Heroes_Woodruff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase ID</th>\n",
       "      <th>SN</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Item ID</th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Lisim78</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>108</td>\n",
       "      <td>Extraction, Quickblade Of Trembling Hands</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lisovynya38</td>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>143</td>\n",
       "      <td>Frenzied Scimitar</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Ithergue48</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>Final Critic</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Chamassasya86</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "      <td>Blindscythe</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iskosia90</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>131</td>\n",
       "      <td>Fury</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Purchase ID             SN  Age Gender  Item ID  \\\n",
       "0            0        Lisim78   20   Male      108   \n",
       "1            1    Lisovynya38   40   Male      143   \n",
       "2            2     Ithergue48   24   Male       92   \n",
       "3            3  Chamassasya86   24   Male      100   \n",
       "4            4      Iskosia90   23   Male      131   \n",
       "\n",
       "                                   Item Name  Price  \n",
       "0  Extraction, Quickblade Of Trembling Hands   3.53  \n",
       "1                          Frenzied Scimitar   1.56  \n",
       "2                               Final Critic   4.88  \n",
       "3                                Blindscythe   3.27  \n",
       "4                                       Fury   1.44  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"Resources/purchase_data.csv\"\n",
    "data_df = pd.read_csv(file)   #Create dataframe from csv file\n",
    "data_df.head()                #Return rows of data from csv into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Players:  576\n",
      "No. of Purchases made: 780\n"
     ]
    }
   ],
   "source": [
    "#Player Count\n",
    "players_count = len(data_df[\"SN\"].unique())   #Determine number of unique players\n",
    "players_count\n",
    "print(\"Total Players: \",players_count)  #Included in dataframe in next step\n",
    "\n",
    "#Total number of purchases\n",
    "purchases = data_df[\"Purchase ID\"].count()   #Use count function for number of purchases\n",
    "print(\"No. of Purchases made:\", purchases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Unique Items sold: 179\n",
      "Avg Price Paid: $ 3.05\n",
      "No. of Purchases made: 780\n",
      "Total Revenue: $ 2379.77\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Players:</th>\n",
       "      <th>No. of Purchases made:</th>\n",
       "      <th>No. of Unique Items sold:</th>\n",
       "      <th>Avg Price Paid:</th>\n",
       "      <th>Total Revenue:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>576</td>\n",
       "      <td>780</td>\n",
       "      <td>179</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$2,379.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Players:  No. of Purchases made:  No. of Unique Items sold:  \\\n",
       "0             576                     780                        179   \n",
       "\n",
       "  Avg Price Paid: Total Revenue:  \n",
       "0           $3.05      $2,379.77  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Purchasing Analysis\n",
    "#Observable trend: Most players make a single purchase. \n",
    "\n",
    "#Number of unique items\n",
    "unique_items = len(data_df[\"Item ID\"].unique())   #Use unique function\n",
    "print(\"No. of Unique Items sold:\", unique_items)\n",
    "\n",
    "#Avg purchase price\n",
    "avg_price = data_df[\"Price\"].mean()    #Use mean function to return average\n",
    "avg_price = round(avg_price,2)\n",
    "print(\"Avg Price Paid: $\", avg_price)\n",
    "\n",
    "#Total number of purchases\n",
    "purchases = data_df[\"Purchase ID\"].count()   #Use count function for number of purchases\n",
    "print(\"No. of Purchases made:\", purchases)\n",
    "\n",
    "#Total revenue\n",
    "revenue = data_df[\"Price\"].sum()      #Use sum function\n",
    "print(\"Total Revenue: $\", revenue)\n",
    "\n",
    "purchasing_analysis_df = pd.DataFrame([{\"Total Players:\": players_count, \"No. of Purchases made:\": purchases,\"No. of Unique Items sold:\": unique_items, \"Avg Price Paid:\": avg_price,\n",
    "                                       \"Total Revenue:\": revenue}])\n",
    "purchasing_analysis_df[\"Avg Price Paid:\"] = purchasing_analysis_df[\"Avg Price Paid:\"].map(\"${:,.2f}\".format)\n",
    "purchasing_analysis_df[\"Total Revenue:\"] = purchasing_analysis_df[\"Total Revenue:\"].map(\"${:,.2f}\".format)\n",
    "purchasing_analysis_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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 Count:</th>\n",
       "      <th>Players %:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>652</td>\n",
       "      <td>83.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>113</td>\n",
