PyMoli_Fixed Report_Purchase Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymoli Purchase Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Trends As Seen in Analysis\n",
    "\n",
    " 1. The majority of Pymoli players are males between the ages of 15-29. These players account for the majority of purchases by count and revenue. \n",
    " 2. The average purchase size is just under \\$3.00. All but two players made 3 or fewer purchases.\n",
    " 3. Our most popular items are $2.35 or under, which is in line with the average purchase price.\n",
    " \n",
    "Potential courses of action include slightly increasing items prices, while keeping the prices under \\$3.00, to earn more money off typical purchasing, or increasing the availability of items below \\$2.35 to encourage more purchasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ------------------------------------------------------\n",
    "# Step 0: Import modules\n",
    "# ------------------------------------------------------\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to Pymoli Data Analysis!\n"
     ]
    }
   ],
   "source": [
    "# ------------------------------------------------------\n",
    "# Step 1: Get the filename, then Read in and clean data.\n",
    "# ------------------------------------------------------\n",
    "\n",
    "# greet the user instructions\n",
    "print(\"Welcome to Pymoli Data Analysis!\")\n",
    "\n",
    "filename = 'data/purchase_data.json'\n",
    "\n",
    "# Read in the data file\n",
    "purchases_df = pd.read_json(filename, orient='records')\n",
    "\n",
    "# make sure everything is the right data type to use\n",
    "purchases_df['Price']= purchases_df['Price'].replace(\"%\",\"\", regex=True).astype(float)\n",
    "\n",
    "# check and remove null values\n",
    "purchases_df.dropna(how='any')\n",
    "purchases_df.to_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------------------------------------------------------\n",
    "## Gender Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Number of Players</th>\n",
       "      <th>Percent of Players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>465</td>\n",
       "      <td>81.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>100</td>\n",
       "      <td>17.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>8</td>\n",
       "      <td>1.40%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Number of Players Percent of Players\n",
       "Male                                 465             81.15%\n",
       "Female                               100             17.45%\n",
       "Other / Non-Disclosed                  8              1.40%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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syDOPHd1YGzXnOj2WTjLgPz/Ki77N3+LG7TkaGDVh+0ZnBvAl8s0JShdCzkeBHECMljw1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\nm0pqfvjgDVpTOfg4HXbc8pt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1051515c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------------------------------------------------------\n",
    "# Step 2: Analyze the players: Look at \n",
    "# Total number of players & Gender demographics (Double-checked in Excel)\n",
    "# -----------------------------------------------------------------------\n",
    "\n",
    "# Total Number of players\n",
    "num_players_total = len(purchases_df.groupby('SN').count())\n",
    "\n",
    "# remove all dupes to account for players who made multiple purchases\n",
    "purchases_noDupes_df = purchases_df.set_index('SN')\n",
    "purchases_noDupes_df = purchases_noDupes_df[~purchases_noDupes_df.index.duplicated(keep='first')]\n",
    "\n",
    "# count and % of male players\n",
    "num_players_male = purchases_noDupes_df[purchases_noDupes_df['Gender']=='Male'].count()['Age']\n",
    "percent_players_male = num_players_male/num_players_total*100\n",
    "\n",
    "# count and % of female players\n",
    "num_players_female = purchases_noDupes_df[purchases_noDupes_df['Gender']=='Female'].count()['Age']\n",
    "percent_players_female = num_players_female/num_players_total*100\n",
    "\n",
    "# count and % of other/non-disclosed players\n",
    "num_players_other = purchases_noDupes_df[purchases_noDupes_df['Gender']=='Other / Non-Disclosed'].count()['Age']\n",
    "percent_players_other = num_players_other/num_players_total*100\n",
    "\n",
    "# create dataframes to hold these result\n",
    "gender_demographics_num_df = pd.DataFrame.from_dict({\"Male\":num_players_male, \"Female\":num_players_female,\\\n",
