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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Purchase ID', 'SN', 'Age', 'Gender', 'Item ID', 'Item Name', 'Price'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\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)\n",
    "print(purchase_data.columns)\n",
    "purchase_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Player Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Display the total number of players\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total 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 Players\n",
       "0            576"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the length function of list of ID's \"SN\", for total players.\n",
    "total_players = len(purchase_data[\"SN\"].value_counts())\n",
    "\n",
    "# Create the data frame to run the player count\n",
    "player_count = pd.DataFrame({\"Total Players\":[total_players]})\n",
    "player_count"
   ]
  },
  {
   "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": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Unique Items</th>        <th class=\"col_heading level0 col1\" >Average Price</th>        <th class=\"col_heading level0 col2\" >Total Purchase Count</th>        <th class=\"col_heading level0 col3\" >Total Revenue</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801row0_col0\" class=\"data row0 col0\" >179</td>\n",
       "                        <td id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801row0_col1\" class=\"data row0 col1\" >$3.05</td>\n",
       "                        <td id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801row0_col2\" class=\"data row0 col2\" >780</td>\n",
       "                        <td id=\"T_55e3a5a4_000d_11eb_b19a_821493ea3801row0_col3\" class=\"data row0 col3\" >$2,379.77</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8909e5ed90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique items, average price, etc. \n",
    "unique_items = len(purchase_data[\"Item Name\"].unique())\n",
    "average_price = (purchase_data[\"Price\"].mean())\n",
    "purchase_count = (purchase_data[\"Purchase ID\"].count())\n",
    "total_rev = (purchase_data[\"Price\"].sum())\n",
    "\n",
    "#create new data frame \n",
    "purchasing_analysis_df = pd.DataFrame({\"Unique Items\": [unique_items], \"Average Price\":[average_price],\n",
    "                                      \"Total Purchase Count\": [purchase_count], \n",
    "                                      \"Total Revenue\": [total_rev]})\n",
    "\n",
    "#display summary data frame\n",
    "purchasing_analysis_df.style.format({'Average Price':\"${:,.2f}\",\n",
    "                         'Total Revenue': '${:,.2f}'})\n"
   ]
  },
  {
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Percentage of Players</th>\n",
       "      <th>Total Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>113.194444</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>19.618056</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>2.604167</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Percentage of Players  Total Count\n",
       "Male                              113.194444          652\n",
       "Female                             19.618056          113\n",
       "Other / Non-Disclosed               2.604167           15"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by gender\n",
    "gender_count = purchase_data[[\"Gender\", \"SN\", \"Purchase ID\", \"Price\"]]\n",
    "gender_count = gender_count.drop_duplicates()\n",
    "final_count = gender_count[\"Gender\"].value_counts()\n",
    "\n",
    "#list the values\n",
    "percentage_players = final_count / total_players * 100\n",
    "\n",
    "#create a new dataframe \n",
    "gender_demographics = pd.DataFrame({ \"Percentage of Players\": percentage_players,\"Total Count\": final_count\n",
    "})\n",
    "gender_demographics\n",
    "\n"
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Purchase Count</th>        <th class=\"col_heading level0 col1\" >Average Purchase Price</th>        <th class=\"col_heading level0 col2\" >Average Purchase Value</th>        <th class=\"col_heading level0 col3\" >Average Purchase Total per Person</th>    </tr>    <tr>        <th class=\"index_name level0\" >Gender</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801level0_row0\" class=\"row_heading level0 row0\" >Male</th>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row0_col0\" class=\"data row0 col0\" >780</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row0_col1\" class=\"data row0 col1\" >$3.05</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row0_col2\" class=\"data row0 col2\" >$2,379.77</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row0_col3\" class=\"data row0 col3\" >3.649954</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801level0_row1\" class=\"row_heading level0 row1\" >Female</th>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row1_col0\" class=\"data row1 col0\" >780</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row1_col1\" class=\"data row1 col1\" >$3.05</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row1_col2\" class=\"data row1 col2\" >$2,379.77</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row1_col3\" class=\"data row1 col3\" >21.059912</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801level0_row2\" class=\"row_heading level0 row2\" >Other / Non-Disclosed</th>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row2_col0\" class=\"data row2 col0\" >780</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row2_col1\" class=\"data row2 col1\" >$3.05</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row2_col2\" class=\"data row2 col2\" >$2,379.77</td>\n",
       "                        <td id=\"T_3c26cae8_001b_11eb_b19a_821493ea3801row2_col3\" class=\"data row2 col3\" >158.651333</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f890a204100>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the total purchases by gender \n",
    "purchase_count = gender_count[\"Purchase ID\"].count()\n",
    "\n",
    "#calculates average prices, purchase totals, etc.\n",
    "average_purchase_price = gender_count[\"Price\"].mean() \n",
