merch_line_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Ecommerce Sales and Growth in 2014 and 2015**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Contents**\n",
    "\n",
    "\n",
    "1. [Project Introduction](1.-Project-Introduction)  \n",
    "2. [Data Preparation](-Data-Preparation)  \n",
    "3. [Data Analysis and Visualization](-Data-Analysis-and-Visualization)  \n",
    "4. [Conclusion](-Conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Project Introduction**\n",
    "\n",
    "**Overview**\n",
    "\n",
    "This project uses data from the [U.S. Census Bureau's 2015 Annual Retail Trade Survey](https://www.census.gov/data/tables/2015/econ/e-stats/2015-e-stats.html). The survey collected information on the ecommerce sales and total sales of various merchandise lines in the U.S. retailing sector in 2014 and 2015. The major focus of this project is to display the author's capacity to clean and wrangle data in the python programming language.   \n",
    "\n",
    "A [2020 survey by Anaconda](https://www.datanami.com/2020/07/06/data-prep-still-dominates-data-scientists-time-survey-finds/#:~:text=Data%20scientists%20spend%20about%2045,data%20scientists%20conducted%20by%20Anaconda) revealed that data scientists spend about 26% of their time on data cleansing. Thus, assuring that data is cleaned and structured appropriately is a vital first step in carrying out analysis. \n",
    "\n",
    "**Objectives**\n",
    "\n",
    "- Provide a general summary of ecommerce sales by merchandise line in the U.S. retailing sector between 2014 and 2015\n",
    "- Answer questions regarding highest earning and fastest growing ecommerce lines between 2014 and 2015\n",
    "- Demonstrate capacity to clean, wrangle, and visualize data in python\n",
    "- Demonstrate ability with the following python libraries: pandas, seaborn\n",
    "\n",
    "**Questions**\n",
    "1. Which three merchandise lines had the highest ecommerce sales in 2014?\n",
    "2. Which three merchandise lines had the highest ecommerce sales in 2015?\n",
    "3. What were the three fastest growing merchandise lines among ecommerce sales between 2014 and 2015?\n",
    "4. How much did ecommerce sales grow as a proportion of total sales in 2014 and 2015?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by importing our libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original file was imported first in order to show the original 'messy' data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table 5.  U.S. Electronic Shopping and Mail-Order Houses (NAICS 4541) - Total and E-commerce Sales by Merchandise Line1: 2015 and 2014</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Estimates are based on data from the 2015 Ann...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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>1</th>\n",
       "      <td>Merchandise Lines</td>\n",
       "      <td>Value of Sales</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y/Y Percent Change</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-commerce as Percent of Total Sales</td>\n",
       "      <td>Percent Distribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Sales</td>\n",
       "      <td>E-commerce Sales</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Table 5.  U.S. Electronic Shopping and Mail-Order Houses (NAICS 4541) - Total and E-commerce Sales by Merchandise Line1: 2015 and 2014  \\\n",
       "0  [Estimates are based on data from the 2015 Ann...                                                                                       \n",
       "1                                  Merchandise Lines                                                                                       \n",
       "2                                                NaN                                                                                       \n",
       "3                                                NaN                                                                                       \n",
       "4                                                NaN                                                                                       \n",
       "\n",
       "       Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4          Unnamed: 5  \\\n",
       "0             NaN        NaN        NaN        NaN                 NaN   \n",
       "1  Value of Sales        NaN        NaN        NaN  Y/Y Percent Change   \n",
       "2             NaN        NaN        NaN        NaN                 NaN   \n",
       "3            2015        NaN       2014        NaN                 NaN   \n",
       "4             NaN        NaN        NaN        NaN                 NaN   \n",
       "\n",
       "  Unnamed: 6                            Unnamed: 7            Unnamed: 8  \\\n",
       "0        NaN                                   NaN                   NaN   \n",
       "1        NaN  E-commerce as Percent of Total Sales  Percent Distribution   \n",
       "2        NaN                                   NaN           Total Sales   \n",
       "3        NaN                                   NaN                   NaN   \n",
       "4        NaN                                   NaN                   NaN   \n",
       "\n",
       "          Unnamed: 9  Unnamed: 10  Unnamed: 11  Unnamed: 12  \n",
       "0                NaN          NaN          NaN          NaN  \n",
       "1                NaN          NaN          NaN          NaN  \n",
       "2   E-commerce Sales          NaN          NaN          NaN  \n",
       "3                NaN          NaN          NaN          NaN  \n",
       "4                NaN          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original file\n",
    "file_original = \"C:\\\\Users\\\\Phillip's PC\\\\ecom_by_merch_line_2015.csv\"\n",
    "df_original = pd.read_csv(file_original)    \n",
    "df_original.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several steps were taken during the import stage to already begin making the data tidier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books and magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing and clothing accessories (includes fo...</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer hardware</td>\n",
       "      <td>S</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer software</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, health aids, and beauty aids</td>\n",
       "      <td>S</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    merchandise_line total_sales_2015  \\\n",
       "0                                Books and magazines           12,583   \n",
       "1  Clothing and clothing accessories (includes fo...           58,188   \n",
       "2                                  Computer hardware                S   \n",
       "3                                  Computer software                S   \n",
       "4                Drugs, health aids, and beauty aids                S   \n",
       "\n",
       "  ecommerce_sales_2015 total_sales_2014 ecommerce_sales_2014  \n",
       "0               11,648           11,879               10,828  \n",
       "1               52,128           53,751               46,830  \n",
       "2               17,459           29,527               16,884  \n",
       "3                    S            9,594                6,491  \n",
       "4               21,469           93,586               19,112  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a header and store as 'header'\n",
    "header = ['merchandise_line', 'total_sales_2015', 'ecommerce_sales_2015', 'total_sales_2014', 'ecommerce_sales_2014']\n",
    "\n",
    "# create a new file\n",
    "file = \"C:\\\\Users\\\\Phillip's PC\\\\ecom_by_merch_line_2015.csv\"\n",
    "\n",
    "# re-import data and store as df, set header as 'header'\n",
