Basic python concepts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,\"b\",3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "student_dict = {\"s_1\":[\"IBM\",\"TCS\"],\"s_2\" :[\"HP\"], \"s_3\":[\"INFOSYS\",\"WIPRO\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['IBM', 'TCS'], ['HP'], ['INFOSYS', 'WIPRO']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[val for k,val in student_dict.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"abe\",1,\"c\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_ex = {\"S1\":[\"HCL\",\"TCS\"],\"S2\":[\"Benz\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S1\n"
     ]
    }
   ],
   "source": [
    "for k,v in dist_ex.items():\n",
    "    if k == \"S1\":\n",
    "        print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.append(\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abe', 1, 'c', 'h']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tup_ex = (1,2,3,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_ex = [1,2,3]\n",
    "dist_ex = {\"Age\":25,\"Weight\":70}\n",
    "\n",
    "tup_ex = (1,2,3,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv(\"Uber Drives.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Uber Drives.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dataframe operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016 21:11</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2016 1:25</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2/2016 20:25</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/5/2016 17:31</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/6/2016 14:42</td>\n",
       "      <td>1/6/2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      START_DATE*       END_DATE* CATEGORY*       START*            STOP*  \\\n",
       "0  1/1/2016 21:11  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1   1/2/2016 1:25   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2  1/2/2016 20:25  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3  1/5/2016 17:31  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4  1/6/2016 14:42  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "   MILES*         PURPOSE*  \n",
       "0     5.1   Meal/Entertain  \n",
       "1     5.0              NaN  \n",
       "2     4.8  Errand/Supplies  \n",
       "3     4.7          Meeting  \n",
       "4    63.7   Customer Visit  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1156, 7)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hasattr(df,\"head\")\n",
    "callable(getattr(df,\"head\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Kar?chi</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>Totals</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12204.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Kar?chi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "1155            Totals               NaN       NaN               NaN   \n",
       "\n",
       "                 STOP*   MILES*        PURPOSE*  \n",
       "1151  Unknown Location      3.9  Temporary Site  \n",
       "1152  Unknown Location     16.2         Meeting  \n",
       "1153           Gampaha      6.4  Temporary Site  \n",
       "1154         Ilukwatta     48.2  Temporary Site  \n",
       "1155               NaN  12204.7             NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*     object\n",
       "END_DATE*       object\n",
       "CATEGORY*       object\n",
       "START*          object\n",
       "STOP*           object\n",
       "MILES*         float64\n",
       "PURPOSE*        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame({\"A\":[\"1\",\"2\",\"3\"],\"B\":[11,12,13],\"C\": [\"12-06-2012\",\"13-06-2015\",\"15-06-2017\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    object\n",
       "B     int64\n",
       "C    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AB\\AppData\\Local\\Temp\\ipykernel_16652\\2705610460.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  temp[\"C\"] = pd.to_datetime(temp[\"C\"])\n"
     ]
    }
   ],
   "source": [
    "temp[\"C\"] = pd.to_datetime(temp[\"C\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"A\"] = pd.to_numeric(temp[\"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A             int64\n",
       "B             int64\n",
       "C    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "temp[\"A\"] = temp[\"A\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A            object\n",
       "B             int64\n",
       "C    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with 'Totals' in the 'START_DATE*' column\n",
    "df = df[df[\"START_DATE*\"] != 'Totals']\n",
    "\n",
    "# Convert the 'START_DATE*' column to datetime\n",
    "df[\"START_DATE*\"] = pd.to_datetime(df[\"START_DATE*\"], format='%m/%d/%Y %H:%M')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'START_DATE*' column to datetime, handling errors\n",
    "df[\"START_DATE*\"] = pd.to_datetime(df[\"START_DATE*\"], format='%m/%d/%Y %H:%M', errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2016-01-01 21:11:00\n",
       "1      2016-01-02 01:25:00\n",
       "2      2016-01-02 20:25:00\n",
       "3      2016-01-05 17:31:00\n",
       "4      2016-01-06 14:42:00\n",