       "      <td>14.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>15</td>\n",
       "      <td>1.92%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Total Count: Players %:\n",
       "Male                            652     83.59%\n",
       "Female                          113     14.49%\n",
       "Other / Non-Disclosed            15      1.92%"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gender Demographics\n",
    "#Observable trend: Males dominate this game's user base at 84%. \n",
    "\n",
    "#Count of Players by gender\n",
    "gender_counts = data_df[\"Gender\"].value_counts()\n",
    "\n",
    "#Percentage of Players by gender\n",
    "pct = (gender_counts/purchases)*100    \n",
    "\n",
    "gender_df = pd.DataFrame({\"Total Count:\": gender_counts, \"Players %:\": pct})                             \n",
    "gender_df[\"Players %:\"] = gender_df[\"Players %:\"].map(\"{:,.2f}%\".format)   #Adds \"%\" and two decimal places\n",
    "gender_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase Count:</th>\n",
       "      <th>Avg Purchase Price:</th>\n",
       "      <th>Avg Purchase Total:</th>\n",
       "      <th>Total Purchase:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</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>113</td>\n",
       "      <td>$3.20</td>\n",
       "      <td>$3.20</td>\n",
       "      <td>$361.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>652</td>\n",
       "      <td>$3.02</td>\n",
       "      <td>$3.02</td>\n",
       "      <td>$1,967.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>15</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$50.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Purchase Count: Avg Purchase Price:  \\\n",
       "Gender                                                       \n",
       "Female                             113               $3.20   \n",
       "Male                               652               $3.02   \n",
       "Other / Non-Disclosed               15               $3.35   \n",
       "\n",
       "                      Avg Purchase Total: Total Purchase:  \n",
       "Gender                                                     \n",
       "Female                              $3.20         $361.94  \n",
       "Male                                $3.02       $1,967.64  \n",
       "Other / Non-Disclosed               $3.35          $50.19  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Purchasing Analysis (Gender)\n",
    "#Purchase Count\n",
    "grouped_data_df = data_df.groupby([\"Gender\"])\n",
    "grouped_data_df[\"Purchase ID\"].count().head() #Returns rows of data\n",
    "\n",
    "\n",
    "#Average Purchase Price by gender\n",
    "avgpurchase_price = grouped_data_df[\"Price\"].mean()\n",
    "avgpurchase_price.head()    #Returns rows of data\n",
    "avgpurchase_price2 = avgpurchase_price.map(\"${:,.2f}\".format)\n",
    "avgpurchase_price2.head()\n",
    "\n",
    "#Total Purchase Value by gender\n",
    "total_value = grouped_data_df[\"Price\"].sum()\n",
    "total_value.head()       #Returns rows of data\n",
    "total_value2 = total_value.map(\"${:,.2f}\".format)\n",
    "total_value2.head()      #Returns rows of data\n",
    "\n",
    "#Average Purchase Total per Person by Gender\n",
    "count2 = grouped_data_df[\"Purchase ID\"].count()\n",
    "gend_totals = (total_value)/count2\n",
    "gend_totals2 = gend_totals.map(\"${:,.2f}\".format)\n",
    "gend_totals2.head()      #Returns rows of data\n",
    "\n",
    "# Organize summary gender data into Data Frame columns\n",
    "gender_summary_df = pd.DataFrame(grouped_data_df[\"Purchase ID\"].count())\n",
    "gender_summary_df = gender_summary_df.rename(columns={\"Purchase ID\":\"Purchase Count:\"})     #Renames column\n",
    "gender_summary_df[\"Avg Purchase Price:\"] = avgpurchase_price2 \n",
    "gender_summary_df[\"Avg Purchase Total:\"] = gend_totals2\n",
    "gender_summary_df[\"Total Purchase:\"] = total_value2\n",
    "gender_summary_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Percentage of Players:</th>\n",
       "      <th>Purchase Count:</th>\n",
       "      <th>Avg Purchase Price:</th>\n",
       "      <th>Avg Purchase Total:</th>\n",
       "      <th>Total Purchase:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Demographics</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>2.95%</td>\n",
       "      <td>23</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$77.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>3.59%</td>\n",
       "      <td>28</td>\n",
       "      <td>$2.96</td>\n",
       "      <td>$2.96</td>\n",
       "      <td>$82.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>17.44%</td>\n",
       "      <td>136</td>\n",
       "      <td>$3.04</td>\n",
       "      <td>$3.04</td>\n",
       "      <td>$412.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>46.79%</td>\n",
       "      <td>365</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$1,114.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>12.95%</td>\n",
       "      <td>101</td>\n",
       "      <td>$2.90</td>\n",
       "      <td>$2.90</td>\n",