    "df = pd.read_csv(file, skiprows = 9, usecols = [i for i in range(5)], names = header, header = 0)                 \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 5)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "merchandise_line        object\n",
       "total_sales_2015        object\n",
       "ecommerce_sales_2015    object\n",
       "total_sales_2014        object\n",
       "ecommerce_sales_2014    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On import, we dealt with the empty rows at the top of the data frame as well as the renaming the columns appropriately. Below, we will examine and tidy the tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, beer, and wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Furniture and home furnishings</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Music and videos</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office equipment and supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sporting goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Toys, hobby goods, and games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Other merchandise2</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Nonmerchandise receipts3</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\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>15</th>\n",
       "      <td>S - Estimate does not meet publication standar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\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>17</th>\n",
       "      <td>Note: Estimates have not been adjusted for pri...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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>19</th>\n",
       "      <td>1 Estimates include data for businesses with o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\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>21</th>\n",
       "      <td>2 Includes other merchandise such as collectib...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\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>23</th>\n",
       "      <td>3 Includes nonmerchandise receipts such as auc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\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>25</th>\n",
       "      <td>Source: U.S. Census Bureau, 2015 Annual Retail...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     merchandise_line total_sales_2015  \\\n",
       "6                                Food, beer, and wine            9,841   \n",
       "7                      Furniture and home furnishings           32,369   \n",
       "8                                    Music and videos                S   \n",
       "9                       Office equipment and supplies           10,613   \n",
       "10                                     Sporting goods           13,104   \n",
       "11                       Toys, hobby goods, and games           12,110   \n",
       "12                                 Other merchandise2           68,378   \n",
       "13                           Nonmerchandise receipts3           29,153   \n",
       "14                                                NaN              NaN   \n",
       "15  S - Estimate does not meet publication standar...              NaN   \n",
       "16                                                NaN              NaN   \n",
       "17  Note: Estimates have not been adjusted for pri...              NaN   \n",
       "18                                                NaN              NaN   \n",
       "19  1 Estimates include data for businesses with o...              NaN   \n",
       "20                                                NaN              NaN   \n",
       "21  2 Includes other merchandise such as collectib...              NaN   \n",
       "22                                                NaN              NaN   \n",
       "23  3 Includes nonmerchandise receipts such as auc...              NaN   \n",
       "24                                                NaN              NaN   \n",
       "25  Source: U.S. Census Bureau, 2015 Annual Retail...              NaN   \n",
       "\n",
       "   ecommerce_sales_2015 total_sales_2014 ecommerce_sales_2014  \n",
       "6                 7,579            8,636                6,349  \n",
       "7                28,883           26,599               23,311  \n",
       "8                     S                S                    S  \n",
       "9                 9,236                S                    S  \n",
       "10               11,681           10,910                9,347  \n",
       "11               10,682           10,623                8,997  \n",
       "12               52,893           55,104               41,543  \n",
       "13               26,374           25,500               22,317  \n",
       "14                  NaN              NaN                  NaN  \n",
       "15                  NaN              NaN                  NaN  \n",
       "16                  NaN              NaN                  NaN  \n",
       "17                  NaN              NaN                  NaN  \n",
       "18                  NaN              NaN                  NaN  \n",
       "19                  NaN              NaN                  NaN  \n",
       "20                  NaN              NaN                  NaN  \n",
       "21                  NaN              NaN                  NaN  \n",
       "22                  NaN              NaN                  NaN  \n",
       "23                  NaN              NaN                  NaN  \n",
       "24                  NaN              NaN                  NaN  \n",
       "25                  NaN              NaN                  NaN  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the tail end of the data frame has several empty rows. If we examine the original file we see that these are notes relating to the some of the missing values, information about the 'other merchandise' and 'non-merchandise' lines, the sources, and the data source. These are all unneeded and so we remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books and magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing and clothing accessories (includes fo...</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer hardware</td>\n",
       "      <td>S</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer software</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, health aids, and beauty aids</td>\n",
       "      <td>S</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics and appliances</td>\n",
       "      <td>30,016</td>\n",
       "      <td>26,050</td>\n",
       "      <td>28,158</td>\n",
       "      <td>24,234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, beer, and wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Furniture and home furnishings</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Music and videos</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office equipment and supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sporting goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Toys, hobby goods, and games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Other merchandise2</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Nonmerchandise receipts3</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     merchandise_line total_sales_2015  \\\n",
       "0                                 Books and magazines           12,583   \n",
       "1   Clothing and clothing accessories (includes fo...           58,188   \n",
       "2                                   Computer hardware                S   \n",
       "3                                   Computer software                S   \n",
       "4                 Drugs, health aids, and beauty aids                S   \n",
       "5                          Electronics and appliances           30,016   \n",