    "                                                     \"Other / Non-Disclosed\":num_players_other}, orient='index')\n",
    "gender_demographics_percent_df = pd.DataFrame.from_dict({\"Male\":percent_players_male, \"Female\":percent_players_female,\\\n",
    "                                                         \"Other / Non-Disclosed\":percent_players_other}, orient='index')\n",
    "\n",
    "# merge dataframes\n",
    "gender_demographics_df = gender_demographics_num_df.merge(gender_demographics_percent_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "\n",
    "# rename columns\n",
    "gender_demographics_df.rename(columns={'0_x': 'Number of Players', '0_y': 'Percent of Players'}, inplace=True)\n",
    "\n",
    "# format results\n",
    "gender_demographics_df['Percent of Players'] = gender_demographics_df['Percent of Players'].map('{0:.2f}%'.format)\n",
    "\n",
    "# print out results\n",
    "display(gender_demographics_df)\n",
    "\n",
    "# create labels and wedge sizes\n",
    "labels = ['Male', 'Female', 'Other / Non-Disclosed']\n",
    "wedge_sizes = [gender_demographics_num_df[0]['Male'],\\\n",
    "               gender_demographics_num_df[0]['Female'],\\\n",
    "               gender_demographics_num_df[0]['Other / Non-Disclosed']]\n",
    "\n",
    "# create pie chart\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(wedge_sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "# ----------------------------------\n",
    "# End Step 2\n",
    "# ----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen above, the majority of Pymoli players are male."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "## Purchasing Analysis\n",
    "### Includes Data on Top Spenders, Overall Purchasing, Purchases Split by Self-Identified Gender, & Age Demographics\n",
    "\n",
    "### Top Spenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Amount Purchased</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Undirrala66</th>\n",
       "      <td>5</td>\n",
       "      <td>$3.41</td>\n",
       "      <td>$17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saedue76</th>\n",
       "      <td>4</td>\n",
       "      <td>$3.39</td>\n",
       "      <td>$13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mindimnya67</th>\n",
       "      <td>4</td>\n",
       "      <td>$3.18</td>\n",
       "      <td>$12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haellysu29</th>\n",
       "      <td>3</td>\n",
       "      <td>$4.24</td>\n",
       "      <td>$12.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eoda93</th>\n",
       "      <td>3</td>\n",
       "      <td>$3.86</td>\n",
       "      <td>$11.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Purchase Count Average Purchase Price Total Amount Purchased\n",
       "SN                                                                       \n",
       "Undirrala66               5                  $3.41                 $17.06\n",
       "Saedue76                  4                  $3.39                 $13.56\n",
       "Mindimnya67               4                  $3.18                 $12.74\n",
       "Haellysu29                3                  $4.24                 $12.73\n",
       "Eoda93                    3                  $3.86                 $11.58"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------------------------------------\n",
    "# Step 3: Purchasing Analysis\n",
    "# 1) Top Spenders\n",
    "# 2) Purchasing Analysis (Total)\n",
    "# 3) Purchasing Analysis (Gender)\n",
    "# 4) Age Demographics\n",
    "# ---------------------------------------------------\n",
    "\n",
    "# ---------------------------------------------------\n",
    "# Question 1 - Top Spenders (Double-checked in Excel)\n",
    "# ---------------------------------------------------\n",
    "\n",
    "# identify top five spender by total puchase value\n",
    "purchases_byPurchaseValue_df = purchases_df.groupby('SN').sum().sort_values('Price',ascending=False)\n",
    "top_five_spenders_bySN = list(purchases_byPurchaseValue_df[0:5].reset_index()['SN'])\n",
    "\n",
    "# create purchase count dataframe\n",
    "top_five_purchase_count_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['SN'].isin(top_five_spenders_bySN)].\\\n",
    "                                                         groupby('SN').count()))\n",
    "top_five_purchase_count_df = top_five_purchase_count_df.drop(['Age', 'Gender', 'Item Name', 'Price'], 1)\n",
    "top_five_purchase_count_df.rename(columns={'Item ID' : 'Purchase Count'}, inplace=True)\n",
    "\n",
    "# create purchase average price dataframe\n",
