HeroesOfPymoli/HeroesOfPymoli_starter.ipynb

{
 "cells": [
  {
   "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": 11,
   "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 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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "# File to Load (Remember to Change These)\n",
    "file = \"Resources/purchase_data.csv\"\n",
    "\n",
    "# Read Purchasing File and store into Pandas data frame\n",
    "purchase_data = pd.read_csv(file)\n",
    "purchase_data.head()\n",
    "                  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Player Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Display the total number of players\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 Number of Players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Number of Players\n",
       "0                      576"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = len(purchase_data[\"SN\"].value_counts())\n",
    "numberofplayers = [{\"Total Number of Players\": players}]\n",
    "numberofplayers_df = pd.DataFrame(numberofplayers)\n",
    "numberofplayers_df\n",
    "\n"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_items = len(purchase_data[\"Item Name\"].unique())\n",
    "average_price = purchase_data[\"Price\"].mean()\n",
    "total_revenue = purchase_data[\"Price\"].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Unique Items</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$2379.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Items Average Price Total Revenue\n",
       "0           179         $3.05      $2379.77"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_purchase_analysis = [{\"Unique Items\": unique_items, \"Average Price\": f'${round(average_price,2)}', \"Total Revenue\": f'${round(total_revenue,2)}'}]\n",
    "total_purchase_analysis_df = pd.DataFrame(total_purchase_analysis)\n",
    "total_purchase_analysis_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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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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 Percentage</th>\n",
       "      <th>Total Count</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>83.6 %</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>14.49 %</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other/Non-Disclosed</th>\n",
       "      <td>1.92 %</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total Percentage  Total Count\n",
       "Gender                                           \n",
       "Male                          83.6 %          652\n",
       "Female                       14.49 %          113\n",
       "Other/Non-Disclosed           1.92 %           15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = purchase_data[\"Gender\"].value_counts()[\"Male\"]\n",
    "female = purchase_data[\"Gender\"].value_counts()[\"Female\"]\n",
    "other = purchase_data[\"Gender\"].value_counts()[\"Other / Non-Disclosed\"]\n",
    "total_gender_counts = male + female + other\n",
    "total_gender_counts\n",
    "# Percentage and Count of Male Players\n",
    "male_percent = f'{round(male/total_gender_counts,3) * 100} %'\n",
    "#Percentage and Count of Female Players\n",
    "female_percent = f'{round(female/total_gender_counts,4) * 100} %'\n",
    "other_percent = f'{round(other/total_gender_counts,4) * 100} %'\n",
    "gender_demographics = {\"Gender\":[\"Male\", \"Female\", \"Other/Non-Disclosed\"],\n",
    "                       \"Total Percentage\": [male_percent, female_percent, other_percent],\n",
    "                       \"Total Count\":[male, female, other]}\n",
    "gender_demographics_df = pd.DataFrame(gender_demographics)\n",
    "gender_df = gender_demographics_df.set_index(\"Gender\")\n",
    "gender_df"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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 Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "      <th>Avg Total Purchase per Person</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>$361.94</td>\n",
       "      <td>$4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>652</td>\n",
       "      <td>$3.02</td>\n",
       "      <td>$1967.64</td>\n",
       "      <td>$4.07</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",
       "      <td>$4.56</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 Avg Total Purchase per Person  \n",
       "Gender                                                                    \n",
       "Female                             $361.94                         $4.47  \n",
       "Male                              $1967.64                         $4.07  \n",
       "Other / Non-Disclosed               $50.19                         $4.56  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. by gender\n",
    "gender_group = purchase_data.groupby(['Gender'])\n",
    "# Using the gender groupby object calculate purchase count, avg. purchase price, avg purchase total \n",
    "gender_analysis_df = pd.DataFrame({\"Purchase Count\":gender_group['Purchase ID'].count(),\n",
    "                                   \"Average Purchase Price\":gender_group['Price'].mean(),\n",
    "                                   \"Total Purchase Value\":gender_group['Price'].sum()})\n",
    "# Format the cells by using a map() method \n",
    "gender_analysis_df['Average Purchase Price'] = gender_analysis_df['Average Purchase Price'].map(\"${:.2f}\".format)\n",
    "gender_analysis_df['Total Purchase Value'] = gender_analysis_df['Total Purchase Value'].map(\"${:.2f}\".format)\n",
    "\n",
    "#gender_analysis_df\n",
    "#Calculate the Avg Total Purchase per Person by a groupby on both the Gender and SN columns, summing the Price column\n",
