Shaena_HeroesOfPymoli_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "file_to_load = \"Resources/purchase_data.csv\"\n",
    "\n",
    "# Read Purchasing File and store into Pandas data frame\n",
    "purchase_data = pd.read_csv(file_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Player Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Display the total number of players\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "576"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_players = len(purchase_data[\"SN\"].unique())\n",
    "total_players"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purchasing Analysis (Total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Run basic calculations to obtain number of unique items, average price, etc.\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display the summary data frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183\n",
      "2.4025683060109286\n",
      "780\n",
      "2379.7699999999973\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Unique Items</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Number of Purchases</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183</td>\n",
       "      <td>$2.40</td>\n",
       "      <td>780</td>\n",
       "      <td>$2,379.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Unique Items Average Price  Number of Purchases Total Revenue\n",
       "0                     183         $2.40                  780     $2,379.77"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_items = len(purchase_data[\"Item ID\"].unique())\n",
    "total_price = sum(purchase_data[\"Price\"].unique())\n",
    "average_price = total_price/number_items\n",
    "number_purch = len(purchase_data[\"Purchase ID\"])\n",
    "total_rev = sum(purchase_data[\"Price\"])\n",
    "print(number_items)\n",
    "print(average_price)\n",
    "print(number_purch)\n",
    "print(total_rev)\n",
    "summary_df = pd.DataFrame({\n",
    "    \"Number of Unique Items\": [number_items],\n",
    "    \"Average Price\": [average_price],\n",
    "    \"Number of Purchases\": [number_purch],\n",
    "    \"Total Revenue\": [total_rev]\n",
    "})\n",
    "\n",
    "#Formatting\n",
    "summary_df[\"Average Price\"]= summary_df[\"Average Price\"].map(\"${:,.2f}\".format)\n",
    "summary_df[\"Total Revenue\"]= summary_df[\"Total Revenue\"].map(\"${:,.2f}\".format)\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Percentage and Count of Male Players\n",
    "\n",
    "\n",
    "* Percentage and Count of Female Players\n",
    "\n",
    "\n",
    "* Percentage and Count of Other / Non-Disclosed\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Percentage of Players</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></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 Percentage of Players\n",
       "Gender                                                 \n",
       "Male                          652                83.59%\n",
       "Female                        113                14.49%\n",
       "Other/ Non-Disclosed           15                 1.92%"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_male = len(purchase_data.loc[(purchase_data[\"Gender\"] == \"Male\")])\n",
    "total_female = len(purchase_data.loc[(purchase_data[\"Gender\"] == \"Female\")])\n",
    "total_other = len(purchase_data.loc[(purchase_data[\"Gender\"] == \"Other / Non-Disclosed\")])\n",
    "total_pop = total_male + total_female + total_other\n",
    "\n",
    "percentage_m = total_male/total_pop * 100\n",
    "percentage_f = total_female/total_pop * 100\n",
    "percentage_o = total_other/total_pop * 100\n",
    "\n",
    "summary = pd.DataFrame({\n",
    "    \"Total Count\" : [total_male, total_female, total_other],\n",
    "    \"Percentage of Players\" : [percentage_m, percentage_f, percentage_o],\n",
    "    \"Gender\" : [\"Male\", \"Female\", \"Other/ Non-Disclosed\"]\n",
    "})\n",
    "#formatting\n",
    "summary = summary.set_index(\"Gender\")\n",
    "summary[\"Percentage of Players\"] = summary[\"Percentage of Players\"].map(\"{:.2f}\".format)\n",
    "summary[\"Percentage of Players\"] = summary[\"Percentage of Players\"].astype(str) + '%'\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Purchasing Analysis (Gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. by gender\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display the summary data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</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>$361.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>652</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>$50.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Purchase Count Average Purchase Price  \\\n",