       "               ...        \n",
       "1150   2016-12-31 01:07:00\n",
       "1151   2016-12-31 13:24:00\n",
       "1152   2016-12-31 15:03:00\n",
       "1153   2016-12-31 21:32:00\n",
       "1154   2016-12-31 22:08:00\n",
       "Name: START_DATE*, Length: 1155, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert data to datetime format\n",
    "pd.to_datetime(df[\"START_DATE*\"],format='%m/%d/%Y %H:%M')\n",
    "# Available time formats: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset summarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AB\\AppData\\Local\\Temp\\ipykernel_16652\\1985922364.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  df.describe(include=\"all\")\n"
     ]
    },
    {
     "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1155</td>\n",
       "      <td>1155</td>\n",
       "      <td>1155</td>\n",
       "      <td>1155</td>\n",
       "      <td>1155</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1154</td>\n",
       "      <td>1154</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016-06-28 23:34:00</td>\n",
       "      <td>6/28/2016 23:59</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1078</td>\n",
       "      <td>201</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2016-12-31 22:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.566840</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.579106</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                START_DATE*        END_DATE* CATEGORY* START* STOP*  \\\n",
       "count                  1155             1155      1155   1155  1155   \n",
       "unique                 1154             1154         2    177   188   \n",
       "top     2016-06-28 23:34:00  6/28/2016 23:59  Business   Cary  Cary   \n",
       "freq                      2                2      1078    201   203   \n",
       "first   2016-01-01 21:11:00              NaN       NaN    NaN   NaN   \n",
       "last    2016-12-31 22:08:00              NaN       NaN    NaN   NaN   \n",
       "mean                    NaN              NaN       NaN    NaN   NaN   \n",
       "std                     NaN              NaN       NaN    NaN   NaN   \n",
       "min                     NaN              NaN       NaN    NaN   NaN   \n",
       "25%                     NaN              NaN       NaN    NaN   NaN   \n",
       "50%                     NaN              NaN       NaN    NaN   NaN   \n",
       "75%                     NaN              NaN       NaN    NaN   NaN   \n",
       "max                     NaN              NaN       NaN    NaN   NaN   \n",
       "\n",
       "             MILES* PURPOSE*  \n",
       "count   1155.000000      653  \n",
       "unique          NaN       10  \n",
       "top             NaN  Meeting  \n",
       "freq            NaN      187  \n",
       "first           NaN      NaN  \n",
       "last            NaN      NaN  \n",
       "mean      10.566840      NaN  \n",
       "std       21.579106      NaN  \n",
       "min        0.500000      NaN  \n",
       "25%        2.900000      NaN  \n",
       "50%        6.000000      NaN  \n",
       "75%       10.400000      NaN  \n",
       "max      310.300000      NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1155 entries, 0 to 1154\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   START_DATE*  1155 non-null   datetime64[ns]\n",
      " 1   END_DATE*    1155 non-null   object        \n",
      " 2   CATEGORY*    1155 non-null   object        \n",
      " 3   START*       1155 non-null   object        \n",
      " 4   STOP*        1155 non-null   object        \n",
      " 5   MILES*       1155 non-null   float64       \n",
      " 6   PURPOSE*     653 non-null    object        \n",
      "dtypes: datetime64[ns](1), float64(1), object(5)\n",
      "memory usage: 72.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cary                201\n",
       "Unknown Location    148\n",
       "Morrisville          85\n",
       "Whitebridge          68\n",
       "Islamabad            57\n",
       "Name: START*, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of unique start locations\n",
    "df[\"START*\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>START_DATE*</th>\n",
       "      <th>START*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>Fort Pierce</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*       START*\n",
       "0 2016-01-01 21:11:00  Fort Pierce\n",
       "1 2016-01-02 01:25:00  Fort Pierce\n",
       "2 2016-01-02 20:25:00  Fort Pierce\n",
       "3 2016-01-05 17:31:00  Fort Pierce"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:4,[0,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*       END_DATE*\n",
       "0 2016-01-01 21:11:00  1/1/2016 21:17\n",
       "1 2016-01-02 01:25:00   1/2/2016 1:37\n",
       "2 2016-01-02 20:25:00  1/2/2016 20:38\n",
       "3 2016-01-05 17:31:00  1/5/2016 17:45"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Positional Indexing\n",
    "df.iloc[0:4,[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*       END_DATE* CATEGORY*\n",
       "0 2016-01-01 21:11:00  1/1/2016 21:17  Business\n",
       "1 2016-01-02 01:25:00   1/2/2016 1:37  Business\n",