    "# then put it into a dataframe and groupby the Gender and take the mean of the summed Price column for each unique player\n",
    "gender_person_group = purchase_data.groupby(['Gender','SN']).sum()\n",
    "gender_person_sum_df = pd.DataFrame(gender_person_group)\n",
    "\n",
    "\n",
    "# Next add this column to the above dataframe\n",
    "gender_analysis_df['Avg Total Purchase per Person'] = gender_person_sum_df.groupby('Gender')['Price'].mean()\n",
    "# Finally format the new column\n",
    "gender_analysis_df['Avg Total Purchase per Person'] = gender_analysis_df['Avg Total Purchase per Person'].map(\"${:.2f}\".format)\n",
    "gender_analysis_df"
   ]
  },
  {
   "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": 24,
   "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",
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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>Total Count</th>\n",
       "      <th>Percentage of Players</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Bins</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>17</td>\n",
       "      <td>2.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>22</td>\n",
       "      <td>3.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>107</td>\n",
       "      <td>18.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>258</td>\n",
       "      <td>44.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>77</td>\n",
       "      <td>13.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>52</td>\n",
       "      <td>9.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>31</td>\n",
       "      <td>5.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>12</td>\n",
       "      <td>2.08%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Count Percentage of Players\n",
       "Age Bins                                   \n",
       "<10                17                 2.95%\n",
       "10-14              22                 3.82%\n",
       "15-19             107                18.58%\n",
       "20-24             258                44.79%\n",
       "25-29              77                13.37%\n",
       "30-34              52                 9.03%\n",
       "35-39              31                 5.38%\n",
       "40+                12                 2.08%"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held   \n",
    "bins = [0, 9, 14, 19, 24, 29, 34, 39, 100]\n",
    "# Create the names for the four bins\n",
    "group_names = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\",\"40+\"]\n",
    "\n",
    "# First create dataframe for unique list of ages - one for each SN value\n",
    "uniq_SN_group = purchase_data.groupby(['SN'])\n",
    "uniq_age_df = pd.DataFrame(uniq_SN_group['Age'].max())\n",
    "\n",
    "\n",
    "# Next use the unique list of ages along with the bins and group names to bin the ages\n",
    "\n",
    "uniq_age_df[\"Age Bins\"] = pd.cut(uniq_age_df[\"Age\"], bins, labels=group_names)\n",
    "\n",
    "#Change the index to be the Age Bins labels instead of the default index\n",
    "uniq_age_df.set_index('Age Bins',inplace=True)\n",
    "\n",
    "# Now count how many values are in each bin by using groupby on the Age Bins labels and\n",
    "# Assign this result to a new dataframe\n",
    "age_bins_df = pd.DataFrame(uniq_age_df.groupby(['Age Bins']).count())\n",
    "\n",
    "# Rename the column to Total Count instead of the Age\n",
    "age_bins_df.rename(columns = {'Age':'Total Count'}, inplace = True) \n",
    "\n",
    "# Calculate the percentage of total players each Age Bin represents and add it to the dataframe\n",
    "age_bins_df['Percentage of Players'] = age_bins_df['Total Count'] / players *100\n",
    "\n",
    "# Format the cells using map() to be 2 decimal places and have a % at the end of the value\n",
    "age_bins_df['Percentage of Players'] = age_bins_df['Percentage of Players'].map(\"{:.2f}%\".format)\n",
    "age_bins_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purchasing Analysis (Age)¶\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Bin the purchase_data data frame by age\n",
    "* Run basic calculations to obtain purchase count, avg. purchase price, avg. \n",
    "* purchase total per person etc. in the table below\n",
    "* Create a summary data frame to hold the results\n",
    "* Optional: give the displayed data cleaner formatting\n",
    "* Display the summary data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "      <th>Avg Total Purchase per Person</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Bins</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>23</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$77.13</td>\n",
       "      <td>$4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>28</td>\n",
       "      <td>$2.96</td>\n",
       "      <td>$82.78</td>\n",
       "      <td>$3.76</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",
       "      <td>$3.86</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",
       "      <td>$4.32</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",
       "      <td>$3.81</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",
       "      <td>$4.12</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",
       "      <td>$4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>13</td>\n",
       "      <td>$2.94</td>\n",
       "      <td>$38.24</td>\n",
       "      <td>$3.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Purchase Count Average Purchase Price Total Purchase Value  \\\n",
       "Age Bins                                                               \n",
       "<10                   23                  $3.35               $77.13   \n",