    "average_purchase_total = gender_count[\"Price\"].sum()\n",
    "average_purchase_per_person = average_purchase_total/final_count\n",
    "\n",
    "#create dataframe with values \n",
    "gender_demographics = pd.DataFrame({\"Purchase Count\": purchase_count, \n",
    "                                    \"Average Purchase Price\": average_purchase_price,\n",
    "                                    \"Average Purchase Value\":average_purchase_total,\n",
    "                                    \"Average Purchase Total per Person\": average_purchase_per_person})\n",
    "\n",
    "#provide index for \"Gender\"\n",
    "gender_demographics.index.name = \"Gender\"\n",
    "\n",
    "#clean formatting\n",
    "gender_demographics.style.format({\"Average Purchase Value\":\"${:,.2f}\",\n",
    "                                  \"Average Purchase Price\":\"${:,.2f}\",\n",
    "                                  \"Avg Purchase Total per Person\":\"${:,.2f}\"})"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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",
       "      <th>Age Demographics</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",
       "      <td>20-24</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",
       "      <td>40+</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",
       "      <td>20-24</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",
       "      <td>20-24</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",
       "      <td>20-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>775</td>\n",
       "      <td>Aethedru70</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>Wolf</td>\n",
       "      <td>3.54</td>\n",
       "      <td>20-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>776</td>\n",
       "      <td>Iral74</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>164</td>\n",
       "      <td>Exiled Doomblade</td>\n",
       "      <td>1.63</td>\n",
       "      <td>20-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>777</td>\n",
       "      <td>Yathecal72</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>Celeste, Incarnation of the Corrupted</td>\n",
       "      <td>3.46</td>\n",
       "      <td>20-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>778</td>\n",
       "      <td>Sisur91</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>Final Critic</td>\n",
       "      <td>4.19</td>\n",
       "      <td>&lt;10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>779</td>\n",
       "      <td>Ennrian78</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Dawn</td>\n",
       "      <td>4.60</td>\n",
       "      <td>20-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>780 rows × 8 columns</p>\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",
       "775          775     Aethedru70   21  Female       60   \n",
       "776          776         Iral74   21    Male      164   \n",
       "777          777     Yathecal72   20    Male       67   \n",
       "778          778        Sisur91    7    Male       92   \n",
       "779          779      Ennrian78   24    Male       50   \n",
       "\n",
       "                                     Item Name  Price Age Demographics  \n",
       "0    Extraction, Quickblade Of Trembling Hands   3.53            20-24  \n",
       "1                            Frenzied Scimitar   1.56              40+  \n",
       "2                                 Final Critic   4.88            20-24  \n",
       "3                                  Blindscythe   3.27            20-24  \n",
       "4                                         Fury   1.44            20-24  \n",
       "..                                         ...    ...              ...  \n",
       "775                                       Wolf   3.54            20-24  \n",
       "776                           Exiled Doomblade   1.63            20-24  \n",
       "777      Celeste, Incarnation of the Corrupted   3.46            20-24  \n",
       "778                               Final Critic   4.19              <10  \n",
       "779                                       Dawn   4.60            20-24  \n",
       "\n",
       "[780 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create bins for ages\n",
    "age_bins = [0, 9.90, 14.90, 19.90, 24.90, 29.90, 34.90, 39.90, 99999]\n",
    "group_names = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40+\"]\n",
    "\n",
    "#database for age demographics\n",
    "purchase_data[\"Age Demographics\"]= pd.cut(purchase_data[\"Age\"], age_bins, labels=group_names)\n",
    "demographic_groups =purchase_data.groupby(\"Age Demographics\")\n",
    "\n",
    "purchase_data\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Purchase Count</th>        <th class=\"col_heading level0 col1\" >Average Purchase Price</th>        <th class=\"col_heading level0 col2\" >Total Purchase Value</th>        <th class=\"col_heading level0 col3\" >Average Purchase Total per Person</th>    </tr>    <tr>        <th class=\"index_name level0\" >Age</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row0_col0\" class=\"data row0 col0\" >9</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row0_col1\" class=\"data row0 col1\" >$3.65</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row0_col2\" class=\"data row0 col2\" >$32.89</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row0_col3\" class=\"data row0 col3\" >$4.70</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row1_col0\" class=\"data row1 col0\" >8</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row1_col1\" class=\"data row1 col1\" >$3.25</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row1_col2\" class=\"data row1 col2\" >$25.97</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row1_col3\" class=\"data row1 col3\" >$4.33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row2\" class=\"row_heading level0 row2\" >9</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row2_col0\" class=\"data row2 col0\" >6</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row2_col1\" class=\"data row2 col1\" >$3.04</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row2_col2\" class=\"data row2 