       "2 2016-01-02 20:25:00  1/2/2016 20:38  Business\n",
       "3 2016-01-05 17:31:00  1/5/2016 17:45  Business"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:4,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>START_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE* CATEGORY*\n",
       "0 2016-01-01 21:11:00  Business\n",
       "1 2016-01-02 01:25:00  Business\n",
       "2 2016-01-02 20:25:00  Business\n",
       "3 2016-01-05 17:31:00  Business\n",
       "4 2016-01-06 14:42:00  Business"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label based indexing\n",
    "df.loc[:,[\"START_DATE*\",\"CATEGORY*\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.loc[df[\"MILES*\"] > 10,[\"MILES*\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.loc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>1/6/2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-10 12:17:00</td>\n",
       "      <td>1/10/2016 12:44</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-01-10 15:08:00</td>\n",
       "      <td>1/10/2016 15:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>New York</td>\n",
       "      <td>Queens</td>\n",
       "      <td>10.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-01-12 16:02:00</td>\n",
       "      <td>1/12/2016 17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>New York</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>15.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-01-13 13:54:00</td>\n",
       "      <td>1/13/2016 14:07</td>\n",
       "      <td>Business</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>Gulfton</td>\n",
       "      <td>11.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           START_DATE*        END_DATE* CATEGORY*       START*  \\\n",
       "4  2016-01-06 14:42:00   1/6/2016 15:49  Business  Fort Pierce   \n",
       "9  2016-01-10 12:17:00  1/10/2016 12:44  Business      Jamaica   \n",
       "10 2016-01-10 15:08:00  1/10/2016 15:51  Business     New York   \n",
       "22 2016-01-12 16:02:00  1/12/2016 17:00  Business     New York   \n",
       "23 2016-01-13 13:54:00  1/13/2016 14:07  Business     Downtown   \n",
       "\n",
       "              STOP*  MILES*        PURPOSE*  \n",
       "4   West Palm Beach    63.7  Customer Visit  \n",
       "9          New York    16.5  Customer Visit  \n",
       "10           Queens    10.8         Meeting  \n",
       "22    Queens County    15.1         Meeting  \n",
       "23          Gulfton    11.2         Meeting  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out all rides greater than 10 miles\n",
    "df.loc[df[\"MILES*\"] > 10].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>MILES*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MILES*\n",
       "7      0.8\n",
       "8      8.3\n",
       "28    10.4\n",
       "30     4.8\n",
       "34    17.1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out all rides to Cary\n",
    "df.loc[df[\"START*\"]==\"Cary\",[\"MILES*\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 7)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out all rides to Cary & Morrisville          \n",
    "df[df[\"START*\"].isin([\"Cary\",\"Morrisville\"])].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 7)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out all rides to Cary & Morrisville          \n",
    "df.loc[df[\"START*\"].isin([\"Cary\",\"Morrisville\"])].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(869, 7)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[~df[\"START*\"].isin([\"Cary\",\"Morrisville\"])].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2016-03-25 16:52:00</td>\n",
       "      <td>3/25/2016 22:22</td>\n",
       "      <td>Business</td>\n",
       "      <td>Latta</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>310.3</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2016-03-25 22:54:00</td>\n",
       "      <td>3/26/2016 1:39</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Kissimmee</td>\n",
       "      <td>201.0</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>2016-10-30 15:22:00</td>\n",
       "      <td>10/30/2016 18:23</td>\n",
       "      <td>Business</td>\n",
       "      <td>Asheville</td>\n",
       "      <td>Mebane</td>\n",
       "      <td>195.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2016-09-27 21:01:00</td>\n",
       "      <td>9/28/2016 2:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>195.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>2016-07-14 16:39:00</td>\n",
       "      <td>7/14/2016 20:05</td>\n",
       "      <td>Business</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>Banner Elk</td>\n",
       "      <td>195.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "269 2016-03-25 16:52:00   3/25/2016 22:22  Business             Latta   \n",
       "270 2016-03-25 22:54:00    3/26/2016 1:39  Business      Jacksonville   \n",
       "881 2016-10-30 15:22:00  10/30/2016 18:23  Business         Asheville   \n",
       "776 2016-09-27 21:01:00    9/28/2016 2:37  Business  Unknown Location   \n",
       "546 2016-07-14 16:39:00   7/14/2016 20:05  Business       Morrisville   \n",
       "\n",