       "Gender                                                       \n",
       "Female                          113                  $3.20   \n",
       "Male                            652                  $3.02   \n",
       "Other/Non-Disclosed              15                  $3.35   \n",
       "\n",
       "                    Total Purchase Value  \n",
       "Gender                                    \n",
       "Female                           $361.94  \n",
       "Male                           $1,967.64  \n",
       "Other/Non-Disclosed               $50.19  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df = purchase_data.loc[(purchase_data[\"Gender\"] == \"Female\")]\n",
    "fnumber_items = len(female_df[\"Item ID\"])\n",
    "faverage_price = female_df[\"Price\"].mean()\n",
    "ftotal_rev = sum(female_df[\"Price\"])\n",
    "\n",
    "male_df = purchase_data.loc[(purchase_data[\"Gender\"] == \"Male\")]\n",
    "mnumber_items = len(male_df[\"Item ID\"])\n",
    "maverage_price = male_df[\"Price\"].mean()\n",
    "mtotal_rev = sum(male_df[\"Price\"])\n",
    "\n",
    "o_df = purchase_data.loc[(purchase_data[\"Gender\"] == \"Other / Non-Disclosed\")]\n",
    "onumber_items = len(o_df[\"Item ID\"])\n",
    "oaverage_price = o_df[\"Price\"].mean()\n",
    "ototal_rev = sum(o_df[\"Price\"])\n",
    "\n",
    "\n",
    "gendersummary = pd.DataFrame({\n",
    "    \"Purchase Count\" : [fnumber_items, mnumber_items, onumber_items],\n",
    "    \"Average Purchase Price\" : [faverage_price, maverage_price, oaverage_price],\n",
    "    \"Total Purchase Value\" : [ftotal_rev, mtotal_rev, ototal_rev],\n",
    "    \"Gender\" : [\"Female\", \"Male\", \"Other/Non-Disclosed\"]\n",
    "})\n",
    "\n",
    "#formatting\n",
    "gendersummary = gendersummary.set_index(\"Gender\")\n",
    "gendersummary[\"Average Purchase Price\"]= gendersummary[\"Average Purchase Price\"].map(\"${:,.2f}\".format)\n",
    "gendersummary[\"Total Purchase Value\"]= gendersummary[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "gendersummary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Establish bins for ages\n",
    "\n",
    "\n",
    "* Categorize the existing players using the age bins. Hint: use pd.cut()\n",
    "\n",
    "\n",
    "* Calculate the numbers and percentages by age group\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Optional: round the percentage column to two decimal points\n",
    "\n",
    "\n",
    "* Display Age Demographics Table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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 Count</th>\n",
       "      <th>Percentage of Players</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>32</td>\n",
       "      <td>4.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>19</td>\n",
       "      <td>2.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>136</td>\n",
       "      <td>17.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>365</td>\n",
       "      <td>46.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>101</td>\n",
       "      <td>12.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>73</td>\n",
       "      <td>9.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>41</td>\n",
       "      <td>5.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>13</td>\n",
       "      <td>1.67%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Count Percentage of Players\n",
       "Age                                     \n",
       "<10             32                 4.10%\n",
       "10-14           19                 2.44%\n",
       "15-19          136                17.44%\n",
       "20-24          365                46.79%\n",
       "25-29          101                12.95%\n",
       "30-34           73                 9.36%\n",
       "35-39           41                 5.26%\n",
       "40+             13                 1.67%"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_SN = purchase_data[[\"SN\", \"Price\", \"Age\"]]\n",
    "bins = [0,10,14,19,24,29,34,39,100]\n",
    "group_names = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\",\"30-34\",\"35-39\",\"40+\"]\n",
    "age_bins = unique_SN.groupby([pd.cut(unique_SN[\"Age\"],bins, labels = group_names)])\n",
    "counts = age_bins.size()\n",
    "df = pd.DataFrame(counts)\n",
    "df.columns = [\"Total Count\"]\n",
    "\n",
    "perc = df.groupby([\"Age\"]).agg({\"Total Count\": \"sum\"})\n",
    "perc1 = round(perc.groupby(level=0).apply(lambda x:\n",
    "                                   100* x / float(df[\"Total Count\"].sum())),2)\n",
    "df\n",