       "6                                Food, beer, and wine            9,841   \n",
       "7                      Furniture and home furnishings           32,369   \n",
       "8                                    Music and videos                S   \n",
       "9                       Office equipment and supplies           10,613   \n",
       "10                                     Sporting goods           13,104   \n",
       "11                       Toys, hobby goods, and games           12,110   \n",
       "12                                 Other merchandise2           68,378   \n",
       "13                           Nonmerchandise receipts3           29,153   \n",
       "\n",
       "   ecommerce_sales_2015 total_sales_2014 ecommerce_sales_2014  \n",
       "0                11,648           11,879               10,828  \n",
       "1                52,128           53,751               46,830  \n",
       "2                17,459           29,527               16,884  \n",
       "3                     S            9,594                6,491  \n",
       "4                21,469           93,586               19,112  \n",
       "5                26,050           28,158               24,234  \n",
       "6                 7,579            8,636                6,349  \n",
       "7                28,883           26,599               23,311  \n",
       "8                     S                S                    S  \n",
       "9                 9,236                S                    S  \n",
       "10               11,681           10,910                9,347  \n",
       "11               10,682           10,623                8,997  \n",
       "12               52,893           55,104               41,543  \n",
       "13               26,374           25,500               22,317  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drip empty / unwatned columns of df's tail\n",
    "df = df.drop(df.index[14:26])\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THe row at index 8 is removed because there is no data for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove row at index 8 (no data)\n",
    "df.drop(df.index[8], inplace = True)\n",
    "df\n",
    "\n",
    "# reset index to 0\n",
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books and magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing and clothing accessories (includes fo...</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer hardware</td>\n",
       "      <td>S</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer software</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, health aids, and beauty aids</td>\n",
       "      <td>S</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics and appliances</td>\n",
       "      <td>30,016</td>\n",
       "      <td>26,050</td>\n",
       "      <td>28,158</td>\n",
       "      <td>24,234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, beer, and wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Furniture and home furnishings</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office equipment and supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sporting goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toys, hobby goods, and games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Other merchandise2</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nonmerchandise receipts3</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     merchandise_line total_sales_2015  \\\n",
       "0                                 Books and magazines           12,583   \n",
       "1   Clothing and clothing accessories (includes fo...           58,188   \n",
       "2                                   Computer hardware                S   \n",
       "3                                   Computer software                S   \n",
       "4                 Drugs, health aids, and beauty aids                S   \n",
       "5                          Electronics and appliances           30,016   \n",
       "6                                Food, beer, and wine            9,841   \n",
       "7                      Furniture and home furnishings           32,369   \n",
       "8                       Office equipment and supplies           10,613   \n",
       "9                                      Sporting goods           13,104   \n",
       "10                       Toys, hobby goods, and games           12,110   \n",
       "11                                 Other merchandise2           68,378   \n",
       "12                           Nonmerchandise receipts3           29,153   \n",
       "\n",
       "   ecommerce_sales_2015 total_sales_2014 ecommerce_sales_2014  \n",
       "0                11,648           11,879               10,828  \n",
       "1                52,128           53,751               46,830  \n",
       "2                17,459           29,527               16,884  \n",
       "3                     S            9,594                6,491  \n",
       "4                21,469           93,586               19,112  \n",
       "5                26,050           28,158               24,234  \n",
       "6                 7,579            8,636                6,349  \n",
       "7                28,883           26,599               23,311  \n",
       "8                 9,236                S                    S  \n",
       "9                11,681           10,910                9,347  \n",
       "10               10,682           10,623                8,997  \n",
       "11               52,893           55,104               41,543  \n",
       "12               26,374           25,500               22,317  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we're going to deal with the cells with an 'S' in them. According to the notes 'Estimate does not meet publication standards because of high sampling variability (coefficient of variation is greater than 30%) or poor response quality (total quantity response rate is less than 50%)'. \n",
    "\n",
    "Instead of removing the row completely (which is an option), we will instead make the value equal to the previous (or next) year. This will allow us to include the merchandise line in the analysis. While maybe not completely accurate, it will serve one of the main purposes of this project - display ability to prepare data for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books and magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing and clothing accessories (includes fo...</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer hardware</td>\n",
       "      <td>30102</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer software</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, health aids, and beauty aids</td>\n",
       "      <td>95943</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics and appliances</td>\n",
       "      <td>30,016</td>\n",
       "      <td>26,050</td>\n",
       "      <td>28,158</td>\n",
       "      <td>24,234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, beer, and wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Furniture and home furnishings</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office equipment and supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sporting goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toys, hobby goods, and games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Other merchandise2</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nonmerchandise receipts3</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     merchandise_line total_sales_2015  \\\n",
       "0                                 Books and magazines           12,583   \n",
       "1   Clothing and clothing accessories (includes fo...           58,188   \n",
       "2                                   Computer hardware            30102   \n",
