYG7 2017-09-09 12:00:00+00:00   \n",
       "\n",
       "                             Participants  Meal Price Type of Meal  \\\n",
       "0                        [\"David Bishop\"]       469.0    Breakfast   \n",
       "1                        [\"David Bishop\"]        22.0       Dinner   \n",
       "2                      [\"Karen Stansell\"]       314.0        Lunch   \n",
       "...                                   ...         ...          ...   \n",
       "50014                     [\"Robin Ramos\"]       160.0       Dinner   \n",
       "50015  [\"Chester Mortimer\",\"Robin Ramos\"]       497.0       Dinner   \n",
       "50016  [\"Chester Mortimer\",\"Robin Ramos\"]       365.0        Lunch   \n",
       "\n",
       "       Heroes Adjustment  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "...                  ...  \n",
       "50014              False  \n",
       "50015              False  \n",
       "50016              False  \n",
       "\n",
       "[50017 rows x 12 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(order_data,invoices,how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>99998</td>\n",
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       "      <td>E4K99D4JR9E40VE1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>100000 rows × 12 columns</p>\n",
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      "text/plain": [
       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      80EYLOKP9E762WKG  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2017-02-18   \n",
       "1      TLEXR1HZWTUTBHPB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2015-07-30   \n",
       "2      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "...                 ...               ...                     ...        ...   \n",
       "99997  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "99998  OF79M3H9C44UM6PO  E4K99D4JR9E40VE1     Fierce Productivity 2015-10-29   \n",
       "99999  EHN2QR78K2RX0FQ9  E4K99D4JR9E40VE1     Fierce Productivity 2015-03-07   \n",
       "\n",
       "       Order Value  Converted           Meal Id              Date of Meal  \\\n",
       "0             4875          1               NaN                       NaT   \n",
       "1             8425          0               NaN                       NaT   \n",
       "2             4837          0  INBUX904GIHI8YBD 2016-05-31 05:00:00+00:00   \n",
       "...            ...        ...               ...                       ...   \n",
       "99997         5138          0  ORWFRT5TUSYGNYG7 2017-09-09 12:00:00+00:00   \n",
       "99998         2036          0               NaN                       NaT   \n",
       "99999         7183          0               NaN                       NaT   \n",
       "\n",
       "                             Participants  Meal Price Type of Meal  \\\n",
       "0                                     NaN         NaN          NaN   \n",
       "1                                     NaN         NaN          NaN   \n",
       "2                        [\"David Bishop\"]       469.0    Breakfast   \n",
       "...                                   ...         ...          ...   \n",
       "99997  [\"Chester Mortimer\",\"Robin Ramos\"]       365.0        Lunch   \n",
       "99998                                 NaN         NaN          NaN   \n",
       "99999                                 NaN         NaN          NaN   \n",
       "\n",
       "      Heroes Adjustment  \n",
       "0                   NaN  \n",
       "1                   NaN  \n",
       "2                 False  \n",
       "...                 ...  \n",
       "99997             False  \n",
       "99998               NaN  \n",
       "99999               NaN  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(order_data,invoices,how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging-on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>8869</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
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       "      <td>False</td>\n",
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       "  </tbody>\n",
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      ],
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       "               Order Id      Company Id_x            Company Name     Date_x  \\\n",
       "0      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "1      97OX39BGVMHODLJM  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2018-09-27   \n",
       "2      041ORQM5OIHTIU6L  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2014-08-24   \n",
       "...                 ...               ...                     ...        ...   \n",
       "50014  STJ6QJC30WPRM93H  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-21   \n",
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       "\n",
       "       Order Value  Converted     Date_y           Meal Id      Company Id_y  \\\n",
       "0             4837          0 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1              343          0 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2             4185          0 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "...            ...        ...        ...               ...               ...   \n",
       "50014         2228          1 2017-09-21  LMX18PNGWCIMG1QW  E4K99D4JR9E40VE1   \n",
       "50015         8869          0 2018-01-28  4U0VH2TGQL30X23X  E4K99D4JR9E40VE1   \n",
       "50016         5138          0 2017-09-06  ORWFRT5TUSYGNYG7  E4K99D4JR9E40VE1   \n",
       "\n",
       "                   Date of Meal                        Participants  \\\n",
       "0     2016-05-31 05:00:00+00:00                    [\"David Bishop\"]   \n",
       "1     2018-10-01 18:00:00+00:00                    [\"David Bishop\"]   \n",
       "2     2014-08-23 12:00:00+00:00                  [\"Karen Stansell\"]   \n",
       "...                         ...                                 ...   \n",
       "50014 2017-09-22 19:00:00+00:00                     [\"Robin Ramos\"]   \n",
       "50015 2018-02-01 20:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "50016 2017-09-09 12:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "\n",
       "       Meal Price Type of Meal  Heroes Adjustment  \n",
       "0           469.0    Breakfast              False  \n",
       "1            22.0       Dinner              False  \n",
       "2           314.0        Lunch              False  \n",
       "...           ...          ...                ...  \n",
       "50014       160.0       Dinner              False  \n",
       "50015       497.0       Dinner              False  \n",
       "50016       365.0        Lunch              False  \n",
       "\n",
       "[50017 rows x 14 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(order_data,invoices,on='Order Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
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       "               Order Id   Company Id_base            Company Name  Date_base  \\\n",
       "0      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "1      97OX39BGVMHODLJM  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2018-09-27   \n",
       "2      041ORQM5OIHTIU6L  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2014-08-24   \n",
       "...                 ...               ...                     ...        ...   \n",
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       "\n",
       "       Order Value  Converted  Date_join           Meal Id   Company Id_join  \\\n",
       "0             4837          0 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1              343          0 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2             4185          0 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "...            ...        ...        ...               ...               ...   \n",
       "50014         2228          1 2017-09-21  LMX18PNGWCIMG1QW  E4K99D4JR9E40VE1   \n",
       "50015         8869          0 2018-01-28  4U0VH2TGQL30X23X  E4K99D4JR9E40VE1   \n",
       "50016         5138          0 2017-09-06  ORWFRT5TUSYGNYG7  E4K99D4JR9E40VE1   \n",
       "\n",
       "                   Date of Meal                        Participants  \\\n",
       "0     2016-05-31 05:00:00+00:00                    [\"David Bishop\"]   \n",
       "1     2018-10-01 18:00:00+00:00                    [\"David Bishop\"]   \n",