    "Age_Demo_table = pd.merge(df, perc1, on=\"Age\")\n",
    "Age_Demo_table.columns = [\"Total Count\" , \"Percentage of Players\"]\n",
    "\n",
    "#formatting\n",
    "Age_Demo_table[\"Percentage of Players\"] = Age_Demo_table[\"Percentage of Players\"].map(\"{:.2f}\".format)\n",
    "Age_Demo_table[\"Percentage of Players\"] = Age_Demo_table[\"Percentage of Players\"].astype(str) + '%'\n",
    "Age_Demo_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purchasing Analysis (Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Bin the purchase_data data frame by age\n",
    "\n",
    "\n",
    "* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display the summary data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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 Count</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Average Total per person</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</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>32</td>\n",
       "      <td>$3.40</td>\n",
       "      <td>$108.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>19</td>\n",
       "      <td>$2.68</td>\n",
       "      <td>$50.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>136</td>\n",
       "      <td>$3.04</td>\n",
       "      <td>$412.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>365</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$1114.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>101</td>\n",
       "      <td>$2.90</td>\n",
       "      <td>$293.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>73</td>\n",
       "      <td>$2.93</td>\n",
       "      <td>$214.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>41</td>\n",
       "      <td>$3.60</td>\n",
       "      <td>$147.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>13</td>\n",
       "      <td>$2.94</td>\n",
       "      <td>$38.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Count Average Price Average Total per person\n",
       "Age                                                      \n",
       "<10             32         $3.40                  $108.96\n",
       "10-14           19         $2.68                   $50.95\n",
       "15-19          136         $3.04                  $412.89\n",
       "20-24          365         $3.05                 $1114.06\n",
       "25-29          101         $2.90                  $293.00\n",
       "30-34           73         $2.93                  $214.00\n",
       "35-39           41         $3.60                  $147.67\n",
       "40+             13         $2.94                   $38.24"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_SN = purchase_data[[\"SN\", \"Price\", \"Age\"]]\n",
    "bins = [0,10,14,19,24,29,34,39,100]\n",
    "group_names = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\",\"30-34\",\"35-39\",\"40+\"]\n",
    "age_bins = unique_SN.groupby([pd.cut(unique_SN[\"Age\"],bins, labels = group_names)])\n",
    "counts = age_bins.size()\n",
    "average = age_bins.mean()[\"Price\"]\n",
    "total = age_bins.sum()[\"Price\"]\n",
    "df = pd.DataFrame({\"Total Count\": counts,\n",
    "                  \"Average Price\": average,\n",
    "                  \"Average Total per person\": total})\n",
    "\n",
    "\n",
    "#formatting\n",
    "df[\"Average Price\"] = df[\"Average Price\"].map(\"${:.2f}\".format)\n",
    "df[\"Average Total per person\"] = df[\"Average Total per person\"].map(\"${:.2f}\".format)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purchasing Analysis (Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Bin the purchase_data data frame by age\n",
    "\n",
    "\n",
    "* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display the summary data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 Purchase Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</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>32</td>\n",
       "      <td>3.405000</td>\n",
       "      <td>108.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>51</td>\n",
       "      <td>3.135490</td>\n",
       "      <td>159.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>187</td>\n",
       "      <td>3.063102</td>\n",
       "      <td>572.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>552</td>\n",
       "      <td>3.055906</td>\n",
       "      <td>1686.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>653</td>\n",
       "      <td>3.031945</td>\n",
       "      <td>1979.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>726</td>\n",
       "      <td>3.021846</td>\n",
       "      <td>2193.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>767</td>\n",
