Heroes of Pymoli Analysis.ipynb

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   "source": [
    "# Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# CSV File\n",
    "csv_path = \"Resources/purchase_data.csv\"\n",
    "\n",
    "# Read CSV into DF\n",
    "data_file = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "<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",
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      "text/plain": [
       "   Total Players\n",
       "0            576"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Player Count: Display the total number of Players\n",
    "\n",
    "Player_Totals = data_file.loc[:,[\"Gender\", \"SN\", \"Age\"]]\n",
    "Player_Totals = Player_Totals.drop_duplicates()\n",
    "Number_of_Players = Player_Totals.count()[0]\n",
    "\n",
    "pd.DataFrame({\"Total Players\" : [Number_of_Players]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Unique Items</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Number of Purchases</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>780</td>\n",
       "      <td>$2379.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Unique Items Average Purchase Price  Total Number of Purchases  \\\n",
       "0                     183                  $3.05                        780   \n",
       "\n",
       "  Total Revenue  \n",
       "0      $2379.77  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Purchasing Analysis (Total) ###\n",
    "    # Number of Unique Items\n",
    "    # Average Purchase Price\n",
    "    # Total Number of Purchases\n",
    "    # Total Revenue\n",
    "    \n",
    "Number_of_Unique_Items = data_file['Item ID'].nunique()\n",
    "Average_Price = data_file['Price'].mean().round(2)\n",
    "Number_of_Purchases = data_file['Purchase ID'].count()\n",
    "Total_Revenue = data_file['Price'].sum().round(2)\n",
    "\n",
    "### Purchasing Analysis DataFrame ###\n",
    "\n",
    "Total_Purchasing_AnalysisDF = pd.DataFrame({\n",
    "    \"Number of Unique Items\":[Number_of_Unique_Items],\n",
    "    \"Average Purchase Price\":[Average_Price],\n",
    "    \"Total Number of Purchases\":[Number_of_Purchases],\n",
    "    \"Total Revenue\":[Total_Revenue]})\n",
    "\n",
    "### Currency/Formatting Added ###\n",
    "Total_Purchasing_AnalysisDF[\"Average Purchase Price\"] = Total_Purchasing_AnalysisDF[\"Average Purchase Price\"].map(\"${:.2f}\".format)\n",
    "Total_Purchasing_AnalysisDF[\"Total Revenue\"] = Total_Purchasing_AnalysisDF[\"Total Revenue\"].map(\"${:.2f}\".format)\n",
    "Total_Purchasing_AnalysisDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Count</th>\n",
       "      <th>Percentage of Players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>484</td>\n",
       "      <td>84.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>81</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>11</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Total Count  Percentage of Players\n",
       "Male                           484                  84.03\n",
       "Female                          81                  14.06\n",
       "Other / Non-Disclosed           11                   1.91"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Gender Demographics ###\n",
    "    # Percentage and Count of Male Players\n",
    "    # Percentage and Count of Female Players\n",
    "    # Percentage and Count of Other / Non Disclosed\n",
    "\n",
    "Gender_Totals = Player_Totals[\"Gender\"].value_counts()\n",
    "Gender_Percents = Gender_Totals/Number_of_Players*100\n",
    "\n",
    "### Gender Demographics DataFrame ###\n",
    "Gender_DemographicsDF = pd.DataFrame({\"Total Count\": Gender_Totals, \"Percentage of Players\": Gender_Percents})\n",
    "\n",
    "### Currency/Formatting Added ###\n",
    "Gender_DemographicsDF = Gender_DemographicsDF.round(2)\n",
    "Gender_DemographicsDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "      <th>Avg Total Purchase Per Person</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>113</td>\n",
       "      <td>$3.20</td>\n",
       "      <td>$361.94</td>\n",
       "      <td>$4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>652</td>\n",
       "      <td>$3.02</td>\n",
       "      <td>$1967.64</td>\n",
       "      <td>$4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other / Non-Disclosed</th>\n",
       "      <td>15</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$50.19</td>\n",
       "      <td>$4.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Purchase Count Average Purchase Price  \\\n",