       "2     2014-08-23 12:00:00+00:00                  [\"Karen Stansell\"]   \n",
       "...                         ...                                 ...   \n",
       "50014 2017-09-22 19:00:00+00:00                     [\"Robin Ramos\"]   \n",
       "50015 2018-02-01 20:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "50016 2017-09-09 12:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "\n",
       "       Meal Price Type of Meal  Heroes Adjustment  \n",
       "0           469.0    Breakfast              False  \n",
       "1            22.0       Dinner              False  \n",
       "2           314.0        Lunch              False  \n",
       "...           ...          ...                ...  \n",
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       "[50017 rows x 14 columns]"
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     "execution_count": 268,
     "metadata": {},
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    }
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   "source": [
    "pd.merge(order_data,invoices,on='Order Id',suffixes=('_base','_join'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>343</td>\n",
       "      <td>0</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>4185</td>\n",
       "      <td>0</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>314.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50014</td>\n",
       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>2228</td>\n",
       "      <td>1</td>\n",
       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>LMX18PNGWCIMG1QW</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-22 19:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\"]</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50015</td>\n",
       "      <td>QHEUIYNC0XQX7GDR</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>8869</td>\n",
       "      <td>0</td>\n",
       "      <td>QHEUIYNC0XQX7GDR</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2018-02-01 20:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>497.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50016</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>5138</td>\n",
       "      <td>0</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>ORWFRT5TUSYGNYG7</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-09 12:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>365.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50017 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id      Company Id_x            Company Name     Date_x  \\\n",
       "0      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "1      97OX39BGVMHODLJM  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2018-09-27   \n",
       "2      041ORQM5OIHTIU6L  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2014-08-24   \n",
       "...                 ...               ...                     ...        ...   \n",
       "50014  STJ6QJC30WPRM93H  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-21   \n",
       "50015  QHEUIYNC0XQX7GDR  E4K99D4JR9E40VE1     Fierce Productivity 2018-01-28   \n",
       "50016  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "\n",
       "       Order Value  Converted            New Id     Date_y           Meal Id  \\\n",
       "0             4837          0  839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD   \n",
       "1              343          0  97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE   \n",
       "2             4185          0  041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS   \n",
       "...            ...        ...               ...        ...               ...   \n",
       "50014         2228          1  STJ6QJC30WPRM93H 2017-09-21  LMX18PNGWCIMG1QW   \n",
       "50015         8869          0  QHEUIYNC0XQX7GDR 2018-01-28  4U0VH2TGQL30X23X   \n",
       "50016         5138          0  NKHFWT5I2J9LPAPG 2017-09-06  ORWFRT5TUSYGNYG7   \n",
       "\n",
       "           Company Id_y              Date of Meal  \\\n",
       "0      LJKS5NK6788CYMUU 2016-05-31 05:00:00+00:00   \n",
       "1      LJKS5NK6788CYMUU 2018-10-01 18:00:00+00:00   \n",
       "2      LJKS5NK6788CYMUU 2014-08-23 12:00:00+00:00   \n",
       "...                 ...                       ...   \n",
       "50014  E4K99D4JR9E40VE1 2017-09-22 19:00:00+00:00   \n",
       "50015  E4K99D4JR9E40VE1 2018-02-01 20:00:00+00:00   \n",
       "50016  E4K99D4JR9E40VE1 2017-09-09 12:00:00+00:00   \n",
       "\n",
       "                             Participants  Meal Price Type of Meal  \\\n",
       "0                        [\"David Bishop\"]       469.0    Breakfast   \n",
       "1                        [\"David Bishop\"]        22.0       Dinner   \n",
       "2                      [\"Karen Stansell\"]       314.0        Lunch   \n",
       "...                                   ...         ...          ...   \n",
       "50014                     [\"Robin Ramos\"]       160.0       Dinner   \n",
       "50015  [\"Chester Mortimer\",\"Robin Ramos\"]       497.0       Dinner   \n",
       "50016  [\"Chester Mortimer\",\"Robin Ramos\"]       365.0        Lunch   \n",
       "\n",
       "       Heroes Adjustment  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "...                  ...  \n",
       "50014              False  \n",
       "50015              False  \n",
       "50016              False  \n",
       "\n",
       "[50017 rows x 15 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(\n",
    "    order_data,\n",
    "    invoices.rename(columns={'Order Id':'New Id'}),\n",
    "    left_on='Order Id',\n",
    "    right_on='New Id'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "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>Order Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Order Value</th>\n",
       "      <th>Converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>80EYLOKP9E762WKG</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2017-02-18</td>\n",
       "      <td>4875</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>TLEXR1HZWTUTBHPB</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>8425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>4837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>99997</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>5138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99998</td>\n",
       "      <td>OF79M3H9C44UM6PO</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2015-10-29</td>\n",
       "      <td>2036</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99999</td>\n",
       "      <td>EHN2QR78K2RX0FQ9</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2015-03-07</td>\n",
       "      <td>7183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      80EYLOKP9E762WKG  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2017-02-18   \n",
       "1      TLEXR1HZWTUTBHPB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2015-07-30   \n",
       "2      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "...                 ...               ...                     ...        ...   \n",
       "99997  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "99998  OF79M3H9C44UM6PO  E4K99D4JR9E40VE1     Fierce Productivity 2015-10-29   \n",
       "99999  EHN2QR78K2RX0FQ9  E4K99D4JR9E40VE1     Fierce Productivity 2015-03-07   \n",
       "\n",
       "       Order Value  Converted  \n",
       "0             4875          1  \n",
       "1             8425          0  \n",
       "2             4837          0  \n",
       "...            ...        ...  \n",
       "99997         5138          0  \n",
       "99998         2036          0  \n",
       "99999         7183          0  \n",
       "\n",
       "[100000 rows x 6 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Rep</th>\n",
       "      <th>Sales Rep Id</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Company Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jessie Mcallister</td>\n",
       "      <td>97UNNAT790E0WM4N</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jessie Mcallister</td>\n",
       "      <td>97UNNAT790E0WM4N</td>\n",
       "      <td>Tangential Sheds</td>\n",
       "      <td>36MFTZOYMTAJP1RK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Jessie Mcallister</td>\n",
       "      <td>97UNNAT790E0WM4N</td>\n",
       "      <td>Two-Mile Grab</td>\n",