       "                STOP*  MILES*        PURPOSE*  \n",
       "269      Jacksonville   310.3  Customer Visit  \n",
       "270         Kissimmee   201.0         Meeting  \n",
       "881            Mebane   195.9             NaN  \n",
       "776  Unknown Location   195.6             NaN  \n",
       "546        Banner Elk   195.3             NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"MILES*\"],ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>2016-11-04 21:04:00</td>\n",
       "      <td>11/4/2016 21:20</td>\n",
       "      <td>Business</td>\n",
       "      <td>Agnew</td>\n",
       "      <td>Cory</td>\n",
       "      <td>4.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>2016-11-06 10:50:00</td>\n",
       "      <td>11/6/2016 11:04</td>\n",
       "      <td>Business</td>\n",
       "      <td>Agnew</td>\n",
       "      <td>Renaissance</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>2016-11-05 08:34:00</td>\n",
       "      <td>11/5/2016 8:43</td>\n",
       "      <td>Business</td>\n",
       "      <td>Agnew</td>\n",
       "      <td>Renaissance</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>2016-11-05 19:20:00</td>\n",
       "      <td>11/5/2016 19:28</td>\n",
       "      <td>Business</td>\n",
       "      <td>Agnew</td>\n",
       "      <td>Agnew</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>2016-10-30 12:58:00</td>\n",
       "      <td>10/30/2016 13:18</td>\n",
       "      <td>Business</td>\n",
       "      <td>Almond</td>\n",
       "      <td>Bryson City</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            START_DATE*         END_DATE* CATEGORY*  START*        STOP*  \\\n",
       "906 2016-11-04 21:04:00   11/4/2016 21:20  Business   Agnew         Cory   \n",
       "911 2016-11-06 10:50:00   11/6/2016 11:04  Business   Agnew  Renaissance   \n",
       "908 2016-11-05 08:34:00    11/5/2016 8:43  Business   Agnew  Renaissance   \n",
       "910 2016-11-05 19:20:00   11/5/2016 19:28  Business   Agnew        Agnew   \n",
       "879 2016-10-30 12:58:00  10/30/2016 13:18  Business  Almond  Bryson City   \n",
       "\n",
       "     MILES* PURPOSE*  \n",
       "906     4.3      NaN  \n",
       "911     2.4      NaN  \n",
       "908     2.2      NaN  \n",
       "910     2.2      NaN  \n",
       "879    15.2      NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"START*\", \"MILES*\"],ascending=[True,False]).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df[\"miles_cat\"] = np.where(df[\"MILES*\"] > 5,\"Long Trip\",\"Short Trip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "      <th>miles_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>Long Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Short Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>Short Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>Short Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>1/6/2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>Long Trip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*       END_DATE* CATEGORY*       START*            STOP*  \\\n",
       "0 2016-01-01 21:11:00  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1 2016-01-02 01:25:00   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2 2016-01-02 20:25:00  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3 2016-01-05 17:31:00  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4 2016-01-06 14:42:00  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "   MILES*         PURPOSE*   miles_cat  \n",
       "0     5.1   Meal/Entertain   Long Trip  \n",
       "1     5.0              NaN  Short Trip  \n",
       "2     4.8  Errand/Supplies  Short Trip  \n",
       "3     4.7          Meeting  Short Trip  \n",
       "4    63.7   Customer Visit   Long Trip  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Long Trip     653\n",
       "Short Trip    502\n",
       "Name: miles_cat, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"miles_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(653, 8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"MILES*\"] > 5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START*\n",
       "Agnew         2.775000\n",
       "Almond       15.200000\n",
       "Apex          5.341176\n",
       "Arabi        17.000000\n",
       "Arlington     4.900000\n",
       "Name: MILES*, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each start loc find the mean distance travelled\n",
    "df.groupby(\"START*\")[\"MILES*\"].agg(\"mean\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AB\\AppData\\Local\\Temp\\ipykernel_16652\\3818415379.py:2: FutureWarning: ['START_DATE*', 'END_DATE*', 'CATEGORY*', 'STOP*', 'PURPOSE*', 'miles_cat'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  df.groupby(\"START*\").agg([\"mean\",\"sum\"]).head()\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">MILES*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>START*</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agnew</th>\n",
       "      <td>2.775000</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almond</th>\n",
       "      <td>15.200000</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apex</th>\n",
       "      <td>5.341176</td>\n",
