HeroesHW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load csv\n",
    "heroes = \"purchase_data_3.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Lirtjaskan85</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>Frenzy, Defender of the Harvest</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Chanjask65</td>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>75</td>\n",
       "      <td>Brutality Ivory Warmace</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Aerithllora36</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>114</td>\n",
       "      <td>Yearning Mageblade</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Aeralria27</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>130</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Haisrisuir60</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>Thorn, Conqueror of the Corrupted</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Purchase ID             SN  Age  Gender  Item ID  \\\n",
       "0            0   Lirtjaskan85   24  Female       69   \n",
       "1            1     Chanjask65   12  Female       75   \n",
       "2            2  Aerithllora36   21  Female      114   \n",
       "3            3     Aeralria27   24    Male      130   \n",
       "4            4   Haisrisuir60   28    Male        9   \n",
       "\n",
       "                           Item Name  Price  \n",
       "0    Frenzy, Defender of the Harvest   4.82  \n",
       "1            Brutality Ivory Warmace   4.12  \n",
       "2                 Yearning Mageblade   2.67  \n",
       "3                              Alpha   4.53  \n",
       "4  Thorn, Conqueror of the Corrupted   4.60  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read with pandas\n",
    "heroes_pd = pd.read_csv(heroes, encoding=\"iso-8859-1\", low_memory=False)\n",
    "heroes_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "581"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate total number of players\n",
    "players = heroes_pd['SN'].nunique()\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Number of Purchases</th>\n",
       "      <th>Number of Unique Items</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.03</td>\n",
       "      <td>780</td>\n",
       "      <td>178</td>\n",
       "      <td>2365.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average Price  Number of Purchases  Number of Unique Items  Total Revenue\n",
       "0           3.03                  780                     178        2365.17"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Purchasing Analysis\n",
    "#find the variables first and then put in summary table\n",
    "average_price = heroes_pd[\"Price\"].mean()\n",
    "items = heroes_pd['Item Name'].nunique()\n",
    "purchases = len(heroes_pd)\n",
    "revenue = heroes_pd['Price'].sum()\n",
    "#create summary table\n",
    "purchasing_summary = pd.DataFrame({\"Number of Unique Items\": [items],\n",
    "                                   \"Average Price\": [average_price],\n",
    "                                   \"Number of Purchases\": [purchases],\n",
    "                                   \"Total Revenue\": [revenue]\n",
    "})\n",
    "purchasing_summary = purchasing_summary.round(2)\n",
    "purchasing_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Percentage of Players</th>\n",
       "      <th>Total Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Males</td>\n",
       "      <td>70.91</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Females</td>\n",
       "      <td>24.61</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other</td>\n",
       "      <td>4.48</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Percentage of Players  Total Count\n",
       "0    Males                  70.91          412\n",
       "1  Females                  24.61          143\n",
       "2    Other                   4.48           26"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gender Demographics\n",
    "#Find amount of men and women players and other / this works\n",
    "grouped_players= heroes_pd.groupby(['SN'], as_index=False).count()\n",
    "\n",
    "#find men, and women, they are 1 and 2 in grouped players...minus from players to find others or null values\n",
    "men = grouped_players[\"Gender\"].value_counts()[1]\n",
    "women = grouped_players[\"Gender\"].value_counts()[2]\n",
    "other = players - men - women\n",
    "\n",
    "#find percents\n",
    "Men_Percent = men/players * 100\n",
    "Women_Percent = women /players * 100\n",