       "      <td>H3JRC7XX7WJAD4ZO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4722</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "      <td>NIW73ZQMGEOB7BHT</td>\n",
       "      <td>Spotty Adoniram</td>\n",
       "      <td>5K17HYJQ6JG6UFU0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4723</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "      <td>NIW73ZQMGEOB7BHT</td>\n",
       "      <td>Physicochemical Impatience</td>\n",
       "      <td>FC1LJXHNH2883N3Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4724</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "      <td>NIW73ZQMGEOB7BHT</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4725 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales Rep      Sales Rep Id                Company Name  \\\n",
       "0     Jessie Mcallister  97UNNAT790E0WM4N      Chimera-Chasing Casbah   \n",
       "1     Jessie Mcallister  97UNNAT790E0WM4N            Tangential Sheds   \n",
       "2     Jessie Mcallister  97UNNAT790E0WM4N               Two-Mile Grab   \n",
       "...                 ...               ...                         ...   \n",
       "4722    Hazel Dickerson  NIW73ZQMGEOB7BHT             Spotty Adoniram   \n",
       "4723    Hazel Dickerson  NIW73ZQMGEOB7BHT  Physicochemical Impatience   \n",
       "4724    Hazel Dickerson  NIW73ZQMGEOB7BHT         Fierce Productivity   \n",
       "\n",
       "            Company Id  \n",
       "0     LJKS5NK6788CYMUU  \n",
       "1     36MFTZOYMTAJP1RK  \n",
       "2     H3JRC7XX7WJAD4ZO  \n",
       "...                ...  \n",
       "4722  5K17HYJQ6JG6UFU0  \n",
       "4723  FC1LJXHNH2883N3Y  \n",
       "4724  E4K99D4JR9E40VE1  \n",
       "\n",
       "[4725 rows x 4 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company Name\n",
       "Chimera-Chasing Casbah        Jessie Mcallister\n",
       "Tangential Sheds              Jessie Mcallister\n",
       "Two-Mile Grab                 Jessie Mcallister\n",
       "                                    ...        \n",
       "Spotty Adoniram                 Hazel Dickerson\n",
       "Physicochemical Impatience      Hazel Dickerson\n",
       "Fierce Productivity             Hazel Dickerson\n",
       "Name: Sales Rep, Length: 4725, dtype: object"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup = sales_team.set_index('Company Name')['Sales Rep']\n",
    "lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Order Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>TLEXR1HZWTUTBHPB</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2015-07-30</td>\n",
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       "      <td>Chimera-Chasing Casbah</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>4837</td>\n",
       "      <td>0</td>\n",
       "      <td>Jessie Mcallister</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>99997</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>5138</td>\n",
       "      <td>0</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99998</td>\n",
       "      <td>OF79M3H9C44UM6PO</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2015-10-29</td>\n",
       "      <td>2036</td>\n",
       "      <td>0</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99999</td>\n",
       "      <td>EHN2QR78K2RX0FQ9</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>Fierce Productivity</td>\n",
       "      <td>2015-03-07</td>\n",
       "      <td>7183</td>\n",
       "      <td>0</td>\n",
       "      <td>Hazel Dickerson</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id        Company Id            Company Name       Date  \\\n",
       "0      80EYLOKP9E762WKG  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2017-02-18   \n",
       "1      TLEXR1HZWTUTBHPB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2015-07-30   \n",
       "2      839FKFW2LLX4LMBB  LJKS5NK6788CYMUU  Chimera-Chasing Casbah 2016-05-27   \n",
       "...                 ...               ...                     ...        ...   \n",
       "99997  NKHFWT5I2J9LPAPG  E4K99D4JR9E40VE1     Fierce Productivity 2017-09-06   \n",
       "99998  OF79M3H9C44UM6PO  E4K99D4JR9E40VE1     Fierce Productivity 2015-10-29   \n",
       "99999  EHN2QR78K2RX0FQ9  E4K99D4JR9E40VE1     Fierce Productivity 2015-03-07   \n",
       "\n",
       "       Order Value  Converted          Sales Rep  \n",
       "0             4875          1  Jessie Mcallister  \n",
       "1             8425          0  Jessie Mcallister  \n",
       "2             4837          0  Jessie Mcallister  \n",
       "...            ...        ...                ...  \n",
       "99997         5138          0    Hazel Dickerson  \n",
       "99998         2036          0    Hazel Dickerson  \n",
       "99999         7183          0    Hazel Dickerson  \n",
       "\n",
       "[100000 rows x 7 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_data['Sales Rep'] = order_data['Company Name'].map(lookup)\n",
    "order_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming / Reshaping DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>469.0</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>314.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-07 19:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\"]</td>\n",
       "      <td>438.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6YLROQT27B6HRF4E</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2015-07-27 12:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\",\"Susan Guerrero\"]</td>\n",
       "      <td>690.0</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>AT0R4DFYYAFOC88Q</td>\n",
       "      <td>2014-07-21</td>\n",
       "      <td>W48JPR1UYWJ18NC6</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-07-17 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\",\"Susan Guerrero\",\"Karen Stanse...</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2DDN2LHS7G85GKPQ</td>\n",
       "      <td>2014-04-29</td>\n",
       "      <td>1MKLAKBOE3SP7YUL</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-30 19:00:00+00:00</td>\n",
       "      <td>[\"Susan Guerrero\",\"David Bishop\"]</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Order Id       Date           Meal Id        Company Id  \\\n",
       "0  839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1  97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2  041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "3  YT796QI18WNGZ7ZJ 2014-04-12  C9SDFHF7553BE247  LJKS5NK6788CYMUU   \n",
       "4  6YLROQT27B6HRF4E 2015-07-28  48EQXS6IHYNZDDZ5  LJKS5NK6788CYMUU   \n",
       "5  AT0R4DFYYAFOC88Q 2014-07-21  W48JPR1UYWJ18NC6  LJKS5NK6788CYMUU   \n",
       "6  2DDN2LHS7G85GKPQ 2014-04-29  1MKLAKBOE3SP7YUL  LJKS5NK6788CYMUU   \n",
       "\n",
       "               Date of Meal  \\\n",
       "0 2016-05-31 05:00:00+00:00   \n",
       "1 2018-10-01 18:00:00+00:00   \n",
       "2 2014-08-23 12:00:00+00:00   \n",
       "3 2014-04-07 19:00:00+00:00   \n",
       "4 2015-07-27 12:00:00+00:00   \n",
       "5 2014-07-17 18:00:00+00:00   \n",
       "6 2014-04-30 19:00:00+00:00   \n",
       "\n",
       "                                        Participants  Meal Price Type of Meal  \\\n",
       "0                                   [\"David Bishop\"]       469.0    Breakfast   \n",
       "1                                   [\"David Bishop\"]        22.0       Dinner   \n",
       "2                                 [\"Karen Stansell\"]       314.0        Lunch   \n",
       "3                                   [\"Addie Patino\"]       438.0       Dinner   \n",
       "4                  [\"Addie Patino\",\"Susan Guerrero\"]       690.0        Lunch   \n",
       "5  [\"David Bishop\",\"Susan Guerrero\",\"Karen Stanse...       181.0       Dinner   \n",
       "6                  [\"Susan Guerrero\",\"David Bishop\"]        14.0       Dinner   \n",
       "\n",
       "   Heroes Adjustment  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  \n",
       "3              False  \n",
       "4              False  \n",
       "5              False  \n",
       "6              False  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Order Id</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
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       "      <td>2DDN2LHS7G85GKPQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Date</td>\n",
       "      <td>2016-05-27 00:00:00</td>\n",
       "      <td>2018-09-27 00:00:00</td>\n",
       "      <td>2014-08-24 00:00:00</td>\n",