       "3                                   Computer software            9,594   \n",
       "4                 Drugs, health aids, and beauty aids            95943   \n",
       "5                          Electronics and appliances           30,016   \n",
       "6                                Food, beer, and wine            9,841   \n",
       "7                      Furniture and home furnishings           32,369   \n",
       "8                       Office equipment and supplies           10,613   \n",
       "9                                      Sporting goods           13,104   \n",
       "10                       Toys, hobby goods, and games           12,110   \n",
       "11                                 Other merchandise2           68,378   \n",
       "12                           Nonmerchandise receipts3           29,153   \n",
       "\n",
       "   ecommerce_sales_2015 total_sales_2014 ecommerce_sales_2014  \n",
       "0                11,648           11,879               10,828  \n",
       "1                52,128           53,751               46,830  \n",
       "2                17,459           29,527               16,884  \n",
       "3                 6,491            9,594                6,491  \n",
       "4                21,469           93,586               19,112  \n",
       "5                26,050           28,158               24,234  \n",
       "6                 7,579            8,636                6,349  \n",
       "7                28,883           26,599               23,311  \n",
       "8                 9,236           10,613                9,236  \n",
       "9                11,681           10,910                9,347  \n",
       "10               10,682           10,623                8,997  \n",
       "11               52,893           55,104               41,543  \n",
       "12               26,374           25,500               22,317  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## impute values into 'S'\n",
    "\n",
    "# computer hardware - make same as 2014 + increase of difference in ecom sales\n",
    "ch = 29527 + (17459 - 16884)\n",
    "ch = str(ch)\n",
    "df.iloc[2, 1] = ch\n",
    " \n",
    "# computer software - make the same as 2014 sales\n",
    "cs = '9,594'\n",
    "df.iloc[3, 1] = cs\n",
    "cs2 = '6,491'\n",
    "df.iloc[3, 2] = cs2\n",
    "\n",
    "# drugs... - make same as 2014 + increase of difference in ecom sales\n",
    "d = 93586 + (21469 - 19112)\n",
    "d = str(d)\n",
    "df.iloc[4, 1] = d\n",
    "\n",
    "# office equipment and supplies - make the same as 2015 sales\n",
    "oes = '10,613'\n",
    "df.iloc[8, 3] = oes\n",
    "oes2 = '9,236'\n",
    "df.iloc[8, 4] = oes2\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will clean up the merchandise labels to be more uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Books and magazines',\n",
       "       'Clothing and clothing accessories (includes footwear)',\n",
       "       'Computer hardware', 'Computer software',\n",
       "       'Drugs, health aids, and beauty aids',\n",
       "       'Electronics and appliances', 'Food, beer, and wine',\n",
       "       'Furniture and home furnishings', 'Office equipment and supplies',\n",
       "       'Sporting goods', 'Toys, hobby goods, and games',\n",
       "       'Other merchandise2', 'Nonmerchandise receipts3'], dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get unique value of merchandise_line column\n",
    "df.merchandise_line.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "merchandise_line = ['books & magazines', 'clothing & accessories', 'computer hardware', 'computer software', \n",
    "                    'drugs, health aids, & beauty aids', 'electronics & appliances', 'food, beer, & wine', \n",
    "                    'home furnishings & furniture', 'office equipment & supplies','sporting goods', \n",
    "                    'toys, hobbies, & games', 'miscellaneous', 'non-merchandise receipts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>books &amp; magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clothing &amp; accessories</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>computer hardware</td>\n",
       "      <td>30102</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>computer software</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drugs, health aids, &amp; beauty aids</td>\n",
       "      <td>95943</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>electronics &amp; appliances</td>\n",
       "      <td>30,016</td>\n",
       "      <td>26,050</td>\n",
       "      <td>28,158</td>\n",
       "      <td>24,234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>food, beer, &amp; wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home furnishings &amp; furniture</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>office equipment &amp; supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sporting goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>toys, hobbies, &amp; games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>miscellaneous</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>non-merchandise receipts</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     merchandise_line total_sales_2015 ecommerce_sales_2015  \\\n",
       "0                   books & magazines           12,583               11,648   \n",
       "1              clothing & accessories           58,188               52,128   \n",
       "2                   computer hardware            30102               17,459   \n",
       "3                   computer software            9,594                6,491   \n",
       "4   drugs, health aids, & beauty aids            95943               21,469   \n",
       "5            electronics & appliances           30,016               26,050   \n",
       "6                  food, beer, & wine            9,841                7,579   \n",
       "7        home furnishings & furniture           32,369               28,883   \n",
       "8         office equipment & supplies           10,613                9,236   \n",
       "9                      sporting goods           13,104               11,681   \n",
       "10             toys, hobbies, & games           12,110               10,682   \n",
       "11                      miscellaneous           68,378               52,893   \n",
       "12           non-merchandise receipts           29,153               26,374   \n",
       "\n",
       "   total_sales_2014 ecommerce_sales_2014  \n",
       "0            11,879               10,828  \n",
       "1            53,751               46,830  \n",
       "2            29,527               16,884  \n",
       "3             9,594                6,491  \n",
       "4            93,586               19,112  \n",
       "5            28,158               24,234  \n",
       "6             8,636                6,349  \n",
       "7            26,599               23,311  \n",
       "8            10,613                9,236  \n",
       "9            10,910                9,347  \n",
       "10           10,623                8,997  \n",
       "11           55,104               41,543  \n",
       "12           25,500               22,317  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['merchandise_line'] = merchandise_line\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When naming the columns above, they were put in lowercase. Later on in the project it was decided that uppercase would look better. Instead of going through and capitalizing each word, we can use a simple one line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# capitalize first letter of each word in\n",
    "df['merchandise_line'] = df.merchandise_line.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>12,583</td>\n",
       "      <td>11,648</td>\n",