col2\" >$18.27</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row2_col3\" class=\"data row2 col3\" >$4.57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row3\" class=\"row_heading level0 row3\" >10</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row3_col0\" class=\"data row3 col0\" >9</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row3_col1\" class=\"data row3 col1\" >$3.54</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row3_col2\" class=\"data row3 col2\" >$31.83</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row3_col3\" class=\"data row3 col3\" >$4.55</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row4\" class=\"row_heading level0 row4\" >11</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row4_col0\" class=\"data row4 col0\" >7</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row4_col1\" class=\"data row4 col1\" >$2.68</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row4_col2\" class=\"data row4 col2\" >$18.79</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row4_col3\" class=\"data row4 col3\" >$3.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row5\" class=\"row_heading level0 row5\" >12</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row5_col1\" class=\"data row5 col1\" >$2.63</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row5_col2\" class=\"data row5 col2\" >$15.80</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row5_col3\" class=\"data row5 col3\" >$3.95</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row6\" class=\"row_heading level0 row6\" >13</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row6_col0\" class=\"data row6 col0\" >4</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row6_col1\" class=\"data row6 col1\" >$2.36</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row6_col2\" class=\"data row6 col2\" >$9.45</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row6_col3\" class=\"data row6 col3\" >$3.15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row7\" class=\"row_heading level0 row7\" >14</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row7_col0\" class=\"data row7 col0\" >2</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row7_col1\" class=\"data row7 col1\" >$3.46</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row7_col2\" class=\"data row7 col2\" >$6.91</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row7_col3\" class=\"data row7 col3\" >$3.46</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row8\" class=\"row_heading level0 row8\" >15</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row8_col0\" class=\"data row8 col0\" >35</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row8_col1\" class=\"data row8 col1\" >$3.02</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row8_col2\" class=\"data row8 col2\" >$105.65</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row8_col3\" class=\"data row8 col3\" >$4.06</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row9\" class=\"row_heading level0 row9\" >16</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row9_col0\" class=\"data row9 col0\" >30</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row9_col1\" class=\"data row9 col1\" >$3.02</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row9_col2\" class=\"data row9 col2\" >$90.56</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row9_col3\" class=\"data row9 col3\" >$3.77</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row10\" class=\"row_heading level0 row10\" >17</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row10_col0\" class=\"data row10 col0\" >22</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row10_col1\" class=\"data row10 col1\" >$2.93</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row10_col2\" class=\"data row10 col2\" >$64.48</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row10_col3\" class=\"data row10 col3\" >$3.39</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row11\" class=\"row_heading level0 row11\" >18</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row11_col0\" class=\"data row11 col0\" >26</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row11_col1\" class=\"data row11 col1\" >$3.16</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row11_col2\" class=\"data row11 col2\" >$82.22</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row11_col3\" class=\"data row11 col3\" >$3.92</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row12\" class=\"row_heading level0 row12\" >19</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row12_col0\" class=\"data row12 col0\" >23</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row12_col1\" class=\"data row12 col1\" >$3.04</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row12_col2\" class=\"data row12 col2\" >$69.98</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row12_col3\" class=\"data row12 col3\" >$4.12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row13\" class=\"row_heading level0 row13\" >20</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row13_col0\" class=\"data row13 col0\" >99</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row13_col1\" class=\"data row13 col1\" >$3.17</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row13_col2\" class=\"data row13 col2\" >$314.32</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row13_col3\" class=\"data row13 col3\" >$4.56</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row14\" class=\"row_heading level0 row14\" >21</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row14_col0\" class=\"data row14 col0\" >62</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row14_col1\" class=\"data row14 col1\" >$2.92</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row14_col2\" class=\"data row14 col2\" >$180.74</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row14_col3\" class=\"data row14 col3\" >$4.20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row15\" class=\"row_heading level0 row15\" >22</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row15_col0\" class=\"data row15 col0\" >70</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row15_col1\" class=\"data row15 col1\" >$2.96</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row15_col2\" class=\"data row15 