       "      <td>90.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arabi</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arlington</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MILES*      \n",
       "                mean   sum\n",
       "START*                    \n",
       "Agnew       2.775000  11.1\n",
       "Almond     15.200000  15.2\n",
       "Apex        5.341176  90.8\n",
       "Arabi      17.000000  17.0\n",
       "Arlington   4.900000   4.9"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each start loc find the mean & the total dist travelled\n",
    "df.groupby(\"START*\").agg([\"mean\",\"sum\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loops and Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic for loop\n",
    "res = []\n",
    "temp = [1,2,3,4]\n",
    "for i in temp:\n",
    "    res.append(i*i)\n",
    "res\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List comprehension way\n",
    "\n",
    "[x*x for x in temp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_sales = pd.read_csv(\"store_sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>store_id</th>\n",
       "      <th>city</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S_1</td>\n",
       "      <td>Texas</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S_2</td>\n",
       "      <td>California</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S_3</td>\n",
       "      <td>California</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S_4</td>\n",
       "      <td>Texas</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S_5</td>\n",
       "      <td>Texas</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_id        city  Jan  Feb  Mar  Apr  May  Jun  July  Aug  Sep  Oct  \\\n",
       "0      S_1       Texas    8   20   13   21   17   20    24   17   16    9   \n",
       "1      S_2  California   12   19   15   15   11   19     7   15   10   11   \n",
       "2      S_3  California   16   16   14   19   23    6    13   13   15   14   \n",
       "3      S_4       Texas    8   18   13   10   14   14     6    8    8   18   \n",
       "4      S_5       Texas   19    5   24    9    5   24    10    5   24   15   \n",
       "\n",
       "   Nov  Dec  \n",
       "0    7    6  \n",
       "1   21   19  \n",
       "2   24    8  \n",
       "3    7   11  \n",
       "4    6   13  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bonus_func(sales_numbers):\n",
    "    return [\"Eligible\" if x > 15 else \"Not Eligible\" for x in sales_numbers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Not Eligible', 'Eligible', 'Eligible']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_func([12,23,56])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Jan           Feb           Mar           Apr           May  \\\n",
       "0  Not Eligible      Eligible  Not Eligible      Eligible      Eligible   \n",
       "1  Not Eligible      Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "2      Eligible      Eligible  Not Eligible      Eligible      Eligible   \n",
       "3  Not Eligible      Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "4      Eligible  Not Eligible      Eligible  Not Eligible  Not Eligible   \n",
       "\n",
       "            Jun          July           Aug           Sep           Oct  \\\n",
       "0      Eligible      Eligible      Eligible      Eligible  Not Eligible   \n",
       "1      Eligible  Not Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "2  Not Eligible  Not Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "3  Not Eligible  Not Eligible  Not Eligible  Not Eligible      Eligible   \n",
       "4      Eligible  Not Eligible  Not Eligible      Eligible  Not Eligible   \n",
       "\n",
       "            Nov           Dec  \n",
       "0  Not Eligible  Not Eligible  \n",
       "1      Eligible      Eligible  \n",
       "2      Eligible  Not Eligible  \n",
       "3  Not Eligible  Not Eligible  \n",
       "4  Not Eligible  Not Eligible  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.iloc[:,2:].apply(bonus_func).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Jan           Feb           Mar           Apr           May  \\\n",
       "0  Not Eligible      Eligible  Not Eligible      Eligible      Eligible   \n",
       "1  Not Eligible      Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "2      Eligible      Eligible  Not Eligible      Eligible      Eligible   \n",
       "3  Not Eligible      Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "4      Eligible  Not Eligible      Eligible  Not Eligible  Not Eligible   \n",
       "\n",
       "            Jun          July           Aug           Sep           Oct  \\\n",
       "0      Eligible      Eligible      Eligible      Eligible  Not Eligible   \n",
       "1      Eligible  Not Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "2  Not Eligible  Not Eligible  Not Eligible  Not Eligible  Not Eligible   \n",
       "3  Not Eligible  Not Eligible  Not Eligible  Not Eligible      Eligible   \n",
       "4      Eligible  Not Eligible  Not Eligible      Eligible  Not Eligible   \n",
       "\n",
       "            Nov           Dec  \n",
       "0  Not Eligible  Not Eligible  \n",