       "      <td>11,879</td>\n",
       "      <td>10,828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>58,188</td>\n",
       "      <td>52,128</td>\n",
       "      <td>53,751</td>\n",
       "      <td>46,830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>30102</td>\n",
       "      <td>17,459</td>\n",
       "      <td>29,527</td>\n",
       "      <td>16,884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "      <td>9,594</td>\n",
       "      <td>6,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>95943</td>\n",
       "      <td>21,469</td>\n",
       "      <td>93,586</td>\n",
       "      <td>19,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics &amp; Appliances</td>\n",
       "      <td>30,016</td>\n",
       "      <td>26,050</td>\n",
       "      <td>28,158</td>\n",
       "      <td>24,234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, Beer, &amp; Wine</td>\n",
       "      <td>9,841</td>\n",
       "      <td>7,579</td>\n",
       "      <td>8,636</td>\n",
       "      <td>6,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home Furnishings &amp; Furniture</td>\n",
       "      <td>32,369</td>\n",
       "      <td>28,883</td>\n",
       "      <td>26,599</td>\n",
       "      <td>23,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Equipment &amp; Supplies</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "      <td>10,613</td>\n",
       "      <td>9,236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>13,104</td>\n",
       "      <td>11,681</td>\n",
       "      <td>10,910</td>\n",
       "      <td>9,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toys, Hobbies, &amp; Games</td>\n",
       "      <td>12,110</td>\n",
       "      <td>10,682</td>\n",
       "      <td>10,623</td>\n",
       "      <td>8,997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>68,378</td>\n",
       "      <td>52,893</td>\n",
       "      <td>55,104</td>\n",
       "      <td>41,543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Non-Merchandise Receipts</td>\n",
       "      <td>29,153</td>\n",
       "      <td>26,374</td>\n",
       "      <td>25,500</td>\n",
       "      <td>22,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     merchandise_line total_sales_2015 ecommerce_sales_2015  \\\n",
       "0                   Books & Magazines           12,583               11,648   \n",
       "1              Clothing & Accessories           58,188               52,128   \n",
       "2                   Computer Hardware            30102               17,459   \n",
       "3                   Computer Software            9,594                6,491   \n",
       "4   Drugs, Health Aids, & Beauty Aids            95943               21,469   \n",
       "5            Electronics & Appliances           30,016               26,050   \n",
       "6                  Food, Beer, & Wine            9,841                7,579   \n",
       "7        Home Furnishings & Furniture           32,369               28,883   \n",
       "8         Office Equipment & Supplies           10,613                9,236   \n",
       "9                      Sporting Goods           13,104               11,681   \n",
       "10             Toys, Hobbies, & Games           12,110               10,682   \n",
       "11                      Miscellaneous           68,378               52,893   \n",
       "12           Non-Merchandise Receipts           29,153               26,374   \n",
       "\n",
       "   total_sales_2014 ecommerce_sales_2014  \n",
       "0            11,879               10,828  \n",
       "1            53,751               46,830  \n",
       "2            29,527               16,884  \n",
       "3             9,594                6,491  \n",
       "4            93,586               19,112  \n",
       "5            28,158               24,234  \n",
       "6             8,636                6,349  \n",
       "7            26,599               23,311  \n",
       "8            10,613                9,236  \n",
       "9            10,910                9,347  \n",
       "10           10,623                8,997  \n",
       "11           55,104               41,543  \n",
       "12           25,500               22,317  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, all of our numerical data is actually the string data type. Later we will convert them to integers, but first we need to remove the commas. This comma removal and data type conversion is achieved through with several for loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13 entries, 0 to 12\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   merchandise_line      13 non-null     object\n",
      " 1   total_sales_2015      13 non-null     int64 \n",
      " 2   ecommerce_sales_2015  13 non-null     int64 \n",
      " 3   total_sales_2014      13 non-null     int64 \n",
      " 4   ecommerce_sales_2014  13 non-null     int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 648.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "##1 remove commas from 2015_total_sales column\n",
    "\n",
    "# get amount column into a list array\n",
    "amount = df['total_sales_2015']\n",
    "\n",
    "# create an empty list\n",
    "new_amount = []\n",
    "\n",
    "# loop over elements in 'amount' and 1) remove commas 2) convert to integer\n",
    "for a in amount:\n",
    "    a = a.replace(',', '')\n",
    "    a = int(a)\n",
    "    new_amount.append(a)\n",
    "\n",
    "# replace old 'amount' column in 'df' with 'new_amount' list\n",
    "df['total_sales_2015'] = new_amount\n",
    "\n",
    "##2 remove commas from 2015_ecommerce_sales column\n",
    "\n",
    "# get amount column into a list array\n",
    "amount = df['ecommerce_sales_2015']\n",
    "\n",
    "# create an empty list\n",
    "new_amount = []\n",
    "\n",
    "# loop over elements in 'amount' and 1) remove commas 2) convert to integer\n",
    "for a in amount:\n",
    "    a = a.replace(',', '')\n",
    "    a = int(a)\n",
    "    new_amount.append(a)\n",
    "\n",
    "# replace old 'amount' column in 'df' with 'new_amount' list\n",
    "df['ecommerce_sales_2015'] = new_amount\n",
    "\n",
    "##3 remove commas from 2014_total_sales column\n",
    "\n",
    "# get amount column into a list array\n",
    "amount = df['total_sales_2014']\n",
    "\n",
    "# create an empty list\n",
    "new_amount = []\n",
    "\n",
    "# loop over elements in 'amount' and 1) remove commas 2) convert to integer\n",
    "for a in amount:\n",
    "    a = a.replace(',', '')\n",
    "    a = int(a)\n",
    "    new_amount.append(a)\n",
    "\n",
    "# replace old 'amount' column in 'df' with 'new_amount' list\n",
    "df['total_sales_2014'] = new_amount\n",
    "\n",
    "##4 remove commas from 2015_ecommerce_sales column\n",
    "\n",
    "# get amount column into a list array\n",
    "amount = df['ecommerce_sales_2014']\n",
    "\n",
    "# create an empty list\n",
    "new_amount = []\n",
    "\n",
    "# loop over elements in 'amount' and 1) remove commas 2) convert to integer\n",
    "for a in amount:\n",
    "    a = a.replace(',', '')\n",
    "    a = int(a)\n",
    "    new_amount.append(a)\n",
    "\n",
    "# replace old 'amount' column in 'df' with 'new_amount' list\n",
    "df['ecommerce_sales_2014'] = new_amount\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will convert our data from a wide to a long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>total_sales_2015</td>\n",
       "      <td>12583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>total_sales_2015</td>\n",
       "      <td>58188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>total_sales_2015</td>\n",
       "      <td>30102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>total_sales_2015</td>\n",
       "      <td>9594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>total_sales_2015</td>\n",