       "      <td>3.052842</td>\n",
       "      <td>2341.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>13</td>\n",
       "      <td>2.941538</td>\n",
       "      <td>38.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Purchase Count  Average Purchase Price  Total Purchase Value\n",
       "Age                                                                \n",
       "<10                32                3.405000                108.96\n",
       "10-14              51                3.135490                159.91\n",
       "15-19             187                3.063102                572.80\n",
       "20-24             552                3.055906               1686.86\n",
       "25-29             653                3.031945               1979.86\n",
       "30-34             726                3.021846               2193.86\n",
       "35-39             767                3.052842               2341.53\n",
       "40+                13                2.941538                 38.24"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preteen_df = purchase_data.loc[(purchase_data[\"Age\"] <= 10)]\n",
    "preteen_items = len(preteen_df[\"Item ID\"])\n",
    "paverage_price = preteen_df[\"Price\"].mean()\n",
    "ptotal_rev = sum(preteen_df[\"Price\"])\n",
    "\n",
    "teen_df = purchase_data.loc[(purchase_data[\"Age\"] <= 14)]\n",
    "teen_items = len(teen_df[\"Item ID\"])\n",
    "taverage_price = teen_df[\"Price\"].mean()\n",
    "ttotal_rev = sum(teen_df[\"Price\"])\n",
    "\n",
    "ya_df = purchase_data.loc[(purchase_data[\"Age\"] <= 19)]\n",
    "ya_items = len(ya_df[\"Item ID\"])\n",
    "yaaverage_price = ya_df[\"Price\"].mean()\n",
    "yatotal_rev = sum(ya_df[\"Price\"])\n",
    "\n",
    "adult_df = purchase_data.loc[(purchase_data[\"Age\"] <= 24)]\n",
    "adult_items = len(adult_df[\"Item ID\"])\n",
    "adultaverage_price = adult_df[\"Price\"].mean()\n",
    "adulttotal_rev = sum(adult_df[\"Price\"])\n",
    "\n",
    "twenty_df = purchase_data.loc[(purchase_data[\"Age\"] <= 29)]\n",
    "twenty_items = len(twenty_df[\"Item ID\"])\n",
    "twenty_price = twenty_df[\"Price\"].mean()\n",
    "twenty_rev = sum(twenty_df[\"Price\"])\n",
    "\n",
    "ythir_df = purchase_data.loc[(purchase_data[\"Age\"] <= 34)]\n",
    "ythir_items = len(ythir_df[\"Item ID\"])\n",
    "ythir_price = ythir_df[\"Price\"].mean()\n",
    "ythir_rev = sum(ythir_df[\"Price\"])\n",
    "\n",
    "thirty_df = purchase_data.loc[(purchase_data[\"Age\"] <= 39)]\n",
    "thirty_items = len(thirty_df[\"Item ID\"])\n",
    "thirty_price = thirty_df[\"Price\"].mean()\n",
    "thirty_rev = sum(thirty_df[\"Price\"])\n",
    "\n",
    "parents_df = purchase_data.loc[(purchase_data[\"Age\"] >= 40)]\n",
    "parents_items = len(parents_df[\"Item ID\"])\n",
    "parents_price = parents_df[\"Price\"].mean()\n",
    "parents_rev = sum(parents_df[\"Price\"])\n",
    "\n",
    "agesummary = pd.DataFrame({\n",
    "    \"Purchase Count\" : [preteen_items, teen_items, ya_items, adult_items, twenty_items, ythir_items, thirty_items, parents_items],\n",
    "    \"Average Purchase Price\" : [paverage_price, taverage_price, yaaverage_price, adultaverage_price, twenty_price, ythir_price, thirty_price, parents_price],\n",
    "    \"Total Purchase Value\" : [ptotal_rev, ttotal_rev, yatotal_rev, adulttotal_rev, twenty_rev, ythir_rev, thirty_rev, parents_rev],\n",
    "    \"Age\" : [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\",\"30-34\",\"35-39\",\"40+\"]\n",
    "})\n",
    "\n",
    "agesummary = agesummary.set_index(\"Age\")\n",
    "agesummary\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Spenders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Run basic calculations to obtain the results in the table below\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Sort the total purchase value column in descending order\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display a preview of the summary data frame\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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 Purchase Value</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>Haillyrgue51</th>\n",
       "      <td>3</td>\n",
       "      <td>$3.17</td>\n",
       "      <td>$9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phistym51</th>\n",
       "      <td>2</td>\n",
       "      <td>$4.75</td>\n",
       "      <td>$9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lamil79</th>\n",
       "      <td>2</td>\n",
       "      <td>$4.64</td>\n",
       "      <td>$9.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aina42</th>\n",
       "      <td>3</td>\n",