       "10-14                 28                  $2.96               $82.78   \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   \n",
       "\n",
       "         Avg Total Purchase per Person  \n",
       "Age Bins                                \n",
       "<10                              $4.54  \n",
       "10-14                            $3.76  \n",
       "15-19                            $3.86  \n",
       "20-24                            $4.32  \n",
       "25-29                            $3.81  \n",
       "30-34                            $4.12  \n",
       "35-39                            $4.76  \n",
       "40+                              $3.19  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bin the purchase_data dataframe by age using the previous bins and labels from above\n",
    "purchase_data[\"Age Bins\"] = pd.cut(purchase_data[\"Age\"], bins, labels=group_names)\n",
    "# Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc\n",
    "age_group = purchase_data.groupby(['Age Bins'])\n",
    "purch_count = age_group['Purchase ID'].count()\n",
    "avg_purch_price = age_group['Price'].mean()\n",
    "total_purch = age_group['Price'].sum()\n",
    "\n",
    "# Create a summary dataframe to hold the results \n",
    "age_purch_df = pd.DataFrame({\"Purchase Count\":purch_count,\"Average Purchase Price\":avg_purch_price,\\\n",
    "                            \"Total Purchase Value\":total_purch})\n",
    "\n",
    "#Calculate the Avg Total Purchase per Person by a groupby on both the Age Bins and SN columns, summing the Price column\n",
    "# then put it into a dataframe and groupby the Age Bins and take the mean of the summed Price column for each unique player\n",
    "age_person_group = purchase_data.groupby(['Age Bins','SN']).sum()\n",
    "age_person_sum_df = pd.DataFrame(age_person_group)\n",
    "\n",
    "# Next add this column to the above dataframe\n",
    "age_purch_df['Avg Total Purchase per Person'] = age_person_sum_df.groupby('Age Bins')['Price'].mean()\n",
    "# Finally format the new columns\n",
    "age_purch_df['Average Purchase Price'] = age_purch_df['Average Purchase Price'].map(\"${:.2f}\".format)\n",
    "age_purch_df['Total Purchase Value'] = age_purch_df['Total Purchase Value'].map(\"${:.2f}\".format)\n",
    "age_purch_df['Avg Total Purchase per Person'] = age_purch_df['Avg Total Purchase per Person'].map(\"${:.2f}\".format)\n",
    "age_purch_df"
   ]
  },
  {
   "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": 32,
   "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>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 Average Purchase Price Total Purchase Value\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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by SN to get information by each unique username\n",
    "top_spend_group = purchase_data.groupby(['SN'])\n",
    "\n",
    "# Sum the spending by each user and sort in descending order to find the top 5 spenders then put into a dataframe\n",
    "top_spend_df = pd.DataFrame(top_spend_group.sum().sort_values('Price',ascending=False))\n",
    "\n",
    "# Take the SN index values from the first 5 rows of data and put into a list \n",
    "#to later use as a mask to get all data for just these top spender users\n",
    "spenders_df = top_spend_df.index.values[0:5]\n",
    "#top5_spenders_SN\n",
    "# Get all rows from purchase_data where the SN is in the spenders\n",
    "spenders_df = purchase_data[purchase_data['SN'].isin(spenders_df)]\n",
    "\n",
    "# Group the spenders_df by SN\n",
    "top_group = spenders_df.groupby(['SN'])\n",
    "\n",
    "# Do basic calculations on the group for Purchase Count,Average Purchase Price and Total Purchase Value\n",
    "purch_count = top_group['Purchase ID'].count()\n",
    "avg_purch = top_group['Price'].mean()\n",
    "total_purch = top_group['Price'].sum()\n",
    "\n",
    "# Create a summary dataframe and add the basic calculations\n",
    "purch_summary_df = pd.DataFrame({\"Purchase Count\":purch_count,\"Average Purchase Price\":avg_purch, \"Total Purchase Value\":total_purch})\n",
    "\n",
    "# Format the cells to 2 decimal places with $ signs at the beginning of the values\n",
    "purch_summary_df['Average Purchase Price'] = purch_summary_df['Average Purchase Price'].map(\"${:.2f}\".format)\n",
    "purch_summary_df['Total Purchase Value'] = purch_summary_df['Total Purchase Value'].map(\"${:.2f}\".format)\n",
    "\n",
    "# Finally sort the values on the Total Purchase Value column in descending order\n",
    "purch_summary_df.sort_values('Total Purchase Value',ascending=False)"
   ]
  },
  {
   "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, average 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": 33,
   "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>Item Price</th>\n",
       "      <th>Total Purchase Value</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>$4.88</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>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>132</th>\n",
       "      <th>Persuasion</th>\n",
       "      <td>9</td>\n",
       "      <td>$3.33</td>\n",
       "      <td>$28.99</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",
       "  </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",
       "108     Extraction, Quickblade Of Trembling Hands                  9   \n",
       "132     Persuasion                                                 9   \n",
       "145     Fiery Glass Crusader                                       9   \n",
       "\n",
       "                                                     Item Price  \\\n",
       "Item ID Item Name                                                 \n",