       "      <td>95943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    merchandise_line        sales_type  amount\n",
       "0                  Books & Magazines  total_sales_2015   12583\n",
       "1             Clothing & Accessories  total_sales_2015   58188\n",
       "2                  Computer Hardware  total_sales_2015   30102\n",
       "3                  Computer Software  total_sales_2015    9594\n",
       "4  Drugs, Health Aids, & Beauty Aids  total_sales_2015   95943"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert 'df' into a long form \n",
    "df_long = pd.melt(df, id_vars = 'merchandise_line', \n",
    "                  value_vars = ['total_sales_2015', 'ecommerce_sales_2015','total_sales_2014', \n",
    "                                'ecommerce_sales_2014'], \n",
    "                  var_name = 'sales_type', value_name = 'amount')\n",
    "df_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the 'sales_type' column we want to remove the year and give it its own column 'year'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014']\n"
     ]
    }
   ],
   "source": [
    "## remove year from 'sales_type' column and create a new column 'year'\n",
    "\n",
    "# turn 'sales_type' into a list array and save it as 'year'\n",
    "year = df_long['sales_type']\n",
    "year = list(year)\n",
    "print(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2015', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014', '2014']\n"
     ]
    }
   ],
   "source": [
    "# create an empty list that will become a new column 'year'\n",
    "year_column = []\n",
    "\n",
    "for element in year:\n",
    "    new_element = element[-4:]\n",
    "    year_column.append(new_element)\n",
    "\n",
    "print(year_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'total_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'ecommerce_sales_2015', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'total_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014', 'ecommerce_sales_2014']\n"
     ]
    }
   ],
   "source": [
    "# turn 'sales_type' into a list array and save it as 'sales_type'\n",
    "sales_type = df_long['sales_type']\n",
    "sales_type = list(sales_type)\n",
    "print(sales_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'total', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce', 'ecommerce']\n"
     ]
    }
   ],
   "source": [
    "# create an empty list that will become a new column 'sales_type'\n",
    "sales_type_column = []\n",
    "\n",
    "for element in sales_type:\n",
    "    new_element = element[:-11]\n",
    "    sales_type_column.append(new_element)\n",
    "\n",
    "print(sales_type_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>total</td>\n",
       "      <td>12583</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>total</td>\n",
       "      <td>58188</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>total</td>\n",
       "      <td>30102</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>total</td>\n",
       "      <td>9594</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>total</td>\n",
       "      <td>95943</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    merchandise_line sales_type  amount  year\n",
       "0                  Books & Magazines      total   12583  2015\n",
       "1             Clothing & Accessories      total   58188  2015\n",
       "2                  Computer Hardware      total   30102  2015\n",
       "3                  Computer Software      total    9594  2015\n",
       "4  Drugs, Health Aids, & Beauty Aids      total   95943  2015"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add 'year' and 'sales_type_column' to 'df'\n",
    "df_long['year'] = year_column\n",
    "df_long['sales_type'] = sales_type_column\n",
    "df_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last, we will rearrange our columns and convert the year column to an int. data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>total</td>\n",
       "      <td>2015</td>\n",
       "      <td>12583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>total</td>\n",
       "      <td>2015</td>\n",
       "      <td>58188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>total</td>\n",
       "      <td>2015</td>\n",
       "      <td>30102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>total</td>\n",
       "      <td>2015</td>\n",
       "      <td>9594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>total</td>\n",
       "      <td>2015</td>\n",
       "      <td>95943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    merchandise_line sales_type  year  amount\n",
       "0                  Books & Magazines      total  2015   12583\n",
       "1             Clothing & Accessories      total  2015   58188\n",
       "2                  Computer Hardware      total  2015   30102\n",
       "3                  Computer Software      total  2015    9594\n",
       "4  Drugs, Health Aids, & Beauty Aids      total  2015   95943"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rearrage columns\n",
    "df_long = df_long[['merchandise_line', 'sales_type', 'year', 'amount']]\n",
    "df_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52 entries, 0 to 51\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   merchandise_line  52 non-null     object\n",
      " 1   sales_type        52 non-null     object\n",
      " 2   year              52 non-null     object\n",
      " 3   amount            52 non-null     int64 \n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_long.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52 entries, 0 to 51\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   merchandise_line  52 non-null     object\n",
      " 1   sales_type        52 non-null     object\n",
      " 2   year              52 non-null     int32 \n",
      " 3   amount            52 non-null     int64 \n",
      "dtypes: int32(1), int64(1), object(2)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# convert 'year' to an integer\n",
    "df_long['year'] = df_long['year'].astype(int)\n",
    "df_long.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And voila! We now have a clean, long data frame. We can now move on to answering some questions about it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3. Data Analysis and Visualization**\n",
    "\n",
    "In this section the 4 questions posed in the introduction are answered. The answer to the question are given first, followed by how explanation on how we arrived at this conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **1. Which three merch lines had the highest ecommerce sales in 2014?**\n",
    "\n",
    "*1. Clothing & Accessories - $46,830,000,000*\n",
    "\n",
    "*2. Electronics & Appliances - $24,234,000,000*\n",
    "\n",
    "*3. Home Furnishings & Furniture - $23,311,000,000*\n",
    "\n",
    "*NOTE: excludes miscellaneous and non-mechandise receipts merch lines*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>11648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>52128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>17459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>6491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>21469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     merchandise_line sales_type  year  amount\n",
       "13                  Books & Magazines  ecommerce  2015   11648\n",
       "14             Clothing & Accessories  ecommerce  2015   52128\n",
       "15                  Computer Hardware  ecommerce  2015   17459\n",
       "16                  Computer Software  ecommerce  2015    6491\n",
       "17  Drugs, Health Aids, & Beauty Aids  ecommerce  2015   21469"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a df with only ecommerce sales\n",
    "is_ecom = df_long['sales_type'] == 'ecommerce'\n",