    "Other_Percent = other /players * 100\n",
    "\n",
    "gender_summary = pd.DataFrame({\"Gender\": [\"Males\", \"Females\", \"Other\"],                    \n",
    "                               \"Percentage of Players\": [Men_Percent, Women_Percent, Other_Percent],\n",
    "                               \"Total Count\": [men, women, other]\n",
    "})\n",
    "gender_summary = gender_summary.round(2)\n",
    "gender_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Normalized Average</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.03</td>\n",
       "      <td>Males</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>642</td>\n",
       "      <td>1945.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.04</td>\n",
       "      <td>Females</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>130</td>\n",
       "      <td>395.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.98</td>\n",
       "      <td>Other</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>8</td>\n",
       "      <td>23.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average Purchase Price   Gender  Normalized Average  Purchase Count  \\\n",
       "0                    3.03    Males               -0.00             642   \n",
       "1                    3.04  Females               -0.00             130   \n",
       "2                    2.98    Other               -0.05               8   \n",
       "\n",
       "   Total Purchase Value  \n",
       "0               1945.51  \n",
       "1                395.80  \n",
       "2                 23.86  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Purchasing Analysis (Gender)\n",
    "male_group = heroes_pd[heroes_pd[\"Gender\"] == \"Male\"]\n",
    "\n",
    "men_items = len(male_group)\n",
    "men_average = male_group[\"Price\"].mean()\n",
    "mentotal =  male_group[\"Price\"].sum()\n",
    "mennormal = men_average - average_price\n",
    "\n",
    "#first filter out for females and then \n",
    "female_group = heroes_pd[heroes_pd[\"Gender\"] == \"Female\"]\n",
    "\n",
    "fem_items = len(female_group)\n",
    "fem_average = female_group[\"Price\"].mean()\n",
    "femtotal =  female_group[\"Price\"].sum()\n",
    "femnormal = men_average - average_price\n",
    "\n",
    "other_group = heroes_pd[heroes_pd[\"Gender\"]==\"Other / Non-Disclosed\"]\n",
    "other_items = len(other_group)\n",
    "other_average = other_group[\"Price\"].mean()\n",
    "othertotal =  other_group[\"Price\"].sum()\n",
    "othernormal = other_average - average_price\n",
    "\n",
    "gender_purchasing = pd.DataFrame({\"Gender\": [\"Males\", \"Females\", \"Other\"],\n",
    "                                   \"Purchase Count\": [men_items, fem_items, other_items],\n",
    "                                   \"Average Purchase Price\": [men_average, fem_average, other_average],\n",
    "                                   \"Total Purchase Value\": [mentotal, femtotal, othertotal],\n",
    "                                   \"Normalized Average\": [mennormal, femnormal, othernormal]\n",
    "})\n",
    "\n",
    "gender_purchasing = gender_purchasing.round(2)\n",
    "gender_purchasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age Group\n",
       "20-24        316\n",
       "25-29        137\n",
       "15-19        132\n",
       "30-34         62\n",
       "35-39         54\n",
       "<10           37\n",
       "10-14         31\n",
       "40+           11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Age Demographics\n",
    "#create bins\n",
    "bins = [0, 9, 14, 19, 24, 29, 34, 39,150]\n",
    "\n",
    "# Create the names for bins\n",
    "group_labels = ['<10','10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40+']\n",
    "\n",
    "AgeGroup = pd.cut(heroes_pd[\"Age\"],bins,labels=group_labels)\n",
    "heroes_pd[\"Age Group\"] = pd.cut(heroes_pd[\"Age\"],bins,labels=group_labels)\n",
    "\n",
    "age_count = pd.DataFrame(pd.value_counts(heroes_pd['Age Group']))\n",
    "age_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Value</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Group</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>37.0</td>\n",
       "      <td>2.917838</td>\n",
       "      <td>107.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>31.0</td>\n",
       "      <td>2.981290</td>\n",
       "      <td>92.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>132.0</td>\n",
       "      <td>3.080455</td>\n",
       "      <td>406.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>316.0</td>\n",