    "top_five_purchase_avg_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['SN'].isin(top_five_spenders_bySN)].\\\n",
    "                                                       groupby('SN').mean()))\n",
    "top_five_purchase_avg_df = top_five_purchase_avg_df.drop(['Age', 'Item ID'], 1)\n",
    "top_five_purchase_avg_df.rename(columns={'Price' : 'Average Purchase Price'}, inplace=True)\n",
    "\n",
    "# create total purchases dataframe\n",
    "top_five_purchase_total_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['SN'].isin(top_five_spenders_bySN)].\\\n",
    "                                                         groupby('SN').sum()))\n",
    "top_five_purchase_total_df = top_five_purchase_total_df.drop(['Age', 'Item ID'], 1)\n",
    "top_five_purchase_total_df.rename(columns={'Price' : 'Total Amount Purchased'}, inplace=True)\n",
    "\n",
    "# merge the dataframes together to create one table\n",
    "top_spenders_analysis_df = top_five_purchase_count_df.merge(top_five_purchase_avg_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "top_spenders_analysis_df = top_spenders_analysis_df.merge(top_five_purchase_total_df, how='outer',\\\n",
    "                                                          left_index=True, right_index=True)\n",
    "\n",
    "# fix the formatting for the currency columns: df['cost'] = df['cost'].map('${:,.2f}'.format)\n",
    "top_spenders_analysis_df['Average Purchase Price'] = top_spenders_analysis_df['Average Purchase Price'].map('${:,.2f}'.format)\n",
    "top_spenders_analysis_df['Total Amount Purchased'] = top_spenders_analysis_df['Total Amount Purchased'].map('${:,.2f}'.format)\n",
    "\n",
    "# sort by total amount purchased\n",
    "top_spenders_analysis_df = top_spenders_analysis_df.sort_values('Total Amount Purchased', ascending=False)\n",
    "display(top_spenders_analysis_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even the top spenders don't make an extraordinary number of purchases. The top 3 spenders made 4-5 purchases, and everyone else made 3 or less.\n",
    "\n",
    "### Overall Purchasing Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Total Revenue</th>\n",
       "      <th>Total Number of Purchases</th>\n",
       "      <th>Average Purchase Total</th>\n",
       "      <th>Number of Unique Items Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$2,286.33</td>\n",
       "      <td>780</td>\n",
       "      <td>$2.93</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Revenue  Total Number of Purchases Average Purchase Total  \\\n",
       "0     $2,286.33                        780                  $2.93   \n",
       "\n",
       "   Number of Unique Items Purchased  \n",
       "0                               183  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------------------------------------\n",
    "# Question 2 - Purchasing Analysis (Total) (Double-checked in Excel)\n",
    "# --------------------------------------------------------------------\n",
    "\n",
    "# number of unique items purchased\n",
    "num_items_unique = len(purchases_df['Item ID'].value_counts())\n",
    "\n",
    "# average purchase price\n",
    "overall_purchase_avg = '${:,.2f}'.format(purchases_df.mean()['Price'])\n",
    "\n",
    "# total number of purchases\n",
    "overall_purchase_count = purchases_df.count()['Item ID']\n",
    "\n",
    "# total revenue\n",
    "overall_purchase_revenue = '${:,.2f}'.format(purchases_df['Price'].sum())\n",
    "\n",
    "# create dataframe to display results\n",
    "purchasing_total_analysis_df = pd.DataFrame({'Number of Unique Items Purchased':[num_items_unique],\\\n",
    "                                             'Average Purchase Total':[overall_purchase_avg],\\\n",
    "                                             'Total Number of Purchases':[overall_purchase_count],\\\n",
    "                                             'Total Revenue':[overall_purchase_revenue]})\n",
    "\n",
    "# rearrange columns in a more sensible way\n",
    "purchasing_total_analysis_df = purchasing_total_analysis_df[['Total Revenue', 'Total Number of Purchases',\\\n",
    "                                                            'Average Purchase Total', 'Number of Unique Items Purchased']]\n",
    "display(purchasing_total_analysis_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average purchase size is under $3.\n",
    "\n",
    "### Purchasing Analysis by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Purchase Count</th>\n",
       "      <th>Percent Purchase (Count)</th>\n",
       "      <th>Total Purchases</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Normalized Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>136</td>\n",