       "      <td>2014-04-12 00:00:00</td>\n",
       "      <td>2015-07-28 00:00:00</td>\n",
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       "      <td>2014-04-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Meal Id</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>W48JPR1UYWJ18NC6</td>\n",
       "      <td>1MKLAKBOE3SP7YUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Company Id</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Date of Meal</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>2014-04-07 19:00:00+00:00</td>\n",
       "      <td>2015-07-27 12:00:00+00:00</td>\n",
       "      <td>2014-07-17 18:00:00+00:00</td>\n",
       "      <td>2014-04-30 19:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Participants</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>[\"Addie Patino\"]</td>\n",
       "      <td>[\"Addie Patino\",\"Susan Guerrero\"]</td>\n",
       "      <td>[\"David Bishop\",\"Susan Guerrero\",\"Karen Stanse...</td>\n",
       "      <td>[\"Susan Guerrero\",\"David Bishop\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Meal Price</td>\n",
       "      <td>469</td>\n",
       "      <td>22</td>\n",
       "      <td>314</td>\n",
       "      <td>438</td>\n",
       "      <td>690</td>\n",
       "      <td>181</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Type of Meal</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Dinner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Heroes Adjustment</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": [
       "                                           0                          1  \\\n",
       "Order Id                    839FKFW2LLX4LMBB           97OX39BGVMHODLJM   \n",
       "Date                     2016-05-27 00:00:00        2018-09-27 00:00:00   \n",
       "Meal Id                     INBUX904GIHI8YBD           J0MMOOPP709DIDIE   \n",
       "Company Id                  LJKS5NK6788CYMUU           LJKS5NK6788CYMUU   \n",
       "Date of Meal       2016-05-31 05:00:00+00:00  2018-10-01 18:00:00+00:00   \n",
       "Participants                [\"David Bishop\"]           [\"David Bishop\"]   \n",
       "Meal Price                               469                         22   \n",
       "Type of Meal                       Breakfast                     Dinner   \n",
       "Heroes Adjustment                      False                      False   \n",
       "\n",
       "                                           2                          3  \\\n",
       "Order Id                    041ORQM5OIHTIU6L           YT796QI18WNGZ7ZJ   \n",
       "Date                     2014-08-24 00:00:00        2014-04-12 00:00:00   \n",
       "Meal Id                     E4UJLQNCI16UX5CS           C9SDFHF7553BE247   \n",
       "Company Id                  LJKS5NK6788CYMUU           LJKS5NK6788CYMUU   \n",
       "Date of Meal       2014-08-23 12:00:00+00:00  2014-04-07 19:00:00+00:00   \n",
       "Participants              [\"Karen Stansell\"]           [\"Addie Patino\"]   \n",
       "Meal Price                               314                        438   \n",
       "Type of Meal                           Lunch                     Dinner   \n",
       "Heroes Adjustment                      False                      False   \n",
       "\n",
       "                                                   4  \\\n",
       "Order Id                            6YLROQT27B6HRF4E   \n",
       "Date                             2015-07-28 00:00:00   \n",
       "Meal Id                             48EQXS6IHYNZDDZ5   \n",
       "Company Id                          LJKS5NK6788CYMUU   \n",
       "Date of Meal               2015-07-27 12:00:00+00:00   \n",
       "Participants       [\"Addie Patino\",\"Susan Guerrero\"]   \n",
       "Meal Price                                       690   \n",
       "Type of Meal                                   Lunch   \n",
       "Heroes Adjustment                              False   \n",
       "\n",
       "                                                                   5  \\\n",
       "Order Id                                            AT0R4DFYYAFOC88Q   \n",
       "Date                                             2014-07-21 00:00:00   \n",
       "Meal Id                                             W48JPR1UYWJ18NC6   \n",
       "Company Id                                          LJKS5NK6788CYMUU   \n",
       "Date of Meal                               2014-07-17 18:00:00+00:00   \n",
       "Participants       [\"David Bishop\",\"Susan Guerrero\",\"Karen Stanse...   \n",
       "Meal Price                                                       181   \n",
       "Type of Meal                                                  Dinner   \n",
       "Heroes Adjustment                                              False   \n",
       "\n",
       "                                                   6  \n",
       "Order Id                            2DDN2LHS7G85GKPQ  \n",
       "Date                             2014-04-29 00:00:00  \n",
       "Meal Id                             1MKLAKBOE3SP7YUL  \n",
       "Company Id                          LJKS5NK6788CYMUU  \n",
       "Date of Meal               2014-04-30 19:00:00+00:00  \n",
       "Participants       [\"Susan Guerrero\",\"David Bishop\"]  \n",
       "Meal Price                                        14  \n",
       "Type of Meal                                  Dinner  \n",
       "Heroes Adjustment                              False  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head(7).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "      <th>Breakfast</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>469.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-07 19:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6YLROQT27B6HRF4E</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2015-07-27 12:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\",\"Susan Guerrero\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>50012</td>\n",
       "      <td>4OMS8ZSA0UX8LHWI</td>\n",
       "      <td>2017-09-20</td>\n",
       "      <td>1TD5MROATV1NHZ4Y</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-21 06:00:00+00:00</td>\n",
       "      <td>[\"Regina Shirley\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50013</td>\n",
       "      <td>RR0VKJN8V0KHNKGG</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>22EX9VZSJKHP4AIP</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2018-03-18 08:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\",\"Chester Mortimer\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50014</td>\n",
       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>LMX18PNGWCIMG1QW</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-22 19:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50015</td>\n",
       "      <td>QHEUIYNC0XQX7GDR</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2018-02-01 20:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50016</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>ORWFRT5TUSYGNYG7</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-09 12:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50017 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Id       Date           Meal Id        Company Id  \\\n",
       "0      839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1      97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2      041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "3      YT796QI18WNGZ7ZJ 2014-04-12  C9SDFHF7553BE247  LJKS5NK6788CYMUU   \n",
       "4      6YLROQT27B6HRF4E 2015-07-28  48EQXS6IHYNZDDZ5  LJKS5NK6788CYMUU   \n",
       "...                 ...        ...               ...               ...   \n",
       "50012  4OMS8ZSA0UX8LHWI 2017-09-20  1TD5MROATV1NHZ4Y  E4K99D4JR9E40VE1   \n",
       "50013  RR0VKJN8V0KHNKGG 2018-03-19  22EX9VZSJKHP4AIP  E4K99D4JR9E40VE1   \n",
       "50014  STJ6QJC30WPRM93H 2017-09-21  LMX18PNGWCIMG1QW  E4K99D4JR9E40VE1   \n",
       "50015  QHEUIYNC0XQX7GDR 2018-01-28  4U0VH2TGQL30X23X  E4K99D4JR9E40VE1   \n",
       "50016  NKHFWT5I2J9LPAPG 2017-09-06  ORWFRT5TUSYGNYG7  E4K99D4JR9E40VE1   \n",
       "\n",
       "                   Date of Meal                        Participants  \\\n",
       "0     2016-05-31 05:00:00+00:00                    [\"David Bishop\"]   \n",
       "1     2018-10-01 18:00:00+00:00                    [\"David Bishop\"]   \n",