       "1      Eligible      Eligible  \n",
       "2      Eligible  Not Eligible  \n",
       "3  Not Eligible  Not Eligible  \n",
       "4  Not Eligible  Not Eligible  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.iloc[:,2:].apply(lambda sales_numbers: [\"Eligible\" if x > 15 else \"Not Eligible\" for x in sales_numbers]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['store_id', 'city', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'July',\n",
       "       'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AB\\AppData\\Local\\Temp\\ipykernel_16652\\2402508055.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  store_sales.groupby(\"city\").agg(\"mean\")\n"
     ]
    },
    {
     "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>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>15.500000</td>\n",
       "      <td>15.272727</td>\n",
       "      <td>14.545455</td>\n",
       "      <td>14.954545</td>\n",
       "      <td>14.090909</td>\n",
       "      <td>15.090909</td>\n",
       "      <td>14.636364</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>15.318182</td>\n",
       "      <td>14.954545</td>\n",
       "      <td>16.409091</td>\n",
       "      <td>13.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>14.744186</td>\n",
       "      <td>15.534884</td>\n",
       "      <td>14.627907</td>\n",
       "      <td>13.976744</td>\n",
       "      <td>13.255814</td>\n",
       "      <td>13.697674</td>\n",
       "      <td>14.813953</td>\n",
       "      <td>16.232558</td>\n",
       "      <td>14.883721</td>\n",
       "      <td>14.279070</td>\n",
       "      <td>15.279070</td>\n",
       "      <td>13.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>13.457143</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>14.485714</td>\n",
       "      <td>15.057143</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>13.114286</td>\n",
       "      <td>13.685714</td>\n",
       "      <td>15.485714</td>\n",
       "      <td>14.685714</td>\n",
       "      <td>13.342857</td>\n",
       "      <td>14.657143</td>\n",
       "      <td>13.685714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Jan        Feb        Mar        Apr        May        Jun  \\\n",
       "city                                                                           \n",
       "Arizona     15.500000  15.272727  14.545455  14.954545  14.090909  15.090909   \n",
       "California  14.744186  15.534884  14.627907  13.976744  13.255814  13.697674   \n",
       "Texas       13.457143  14.428571  14.485714  15.057143  13.600000  13.114286   \n",
       "\n",
       "                 July        Aug        Sep        Oct        Nov        Dec  \n",
       "city                                                                          \n",
       "Arizona     14.636364  15.500000  15.318182  14.954545  16.409091  13.318182  \n",
       "California  14.813953  16.232558  14.883721  14.279070  15.279070  13.604651  \n",
       "Texas       13.685714  15.485714  14.685714  13.342857  14.657143  13.685714  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.groupby(\"city\").agg(\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other  Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*      0\n",
       "END_DATE*        0\n",
       "CATEGORY*        0\n",
       "START*           0\n",
       "STOP*            0\n",
       "MILES*           0\n",
       "PURPOSE*       502\n",
       "miles_cat        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*      0\n",
       "END_DATE*        0\n",
       "CATEGORY*        0\n",
       "START*           0\n",
       "STOP*            0\n",
       "MILES*           0\n",
       "PURPOSE*       502\n",
       "miles_cat        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meeting            187\n",
       "Meal/Entertain     160\n",
       "Errand/Supplies    128\n",
       "Customer Visit     101\n",
       "Temporary Site      50\n",
       "Between Offices     18\n",
       "Moving               4\n",
       "Airport/Travel       3\n",
       "Charity ($)          1\n",
       "Commute              1\n",
       "Name: PURPOSE*, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill NA with most common values\n",
    "df[\"PURPOSE*\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna({\"PURPOSE*\":\"Meeting\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meeting            689\n",
       "Meal/Entertain     160\n",
       "Errand/Supplies    128\n",
       "Customer Visit     101\n",
       "Temporary Site      50\n",
       "Between Offices     18\n",
       "Moving               4\n",
       "Airport/Travel       3\n",
       "Charity ($)          1\n",
       "Commute              1\n",
       "Name: PURPOSE*, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PURPOSE*\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>store_id</th>\n",
       "      <th>city</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S_1</td>\n",
       "      <td>Texas</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S_2</td>\n",
       "      <td>California</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S_3</td>\n",
       "      <td>California</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S_4</td>\n",