       "      <td>17.44%</td>\n",
       "      <td>$382.91</td>\n",
       "      <td>$2.82</td>\n",
       "      <td>$3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>633</td>\n",
       "      <td>81.15%</td>\n",
       "      <td>$1,867.68</td>\n",
       "      <td>$2.95</td>\n",
       "      <td>$4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>11</td>\n",
       "      <td>1.41%</td>\n",
       "      <td>$35.74</td>\n",
       "      <td>$3.25</td>\n",
       "      <td>$4.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Purchase Count Percent Purchase (Count)  \\\n",
       "Gender                                                           \n",
       "Female                            136                   17.44%   \n",
       "Male                              633                   81.15%   \n",
       "Other / Non-Disclosed              11                    1.41%   \n",
       "\n",
       "                      Total Purchases Average Purchase Price Normalized Total  \n",
       "Gender                                                                         \n",
       "Female                        $382.91                  $2.82            $3.83  \n",
       "Male                        $1,867.68                  $2.95            $4.02  \n",
       "Other / Non-Disclosed          $35.74                  $3.25            $4.47  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------------------------------------\n",
    "# Question 3 - Purchasing Analysis (Gender) (Double-checked with Lena)\n",
    "# --------------------------------------------------------------------\n",
    "\n",
    "# purchase count by gender\n",
    "gender_purchase_count_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Gender').count()))\n",
    "gender_purchase_count_df = gender_purchase_count_df.drop(['Age', 'Item Name', 'SN', 'Price'], 1)\n",
    "gender_purchase_count_df.rename(columns={'Item ID' : 'Purchase Count'}, inplace=True)\n",
    "\n",
    "# average purchase price by gender\n",
    "gender_purchase_avg_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Gender').mean()))\n",
    "gender_purchase_avg_df = gender_purchase_avg_df.drop(['Age', 'Item ID'], 1)\n",
    "gender_purchase_avg_df.rename(columns={'Price' : 'Average Purchase Price'}, inplace=True)\n",
    "\n",
    "# sum of all purchases by gender\n",
    "gender_purchase_total_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Gender').sum()))\n",
    "gender_purchase_total_df = gender_purchase_total_df.drop(['Age', 'Item ID'], 1)\n",
    "gender_purchase_total_df.rename(columns={'Price' : 'Total Purchases'}, inplace=True)\n",
    "\n",
    "# create dataframe to display results\n",
    "purchasing_gender_analysis_df = gender_purchase_count_df.merge(gender_purchase_total_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "purchasing_gender_analysis_df = purchasing_gender_analysis_df.merge(gender_purchase_avg_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "\n",
    "# add normalized totals\n",
    "purchasing_gender_analysis_df['Normalized Total'] = purchasing_gender_analysis_df['Total Purchases']/gender_demographics_df['Number of Players']\n",
    "\n",
    "# add percent count column\n",
    "purchasing_gender_analysis_df['Percent Purchase (Count)'] = purchasing_gender_analysis_df['Purchase Count']/\\\n",
    "                                                            int(purchasing_total_analysis_df['Total Number of Purchases'])*100\n",
    "\n",
    "# format columns\n",
    "purchasing_gender_analysis_df['Total Purchases'] = purchasing_gender_analysis_df['Total Purchases'].map('${:,.2f}'.format)\n",
    "purchasing_gender_analysis_df['Average Purchase Price'] = purchasing_gender_analysis_df['Average Purchase Price'].map('${:,.2f}'.format)\n",
    "purchasing_gender_analysis_df['Normalized Total'] = purchasing_gender_analysis_df['Normalized Total'].map('${:,.2f}'.format)\n",
    "purchasing_gender_analysis_df['Percent Purchase (Count)'] = purchasing_gender_analysis_df['Percent Purchase (Count)'].map('{0:.2f}%'.format)\n",
    "\n",
    "# rearrange columns\n",
    "purchasing_gender_analysis_df = purchasing_gender_analysis_df[['Purchase Count', 'Percent Purchase (Count)',\\\n",
    "                                                               'Total Purchases','Average Purchase Price',\\\n",
    "                                                               'Normalized Total']]\n",
    "\n",