    "ecom = df_long[is_ecom]\n",
    "\n",
    "## create an alternate ecom df without two merch cats\n",
    "\n",
    "# remove miscellaneous in merch line\n",
    "no_misc = ecom['merchandise_line'] != 'Miscellaneous'\n",
    "ecom1 = ecom[no_misc]\n",
    "\n",
    "# remove non-merchandise receipts\n",
    "no_non = ecom1['merchandise_line'] != 'Non-Merchandise Receipts'\n",
    "ecom1 = ecom1[no_non]\n",
    "\n",
    "ecom1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2014</td>\n",
       "      <td>46830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Electronics &amp; Appliances</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2014</td>\n",
       "      <td>24234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Home Furnishings &amp; Furniture</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2014</td>\n",
       "      <td>23311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                merchandise_line sales_type  year  amount\n",
       "40        Clothing & Accessories  ecommerce  2014   46830\n",
       "44      Electronics & Appliances  ecommerce  2014   24234\n",
       "46  Home Furnishings & Furniture  ecommerce  2014   23311"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create 2014 df\n",
    "ecom2014 = ecom1['year'] == 2014\n",
    "ecom2014 = ecom1[ecom2014]\n",
    "ecom2014\n",
    "\n",
    "ecom2014.sort_values(by = 'amount', ascending = False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set size and style\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.set(style = 'white')\n",
    "\n",
    "# create barplot\n",
    "fig1 = sns.barplot(x = 'amount', y = 'merchandise_line', data = ecom2014, order = ecom2014.sort_values('amount', ascending = False).merchandise_line)\n",
    "\n",
    "# labels \n",
    "plt.xlabel('Amount (in millions)', fontsize = 14)\n",
    "plt.ylabel(None)\n",
    "plt.title('Ecommerce Sales 2014', fontsize = 24)\n",
    "\n",
    "# legend\n",
    "\n",
    "# remove top and right border from plot\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **2. Which three merch lines had the highest ecommerce sales in 2015?**\n",
    "\n",
    "*1. Clothing & Acessories - $52,128,000,000*\n",
    "\n",
    "*2. Home Furnishings & Furniture - $28,883,000,000*\n",
    "\n",
    "*3. Electronics & Appliances - $26,050,000,000*\n",
    "\n",
    "*NOTE: excludes miscellaneous and non-mechandise receipts merch lines*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>52128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Home Furnishings &amp; Furniture</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>28883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Electronics &amp; Appliances</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>26050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                merchandise_line sales_type  year  amount\n",
       "14        Clothing & Accessories  ecommerce  2015   52128\n",
       "20  Home Furnishings & Furniture  ecommerce  2015   28883\n",
       "18      Electronics & Appliances  ecommerce  2015   26050"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an ecom2015 df\n",
    "ecom2015 = ecom1['year'] == 2015\n",
    "ecom2015 = ecom1[ecom2015]\n",
    "ecom2015\n",
    "\n",
    "# sort for largest\n",
    "ecom2015.sort_values(by = 'amount', ascending = False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set size and style\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.set(style = 'white')\n",
    "\n",
    "# create barplot\n",
    "fig2 = sns.barplot(x = 'amount', y = 'merchandise_line', data = ecom2015, order = ecom2015.sort_values('amount', ascending = False).merchandise_line)\n",
    "\n",
    "# labels \n",
    "plt.xlabel('Amount (in millions)', fontsize = 14)\n",
    "plt.ylabel(None)\n",
    "plt.title('Ecommerce Sales 2015', fontsize = 24)\n",
    "\n",
    "# legend\n",
    "\n",
    "# remove top and right border from plot\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **3. What were the three fastest growing merch lines among ecommerce sales between 2014 and 2015?**\n",
    "\n",
    "*1. Home Furnishings & Furnitre - $5,572,000,000*\n",
    "\n",
    "*2. Clothing & Accessories - $5,298,000,000*\n",
    "\n",
    "*3. Drug, Health Aids, & Beauty Aids - $2,3547,000,000*\n",
    "\n",
    "*NOTE: excludes miscellaneous and non-mechandise receipts merch lines*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>12583</td>\n",
       "      <td>11648</td>\n",
       "      <td>11879</td>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>58188</td>\n",
       "      <td>52128</td>\n",
       "      <td>53751</td>\n",
       "      <td>46830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>30102</td>\n",
       "      <td>17459</td>\n",
       "      <td>29527</td>\n",
       "      <td>16884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>9594</td>\n",
       "      <td>6491</td>\n",
       "      <td>9594</td>\n",
       "      <td>6491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>95943</td>\n",
       "      <td>21469</td>\n",
       "      <td>93586</td>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics &amp; Appliances</td>\n",
       "      <td>30016</td>\n",
       "      <td>26050</td>\n",
       "      <td>28158</td>\n",
       "      <td>24234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food, Beer, &amp; Wine</td>\n",
       "      <td>9841</td>\n",
       "      <td>7579</td>\n",
       "      <td>8636</td>\n",
       "      <td>6349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home Furnishings &amp; Furniture</td>\n",
       "      <td>32369</td>\n",
       "      <td>28883</td>\n",
       "      <td>26599</td>\n",
       "      <td>23311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Equipment &amp; Supplies</td>\n",
       "      <td>10613</td>\n",
       "      <td>9236</td>\n",
       "      <td>10613</td>\n",
       "      <td>9236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>13104</td>\n",
       "      <td>11681</td>\n",
       "      <td>10910</td>\n",
       "      <td>9347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toys, Hobbies, &amp; Games</td>\n",
       "      <td>12110</td>\n",
       "      <td>10682</td>\n",
       "      <td>10623</td>\n",
       "      <td>8997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     merchandise_line  total_sales_2015  ecommerce_sales_2015  \\\n",
       "0                   Books & Magazines             12583                 11648   \n",
       "1              Clothing & Accessories             58188                 52128   \n",
       "2                   Computer Hardware             30102                 17459   \n",
       "3                   Computer Software              9594                  6491   \n",
       "4   Drugs, Health Aids, & Beauty Aids             95943                 21469   \n",
       "5            Electronics & Appliances             30016                 26050   \n",
       "6                  Food, Beer, & Wine              9841                  7579   \n",
       "7        Home Furnishings & Furniture             32369                 28883   \n",
       "8         Office Equipment & Supplies             10613                  9236   \n",
       "9                      Sporting Goods             13104                 11681   \n",
       "10             Toys, Hobbies, & Games             12110                 10682   \n",
       "\n",
       "    total_sales_2014  ecommerce_sales_2014  \n",
       "0              11879                 10828  \n",
       "1              53751                 46830  \n",
       "2              29527                 16884  \n",
       "3               9594                  6491  \n",
       "4              93586                 19112  \n",
       "5              28158                 24234  \n",