       "      <td>Texas</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S_5</td>\n",
       "      <td>Texas</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_id        city  Jan  Feb  Mar  Apr  May  Jun  July  Aug  Sep  Oct  \\\n",
       "0      S_1       Texas    8   20   13   21   17   20    24   17   16    9   \n",
       "1      S_2  California   12   19   15   15   11   19     7   15   10   11   \n",
       "2      S_3  California   16   16   14   19   23    6    13   13   15   14   \n",
       "3      S_4       Texas    8   18   13   10   14   14     6    8    8   18   \n",
       "4      S_5       Texas   19    5   24    9    5   24    10    5   24   15   \n",
       "\n",
       "   Nov  Dec  \n",
       "0    7    6  \n",
       "1   21   19  \n",
       "2   24    8  \n",
       "3    7   11  \n",
       "4    6   13  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_mat = np.array(store_sales.iloc[:,2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8, 20, 13, ...,  9,  7,  6],\n",
       "       [12, 19, 15, ..., 11, 21, 19],\n",
       "       [16, 16, 14, ..., 14, 24,  8],\n",
       "       ...,\n",
       "       [16,  9,  6, ..., 22, 17, 22],\n",
       "       [18, 16,  9, ..., 17, 19, 10],\n",
       "       [ 5, 23, 17, ..., 20,  5,  9]], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 13, 21, ...,  9,  7,  6],\n",
       "       [19, 15, 15, ..., 11, 21, 19],\n",
       "       [16, 14, 19, ..., 14, 24,  8],\n",
       "       ...,\n",
       "       [ 9,  6, 14, ..., 22, 17, 22],\n",
       "       [16,  9,  5, ..., 17, 19, 10],\n",
       "       [23, 17, 24, ..., 20,  5,  9]], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_mat_lag = sales_mat[:,1:]\n",
    "sales_mat_lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_mat_lag = sales_mat[:,1:]\n",
    "sales_mat_lag = np.c_[sales_mat_lag,np.zeros(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_diff = sales_mat_lag - sales_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "perct_change = pd.DataFrame(sales_diff/sales_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>-0.190476</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.291667</td>\n",
       "      <td>-0.058824</td>\n",
       "      <td>-0.437500</td>\n",
       "      <td>-0.222222</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>-0.210526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.266667</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>-0.631579</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>-0.095238</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>-0.739130</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>-0.277778</td>\n",
       "      <td>-0.230769</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.571429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>-0.611111</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.736842</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>-0.625000</td>\n",
       "      <td>-0.444444</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>-0.583333</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  1.500000 -0.350000  0.615385 -0.190476  0.176471  0.200000 -0.291667   \n",
       "1  0.583333 -0.210526  0.000000 -0.266667  0.727273 -0.631579  1.142857   \n",
       "2  0.000000 -0.125000  0.357143  0.210526 -0.739130  1.166667  0.000000   \n",
       "3  1.250000 -0.277778 -0.230769  0.400000  0.000000 -0.571429  0.333333   \n",
       "4 -0.736842  3.800000 -0.625000 -0.444444  3.800000 -0.583333 -0.500000   \n",
       "\n",
       "         7         8         9         10   11  \n",
       "0 -0.058824 -0.437500 -0.222222 -0.142857 -1.0  \n",
       "1 -0.333333  0.100000  0.909091 -0.095238 -1.0  \n",
       "2  0.153846 -0.066667  0.714286 -0.666667 -1.0  \n",
       "3  0.000000  1.250000 -0.611111  0.571429 -1.0  \n",
       "4  3.800000 -0.375000 -0.600000  1.166667 -1.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perct_change.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_mat = np.random.uniform(low=0.05, high=0.25, size=(100,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Find total annual profit for each store\n",
    "profit_mat = profit_mat.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 100)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "prof_matrix = np.dot(sales_mat,profit_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prof_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis (Univariate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"housing_prices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AB\\AppData\\Local\\Temp\\ipykernel_16652\\3592246963.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(housing[\"LotArea\"], kde=False);\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze the spread of the \"LotArea\" column\n",
    "\n",
    "sns.distplot(housing[\"LotArea\"], kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# different types of housing exterios\n",
    "\n",
    "plot = sns.countplot(x = \"Exterior1st\", data = housing)\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"LotArea\", y = \"SalePrice\", data = housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50     9478.50\n",