col2\" >$206.85</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row15_col3\" class=\"data row15 col3\" >$4.22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row16\" class=\"row_heading level0 row16\" >23</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row16_col0\" class=\"data row16 col0\" >67</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row16_col1\" class=\"data row16 col1\" >$3.01</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row16_col2\" class=\"data row16 col2\" >$201.93</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row16_col3\" class=\"data row16 col3\" >$4.12</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row17\" class=\"row_heading level0 row17\" >24</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row17_col0\" class=\"data row17 col0\" >67</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row17_col1\" class=\"data row17 col1\" >$3.14</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row17_col2\" class=\"data row17 col2\" >$210.22</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row17_col3\" class=\"data row17 col3\" >$4.38</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row18\" class=\"row_heading level0 row18\" >25</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row18_col0\" class=\"data row18 col0\" >59</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row18_col1\" class=\"data row18 col1\" >$3.08</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row18_col2\" class=\"data row18 col2\" >$181.90</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row18_col3\" class=\"data row18 col3\" >$4.23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row19\" class=\"row_heading level0 row19\" >26</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row19_col0\" class=\"data row19 col0\" >14</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row19_col1\" class=\"data row19 col1\" >$2.87</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row19_col2\" class=\"data row19 col2\" >$40.19</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row19_col3\" class=\"data row19 col3\" >$3.65</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row20\" class=\"row_heading level0 row20\" >27</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row20_col0\" class=\"data row20 col0\" >10</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row20_col1\" class=\"data row20 col1\" >$2.72</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row20_col2\" class=\"data row20 col2\" >$27.23</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row20_col3\" class=\"data row20 col3\" >$3.03</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row21\" class=\"row_heading level0 row21\" >28</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row21_col0\" class=\"data row21 col0\" >5</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row21_col1\" class=\"data row21 col1\" >$1.69</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row21_col2\" class=\"data row21 col2\" >$8.45</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row21_col3\" class=\"data row21 col3\" >$2.11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row22\" class=\"row_heading level0 row22\" >29</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row22_col0\" class=\"data row22 col0\" >13</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row22_col1\" class=\"data row22 col1\" >$2.71</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row22_col2\" class=\"data row22 col2\" >$35.23</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row22_col3\" class=\"data row22 col3\" >$3.52</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row23\" class=\"row_heading level0 row23\" >30</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row23_col0\" class=\"data row23 col0\" >35</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row23_col1\" class=\"data row23 col1\" >$3.15</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row23_col2\" class=\"data row23 col2\" >$110.32</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row23_col3\" class=\"data row23 col3\" >$4.41</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row24\" class=\"row_heading level0 row24\" >31</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row24_col0\" class=\"data row24 col0\" >7</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row24_col1\" class=\"data row24 col1\" >$3.44</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row24_col2\" class=\"data row24 col2\" >$24.05</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row24_col3\" class=\"data row24 col3\" >$4.81</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row25\" class=\"row_heading level0 row25\" >32</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row25_col0\" class=\"data row25 col0\" >8</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row25_col1\" class=\"data row25 col1\" >$2.84</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row25_col2\" class=\"data row25 col2\" >$22.69</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row25_col3\" class=\"data row25 col3\" >$3.78</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row26\" class=\"row_heading level0 row26\" >33</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row26_col0\" class=\"data row26 col0\" >14</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row26_col1\" class=\"data row26 col1\" >$2.49</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row26_col2\" class=\"data row26 col2\" >$34.81</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row26_col3\" class=\"data row26 col3\" >$3.87</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row27\" class=\"row_heading level0 row27\" >34</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row27_col0\" class=\"data row27 col0\" >9</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row27_col1\" class=\"data row27 col1\" >$2.46</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row27_col2\" class=\"data row27 col2\" >$22.13</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row27_col3\" class=\"data row27 col3\" >$3.16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row28\" class=\"row_heading level0 row28\" >35</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row28_col0\" class=\"data row28 