    "display(purchasing_gender_analysis_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as the majority of users are male, the majority of purchases are made by male users. They accounted for 81.15% of purchases by count and 81.69% by purchase amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Number of Users</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Total Purchases</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Normalized Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>$83.46</td>\n",
       "      <td>$2.98</td>\n",
       "      <td>$4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>$96.95</td>\n",
       "      <td>$2.77</td>\n",
       "      <td>$4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>100</td>\n",
       "      <td>133</td>\n",
       "      <td>$386.42</td>\n",
       "      <td>$2.91</td>\n",
       "      <td>$3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>259</td>\n",
       "      <td>336</td>\n",
       "      <td>$978.77</td>\n",
       "      <td>$2.91</td>\n",
       "      <td>$3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>87</td>\n",
       "      <td>125</td>\n",
       "      <td>$370.33</td>\n",
       "      <td>$2.96</td>\n",
       "      <td>$4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>47</td>\n",
       "      <td>64</td>\n",
       "      <td>$197.25</td>\n",
       "      <td>$3.08</td>\n",
       "      <td>$4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>27</td>\n",
       "      <td>42</td>\n",
       "      <td>$119.40</td>\n",
       "      <td>$2.84</td>\n",
       "      <td>$4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-44</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>$51.03</td>\n",
       "      <td>$3.19</td>\n",
       "      <td>$5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45+</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>$2.72</td>\n",
       "      <td>$2.72</td>\n",
       "      <td>$2.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Number of Users  Purchase Count Total Purchases  \\\n",
       "Age Range                                                    \n",
       "<10                     19              28          $83.46   \n",
       "10-14                   23              35          $96.95   \n",
       "15-19                  100             133         $386.42   \n",
       "20-24                  259             336         $978.77   \n",
       "25-29                   87             125         $370.33   \n",
       "30-34                   47              64         $197.25   \n",
       "35-39                   27              42         $119.40   \n",
       "40-44                   10              16          $51.03   \n",
       "45+                      1               1           $2.72   \n",
       "\n",
       "          Average Purchase Price Normalized Total  \n",
       "Age Range                                          \n",
       "<10                        $2.98            $4.39  \n",
       "10-14                      $2.77            $4.22  \n",
       "15-19                      $2.91            $3.86  \n",
       "20-24                      $2.91            $3.78  \n",
       "25-29                      $2.96            $4.26  \n",
       "30-34                      $3.08            $4.20  \n",
       "35-39                      $2.84            $4.42  \n",
       "40-44                      $3.19            $5.10  \n",
       "45+                        $2.72            $2.72  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ----------------------------------------------------------------------------\n",
    "# Question 4 - Purchasing Analysis (Age) (partially double-checked with Excel)\n",
    "# ----------------------------------------------------------------------------\n",
    "ages_df = purchases_df.sort_values('Age')\n",
    "\n",
    "# create bins: <10, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-45, 45+\n",
    "bins = [0, 10, 15, 20, 25, 30, 35, 40, 45, 100]\n",
    "\n",
    "# name the bins\n",
    "bin_names = ['<10', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45+']\n",
    "\n",
    "# create a usable version of the dataframe for analysis\n",
    "purchases_df['Age Range'] = pd.cut(purchases_df['Age'], bins, labels=bin_names, right=False)\n",
    "\n",
    "# generate dataframes with the data we need\n",
    "\n",
    "# purchase count\n",
    "age_purchase_count_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Age Range').count()))\n",
    "age_purchase_count_df = age_purchase_count_df.drop(['Age', 'Gender', 'Item ID', 'Item Name', 'Price'], 1)\n",
    "age_purchase_count_df.rename(columns={'SN' : 'Purchase Count'}, inplace=True)\n",
    "\n",