       "0.95    17401.15\n",
       "0.99    37567.64\n",
       "Name: LotArea, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are outliers skewing the relationship.. Redraw the relationshp after removing very large values\n",
    "housing[\"LotArea\"].quantile([0.5,0.95,0.99])\n",
    "housing_sub = housing.loc[housing[\"LotArea\"] < housing[\"LotArea\"].quantile(0.95)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"LotArea\", y = \"SalePrice\", data = housing_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2638433538714056"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.4302099912897768"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"LotArea\"].corr(housing[\"SalePrice\"])\n",
    "housing_sub[\"LotArea\"].corr(housing_sub[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting multiple graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf_cols = [col_name for col_name in housing.columns if \"SF\" in col_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sf_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows = 3,ncols = 3, figsize = (10,10))\n",
    "for i in range(0, len(sf_cols)):\n",
    "    rows = i // 3\n",
    "    cols = i % 3\n",
    "    ax = axs[rows, cols]\n",
    "    plot = sns.regplot(x = sf_cols[i], y = \"SalePrice\", data = housing, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VinylSd    515\n",
       "HdBoard    222\n",
       "MetalSd    220\n",
       "Wd Sdng    206\n",
       "Plywood    108\n",
       "CemntBd     61\n",
       "BrkFace     50\n",
       "WdShing     26\n",
       "Stucco      25\n",
       "AsbShng     20\n",
       "BrkComm      2\n",
       "Stone        2\n",
       "AsphShn      1\n",
       "ImStucc      1\n",
       "CBlock       1\n",
       "Name: Exterior1st, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Is the price of the house impacted by the Exterior covering on house\n",
    "housing[\"Exterior1st\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize = (10,10))\n",
    "sns.boxplot(x = \"Exterior1st\", y = \"SalePrice\", data = housing, ax=axs);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_3_exterior = housing[\"Exterior1st\"].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_3_exterior = list(top_3_exterior.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"Exterior_new\"] = np.where(housing[\"Exterior1st\"].isin(top_3_exterior),housing[\"Exterior1st\"],\"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VinylSd    515\n",
       "Others     503\n",
       "HdBoard    222\n",
       "MetalSd    220\n",
       "Name: Exterior_new, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"Exterior_new\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.boxplot(x = \"Exterior_new\", y = \"SalePrice\", data = housing);\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"new_house\"] = np.where(housing[\"YearBuilt\"] > 2000,\"New\",\"Old\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Old    1096\n",
       "New     364\n",
       "Name: new_house, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"new_house\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"new_house\", y = \"SalePrice\", hue = \"Exterior_new\", data = housing);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior_new</th>\n",
       "      <th>new_house</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HdBoard</th>\n",
       "      <th>New</th>\n",
       "      <td>1</td>\n",
       "      <td>198500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>221</td>\n",
       "      <td>149900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MetalSd</th>\n",
       "      <th>New</th>\n",
       "      <td>19</td>\n",
       "      <td>222000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>201</td>\n",
       "      <td>135000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Others</th>\n",
       "      <th>New</th>\n",
       "      <td>35</td>\n",
       "      <td>303477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>468</td>\n",
       "      <td>142976.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">VinylSd</th>\n",
       "      <th>New</th>\n",
       "      <td>309</td>\n",
       "      <td>219500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>206</td>\n",
       "      <td>167075.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SalePrice          \n",
       "                           count    median\n",
       "Exterior_new new_house                    \n",
       "HdBoard      New               1  198500.0\n",
       "             Old             221  149900.0\n",
       "MetalSd      New              19  222000.0\n",
       "             Old             201  135000.0\n",
       "Others       New              35  303477.0\n",
       "             Old             468  142976.5\n",
       "VinylSd      New             309  219500.0\n",
       "             Old             206  167075.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.groupby([\"Exterior_new\",\"new_house\"]).agg({\"SalePrice\":[\"count\", \"median\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"new_house\", data = housing, hue = \"Exterior_new\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}