       "2     2014-08-23 12:00:00+00:00                  [\"Karen Stansell\"]   \n",
       "3     2014-04-07 19:00:00+00:00                    [\"Addie Patino\"]   \n",
       "4     2015-07-27 12:00:00+00:00   [\"Addie Patino\",\"Susan Guerrero\"]   \n",
       "...                         ...                                 ...   \n",
       "50012 2017-09-21 06:00:00+00:00                  [\"Regina Shirley\"]   \n",
       "50013 2018-03-18 08:00:00+00:00  [\"Robin Ramos\",\"Chester Mortimer\"]   \n",
       "50014 2017-09-22 19:00:00+00:00                     [\"Robin Ramos\"]   \n",
       "50015 2018-02-01 20:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "50016 2017-09-09 12:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "\n",
       "       Heroes Adjustment  Breakfast  Dinner  Lunch  \n",
       "0                  False      469.0     0.0    0.0  \n",
       "1                  False        0.0    22.0    0.0  \n",
       "2                  False        0.0     0.0  314.0  \n",
       "3                  False        0.0   438.0    0.0  \n",
       "4                  False        0.0     0.0  690.0  \n",
       "...                  ...        ...     ...    ...  \n",
       "50012              False        9.0     0.0    0.0  \n",
       "50013              False       25.0     0.0    0.0  \n",
       "50014              False        0.0   160.0    0.0  \n",
       "50015              False        0.0   497.0    0.0  \n",
       "50016              False        0.0     0.0  365.0  \n",
       "\n",
       "[50017 rows x 10 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 15)\n",
    "\n",
    "melt_experiment = pd.merge(\n",
    "    invoices,\n",
    "    pd.get_dummies(invoices['Type of Meal']) * dummies.mul(invoices['Meal Price'].values,axis=0),\n",
    "    left_index=True,\n",
    "    right_index=True\n",
    ")\n",
    "del melt_experiment['Type of Meal']\n",
    "del melt_experiment['Meal Price']\n",
    "melt_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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",
       "\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>Order Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meal Id</th>\n",
       "      <th>Company Id</th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Participants</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>839FKFW2LLX4LMBB</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>INBUX904GIHI8YBD</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2016-05-31 05:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97OX39BGVMHODLJM</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>J0MMOOPP709DIDIE</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2018-10-01 18:00:00+00:00</td>\n",
       "      <td>[\"David Bishop\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>041ORQM5OIHTIU6L</td>\n",
       "      <td>2014-08-24</td>\n",
       "      <td>E4UJLQNCI16UX5CS</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-08-23 12:00:00+00:00</td>\n",
       "      <td>[\"Karen Stansell\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>YT796QI18WNGZ7ZJ</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>C9SDFHF7553BE247</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2014-04-07 19:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6YLROQT27B6HRF4E</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>48EQXS6IHYNZDDZ5</td>\n",
       "      <td>LJKS5NK6788CYMUU</td>\n",
       "      <td>2015-07-27 12:00:00+00:00</td>\n",
       "      <td>[\"Addie Patino\",\"Susan Guerrero\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>150046</td>\n",
       "      <td>4OMS8ZSA0UX8LHWI</td>\n",
       "      <td>2017-09-20</td>\n",
       "      <td>1TD5MROATV1NHZ4Y</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-21 06:00:00+00:00</td>\n",
       "      <td>[\"Regina Shirley\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150047</td>\n",
       "      <td>RR0VKJN8V0KHNKGG</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>22EX9VZSJKHP4AIP</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2018-03-18 08:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\",\"Chester Mortimer\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150048</td>\n",
       "      <td>STJ6QJC30WPRM93H</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>LMX18PNGWCIMG1QW</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-22 19:00:00+00:00</td>\n",
       "      <td>[\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150049</td>\n",
       "      <td>QHEUIYNC0XQX7GDR</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>4U0VH2TGQL30X23X</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2018-02-01 20:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150050</td>\n",
       "      <td>NKHFWT5I2J9LPAPG</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>ORWFRT5TUSYGNYG7</td>\n",
       "      <td>E4K99D4JR9E40VE1</td>\n",
       "      <td>2017-09-09 12:00:00+00:00</td>\n",
       "      <td>[\"Chester Mortimer\",\"Robin Ramos\"]</td>\n",
       "      <td>False</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150051 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Order Id       Date           Meal Id        Company Id  \\\n",
       "0       839FKFW2LLX4LMBB 2016-05-27  INBUX904GIHI8YBD  LJKS5NK6788CYMUU   \n",
       "1       97OX39BGVMHODLJM 2018-09-27  J0MMOOPP709DIDIE  LJKS5NK6788CYMUU   \n",
       "2       041ORQM5OIHTIU6L 2014-08-24  E4UJLQNCI16UX5CS  LJKS5NK6788CYMUU   \n",
       "3       YT796QI18WNGZ7ZJ 2014-04-12  C9SDFHF7553BE247  LJKS5NK6788CYMUU   \n",
       "4       6YLROQT27B6HRF4E 2015-07-28  48EQXS6IHYNZDDZ5  LJKS5NK6788CYMUU   \n",
       "...                  ...        ...               ...               ...   \n",
       "150046  4OMS8ZSA0UX8LHWI 2017-09-20  1TD5MROATV1NHZ4Y  E4K99D4JR9E40VE1   \n",
       "150047  RR0VKJN8V0KHNKGG 2018-03-19  22EX9VZSJKHP4AIP  E4K99D4JR9E40VE1   \n",
       "150048  STJ6QJC30WPRM93H 2017-09-21  LMX18PNGWCIMG1QW  E4K99D4JR9E40VE1   \n",
       "150049  QHEUIYNC0XQX7GDR 2018-01-28  4U0VH2TGQL30X23X  E4K99D4JR9E40VE1   \n",
       "150050  NKHFWT5I2J9LPAPG 2017-09-06  ORWFRT5TUSYGNYG7  E4K99D4JR9E40VE1   \n",
       "\n",
       "                    Date of Meal                        Participants  \\\n",
       "0      2016-05-31 05:00:00+00:00                    [\"David Bishop\"]   \n",
       "1      2018-10-01 18:00:00+00:00                    [\"David Bishop\"]   \n",
       "2      2014-08-23 12:00:00+00:00                  [\"Karen Stansell\"]   \n",
       "3      2014-04-07 19:00:00+00:00                    [\"Addie Patino\"]   \n",
       "4      2015-07-27 12:00:00+00:00   [\"Addie Patino\",\"Susan Guerrero\"]   \n",
       "...                          ...                                 ...   \n",
       "150046 2017-09-21 06:00:00+00:00                  [\"Regina Shirley\"]   \n",
       "150047 2018-03-18 08:00:00+00:00  [\"Robin Ramos\",\"Chester Mortimer\"]   \n",
       "150048 2017-09-22 19:00:00+00:00                     [\"Robin Ramos\"]   \n",
       "150049 2018-02-01 20:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "150050 2017-09-09 12:00:00+00:00  [\"Chester Mortimer\",\"Robin Ramos\"]   \n",
       "\n",
       "        Heroes Adjustment Type of Meal  Expenses  \n",
       "0                   False    Breakfast     469.0  \n",
       "1                   False    Breakfast       0.0  \n",
       "2                   False    Breakfast       0.0  \n",
       "3                   False    Breakfast       0.0  \n",
       "4                   False    Breakfast       0.0  \n",
       "...                   ...          ...       ...  \n",
       "150046              False        Lunch       0.0  \n",
       "150047              False        Lunch       0.0  \n",
       "150048              False        Lunch       0.0  \n",
       "150049              False        Lunch       0.0  \n",
       "150050              False        Lunch     365.0  \n",
       "\n",
       "[150051 rows x 9 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(\n",
    "    frame=melt_experiment,\n",
    "    id_vars=['Order Id', 'Date', 'Meal Id', 'Company Id', 'Date of Meal','Participants', 'Heroes Adjustment'],\n",
    "    value_vars=['Breakfast', 'Dinner', 'Lunch'],\n",
    "    var_name='Type of Meal',\n",