    "# purchase total\n",
    "age_purchase_total_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Age Range').sum()))\n",
    "age_purchase_total_df = age_purchase_total_df.drop(['Age', 'Item ID'], 1)\n",
    "age_purchase_total_df.rename(columns={'Price' : 'Total Purchases'}, inplace=True)\n",
    "\n",
    "# average purchase price\n",
    "age_purchase_avg_df = pd.DataFrame.from_dict(dict(purchases_df.groupby('Age Range').mean()))\n",
    "age_purchase_avg_df = age_purchase_avg_df.drop(['Age', 'Item ID'], 1)\n",
    "age_purchase_avg_df.rename(columns={'Price' : 'Average Purchase Price'}, inplace=True)\n",
    "\n",
    "# create age demographics for funzies and for later use\n",
    "purchases_noDupes_round2_df = purchases_df.set_index('SN')\n",
    "purchases_noDupes_round2_df = purchases_noDupes_round2_df[~purchases_noDupes_round2_df.index.duplicated(keep='first')]\n",
    "age_demographics_df = purchases_noDupes_round2_df.groupby('Age Range').count()\n",
    "age_demographics_df = age_demographics_df.drop(['Age', 'Gender', 'Item Name', 'Price'], 1)\n",
    "age_demographics_df.rename(columns={'Item ID' : 'Number of Users'}, inplace=True)\n",
    "\n",
    "# make a dataframe to store the results\n",
    "purchasing_age_analysis_df = age_demographics_df.merge(age_purchase_count_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "purchasing_age_analysis_df = purchasing_age_analysis_df.merge(age_purchase_total_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "purchasing_age_analysis_df = purchasing_age_analysis_df.merge(age_purchase_avg_df, how='outer',\\\n",
    "                                                            left_index=True, right_index=True)\n",
    "\n",
    "# normalized totals\n",
    "purchasing_age_analysis_df[\"Normalized Total\"] = purchasing_age_analysis_df['Total Purchases']/age_demographics_df['Number of Users']\n",
    "\n",
    "# format \n",
    "purchasing_age_analysis_df['Total Purchases'] = purchasing_age_analysis_df['Total Purchases'].map('${:,.2f}'.format)\n",
    "purchasing_age_analysis_df['Average Purchase Price'] = purchasing_age_analysis_df['Average Purchase Price'].map('${:,.2f}'.format)\n",
    "purchasing_age_analysis_df['Normalized Total'] = purchasing_age_analysis_df['Normalized Total'].map('${:,.2f}'.format)\n",
    "\n",
    "# print results\n",
    "display(purchasing_age_analysis_df)\n",
    "\n",
    "# --------------------------------------\n",
    "# End Step 3\n",
    "# --------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Item Name</th>\n",
       "      <th>Age Range</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Betrayal, Whisper of Grieving Widows</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>$2.35</td>\n",
       "      <td>$25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Arcane Gem</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>$2.23</td>\n",
       "      <td>$24.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Serenity</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>$1.49</td>\n",
       "      <td>$13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Trickster</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>$2.07</td>\n",
       "      <td>$18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Woeful Adamantite Claymore</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>$1.24</td>\n",
       "      <td>$11.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Item Name  Age Range  Purchase Count  \\\n",
       "Item ID                                                                    \n",
       "39       Betrayal, Whisper of Grieving Widows         11              11   \n",
       "84                                 Arcane Gem         11              11   \n",
       "13                                   Serenity          9               9   \n",
       "31                                  Trickster          9               9   \n",
       "175                Woeful Adamantite Claymore          9               9   \n",
       "\n",
       "         Price Total Purchases  \n",
       "Item ID                         \n",
       "39       $2.35          $25.85  \n",
       "84       $2.23          $24.53  \n",
       "13       $1.49          $13.41  \n",
       "31       $2.07          $18.63  \n",
       "175      $1.24          $11.16  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------\n",
    "# Step 4: Items analysis\n",
    "# 1) Most Popular Items\n",
    "# 2) Most Profitable Items\n",
    "# --------------------------------------\n",
    "\n",
    "# most popular items\n",