col0\" >14</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row28_col1\" class=\"data row28 col1\" >$3.72</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row28_col2\" class=\"data row28 col2\" >$52.03</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row28_col3\" class=\"data row28 col3\" >$5.20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row29\" class=\"row_heading level0 row29\" >36</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row29_col0\" class=\"data row29 col0\" >5</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row29_col1\" class=\"data row29 col1\" >$2.53</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row29_col2\" class=\"data row29 col2\" >$12.66</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row29_col3\" class=\"data row29 col3\" >$2.53</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row30\" class=\"row_heading level0 row30\" >37</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row30_col0\" class=\"data row30 col0\" >7</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row30_col1\" class=\"data row30 col1\" >$3.64</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row30_col2\" class=\"data row30 col2\" >$25.51</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row30_col3\" class=\"data row30 col3\" >$5.10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row31\" class=\"row_heading level0 row31\" >38</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row31_col0\" class=\"data row31 col0\" >9</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row31_col1\" class=\"data row31 col1\" >$3.79</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row31_col2\" class=\"data row31 col2\" >$34.15</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row31_col3\" class=\"data row31 col3\" >$6.83</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row32\" class=\"row_heading level0 row32\" >39</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row32_col0\" class=\"data row32 col0\" >6</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row32_col1\" class=\"data row32 col1\" >$3.89</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row32_col2\" class=\"data row32 col2\" >$23.32</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row32_col3\" class=\"data row32 col3\" >$3.89</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row33\" class=\"row_heading level0 row33\" >40</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row33_col0\" class=\"data row33 col0\" >6</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row33_col1\" class=\"data row33 col1\" >$2.79</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row33_col2\" class=\"data row33 col2\" >$16.71</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row33_col3\" class=\"data row33 col3\" >$3.34</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row34\" class=\"row_heading level0 row34\" >41</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row34_col0\" class=\"data row34 col0\" >2</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row34_col1\" class=\"data row34 col1\" >$3.27</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row34_col2\" class=\"data row34 col2\" >$6.54</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row34_col3\" class=\"data row34 col3\" >$3.27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row35\" class=\"row_heading level0 row35\" >42</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row35_col0\" class=\"data row35 col0\" >1</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row35_col1\" class=\"data row35 col1\" >$3.93</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row35_col2\" class=\"data row35 col2\" >$3.93</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row35_col3\" class=\"data row35 col3\" >$3.93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row36\" class=\"row_heading level0 row36\" >43</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row36_col0\" class=\"data row36 col0\" >1</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row36_col1\" class=\"data row36 col1\" >$4.00</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row36_col2\" class=\"data row36 col2\" >$4.00</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row36_col3\" class=\"data row36 col3\" >$4.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row37\" class=\"row_heading level0 row37\" >44</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row37_col0\" class=\"data row37 col0\" >2</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row37_col1\" class=\"data row37 col1\" >$2.68</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row37_col2\" class=\"data row37 col2\" >$5.36</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row37_col3\" class=\"data row37 col3\" >$2.68</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801level0_row38\" class=\"row_heading level0 row38\" >45</th>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row38_col0\" class=\"data row38 col0\" >1</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row38_col1\" class=\"data row38 col1\" >$1.70</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row38_col2\" class=\"data row38 col2\" >$1.70</td>\n",
       "                        <td id=\"T_5a1f20ee_000d_11eb_b19a_821493ea3801row38_col3\" class=\"data row38 col3\" >$1.70</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8907c0be20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counts purchases by age group and calculatess averages \n",
    "age_grouped = purchase_data.groupby(\"Age\")\n",
    "age_grouped = purchase_data.groupby(\"Age\")\n",
    "age_purchase_count = age_grouped[\"Purchase ID\"].count()\n",
    "average_purchase_price_age = age_grouped[\"Price\"].mean()\n",
    "total_purchase_value = age_grouped[\"Price\"].sum()\n",
    "total_count_age = age_grouped[\"SN\"].nunique()\n",
    "average_purchase_per_person = total_purchase_value/total_count_age\n",
    "\n",
    "\n",
    "#create the dataframe\n",
    "age_demographics = pd.DataFrame({\"Purchase Count\": age_purchase_count,\n",
    "                                 \"Average Purchase Price\": average_purchase_price_age,\n",
    "                                 \"Total Purchase Value\":total_purchase_value,\n",