       "92      Final Critic                                      $4.88   \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm      $4.23   \n",
       "108     Extraction, Quickblade Of Trembling Hands         $3.53   \n",
       "132     Persuasion                                        $3.33   \n",
       "145     Fiery Glass Crusader                              $4.58   \n",
       "\n",
       "                                                     Total Purchase Value  \n",
       "Item ID Item Name                                                          \n",
       "92      Final Critic                                               $59.99  \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               $50.76  \n",
       "108     Extraction, Quickblade Of Trembling Hands                  $31.77  \n",
       "132     Persuasion                                                 $28.99  \n",
       "145     Fiery Glass Crusader                                       $41.22  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the Item ID, Item Name, and Item Price columns from the purchase_data dataframe\n",
    "item_df = purchase_data.loc[:,[\"Item ID\",\"Item Name\",\"Price\"]]\n",
    "#item_df.head()\n",
    "#Group by Item ID and Item Name\n",
    "item_group = item_df.groupby(['Item ID','Item Name'])\n",
    "\n",
    "# Count the number of values in Price column grouped by Item ID \n",
    "# then sort in descending order to determine the most popular items then put into a dataframe\n",
    "pop_df = pd.DataFrame(item_group.count().sort_values('Price',ascending=False))\n",
    "# Take the Item ID index values from the first 5 rows of data and put into a list  \n",
    "# to later use as a mask to get all data for just these top 5 most popular Item ID\n",
    "popular = pop_df.index.values[0:5]\n",
    "popular_ItemID_list = [i[0] for i in popular]\n",
    "\n",
    "# Get all rows from item_df where the Item ID is in the top5_popular_ItemID_list \n",
    "popular_df = item_df[item_df['Item ID'].isin(popular_ItemID_list)]\n",
    "\n",
    "# Group the top5_popular_df by Item ID and Item Name\n",
    "popular_group = popular_df.groupby(['Item ID','Item Name'])\n",
    "\n",
    "# Do basic calculations on the group for Purchase Count,Item Price and Total Purchase Value\n",
    "pop_count = popular_group['Price'].count()\n",
    "pop_price = popular_group['Price'].max()\n",
    "pop_total = popular_group['Price'].sum()\n",
    "\n",
    "# Create a summary dataframe and add the basic calculations\n",
    "pop_summary_df = pd.DataFrame({\"Purchase Count\":pop_count,\"Item Price\":pop_price,\n",
    "                                     \"Total Purchase Value\":pop_total})\n",
    "\n",
    "# Format the cells to 2 decimal places with $ signs at the beginning of the values\n",
    "pop_summary_df['Item Price'] = pop_summary_df['Item Price'].map(\"${:.2f}\".format)\n",
    "pop_summary_df['Total Purchase Value'] = pop_summary_df['Total Purchase Value'].map(\"${:.2f}\".format)\n",
    "\n",
    "# Finally sort the values on the Total Purchase Value column in descending order\n",
    "pop_summary_df.sort_values('Purchase Count',ascending=False)"
   ]
  },
  {
   "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": 34,
   "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>Item Price</th>\n",
       "      <th>Total Purchase Value</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>$4.88</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                                      $4.88   \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 Value  \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": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Count the number of values in Price column grouped by Item ID \n",
    "# then sort in descending order to determine the most popular items then put into a dataframe\n",
    "top_profit_df = pd.DataFrame(item_group.sum().sort_values('Price',ascending=False))\n",
    "#top_profit_df.head(5)\n",
    "# Take the Item ID index values from the first 5 rows of data and put into a list  \n",
    "# to later use as a mask to get all data for just these top 5 most popular Item ID\n",
    "profit = top_profit_df.index.values[0:5]\n",
    "profit_ItemID_list = [i[0] for i in profit]\n",
    "#top5_profit_ItemID_list\n",
    "\n",
    "# Get all rows from item_df where the Item ID is in the top5_profit_ItemID_list \n",
    "profit_df = item_df[item_df['Item ID'].isin(profit_ItemID_list)]\n",
    "\n",
    "# Group the top5_popular_df by Item ID and Item Name\n",
    "profit_group = profit_df.groupby(['Item ID','Item Name'])\n",
    "\n",
    "# Do basic calculations on the group for Purchase Count,Item Price and Total Purchase Value\n",
    "profit_count = profit_group['Price'].count()\n",
    "profit_price = profit_group['Price'].max()\n",
    "profit_total = profit_group['Price'].sum()\n",
    "# Create a summary dataframe and add the basic calculations\n",
    "profit_summary_df = pd.DataFrame({\"Purchase Count\":profit_count,\"Item Price\":profit_price,\n",
    "                                     \"Total Purchase Value\":profit_total})\n",
    "\n",
    "# Format the cells to 2 decimal places with $ signs at the beginning of the values\n",
    "profit_summary_df['Item Price'] = profit_summary_df['Item Price'].map(\"${:.2f}\".format)\n",
    "profit_summary_df['Total Purchase Value'] = profit_summary_df['Total Purchase Value'].map(\"${:.2f}\".format)\n",
    "\n",
    "# Finally sort the values on the Total Purchase Value column in descending order\n",
    "profit_summary_df.sort_values('Total Purchase Value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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