    "    value_name='Expenses'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Meal Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>301.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>729.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>336.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14175 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Meal Price\n",
       "Company Id       Type of Meal            \n",
       "007RF0BCBVMXTL80 Breakfast     301.666667\n",
       "                 Dinner        453.000000\n",
       "                 Lunch         112.000000\n",
       "...                                   ...\n",
       "ZZYB9JLJKXRH7U6O Breakfast     729.000000\n",
       "                 Dinner        336.000000\n",
       "                 Lunch         149.000000\n",
       "\n",
       "[14175 rows x 1 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.groupby(['Company Id','Type of Meal']).agg(\n",
    "    {'Meal Price':np.mean}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = invoices.groupby(['Company Id','Type of Meal']).agg(\n",
    "    Avg_Price = pd.NamedAgg(column='Meal Price', aggfunc=np.mean)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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",
       "    .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>Type of Meal</th>\n",
       "      <th>Breakfast</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>007RF0BCBVMXTL80</td>\n",
       "      <td>301.666667</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>373.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>00CVEIVO8C92FFBO</td>\n",
       "      <td>346.500000</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>426.428571</td>\n",
       "      <td>403.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>00OFNRZV8983KC3L</td>\n",
       "      <td>571.500000</td>\n",
       "      <td>665.333333</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>550.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZZY3WSHGJWPKJBCV</td>\n",
       "      <td>448.571429</td>\n",
       "      <td>351.428571</td>\n",
       "      <td>511.333333</td>\n",
       "      <td>419.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>480.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>All</td>\n",
       "      <td>404.625338</td>\n",
       "      <td>400.075604</td>\n",
       "      <td>405.087956</td>\n",
       "      <td>403.257073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4726 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Type of Meal       Breakfast      Dinner       Lunch         All\n",
       "Company Id                                                      \n",
       "007RF0BCBVMXTL80  301.666667  453.000000  112.000000  373.500000\n",
       "00CVEIVO8C92FFBO  346.500000  425.000000  426.428571  403.285714\n",
       "00OFNRZV8983KC3L  571.500000  665.333333  357.000000  550.428571\n",
       "...                      ...         ...         ...         ...\n",
       "ZZY3WSHGJWPKJBCV  448.571429  351.428571  511.333333  419.647059\n",
       "ZZYB9JLJKXRH7U6O  729.000000  336.000000  149.000000  480.636364\n",
       "All               404.625338  400.075604  405.087956  403.257073\n",
       "\n",
       "[4726 rows x 4 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(\n",
    "    invoices,\n",
    "    index=['Company Id'],\n",
    "    columns=['Type of Meal'],\n",
    "    values='Meal Price',\n",
    "    aggfunc=np.mean,\n",
    "    margins=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stack / Unstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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",
       "        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></th>\n",
       "      <th></th>\n",
       "      <th>Meal Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">Breakfast</td>\n",
       "      <td>False</td>\n",
       "      <td>301.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "      <td>453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Lunch</td>\n",
       "      <td>False</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28350 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Meal Price\n",
       "Company Id       Type of Meal Heroes Adjustment            \n",
       "007RF0BCBVMXTL80 Breakfast    False              301.666667\n",
       "                              True                      NaN\n",
       "                 Dinner       False              453.000000\n",
       "...                                                     ...\n",
       "ZZYB9JLJKXRH7U6O Dinner       True                      NaN\n",
       "                 Lunch        False              149.000000\n",
       "                              True                      NaN\n",
       "\n",
       "[28350 rows x 1 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = invoices.groupby(['Company Id','Type of Meal','Heroes Adjustment']).agg(\n",
    "    {'Meal Price':np.mean}\n",
    ")\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead tr th {\n",
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       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Meal Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Heroes Adjustment</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>301.666667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14175 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Meal Price      \n",
       "Heroes Adjustment                   False True \n",
       "Company Id       Type of Meal                  \n",
       "007RF0BCBVMXTL80 Breakfast     301.666667   NaN\n",
       "                 Dinner        453.000000   NaN\n",
       "                 Lunch         112.000000   NaN\n",
       "...                                   ...   ...\n",
       "ZZYB9JLJKXRH7U6O Breakfast     729.000000   NaN\n",
       "                 Dinner        336.000000   NaN\n",
       "                 Lunch         149.000000   NaN\n",
       "\n",
       "[14175 rows x 2 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th colspan=\"3\" halign=\"left\">Meal Price</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th>Breakfast</th>\n",
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       "      <td rowspan=\"2\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
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       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>True</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>00CVEIVO8C92FFBO</td>\n",
       "      <td>False</td>\n",
       "      <td>346.500000</td>\n",
       "      <td>425.0</td>\n",
       "      <td>426.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZZY3WSHGJWPKJBCV</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>False</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>336.0</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9450 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Meal Price                   \n",
       "Type of Meal                         Breakfast Dinner       Lunch\n",
       "Company Id       Heroes Adjustment                               \n",
       "007RF0BCBVMXTL80 False              301.666667  453.0  112.000000\n",
       "                 True                      NaN    NaN         NaN\n",
       "00CVEIVO8C92FFBO False              346.500000  425.0  426.428571\n",
       "...                                        ...    ...         ...\n",
       "ZZY3WSHGJWPKJBCV True                      NaN    NaN         NaN\n",
       "ZZYB9JLJKXRH7U6O False              729.000000  336.0  149.000000\n",
       "                 True                      NaN    NaN         NaN\n",
       "\n",
       "[9450 rows x 3 columns]"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.unstack(level='Type of Meal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Meal Price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Date of Meal</th>\n",
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       "      <th>max</th>\n",
       "      <th>min</th>\n",
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       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th>Type of Meal</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",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>545.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>301.666667</td>\n",
       "      <td>2015-09-17 06:00:00+00:00</td>\n",
       "      <td>2014-01-10 07:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>729.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>2018-09-25 20:00:00+00:00</td>\n",