       "Gender                                                         \n",
       "Female                            113                  $3.20   \n",
       "Male                              652                  $3.02   \n",
       "Other / Non-Disclosed              15                  $3.35   \n",
       "\n",
       "                      Total Purchase Value Avg Total Purchase Per Person  \n",
       "Gender                                                                    \n",
       "Female                             $361.94                         $4.47  \n",
       "Male                              $1967.64                         $4.07  \n",
       "Other / Non-Disclosed               $50.19                         $4.56  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Purchasing Analysis (Gender) ###\n",
    "    # Purchase Count \n",
    "    # Average Purchase Price \n",
    "    # Total Purchase Value \n",
    "    # Average Purchase Total per Person by Gender\n",
    "\n",
    "Gender_Purchase_Count = data_file.groupby([\"Gender\"])[\"Price\"].count().rename(\"Purchase Count\")\n",
    "Average_Gender_Purchase_Price = data_file.groupby([\"Gender\"])[\"Price\"].mean().rename(\"Average Purchase Price\")\n",
    "Total_Gender_Purchase_Value = data_file.groupby([\"Gender\"])[\"Price\"].sum().rename(\"Total Purchase Value\")\n",
    "Average_Purchase_Total_PG = Total_Gender_Purchase_Value / Gender_DemographicsDF[\"Total Count\"]\n",
    "\n",
    "\n",
    "### Purchasing Analysis (Gender) DataFrame ###\n",
    "Gender_Purchasing_AnalysisDF = pd.DataFrame({\"Purchase Count\": Gender_Purchase_Count,\n",
    "                                            \"Average Purchase Price\": Average_Gender_Purchase_Price,\n",
    "                                            \"Total Purchase Value\": Total_Gender_Purchase_Value,\n",
    "                                            \"Avg Total Purchase Per Person\": Average_Purchase_Total_PG})\n",
    "### Currency/Formatting Added ###\n",
    "Gender_Purchasing_AnalysisDF[\"Average Purchase Price\"] = Gender_Purchasing_AnalysisDF[\"Average Purchase Price\"].map(\"${:.2f}\".format) \n",
    "Gender_Purchasing_AnalysisDF[\"Total Purchase Value\"] = Gender_Purchasing_AnalysisDF[\"Total Purchase Value\"].map(\"${:.2f}\".format)\n",
    "Gender_Purchasing_AnalysisDF[\"Avg Total Purchase Per Person\"] = Gender_Purchasing_AnalysisDF[\"Avg Total Purchase Per Person\"].map(\"${:.2f}\".format)\n",
    "Gender_Purchasing_AnalysisDF = Gender_Purchasing_AnalysisDF.loc[:, [\"Purchase Count\", \"Average Purchase Price\", \"Total Purchase Value\", \"Avg Total Purchase Per Person\"]]\n",
    "Gender_Purchasing_AnalysisDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Count</th>\n",
       "      <th>Percentage of Players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>17</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>22</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>107</td>\n",
       "      <td>18.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>258</td>\n",
       "      <td>44.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>77</td>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>52</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>31</td>\n",
       "      <td>5.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>12</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Count  Percentage of Players\n",
       "<10             17                   2.95\n",
       "10-14           22                   3.82\n",
       "15-19          107                  18.58\n",
       "20-24          258                  44.79\n",
       "25-29           77                  13.37\n",
       "30-34           52                   9.03\n",
       "35-39           31                   5.38\n",
       "40+             12                   2.08"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Age Demographics ###\n",
    "\n",
    "    # Establish the bins\n",
    "bins = [0, 9.90, 14.90, 19.90, 24.90, 29.90, 34.90, 39.90, 99999]\n",
    "bin_labels = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40+\"]\n",
    "    \n",
    "    # Purchase Count\n",
    "    # Average Purchase Price\n",
    "    # Total Purchase Value\n",
    "    # Average Purchase Total per Person by Age Group\n",
    "\n",
    "Player_Totals[\"Age Ranges\"] = pd.cut(Player_Totals[\"Age\"], bins, labels = bin_labels)\n",
    "Age_Demographics_Totals = Player_Totals[\"Age Ranges\"].value_counts()\n",
    "Age_Demographics_Percents = Age_Demographics_Totals / Number_of_Players * 100\n",
    "\n",
    "### Age Demographics Data Frame ###\n",