       "      <td>$293.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>9.36%</td>\n",
       "      <td>73</td>\n",
       "      <td>$2.93</td>\n",
       "      <td>$2.93</td>\n",
       "      <td>$214.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>5.26%</td>\n",
       "      <td>41</td>\n",
       "      <td>$3.60</td>\n",
       "      <td>$3.60</td>\n",
       "      <td>$147.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>1.67%</td>\n",
       "      <td>13</td>\n",
       "      <td>$2.94</td>\n",
       "      <td>$2.94</td>\n",
       "      <td>$38.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Percentage of Players:  Purchase Count: Avg Purchase Price:  \\\n",
       "Age Demographics                                                               \n",
       "<10                               2.95%               23               $3.35   \n",
       "10-14                             3.59%               28               $2.96   \n",
       "15-19                            17.44%              136               $3.04   \n",
       "20-24                            46.79%              365               $3.05   \n",
       "25-29                            12.95%              101               $2.90   \n",
       "30-34                             9.36%               73               $2.93   \n",
       "35-39                             5.26%               41               $3.60   \n",
       "40+                               1.67%               13               $2.94   \n",
       "\n",
       "                 Avg Purchase Total: Total Purchase:  \n",
       "Age Demographics                                      \n",
       "<10                            $3.35          $77.13  \n",
       "10-14                          $2.96          $82.78  \n",
       "15-19                          $3.04         $412.89  \n",
       "20-24                          $3.05       $1,114.06  \n",
       "25-29                          $2.90         $293.00  \n",
       "30-34                          $2.93         $214.00  \n",
       "35-39                          $3.60         $147.67  \n",
       "40+                            $2.94          $38.24  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age Demographics\n",
    "#Observable trend: Age group 20-24 dominate this game's user base at 47%. \n",
    "\n",
    "#The below each broken into bins of 4 years (i.e. 0-10, 10-14, 15-19, etc.)\n",
    "agebins = [0,9.9,14.9,19.9,24.9,29.9,34.9,39.9,99]\n",
    "binnames = [\"<10\",\"10-14\",\"15-19\",\"20-24\",\"25-29\",\"30-34\",\"35-39\",\"40+\"]\n",
    "\n",
    "age_data_df = data_df\n",
    "age_data_df[\"Age Demographics\"] = pd.cut(data_df[\"Age\"], agebins, labels=binnames)\n",
    "age_data_df\n",
    "\n",
    "#Purchase Count\n",
    "age_data_df = age_data_df.groupby(\"Age Demographics\")\n",
    "age_data_df.count()\n",
    "\n",
    "#Calculate percentages of purchases of each age group\n",
    "age_summary_df = pd.DataFrame(age_data_df.count())\n",
    "age_summary_df[\"SN\"] = (age_summary_df[\"SN\"]/purchases)*100\n",
    "age_summary_df\n",
    "\n",
    "#Format percentages \n",
    "age_summary_df[\"SN\"] = age_summary_df[\"SN\"].map(\"{:,.2f}%\".format)\n",
    "age_summary_df\n",
    "\n",
    "#Limit columns to \"Age Demographics\", \"Purchase ID\", and \"SN\"\n",
    "summary2_df = age_summary_df[[\"Purchase ID\",\"SN\"]]\n",
    "summary2_df\n",
    "\n",
    "#Average Purchase Price\n",
    "avgprice_age = age_data_df[\"Price\"].mean()\n",
    "avgprice2_age = avgprice_age.map(\"${:,.2f}\".format)\n",
    "avgprice2_age\n",
    "\n",
    "#Total Purchase Value\n",
    "totalvalue_age = age_data_df[\"Price\"].sum()\n",
    "totalvalue2_age = totalvalue_age.map(\"${:,.2f}\".format)\n",
    "totalvalue2_age\n",
    "\n",
    "#Average Purchase Total per Person by Age Group\n",
    "totals_age = totalvalue_age/age_data_df[\"Purchase ID\"].count()\n",
    "totals2_age = totals_age.map(\"${:,.2f}\".format)\n",
    "totals2_age\n",
    "\n",
    "# Organize summary gender data, get all columns to organized Data Frame, add needed columns to it\n",
    "# Rename the columns\n",
    "\n",
    "age_summary_df[\"Avg Purchase Price:\"] = avgprice2_age  \n",
    "age_summary_df[\"Total Purchase:\"] = totalvalue2_age\n",
    "age_summary_df[\"Avg Purchase Total:\"] = totals2_age \n",
    "age_summary_df\n",
    "\n",
    "summary_age_df = age_summary_df.rename(columns={\"Purchase ID\":\"Purchase Count:\",\"SN\":\"Percentage of Players:\"})\n",
    "\n",
    "#Limit columns to \"Age Demographics\", \"Purchase ID\", and \"SN\"\n",
    "summary_age_df = summary_age_df[[\"Percentage of Players:\",\"Purchase Count:\",\"Avg Purchase Price:\",\"Avg Purchase Total:\",\"Total Purchase:\"]]\n",
    "summary_age_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Avg Purchase Price:</th>\n",
       "      <th>Total Purchase:</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>Lisosia93</th>\n",
       "      <td>5</td>\n",
       "      <td>$3.79</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idastidru52</th>\n",
       "      <td>4</td>\n",
       "      <td>$3.86</td>\n",
       "      <td>15.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chamjask73</th>\n",