       "      <td>3.008196</td>\n",
       "      <td>950.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>137.0</td>\n",
       "      <td>3.045328</td>\n",
       "      <td>417.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2.925806</td>\n",
       "      <td>181.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>54.0</td>\n",
       "      <td>3.175926</td>\n",
       "      <td>171.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.406364</td>\n",
       "      <td>37.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Purchase Count  Average Purchase Value  Total Purchase Value\n",
       "Age Group                                                              \n",
       "<10                  37.0                2.917838                107.96\n",
       "10-14                31.0                2.981290                 92.42\n",
       "15-19               132.0                3.080455                406.62\n",
       "20-24               316.0                3.008196                950.59\n",
       "25-29               137.0                3.045328                417.21\n",
       "30-34                62.0                2.925806                181.40\n",
       "35-39                54.0                3.175926                171.50\n",
       "40+                  11.0                3.406364                 37.47"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Age Analysis\n",
    "groupby_age = heroes_pd['Price'].groupby(heroes_pd['Age Group'])\n",
    "groupby_analysis = pd.DataFrame(heroes_pd['Price'].groupby(heroes_pd['Age Group']).describe())\n",
    "groupby_analysis \n",
    "groupby_analysis = groupby_analysis.drop('std', 1)\n",
    "groupby_analysis = groupby_analysis.drop('min', 1)\n",
    "groupby_analysis = groupby_analysis.drop('25%', 1)\n",
    "groupby_analysis = groupby_analysis.drop('50%', 1)\n",
    "groupby_analysis = groupby_analysis.drop('75%', 1)\n",
    "groupby_analysis = groupby_analysis.drop('max', 1)\n",
    "groupby_analysis\n",
    "groupby_analysis[\"Total Purchase Value\"] = groupby_analysis[\"count\"] * groupby_analysis[\"mean\"]\n",
    "groupby_analysis.round(2)\n",
    "groupby_analysis.rename(index=str, columns={\"count\": \"Purchase Count\", \"mean\": \"Average Purchase Value\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SN</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chamalo71</td>\n",
       "      <td>4</td>\n",
       "      <td>3.362500</td>\n",
       "      <td>13.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Strithenu87</td>\n",
       "      <td>4</td>\n",
       "      <td>3.207500</td>\n",
       "      <td>12.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mindosia50</td>\n",
       "      <td>3</td>\n",
       "      <td>4.003333</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aeralria27</td>\n",
       "      <td>3</td>\n",
       "      <td>3.793333</td>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eudai71</td>\n",
       "      <td>3</td>\n",
       "      <td>3.790000</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SN  Purchase Count  Average Purchase Price  Total Purchase Value\n",
       "0    Chamalo71               4                3.362500                 13.45\n",
       "1  Strithenu87               4                3.207500                 12.83\n",
       "2   Mindosia50               3                4.003333                 12.01\n",
       "3   Aeralria27               3                3.793333                 11.38\n",
       "4      Eudai71               3                3.790000                 11.37"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 5 Spenders\n",
    "\n",
    "sn_price = heroes_pd.groupby([\"SN\"])['Price'].sum()\n",
    "sn_pur = heroes_pd.groupby([\"SN\"])['Price'].count()\n",
    "sn_users = heroes_pd.groupby([\"SN\"])\n",
    "avg_sn = sn_price / sn_pur\n",
    "\n",
    "top_sn = pd.DataFrame({\"Purchase Count\": sn_pur,\"Average Purchase Price\":avg_sn, \"Total Purchase Value\": sn_price})\n",
    "\n",
    "top_sn = top_sn.sort_values(\"Total Purchase Value\", ascending=False)\n",
    "top_sn = top_sn[[\"Purchase Count\",\"Average Purchase Price\", \"Total Purchase Value\"]]\n",
    "\n",
    "top_sn.reset_index(inplace=True)\n",
    "top_sn.round(2)\n",
    "top_sn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hatred</td>\n",
       "      <td>11</td>\n",
       "      <td>4.59</td>\n",
       "      <td>50.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Misery's End</td>\n",