    "Age_DemographicsDF = pd.DataFrame({\"Total Count\": Age_Demographics_Totals, \"Percentage of Players\": Age_Demographics_Percents})\n",
    "\n",
    "### Currency/Formatting Added ###\n",
    "Age_DemographicsDF = Age_DemographicsDF.round(2)\n",
    "Age_DemographicsDF.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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    {
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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 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               $13.10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Top Spenders ###\n",
    "    # Identify top 5 spenders in the game by total purchase value\n",
    "    # SN\n",
    "    # Purchase Count\n",
    "    # Average Purchase Price\n",
    "    # Total Purchase Value\n",
    "\n",
    "Spender_Analysis = data_file.groupby(\"SN\")\n",
    "\n",
    "Top_Purchase_Count = Spender_Analysis[\"Purchase ID\"].count().round(2)\n",
    "Avg_Spender_Price = Spender_Analysis[\"Price\"].mean().round(2)\n",
    "Total_Spender_Purchase = Spender_Analysis[\"Price\"].sum().round(2)\n",
    "\n",
    "### Top Spenders Data Frame ###\n",
    "Top_Spenders_Analysis = pd.DataFrame({\"Purchase Count\": Top_Purchase_Count,\n",
    "                               \"Average Purchase Price\" : Avg_Spender_Price,\n",
    "                                \"Total Purchase Value\": Total_Spender_Purchase\n",
    "})\n",
    "### Currency/Formatting Added ###                                   \n",
    "Top_5_Spenders_DataFrame = Top_Spenders_Analysis.sort_values([\"Total Purchase Value\"], ascending=False).head(5) \n",
    "Top_5_Spenders_DataFrame[\"Average Purchase Price\"] = Top_5_Spenders_DataFrame[\"Average Purchase Price\"].map(\"${:.2f}\".format)\n",
    "Top_5_Spenders_DataFrame[\"Total Purchase Value\"] = Top_5_Spenders_DataFrame[\"Total Purchase Value\"].map(\"${:.2f}\".format)\n",
    "Top_5_Spenders_DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Item Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th>Item Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <td>12</td>\n",
       "      <td>$4.23</td>\n",
       "      <td>$50.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.58</td>\n",
       "      <td>$41.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <th>Extraction, Quickblade Of Trembling Hands</th>\n",
       "      <td>9</td>\n",
       "      <td>$3.53</td>\n",
       "      <td>$31.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>Nirvana</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.90</td>\n",
       "      <td>$44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>Pursuit, Cudgel of Necromancy</th>\n",
       "      <td>8</td>\n",
       "      <td>$1.02</td>\n",
       "      <td>$8.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  \\\n",
       "Item ID Item Name                                                      \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm              12   \n",
       "145     Fiery Glass Crusader                                       9   \n",
       "108     Extraction, Quickblade Of Trembling Hands                  9   \n",
       "82      Nirvana                                                    9   \n",
       "19      Pursuit, Cudgel of Necromancy                              8   \n",
       "\n",
       "                                                     Item Price  \\\n",
       "Item ID Item Name                                                 \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm      $4.23   \n",
       "145     Fiery Glass Crusader                              $4.58   \n",
       "108     Extraction, Quickblade Of Trembling Hands         $3.53   \n",
       "82      Nirvana                                           $4.90   \n",
       "19      Pursuit, Cudgel of Necromancy                     $1.02   \n",
       "\n",
       "                                                     Total Purchase Value  \n",
       "Item ID Item Name                                                          \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               $50.76  \n",
       "145     Fiery Glass Crusader                                       $41.22  \n",
       "108     Extraction, Quickblade Of Trembling Hands                  $31.77  \n",
       "82      Nirvana                                                    $44.10  \n",
       "19      Pursuit, Cudgel of Necromancy                               $8.16  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Most Popular Items ###\n",