       "      <td>2014-12-29 20:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>112.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2015-11-27 12:00:00+00:00</td>\n",
       "      <td>2015-11-27 12:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>919.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>2018-12-16 06:00:00+00:00</td>\n",
       "      <td>2014-01-19 06:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>564.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>2016-02-07 19:00:00+00:00</td>\n",
       "      <td>2014-05-11 19:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lunch</td>\n",
       "      <td>207.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>2018-08-14 12:00:00+00:00</td>\n",
       "      <td>2018-03-04 12:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14175 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Meal Price                     \\\n",
       "                                     max    min        mean   \n",
       "Company Id       Type of Meal                                 \n",
       "007RF0BCBVMXTL80 Breakfast         545.0   74.0  301.666667   \n",
       "                 Dinner            729.0  156.0  453.000000   \n",
       "                 Lunch             112.0  112.0  112.000000   \n",
       "...                                  ...    ...         ...   \n",
       "ZZYB9JLJKXRH7U6O Breakfast         919.0  341.0  729.000000   \n",
       "                 Dinner            564.0  244.0  336.000000   \n",
       "                 Lunch             207.0   91.0  149.000000   \n",
       "\n",
       "                                           Date of Meal  \\\n",
       "                                                    max   \n",
       "Company Id       Type of Meal                             \n",
       "007RF0BCBVMXTL80 Breakfast    2015-09-17 06:00:00+00:00   \n",
       "                 Dinner       2018-09-25 20:00:00+00:00   \n",
       "                 Lunch        2015-11-27 12:00:00+00:00   \n",
       "...                                                 ...   \n",
       "ZZYB9JLJKXRH7U6O Breakfast    2018-12-16 06:00:00+00:00   \n",
       "                 Dinner       2016-02-07 19:00:00+00:00   \n",
       "                 Lunch        2018-08-14 12:00:00+00:00   \n",
       "\n",
       "                                                         \n",
       "                                                    min  \n",
       "Company Id       Type of Meal                            \n",
       "007RF0BCBVMXTL80 Breakfast    2014-01-10 07:00:00+00:00  \n",
       "                 Dinner       2014-12-29 20:00:00+00:00  \n",
       "                 Lunch        2015-11-27 12:00:00+00:00  \n",
       "...                                                 ...  \n",
       "ZZYB9JLJKXRH7U6O Breakfast    2014-01-19 06:00:00+00:00  \n",
       "                 Dinner       2014-05-11 19:00:00+00:00  \n",
       "                 Lunch        2018-03-04 12:00:00+00:00  \n",
       "\n",
       "[14175 rows x 5 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_test = invoices.groupby(['Company Id','Type of Meal']).agg({\n",
    "    'Meal Price':[max,min,np.mean],\n",
    "    'Date of Meal':[max,min],\n",
    "})\n",
    "stack_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date of Meal</th>\n",
       "      <th>Meal Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th>Type of Meal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td rowspan=\"3\" valign=\"top\">Breakfast</td>\n",
       "      <td>max</td>\n",
       "      <td>2015-09-17 06:00:00+00:00</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>NaT</td>\n",
       "      <td>301.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2014-01-10 07:00:00+00:00</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td rowspan=\"3\" valign=\"top\">Lunch</td>\n",
       "      <td>max</td>\n",
       "      <td>2018-08-14 12:00:00+00:00</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>NaT</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2018-03-04 12:00:00+00:00</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41403 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Date of Meal  Meal Price\n",
       "Company Id       Type of Meal                                           \n",
       "007RF0BCBVMXTL80 Breakfast    max  2015-09-17 06:00:00+00:00  545.000000\n",
       "                              mean                       NaT  301.666667\n",
       "                              min  2014-01-10 07:00:00+00:00   74.000000\n",
       "...                                                      ...         ...\n",
       "ZZYB9JLJKXRH7U6O Lunch        max  2018-08-14 12:00:00+00:00  207.000000\n",
       "                              mean                       NaT  149.000000\n",
       "                              min  2018-03-04 12:00:00+00:00   91.000000\n",
       "\n",
       "[41403 rows x 2 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_test.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">007RF0BCBVMXTL80</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">Breakfast</td>\n",
       "      <td>Date of Meal</td>\n",
       "      <td>2015-09-17 06:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>Meal Price</td>\n",
       "      <td>545</td>\n",
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       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dinner</td>\n",
       "      <td>Date of Meal</td>\n",
       "      <td>2018-09-25 20:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-29 20:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">ZZYB9JLJKXRH7U6O</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>Meal Price</td>\n",
       "      <td>564</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Lunch</td>\n",
       "      <td>Date of Meal</td>\n",
       "      <td>2018-08-14 12:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-04 12:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Meal Price</td>\n",
       "      <td>207</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27602 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  max  \\\n",
       "Company Id       Type of Meal                                           \n",
       "007RF0BCBVMXTL80 Breakfast    Date of Meal  2015-09-17 06:00:00+00:00   \n",
       "                              Meal Price                          545   \n",
       "                 Dinner       Date of Meal  2018-09-25 20:00:00+00:00   \n",
       "...                                                               ...   \n",
       "ZZYB9JLJKXRH7U6O Dinner       Meal Price                          564   \n",
       "                 Lunch        Date of Meal  2018-08-14 12:00:00+00:00   \n",
       "                              Meal Price                          207   \n",
       "\n",
       "                                                  mean  \\\n",
       "Company Id       Type of Meal                            \n",
       "007RF0BCBVMXTL80 Breakfast    Date of Meal         NaN   \n",
       "                              Meal Price    301.666667   \n",
       "                 Dinner       Date of Meal         NaN   \n",
       "...                                                ...   \n",
       "ZZYB9JLJKXRH7U6O Dinner       Meal Price    336.000000   \n",
       "                 Lunch        Date of Meal         NaN   \n",
       "                              Meal Price    149.000000   \n",
       "\n",
       "                                                                  min  \n",
       "Company Id       Type of Meal                                          \n",
       "007RF0BCBVMXTL80 Breakfast    Date of Meal  2014-01-10 07:00:00+00:00  \n",
       "                              Meal Price                           74  \n",
       "                 Dinner       Date of Meal  2014-12-29 20:00:00+00:00  \n",
       "...                                                               ...  \n",
       "ZZYB9JLJKXRH7U6O Dinner       Meal Price                          244  \n",
       "                 Lunch        Date of Meal  2018-03-04 12:00:00+00:00  \n",
       "                              Meal Price                           91  \n",
       "\n",
       "[27602 rows x 3 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_test.stack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>three</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>one</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