    "                                 \"Average Purchase Total per Person\": average_purchase_per_person})\n",
    "\n",
    "#clean formatting\n",
    "age_demographics.style.format({\"Average Purchase Price\":\"${:,.2f}\",\n",
    "                               \"Total Purchase Value\":\"${:,.2f}\",\n",
    "                               \"Average Purchase Total per Person\":\"${:,.2f}\"})"
   ]
  },
  {
   "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": 13,
   "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               $13.10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define top spenders \n",
    "top_spenders = purchase_data[\"Item ID\"].groupby(purchase_data[\"SN\"]).count()\n",
    "\n",
    "#create a dataframe for top spenders \n",
    "top_spenders= pd.DataFrame(data=top_spenders)\n",
    "top_spenders.columns = [\"Purchase Count\"]\n",
    "\n",
    "top_spenders[\"Average Purchase Price\"] = round(purchase_data['Price'].groupby(purchase_data['SN']).mean(),2)\n",
    "top_spenders[\"Total Purchase Value\"] = purchase_data['Price'].groupby(purchase_data['SN']).sum()\n",
    "\n",
    "\n",
    "top_spenders.sort_values(by=[\"Total Purchase Value\"], ascending=False, inplace=True)\n",
    "\n",
    "top_spenders[\"Average Purchase Price\"] = top_spenders[\"Average Purchase Price\"].map(\"${:,.2f}\".format)\n",
    "top_spenders[\"Total Purchase Value\"] = top_spenders[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "top_spenders.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, 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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\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>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>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>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>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>37</th>\n",
       "      <th>Shadow Strike, Glory of Ending Hope</th>\n",
       "      <td>8</td>\n",
       "      <td>$3.16</td>\n",
       "      <td>$25.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  Price  \\\n",
       "Item ID Item Name                                                             \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm              12  $4.23   \n",
       "108     Extraction, Quickblade Of Trembling Hands                  9  $3.53   \n",
       "145     Fiery Glass Crusader                                       9  $4.58   \n",
       "82      Nirvana                                                    9  $4.90   \n",
       "37      Shadow Strike, Glory of Ending Hope                        8  $3.16   \n",
       "\n",
       "                                                     Total Purchase Value  \n",
       "Item ID Item Name                                                          \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               $50.76  \n",
       "108     Extraction, Quickblade Of Trembling Hands                  $31.77  \n",
       "145     Fiery Glass Crusader                                       $41.22  \n",
       "82      Nirvana                                                    $44.10  \n",
       "37      Shadow Strike, Glory of Ending Hope                        $25.28  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define items and set the index \n",
    "most_items = purchase_data.groupby(['Item ID', 'Item Name','Price'])['Price'].agg(['count','sum'])\n",
    "most_items.columns = ['Purchase Count', 'Total Purchase Value']\n",
    "most_items.reset_index(inplace=True)\n",
    "most_items.set_index(['Item ID','Item Name'] ,inplace=True)\n",
    "most_items = most_items[['Purchase Count', 'Price', 'Total Purchase Value']]\n",
    "\n",
    "#organizes the purchase count column \n",
    "popular_items = most_items.sort_values(by='Purchase Count', ascending=False)\n",
    "\n",
    "#clean formatting \n",
    "popular_items['Price'] = popular_items['Price'].map('${:,.2f}'.format)\n",
    "popular_items['Total Purchase Value'] = popular_items['Total Purchase Value'].map('${:,.2f}'.format)\n",
    "popular_items.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": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<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>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>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>92</th>\n",
       "      <th>Final Critic</th>\n",
       "      <td>8</td>\n",
       "      <td>$4.88</td>\n",
       "      <td>$39.04</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  Price  \\\n",
       "Item ID Item Name                                                             \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm              12  $4.23   \n",
       "82      Nirvana                                                    9  $4.90   \n",
       "145     Fiery Glass Crusader                                       9  $4.58   \n",
       "92      Final Critic                                               8  $4.88   \n",
       "103     Singed Scalpel                                             8  $4.35   \n",
       "\n",
       "                                                     Total Purchase Value  \n",
       "Item ID Item Name                                                          \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               $50.76  \n",
       "82      Nirvana                                                    $44.10  \n",
       "145     Fiery Glass Crusader                                       $41.22  \n",
       "92      Final Critic                                               $39.04  \n",
       "103     Singed Scalpel                                             $34.80  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in descending \n",
    "profitable_items = most_items.sort_values(by='Total Purchase Value', ascending=False)\n",
    "\n",
    "#the data frame \n",
    "profitable_items['Price'] = profitable_items['Price'].map('${:,.2f}'.format)\n",
    "profitable_items['Total Purchase Value'] = profitable_items['Total Purchase Value'].map('${:,.2f}'.format)\n",
    "profitable_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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