    "    # Identify the 5 most popular items by purchase count\n",
    "    # Item ID\n",
    "    # Item Name\n",
    "    # Purchase Count\n",
    "    # Item Price\n",
    "    # Total Purchase Value\n",
    "    \n",
    "Most_Popular_Group = data_file.groupby([\"Item ID\", \"Item Name\"])\n",
    "Popular_Purchase_Count = Most_Popular_Group[\"Price\"].count()\n",
    "Popular_Purchase_Value = Most_Popular_Group[\"Price\"].sum()\n",
    "Popular_Item_Price = Popular_Purchase_Value/Popular_Purchase_Count\n",
    "\n",
    "### Most Popular Items DataFrame ###\n",
    "\n",
    "Most_Popular_Items = pd.DataFrame({\"Purchase Count\" : Popular_Purchase_Count,\n",
    "                                  \"Item Price\": Popular_Item_Price,\n",
    "                                  \"Total Purchase Value\": Popular_Purchase_Value})\n",
    "\n",
    "Most_Popular_Items = Most_Popular_Items.sort_values(\"Purchase Count\", ascending=False)\n",
    "\n",
    "### Currency/Formatting Added ###                                   \n",
    "Most_Popular_Items[\"Item Price\"] = Most_Popular_Items[\"Item Price\"].map(\"${:,.2f}\".format)\n",
    "Most_Popular_Items[\"Total Purchase Value\"] = Most_Popular_Items[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "Most_Popular_Items.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Item Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item ID</th>\n",
       "      <th>Item Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <th>Oathbreaker, Last Hope of the Breaking Storm</th>\n",
       "      <td>12</td>\n",
       "      <td>$4.23</td>\n",
       "      <td>$50.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <th>Fiery Glass Crusader</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.58</td>\n",
       "      <td>$41.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <th>Extraction, Quickblade Of Trembling Hands</th>\n",
       "      <td>9</td>\n",
       "      <td>$3.53</td>\n",
       "      <td>$31.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>Nirvana</th>\n",
       "      <td>9</td>\n",
       "      <td>$4.90</td>\n",
       "      <td>$44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>Pursuit, Cudgel of Necromancy</th>\n",
       "      <td>8</td>\n",
       "      <td>$1.02</td>\n",
       "      <td>$8.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Purchase Count  \\\n",
       "Item ID Item Name                                                      \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm              12   \n",
       "145     Fiery Glass Crusader                                       9   \n",
       "108     Extraction, Quickblade Of Trembling Hands                  9   \n",
       "82      Nirvana                                                    9   \n",
       "19      Pursuit, Cudgel of Necromancy                              8   \n",
       "\n",
       "                                                     Item Price  \\\n",
       "Item ID Item Name                                                 \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm      $4.23   \n",
       "145     Fiery Glass Crusader                              $4.58   \n",
       "108     Extraction, Quickblade Of Trembling Hands         $3.53   \n",
       "82      Nirvana                                           $4.90   \n",
       "19      Pursuit, Cudgel of Necromancy                     $1.02   \n",
       "\n",
       "                                                     Total Purchase Value  \n",
       "Item ID Item Name                                                          \n",
       "178     Oathbreaker, Last Hope of the Breaking Storm               $50.76  \n",
       "145     Fiery Glass Crusader                                       $41.22  \n",
       "108     Extraction, Quickblade Of Trembling Hands                  $31.77  \n",
       "82      Nirvana                                                    $44.10  \n",
       "19      Pursuit, Cudgel of Necromancy                               $8.16  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Most Profitable Items ###\n",
    "    # Item ID\n",
    "    # Item Name\n",
    "    # Purchase Count\n",
    "    # Item Price\n",
    "    # Total Purchase Value\n",
    "    \n",
    "Most_Profitable_Items = Most_Popular_Items.sort_values(\"Total Purchase Value\", ascending=False)\n",
    "Most_Profitable_Items = Most_Popular_Items.loc[:,[\"Purchase Count\", \"Item Price\", \"Total Purchase Value\"]]\n",
    "\n",
    "Most_Profitable_Items.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase Count</th>\n",