       "      <td>$3.07</td>\n",
       "      <td>$9.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saesrideu94</th>\n",
       "      <td>2</td>\n",
       "      <td>$4.59</td>\n",
       "      <td>$9.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Purchase Count Average Purchase Price Total Purchase Value\n",
       "SN                                                                      \n",
       "Haillyrgue51               3                  $3.17                $9.50\n",
       "Phistym51                  2                  $4.75                $9.50\n",
       "Lamil79                    2                  $4.64                $9.29\n",
       "Aina42                     3                  $3.07                $9.22\n",
       "Saesrideu94                2                  $4.59                $9.18"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SN_purchase_count = purchase_data.groupby([\"SN\"]).count()[\"Price\"].rename(\"Purchase Count\")\n",
    "SN_average = purchase_data.groupby([\"SN\"]).mean()[\"Price\"].rename(\"Average Purchase Price\")\n",
    "SN_total = purchase_data.groupby([\"SN\"]).sum()[\"Price\"].rename(\"Total\")\n",
    "sn_df = pd.DataFrame({\"Purchase Count\": SN_purchase_count,\n",
    "                     \"Average Purchase Price\": SN_average,\n",
    "                     \"Total Purchase Value\": SN_total})\n",
    "\n",
    "sn_df[\"Average Purchase Price\"] = sn_df[\"Average Purchase Price\"].map(\"${:.2f}\".format)\n",
    "sn_df[\"Total Purchase Value\"] = sn_df[\"Total Purchase Value\"].map(\"${:.2f}\".format)\n",
    "sn_df = sn_df.sort_values(by=[\"Total Purchase Value\"], ascending =False)\n",
    "sn_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Popular Items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Retrieve the Item ID, Item Name, and Item Price columns\n",
    "\n",
    "\n",
    "* Group by Item ID and Item Name. Perform calculations to obtain purchase count, item price, and total purchase value\n",
    "\n",
    "\n",
    "* Create a summary data frame to hold the results\n",
    "\n",
    "\n",
    "* Sort the purchase count column in descending order\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display a preview of the summary data frame\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Purchase Value</th>\n",
       "      <th>Item Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Name</th>\n",
       "      <th>Item ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <th>178</th>\n",
       "      <td>12</td>\n",
       "      <td>$50.76</td>\n",
       "      <td>$4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extraction, Quickblade Of Trembling Hands</th>\n",
       "      <th>108</th>\n",
       "      <td>9</td>\n",
       "      <td>$31.77</td>\n",
       "      <td>$3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nirvana</th>\n",
       "      <th>82</th>\n",
       "      <td>9</td>\n",
       "      <td>$44.10</td>\n",
       "      <td>$4.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <th>145</th>\n",
       "      <td>9</td>\n",
       "      <td>$41.22</td>\n",
       "      <td>$4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pursuit, Cudgel of Necromancy</th>\n",
       "      <th>19</th>\n",
       "      <td>8</td>\n",
       "      <td>$8.16</td>\n",
       "      <td>$1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  \\\n",
       "Item Name                                    Item ID                   \n",
       "Oathbreaker, Last Hope of the Breaking Storm 178                  12   \n",
       "Extraction, Quickblade Of Trembling Hands    108                   9   \n",
       "Nirvana                                      82                    9   \n",
       "Fiery Glass Crusader                         145                   9   \n",
       "Pursuit, Cudgel of Necromancy                19                    8   \n",
       "\n",
       "                                                     Purchase Value Item Price  \n",
       "Item Name                                    Item ID                            \n",
       "Oathbreaker, Last Hope of the Breaking Storm 178             $50.76      $4.23  \n",
       "Extraction, Quickblade Of Trembling Hands    108             $31.77      $3.53  \n",
       "Nirvana                                      82              $44.10      $4.90  \n",
       "Fiery Glass Crusader                         145             $41.22      $4.58  \n",
       "Pursuit, Cudgel of Necromancy                19               $8.16      $1.02  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_count = purchase_data.groupby([\"Item Name\", \"Item ID\"]).count()[\"Purchase ID\"]\n",