       "      <td>3</td>\n",
       "      <td>$4.61</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iral74</th>\n",
       "      <td>4</td>\n",
       "      <td>$3.40</td>\n",
       "      <td>13.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iskadarya95</th>\n",
       "      <td>3</td>\n",
       "      <td>$4.37</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Purchase Count: Avg Purchase Price:  Total Purchase:\n",
       "SN                                                               \n",
       "Lisosia93                  5               $3.79            18.96\n",
       "Idastidru52                4               $3.86            15.45\n",
       "Chamjask73                 3               $4.61            13.83\n",
       "Iral74                     4               $3.40            13.62\n",
       "Iskadarya95                3               $4.37            13.10"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top Spenders\n",
    "#Observable trend: Top purchasers do not exceed 5 purchases with no trend for avg purchase price.\n",
    "\n",
    "#Identify the the top 5 spenders in the game by total purchase value, then list (in a table):\n",
    "\n",
    "#Player Count\n",
    "file = \"Resources/purchase_data.csv\"\n",
    "data_df = pd.read_csv(file)   #Create dataframe from csv file\n",
    "data_df.head()                #Return rows of data from csv into dataframe\n",
    "\n",
    "players_count = len(data_df[\"SN\"].unique())   #Determine number of unique players\n",
    "players_count\n",
    "\n",
    "#Purchase Count\n",
    "moneybags_data = data_df.groupby([\"SN\"])\n",
    "moneybags_count = moneybags_data[\"Purchase ID\"].count()\n",
    "\n",
    "#Total Purchase Value\n",
    "total_purchase_SN = moneybags_data[\"Price\"].sum()\n",
    "#total_purchase_SN = total_purchase_SN.map(\"${:,.2f}\".format)\n",
    "\n",
    "#Average Purchase Price\n",
    "Avg_moneybags = moneybags_data[\"Price\"].mean()\n",
    "Avg_moneybags\n",
    "Avg_moneybags2 = Avg_moneybags.map(\"${:,.2f}\".format)\n",
    "\n",
    "\n",
    "#Summary of Top 5 Spenders \n",
    "moneybags_summary_df = pd.DataFrame({\"Purchase Count:\": moneybags_count, \"Avg Purchase Price:\": Avg_moneybags2,\"Total Purchase:\":total_purchase_SN}) \n",
    "moneybags_summary_df = moneybags_summary_df.sort_values(by=\"Total Purchase:\", ascending=False)\n",
    "moneybags_summary_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase Count:</th>\n",
       "      <th>Item Price:</th>\n",
       "      <th>Total Purchase:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th>Item Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>Final Critic</th>\n",
       "      <td>13</td>\n",
       "      <td>$4.61</td>\n",
       "      <td>$59.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <td>12</td>\n",
       "      <td>$4.23</td>\n",
       "      <td>$50.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.58</td>\n",
       "      <td>$41.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <th>Persuasion</th>\n",
       "      <td>9</td>\n",
       "      <td>$3.22</td>\n",
       "      <td>$28.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <th>Extraction, Quickblade Of Trembling Hands</th>\n",
       "      <td>9</td>\n",
       "      <td>$3.53</td>\n",
       "      <td>$31.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>Nirvana</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.90</td>\n",
       "      <td>$44.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count:  \\\n",
       "Item ID Item Name                                                       \n",
       "92      Final Critic                                               13   \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               12   \n",
       "145     Fiery Glass Crusader                                        9   \n",
       "132     Persuasion                                                  9   \n",
       "108     Extraction, Quickblade Of Trembling Hands                   9   \n",
       "82      Nirvana                                                     9   \n",
       "\n",
       "                                                     Item Price:  \\\n",
       "Item ID Item Name                                                  \n",
       "92      Final Critic                                       $4.61   \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm       $4.23   \n",
       "145     Fiery Glass Crusader                               $4.58   \n",
       "132     Persuasion                                         $3.22   \n",
       "108     Extraction, Quickblade Of Trembling Hands          $3.53   \n",
       "82      Nirvana                                            $4.90   \n",
       "\n",
       "                                                     Total Purchase:  \n",
       "Item ID Item Name                                                     \n",
       "92      Final Critic                                          $59.99  \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm          $50.76  \n",
       "145     Fiery Glass Crusader                                  $41.22  \n",