       "      <th>Average Purchase Price</th>\n",
       "      <th>Total Purchase Value</th>\n",
       "      <th>Avg Total Purchase Per Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>28</td>\n",
       "      <td>$2.96</td>\n",
       "      <td>$82.78</td>\n",
       "      <td>$3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>136</td>\n",
       "      <td>$3.04</td>\n",
       "      <td>$412.89</td>\n",
       "      <td>$3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>365</td>\n",
       "      <td>$3.05</td>\n",
       "      <td>$1,114.06</td>\n",
       "      <td>$4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>101</td>\n",
       "      <td>$2.90</td>\n",
       "      <td>$293.00</td>\n",
       "      <td>$3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>73</td>\n",
       "      <td>$2.93</td>\n",
       "      <td>$214.00</td>\n",
       "      <td>$4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>41</td>\n",
       "      <td>$3.60</td>\n",
       "      <td>$147.67</td>\n",
       "      <td>$4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40+</th>\n",
       "      <td>13</td>\n",
       "      <td>$2.94</td>\n",
       "      <td>$38.24</td>\n",
       "      <td>$3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;10</th>\n",
       "      <td>23</td>\n",
       "      <td>$3.35</td>\n",
       "      <td>$77.13</td>\n",
       "      <td>$4.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Purchase Count Average Purchase Price Total Purchase Value  \\\n",
       "10-14             28                  $2.96               $82.78   \n",
       "15-19            136                  $3.04              $412.89   \n",
       "20-24            365                  $3.05            $1,114.06   \n",
       "25-29            101                  $2.90              $293.00   \n",
       "30-34             73                  $2.93              $214.00   \n",
       "35-39             41                  $3.60              $147.67   \n",
       "40+               13                  $2.94               $38.24   \n",
       "<10               23                  $3.35               $77.13   \n",
       "\n",
       "      Avg Total Purchase Per Person  \n",
       "10-14                         $3.76  \n",
       "15-19                         $3.86  \n",
       "20-24                         $4.32  \n",
       "25-29                         $3.81  \n",
       "30-34                         $4.12  \n",
       "35-39                         $4.76  \n",
       "40+                           $3.19  \n",
       "<10                           $4.54  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Purchasing Analysis (Age) ###\n",
    "    # Establish the bins #\n",
    "bins = [0, 9.90, 14.90, 19.90, 24.90, 29.90, 34.90, 39.90, 99999]\n",
    "bin_labels = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40+\"]\n",
    "\n",
    "\n",
    "data_file[\"Age Ranges\"] = pd.cut(data_file[\"Age\"], bins, labels= bin_labels)\n",
    "\n",
    "Age_Purchase_Counts = data_file.groupby([\"Age Ranges\"])[\"Price\"].count()\n",
    "Age_Average_Price = data_file.groupby([\"Age Ranges\"])[\"Price\"].mean().round(2)\n",
    "Age_Purchase_Value = data_file.groupby([\"Age Ranges\"])[\"Price\"].sum().round(2)\n",
    "Average_Purchase_Total_Per_Person = Age_Purchase_Value / Age_DemographicsDF[\"Total Count\"].round(2)\n",
    "\n",
    "### Purchasing Analysis (Age) DataFrame ###\n",
    "Age_Purchasing_Analysis = pd.DataFrame({\"Purchase Count\": Age_Purchase_Counts,\n",
    "                                            \"Average Purchase Price\": Age_Average_Price,\n",
    "                                            \"Total Purchase Value\": Age_Purchase_Value,\n",
    "                                            \"Avg Total Purchase Per Person\": Average_Purchase_Total_Per_Person})\n",
    "\n",
    "### Currency/Formatting Added ###\n",
    "Age_Purchasing_Analysis[\"Purchase Count\"] = Age_Purchasing_Analysis[\"Purchase Count\"].map(\"{:,}\".format)\n",
    "Age_Purchasing_Analysis[\"Average Purchase Price\"] = Age_Purchasing_Analysis[\"Average Purchase Price\"].map(\"${:,.2f}\".format)\n",
    "Age_Purchasing_Analysis[\"Total Purchase Value\"] = Age_Purchasing_Analysis[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\n",
    "Age_Purchasing_Analysis[\"Avg Total Purchase Per Person\"] = Age_Purchasing_Analysis[\"Avg Total Purchase Per Person\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "Age_Purchasing_Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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