    "items_byPopularity_df = purchases_df.groupby('Item ID').count().sort_values('Price', ascending=False)\n",
    "top_five_item_byPopularity = list(items_byPopularity_df[0:5].reset_index()['Item ID'])\n",
    "\n",
    "# create purchase count dataframe\n",
    "top_five_item_popular_count_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['Item ID'].isin(top_five_item_byPopularity)].\\\n",
    "                                                          groupby('Item ID').count()))\n",
    "top_five_item_popular_count_df = top_five_item_popular_count_df.drop(['Age', 'SN', 'Gender', 'Price'], 1)\n",
    "top_five_item_popular_count_df.rename(columns={'Item Name' : 'Purchase Count'}, inplace=True)\n",
    "\n",
    "# create item price dataframe\n",
    "top_five_popular_price_df = purchases_df[purchases_df['Item ID'].isin(top_five_item_byPopularity)]\n",
    "top_five_popular_price_df = top_five_popular_price_df.set_index('Item ID')\n",
    "top_five_popular_price_df = top_five_popular_price_df[~top_five_popular_price_df.\\\n",
    "                                                      index.duplicated(keep='first')].drop(['Age', 'Gender', 'Item Name',\\\n",
    "                                                                                            'SN','Age Range'], 1)\n",
    "\n",
    "# create item name dataframe\n",
    "top_five_popular_names_df = purchases_df[purchases_df['Item ID'].isin(top_five_item_byPopularity)]\n",
    "top_five_popular_names_df = top_five_popular_names_df.set_index('Item ID')\n",
    "top_five_popular_names_df = top_five_popular_names_df[~top_five_popular_names_df.\\\n",
    "                                                      index.duplicated(keep='first')].drop(['Age', 'Gender', 'Price',\\\n",
    "                                                                                            'SN','Age Range'], 1)\n",
    "\n",
    "# create total purchases dataframe\n",
    "top_five_item_popular_total_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['Item ID'].isin(top_five_item_byPopularity)].\\\n",
    "                                                             groupby('Item ID').sum()))\n",
    "top_five_item_popular_total_df = top_five_item_popular_total_df.drop('Age', 1)\n",
    "top_five_item_popular_total_df.rename(columns={'Price' : 'Total Purchases'}, inplace=True)\n",
    "\n",
    "# make a dataframe to store the results\n",
    "top_five_items_byPurchaseCount_df = top_five_popular_names_df.merge(top_five_item_popular_count_df, how='outer',\\\n",
    "                                                                    left_index=True, right_index=True)\n",
    "top_five_items_byPurchaseCount_df = top_five_items_byPurchaseCount_df.merge(top_five_popular_price_df, how='outer',\\\n",
    "                                                                              left_index=True, right_index=True)\n",
    "top_five_items_byPurchaseCount_df = top_five_items_byPurchaseCount_df.merge(top_five_item_popular_total_df,\\\n",
    "                                                                            how='outer', left_index=True,\\\n",
    "                                                                            right_index=True)\n",
    "\n",
    "# format the data\n",
    "top_five_items_byPurchaseCount_df['Price'] = top_five_items_byPurchaseCount_df['Price'].map('${:,.2f}'.format)\n",
    "top_five_items_byPurchaseCount_df['Total Purchases'] = top_five_items_byPurchaseCount_df['Total Purchases'].map('${:,.2f}'.format)\n",
    "top_five_items_byPurchaseCount_df = top_five_items_byPurchaseCount_df.sort_values('Purchase Count',\\\n",
    "                                                                                    ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "display(top_five_items_byPurchaseCount_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Item Name</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Retribution Axe</td>\n",
       "      <td>9</td>\n",
       "      <td>$4.14</td>\n",
       "      <td>$37.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Spectral Diamond Doomblade</td>\n",
       "      <td>7</td>\n",
       "      <td>$4.25</td>\n",
       "      <td>$29.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Orenmir</td>\n",
       "      <td>6</td>\n",
       "      <td>$4.95</td>\n",
       "      <td>$29.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Singed Scalpel</td>\n",
       "      <td>6</td>\n",
       "      <td>$4.87</td>\n",
       "      <td>$29.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Splitter, Foe Of Subtlety</td>\n",
       "      <td>8</td>\n",
       "      <td>$3.61</td>\n",