       "132     Persuasion                                            $28.99  \n",
       "108     Extraction, Quickblade Of Trembling Hands             $31.77  \n",
       "82      Nirvana                                               $44.10  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Items\n",
    "#Observable trend: Most players make a single purchase. Only 23% of the players made multiple purchases.\n",
    "\n",
    "#Identify the 5 most popular items by purchase count, then list (in a table):\n",
    "bestsellers = data_df.groupby([\"Item ID\", \"Item Name\"])\n",
    "bestsellers_count= bestsellers[\"Item ID\"].count()\n",
    "\n",
    "#Item Price\n",
    "item_price = bestsellers_df[\"Price\"].mean()\n",
    "item_price = item_price.map(\"${:,.2f}\".format)\n",
    "item_price\n",
    "\n",
    "#Total Purchase Value\n",
    "item_total = bestsellers_df[\"Price\"].sum()\n",
    "item_total2 = item_total.map(\"${:,.2f}\".format)\n",
    "item_total2\n",
    "\n",
    "# Summary of Most Popular Items grouped by Item, rename \"Purchase ID\" column, using .rename(columns={})\n",
    "item_analysis_df = pd.DataFrame({\"Purchase ID\": bestsellers_count, \"Item Price:\": item_price,\"Total Purchase:\":item_total2}) \n",
    "item_analysis2_df = item_analysis_df.rename(columns={\"Purchase ID\":\"Purchase Count:\"})\n",
    "item_analysis3_df = item_analysis2_df.sort_values(by=\"Purchase Count:\", ascending=False)   #Sort by sales volume\n",
    "item_analysis3_df.head(6)   #Though top 5 requested there are four tied at third place.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Item Price:</th>\n",
       "      <th>Total Purchase:</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th>Item Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>Final Critic</th>\n",
       "      <td>13</td>\n",
       "      <td>$4.61</td>\n",
       "      <td>59.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <td>12</td>\n",
       "      <td>$4.23</td>\n",
       "      <td>50.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>Nirvana</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.90</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.58</td>\n",
       "      <td>41.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <th>Singed Scalpel</th>\n",
       "      <td>8</td>\n",
       "      <td>$4.35</td>\n",
       "      <td>34.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  \\\n",
       "Item ID Item Name                                                      \n",
       "92      Final Critic                                              13   \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm              12   \n",
       "82      Nirvana                                                    9   \n",
       "145     Fiery Glass Crusader                                       9   \n",
       "103     Singed Scalpel                                             8   \n",
       "\n",
       "                                                     Item Price:  \\\n",
       "Item ID Item Name                                                  \n",
       "92      Final Critic                                       $4.61   \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm       $4.23   \n",
       "82      Nirvana                                            $4.90   \n",
       "145     Fiery Glass Crusader                               $4.58   \n",
       "103     Singed Scalpel                                     $4.35   \n",
       "\n",
       "                                                      Total Purchase:  \n",
       "Item ID Item Name                                                      \n",
       "92      Final Critic                                            59.99  \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm            50.76  \n",
       "82      Nirvana                                                 44.10  \n",
       "145     Fiery Glass Crusader                                    41.22  \n",
       "103     Singed Scalpel                                          34.80  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Profitable Items\n",
    "#Technically because we do not have item production cost we cannot calculate most profitable. I am interpreting the question as the items generating the most revenue.\n",
    "\n",
    "bestsellers = data_df.groupby([\"Item ID\", \"Item Name\"])\n",
    "bestsellers_count= bestsellers[\"Item ID\"].count()\n",
    "\n",
    "item_total = bestsellers_df[\"Price\"].sum()\n",
    "\n",
    "item_analysis_df = pd.DataFrame({\"Purchase Count\": bestsellers_count, \"Item Price:\": item_price,\"Total Purchase:\":item_total})\n",
    "\n",
    "top5_df = item_analysis_df.sort_values(by=\"Total Purchase:\", ascending=False)   #Sort by sales volume\n",
    "top5_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observable trends based on the data:\n",
    "#Most players make a single purchase. At most, 35% of the players made multiple purchases.\n",
    "#Males dominate this game's user base at 84%. \n",
    "#Age group 20-24 dominate this game's user base at 47%. \n",
    "#Top purchasers do not exceed 5 purchases with no trend for avg purchase price."
   ]
  }
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