       "      <td>9</td>\n",
       "      <td>4.90</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agatha</td>\n",
       "      <td>8</td>\n",
       "      <td>4.93</td>\n",
       "      <td>39.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apocalyptic Battlescythe</td>\n",
       "      <td>8</td>\n",
       "      <td>4.85</td>\n",
       "      <td>38.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Oculus, Token of Lost Worlds</td>\n",
       "      <td>8</td>\n",
       "      <td>4.61</td>\n",
       "      <td>36.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Item Name  Purchase Count  Average Purchase Price  \\\n",
       "0                            Hatred              11                    4.59   \n",
       "1                      Misery's End               9                    4.90   \n",
       "2                            Agatha               8                    4.93   \n",
       "3          Apocalyptic Battlescythe               8                    4.85   \n",
       "4  The Oculus, Token of Lost Worlds               8                    4.61   \n",
       "\n",
       "   Total Purchase Value  \n",
       "0                 50.49  \n",
       "1                 44.10  \n",
       "2                 39.44  \n",
       "3                 38.80  \n",
       "4                 36.88  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Items by Total Price\n",
    "item_price = heroes_pd.groupby([\"Item Name\"])['Price'].sum()\n",
    "item_num = heroes_pd.groupby([\"Item Name\"])['Price'].count()\n",
    "item_name = heroes_pd.groupby([\"Item Name\"])\n",
    "avg_item = item_price / item_num\n",
    "\n",
    "best_item = pd.DataFrame({\"Purchase Count\": item_num,\"Average Purchase Price\":avg_item, \"Total Purchase Value\": item_price})\n",
    "\n",
    "best_item = best_item.sort_values(\"Total Purchase Value\", ascending=False)\n",
    "best_item = best_item[[\"Purchase Count\",\"Average Purchase Price\", \"Total Purchase Value\"]]\n",
    "\n",
    "best_item.reset_index(inplace=True)\n",
    "best_item.round(2)\n",
    "best_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hatred</td>\n",
       "      <td>11</td>\n",
       "      <td>4.59</td>\n",
       "      <td>50.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Final Critic</td>\n",
       "      <td>11</td>\n",
       "      <td>2.70</td>\n",
       "      <td>29.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Primitive Blade</td>\n",
       "      <td>9</td>\n",
       "      <td>1.39</td>\n",
       "      <td>12.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Misery's End</td>\n",
       "      <td>9</td>\n",
       "      <td>4.90</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Oculus, Token of Lost Worlds</td>\n",
       "      <td>8</td>\n",
       "      <td>4.61</td>\n",
       "      <td>36.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Item Name  Purchase Count  Average Purchase Price  \\\n",
       "0                            Hatred              11                    4.59   \n",
       "1                      Final Critic              11                    2.70   \n",
       "2                   Primitive Blade               9                    1.39   \n",
       "3                      Misery's End               9                    4.90   \n",
       "4  The Oculus, Token of Lost Worlds               8                    4.61   \n",
       "\n",
       "   Total Purchase Value  \n",
       "0                 50.49  \n",
       "1                 29.70  \n",
       "2                 12.51  \n",
       "3                 44.10  \n",
       "4                 36.88  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Items by Number of Purchases\n",
    "item_price = heroes_pd.groupby([\"Item Name\"])['Price'].sum()\n",
    "item_num = heroes_pd.groupby([\"Item Name\"])['Price'].count()\n",
    "item_name = heroes_pd.groupby([\"Item Name\"])\n",
    "avg_item = item_price / item_num\n",
    "\n",
    "best_item = pd.DataFrame({\"Purchase Count\": item_num,\"Average Purchase Price\":avg_item, \"Total Purchase Value\": item_price})\n",
    "\n",
    "best_item = best_item.sort_values(\"Purchase Count\", ascending=False)\n",
    "best_item = best_item[[\"Purchase Count\",\"Average Purchase Price\", \"Total Purchase Value\"]]\n",
    "\n",
    "best_item.reset_index(inplace=True)\n",
    "best_item.round(2)\n",
    "best_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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