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       "      <td>two</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>three</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    level_0 level_1  0\n",
       "0         0     one  3\n",
       "1         0     two  5\n",
       "2         0   three  7\n",
       "..      ...     ... ..\n",
       "27        9     one  9\n",
       "28        9     two  5\n",
       "29        9   three  2\n",
       "\n",
       "[30 rows x 3 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    data = {\n",
    "        'one': np.random.randint(2,10,size=10),\n",
    "        'two': np.random.randint(4,7,size=10),\n",
    "        'three': np.random.randint(0,12,size=10)\n",
    "    }\n",
    ").stack().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>Company Id</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>03EQ4VP4P0H9DPWU</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>05Q86EK4NTLK9R2L</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>06YCP4EVJEC9D0VJ</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZOQ9K5QU63RH4588</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZSBTR2Y27TGD8OTL</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZXI0NZDW4R201UZA</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Avg_Price  Meal Price\n",
       "Company Id       Type of Meal                       \n",
       "03EQ4VP4P0H9DPWU Dinner              NaN         NaN\n",
       "05Q86EK4NTLK9R2L Dinner              NaN         NaN\n",
       "06YCP4EVJEC9D0VJ Lunch               NaN         NaN\n",
       "...                                  ...         ...\n",
       "ZOQ9K5QU63RH4588 Breakfast           NaN         NaN\n",
       "ZSBTR2Y27TGD8OTL Lunch               NaN         NaN\n",
       "ZXI0NZDW4R201UZA Dinner              NaN         NaN\n",
       "\n",
       "[374 rows x 2 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[res['Meal Price'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Type of Meal</th>\n",
       "      <th>Breakfast</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>007RF0BCBVMXTL80</th>\n",
       "      <td>301.666667</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>373.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00CVEIVO8C92FFBO</th>\n",
       "      <td>346.500000</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>426.428571</td>\n",
       "      <td>403.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00OFNRZV8983KC3L</th>\n",
       "      <td>571.500000</td>\n",
       "      <td>665.333333</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>550.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZY3WSHGJWPKJBCV</th>\n",
       "      <td>448.571429</td>\n",
       "      <td>351.428571</td>\n",
       "      <td>511.333333</td>\n",
       "      <td>419.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZYB9JLJKXRH7U6O</th>\n",
       "      <td>729.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>480.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>404.625338</td>\n",
       "      <td>400.075604</td>\n",
       "      <td>405.087956</td>\n",
       "      <td>403.257073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4726 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Type of Meal       Breakfast      Dinner       Lunch         All\n",
       "Company Id                                                      \n",
       "007RF0BCBVMXTL80  301.666667  453.000000  112.000000  373.500000\n",
       "00CVEIVO8C92FFBO  346.500000  425.000000  426.428571  403.285714\n",
       "00OFNRZV8983KC3L  571.500000  665.333333  357.000000  550.428571\n",
       "...                      ...         ...         ...         ...\n",
       "ZZY3WSHGJWPKJBCV  448.571429  351.428571  511.333333  419.647059\n",
       "ZZYB9JLJKXRH7U6O  729.000000  336.000000  149.000000  480.636364\n",
       "All               404.625338  400.075604  405.087956  403.257073\n",
       "\n",
       "[4726 rows x 4 columns]"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(\n",
    "    invoices,\n",
    "    index='Company Id',\n",
    "    columns='Type of Meal',\n",
    "    values='Meal Price',\n",
    "    aggfunc=np.mean,\n",
    "    margins=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company Id        Type of Meal\n",
       "007RF0BCBVMXTL80  Breakfast        905.0\n",
       "                  Dinner          2718.0\n",
       "                  Lunch            112.0\n",
       "                                   ...  \n",
       "ZZYB9JLJKXRH7U6O  Breakfast       3645.0\n",
       "                  Dinner          1344.0\n",
       "                  Lunch            298.0\n",
       "Length: 13801, dtype: float64"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.groupby(['Company Id','Type of Meal'])['Meal Price'].sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meal Id                            \n",
       "INBUX904GIHI8YBD  Order Id                839FKFW2LLX4LMBB\n",
       "                  Date                 2016-05-27 00:00:00\n",
       "                  Company Id              LJKS5NK6788CYMUU\n",
       "                                              ...         \n",
       "ORWFRT5TUSYGNYG7  Meal Price                           365\n",
       "                  Type of Meal                       Lunch\n",
       "                  Heroes Adjustment                  False\n",
       "Length: 400136, dtype: object"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.set_index('Meal Id').stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Order Id                839FKFW2LLX4LMBB\n",
       "       Date                 2016-05-27 00:00:00\n",
       "       Meal Id                 INBUX904GIHI8YBD\n",
       "                                   ...         \n",
       "50016  Meal Price                           365\n",
       "       Type of Meal                       Lunch\n",
       "       Heroes Adjustment                  False\n",
       "Length: 450153, dtype: object"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices['Meal Price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_numeric(invoices['Meal Price'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoices['Meal Price'].apply(lambda x: type(x)).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas.util.testing as tm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm.makeBoolIndex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm.N, tm.K = 15, 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "berlin_timezone = pytz.timezone('Europe/Berlin')\n",
    "\n",
    "test = pd.DataFrame({\n",
    "    'date_from_first_source':tm.makeDateIndex(),\n",
    "    'date_from_source': [datetime.datetime(2000, 1, np.random.randint(1,25), 8, 15, 12, 0, berlin_timezone) for i in range(10)]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.to_csv('datetime_test.csv',index=False)\n",
    "test = pd.read_csv('datetime_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime(test['date_from_first_source'])\n",
    "pd.to_datetime(test['date_from_source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['date_from_first_source'] - test['date_from_source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.datetime(2019,8,1,3,4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unaware = datetime.datetime(2011, 8, 15, 8, 15, 12, 0)\n",
    "aware = datetime.datetime(2011, 8, 15, 8, 15, 12, 0, pytz.UTC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unaware.isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aware.isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pytz?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "amsterdam_time_zone = pytz.timezone('Europe/Amsterdam')\n",
    "berlin_time_zone = pytz.timezone('Europe/Berlin')\n",
    "us_eastern_time_zone = pytz.timezone('US/Eastern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(datetime.datetime.now(amsterdam_time_zone))\n",
    "print(datetime.datetime.now(berlin_time_zone))\n",
    "print(datetime.datetime.now(us_eastern_time_zone))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.datetime.now(amsterdam_time_zone) - datetime.datetime.now(us_eastern_time_zone)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_data = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/order_leads.csv')\n",
    "sales_team = pd.read_csv('https://raw.githubusercontent.com/FBosler/you-datascientist/master/sales_team.csv')"
   ]
  }
 ],
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