    "items_purchase_value = purchase_data.groupby([\"Item Name\", \"Item ID\"]).sum()[\"Price\"]\n",
    "item_purchase_price = purchase_data.groupby([\"Item Name\", \"Item ID\"]).mean()[\"Price\"]\n",
    "summary = pd.DataFrame({\"Purchase Count\": purchase_count,\n",
    "           \"Purchase Value\": items_purchase_value,\n",
    "                       \"Item Price\": item_purchase_price})\n",
    "\n",
    "#formatting\n",
    "summary_sort = summary.sort_values(\"Purchase Count\", ascending=False)\n",
    "summary_sort[\"Purchase Value\"]= summary_sort[\"Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "summary_sort[\"Item Price\"]= summary_sort[\"Item Price\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "summary_sort.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Profitable Items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sort the above table by total purchase value in descending order\n",
    "\n",
    "\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "\n",
    "\n",
    "* Display a preview of the data frame\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Purchase Value</th>\n",
       "      <th>Item Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Name</th>\n",
       "      <th>Item ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <th>178</th>\n",
       "      <td>12</td>\n",
       "      <td>$50.76</td>\n",
       "      <td>$4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nirvana</th>\n",
       "      <th>82</th>\n",
       "      <td>9</td>\n",
       "      <td>$44.10</td>\n",
       "      <td>$4.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <th>145</th>\n",
       "      <td>9</td>\n",
       "      <td>$41.22</td>\n",
       "      <td>$4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final Critic</th>\n",
       "      <th>92</th>\n",
       "      <td>8</td>\n",
       "      <td>$39.04</td>\n",
       "      <td>$4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singed Scalpel</th>\n",
       "      <th>103</th>\n",
       "      <td>8</td>\n",
       "      <td>$34.80</td>\n",
       "      <td>$4.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  \\\n",
       "Item Name                                    Item ID                   \n",
       "Oathbreaker, Last Hope of the Breaking Storm 178                  12   \n",
       "Nirvana                                      82                    9   \n",
       "Fiery Glass Crusader                         145                   9   \n",
       "Final Critic                                 92                    8   \n",
       "Singed Scalpel                               103                   8   \n",
       "\n",
       "                                                     Purchase Value Item Price  \n",
       "Item Name                                    Item ID                            \n",
       "Oathbreaker, Last Hope of the Breaking Storm 178             $50.76      $4.23  \n",
       "Nirvana                                      82              $44.10      $4.90  \n",
       "Fiery Glass Crusader                         145             $41.22      $4.58  \n",
       "Final Critic                                 92              $39.04      $4.88  \n",
       "Singed Scalpel                               103             $34.80      $4.35  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_sort = summary.sort_values(\"Purchase Value\", ascending=False)\n",
    "summary_sort[\"Purchase Value\"]= summary_sort[\"Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "summary_sort[\"Item Price\"]= summary_sort[\"Item Price\"].map(\"${:,.2f}\".format)\n",
    "summary_sort.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observational Trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Of the 1163 active players, the vast majority are male (84%). There also exists, a smaller, but notable proportion of female players (14%).\n",
    "\n",
    "* Our peak age demographic falls between 20-24 (44.8%) with secondary groups falling between 15-19 (18.60%) and 25-29 (13.4%).  \n",
    "\n",
    "* The purchases data also reflects the above observation with male gamers spending a total of 1967.64 on game ad ons Which is over five times the amount spent by female gamers $361.94. \n",
    "\n",
    "* Even though the peak demographis falls between 20-24 the age demographic that spends the most money is actually 30-34 and 35-40. \n",
    "\n",
    "* Oathbreaker, Last Hope of the Breaking Storm is by far the most profitable item purchase. \n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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