       "6               8636                  6349  \n",
       "7              26599                 23311  \n",
       "8              10613                  9236  \n",
       "9              10910                  9347  \n",
       "10             10623                  8997  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'df' to be used in next analysis \n",
    "\n",
    "# remove miscellaneous and non merch rows\n",
    "df1 = df.drop(df.index[[11, 12]])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-226-0c5c898b54a6>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ecom2015['growth'] = growth\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>merchandise_line</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "      <th>growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Home Furnishings &amp; Furniture</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>28883</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>52128</td>\n",
       "      <td>5298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>ecommerce</td>\n",
       "      <td>2015</td>\n",
       "      <td>21469</td>\n",
       "      <td>2357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     merchandise_line sales_type  year  amount  growth\n",
       "20       Home Furnishings & Furniture  ecommerce  2015   28883    5572\n",
       "14             Clothing & Accessories  ecommerce  2015   52128    5298\n",
       "17  Drugs, Health Aids, & Beauty Aids  ecommerce  2015   21469    2357"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a growth column from 'df'\n",
    "growth = df1['ecommerce_sales_2015'] - df1['ecommerce_sales_2014']\n",
    "growth = list(growth)\n",
    "\n",
    "# add 'growth' to 'ecom2015' as a new column called 'growth'\n",
    "ecom2015['growth'] = growth\n",
    "ecom2015.head()\n",
    "\n",
    "# sort for largest\n",
    "growth2015 = ecom2015.sort_values(by = 'growth', ascending = False)\n",
    "growth2015.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set size and style\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.set(style = 'white')\n",
    "\n",
    "# create barplot\n",
    "fig3 = sns.barplot(x = 'growth', y = 'merchandise_line', data = growth2015)\n",
    "\n",
    "# labels \n",
    "plt.xlabel('Amount (in millions)', fontsize = 14)\n",
    "plt.ylabel(None)\n",
    "plt.title('Growth by Merchandise Line between 2014 - 2015', fontsize = 24)\n",
    "\n",
    "# remove top and right border from plot\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **4. How much did ecommerce sales grow as a proportion of total sales in 2014 and 2015?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Between 2014 and 2015 ecommerce sales increased by **3%** from **62%** of the total sales in 2014 to **65%** of the total sales in 2015.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the original dataframe we will be working with for this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>merchandise_line</th>\n",
       "      <th>total_sales_2015</th>\n",
       "      <th>ecommerce_sales_2015</th>\n",
       "      <th>total_sales_2014</th>\n",
       "      <th>ecommerce_sales_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Books &amp; Magazines</td>\n",
       "      <td>12583</td>\n",
       "      <td>11648</td>\n",
       "      <td>11879</td>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clothing &amp; Accessories</td>\n",
       "      <td>58188</td>\n",
       "      <td>52128</td>\n",
       "      <td>53751</td>\n",
       "      <td>46830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Hardware</td>\n",
       "      <td>30102</td>\n",
       "      <td>17459</td>\n",
       "      <td>29527</td>\n",
       "      <td>16884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Software</td>\n",
       "      <td>9594</td>\n",
       "      <td>6491</td>\n",
       "      <td>9594</td>\n",
       "      <td>6491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drugs, Health Aids, &amp; Beauty Aids</td>\n",
       "      <td>95943</td>\n",
       "      <td>21469</td>\n",
       "      <td>93586</td>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    merchandise_line  total_sales_2015  ecommerce_sales_2015  \\\n",
       "0                  Books & Magazines             12583                 11648   \n",
       "1             Clothing & Accessories             58188                 52128   \n",
       "2                  Computer Hardware             30102                 17459   \n",
       "3                  Computer Software              9594                  6491   \n",
       "4  Drugs, Health Aids, & Beauty Aids             95943                 21469   \n",
       "\n",
       "   total_sales_2014  ecommerce_sales_2014  \n",
       "0             11879                 10828  \n",
       "1             53751                 46830  \n",
       "2             29527                 16884  \n",
       "3              9594                  6491  \n",
       "4             93586                 19112  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do some math to find out the percentage of total sales ecommerce accounted for in each year, followed by the difference (aka increase in growth)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.62\n",
      "0.65\n",
      "0.03\n"
     ]
    }
   ],
   "source": [
    "proportion2014 = round(df1['ecommerce_sales_2014'].sum() / df1['total_sales_2014'].sum(), 2)\n",
    "proportion2015 = round(df1['ecommerce_sales_2015'].sum() / df1['total_sales_2015'].sum(), 2)\n",
    "prop_increase = round(proportion2015 - proportion2014, 3) \n",
    "print(proportion2014)\n",
    "print(proportion2015)\n",
    "print(prop_increase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set size and style\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.set(style = 'white')\n",
    "\n",
    "# create barplot\n",
    "fig4 = sns.barplot(x = 'year', y = 'amount', hue = 'sales_type', data = df_long, ci = None)\n",
    "\n",
    "# labels \n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Amount (in millions)', fontsize = 14)\n",
    "plt.title('Ecommerce as a Proportion of Total Retail Sales', fontsize = 24)\n",
    "\n",
    "# legend\n",
    "fig4.legend(frameon = False)\n",
    "\n",
    "# remove top and right border from plot\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4. Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the project, the following findings were revealed:\n",
    "\n",
    "1. The highest earning ecommerce merchandise lines of 2014 were: **Clothing & Accessories** *(46,830,000,000)*, **Electronics & Appliances** *(24,234,000,000)*, and **Home Furnishings & Furniture** *(23,311,000,000)*.    \n",
    "\n",
    "2. The highest earning ecommerce merchandise lines of 2015 were: **Clothing & Accessories** *(52,128,000,000)*, **Home Furnishings & Furniture** *(28,883,000,000)*, and **Electronics & Appliances** *(26,050,000,000)*.  \n",
    "\n",
    "3. The ecommerce merchandise lines which grew the most from 2014 to 2015 were: **Home Furnishings & Furnitre** *(5,572,000,000)*, **Clothing & Accessories** *(5,298,000,000)*, and **Drug, Health Aids, & Beauty Aids** *(2,3547,000,000)*.  \n",
    "\n",
    "4. Between 2014 and 2015 ecommerce sales increased by **3%** from **62%** of the total sales in 2014 to **65%** of the total sales in 2015.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Sources**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Data Source**\n",
    "\n",
    "https://www.census.gov/data/tables/2015/econ/e-stats/2015-e-stats.html\n",
    "\n",
    "**Anaconda Survey**\n",
    "\n",
    "https://www.datanami.com/2020/07/06/data-prep-still-dominates-data-scientists-time-survey-finds/#:~:text=Data%20scientists%20spend%20about%2045,data%20scientists%20conducted%20by%20Anaconda"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}