       "      <td>$28.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Item Name  Purchase Count  Price Total Purchases\n",
       "Item ID                                                                   \n",
       "34                  Retribution Axe               9  $4.14          $37.26\n",
       "115      Spectral Diamond Doomblade               7  $4.25          $29.75\n",
       "32                          Orenmir               6  $4.95          $29.70\n",
       "103                  Singed Scalpel               6  $4.87          $29.22\n",
       "107       Splitter, Foe Of Subtlety               8  $3.61          $28.88"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most profitable items\n",
    "items_byTotalPurchases_df = purchases_df.groupby('Item ID').sum().sort_values('Price',ascending=False)\n",
    "top_five_item_profit = list(items_byTotalPurchases_df[0:5].reset_index()['Item ID'])\n",
    "\n",
    "# create purchase count dataframe\n",
    "top_five_item_purchase_count_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['Item ID'].isin(top_five_item_profit)].\\\n",
    "                                                         groupby('Item ID').count()))\n",
    "top_five_item_purchase_count_df = top_five_item_purchase_count_df.drop(['Age', 'Age Range', 'SN', 'Gender', 'Price'], 1)\n",
    "top_five_item_purchase_count_df.rename(columns={'Item Name' : 'Purchase Count'}, inplace=True)\n",
    "\n",
    "# create item price dataframe\n",
    "top_five_item_price_df = purchases_df[purchases_df['Item ID'].isin(top_five_item_profit)]\n",
    "top_five_item_price_df = top_five_item_price_df.set_index('Item ID')\n",
    "top_five_item_price_df = top_five_item_price_df[~top_five_item_price_df.\\\n",
    "                                                index.duplicated(keep='first')].drop(['Age', 'Gender', 'Item Name',\\\n",
    "                                                                                      'SN', 'Age Range'], 1)\n",
    "# create item name dataframe\n",
    "top_five_item_names_df = purchases_df[purchases_df['Item ID'].isin(top_five_item_profit)]\n",
    "top_five_item_names_df = top_five_item_names_df.set_index('Item ID')\n",
    "top_five_item_names_df = top_five_item_names_df[~top_five_item_names_df.\\\n",
    "                                                index.duplicated(keep='first')].drop(['Age', 'Gender', 'Price',\\\n",
    "                                                                                      'SN', 'Age Range'], 1)\n",
    "\n",
    "# create total purchases dataframe\n",
    "top_five_item_purchase_total_df = pd.DataFrame.from_dict(dict(purchases_df[purchases_df['Item ID'].isin(top_five_item_profit)].\\\n",
    "                                                         groupby('Item ID').sum()))\n",
    "top_five_item_purchase_total_df = top_five_item_purchase_total_df.drop('Age', 1)\n",
    "top_five_item_purchase_total_df.rename(columns={'Price' : 'Total Purchases'}, inplace=True)\n",
    "\n",
    "# make a dataframe to store the results\n",
    "top_five_items_byTotalPurchases_df = top_five_item_names_df.merge(top_five_item_purchase_count_df, how='outer',\\\n",
    "                                                                  left_index=True, right_index=True)\n",
    "top_five_items_byTotalPurchases_df = top_five_items_byTotalPurchases_df.merge(top_five_item_price_df, how='outer',\\\n",
    "                                                                              left_index=True, right_index=True)\n",
    "top_five_items_byTotalPurchases_df = top_five_items_byTotalPurchases_df.merge(top_five_item_purchase_total_df,\\\n",
    "                                                                              how='outer', left_index=True,\\\n",
    "                                                                              right_index=True)\n",
    "\n",
    "# format the data\n",
    "top_five_items_byTotalPurchases_df['Price'] = top_five_items_byTotalPurchases_df['Price'].map('${:,.2f}'.format)\n",
    "top_five_items_byTotalPurchases_df['Total Purchases'] = top_five_items_byTotalPurchases_df['Total Purchases'].map('${:,.2f}'.format)\n",
    "top_five_items_byTotalPurchases_df = top_five_items_byTotalPurchases_df.sort_values('Total Purchases',\\\n",
    "                                                                                   ascending=False)\n",
    "display(top_five_items_byTotalPurchases_df)\n",
    "# ----------------------------------\n",
    "# End Step 4\n",
    "# ----------------------------------"
   ]
  }
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