#Analysis Report

1. 81% of players are male which also make the most of the percentage of the revenue.
2. The game is most popular among the age group of 15 years to 25 years and after that age group there is drastic decline in player's interest.
3. The most popular items are below the average price of most of the items.
4. However the most popular items are not the most profitable items. The most profitable items are the item number 34 and 115 which are more pricy.

Code

import pandas as pd

from pprint import pprint

data = pd.read_json('purchase_data.json')
# View data
# print(data.head())

# Number of players
unique_players = len(data["SN"].unique())
pd.DataFrame({"Total Players": [unique_players]})

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	Total Players
0	573

Purchasing Analysis (Total)

temp_df = pd.DataFrame()
# * Number of Unique Items
unique_items = len(data['Item ID'].unique())
temp_df["Number of Unique Items"] = [unique_items]

# * Average Purchase Price
avg_price = round(data["Price"].mean(), 2)
temp_df["Average Purchase Price"] = avg_price

# * Total Number of Purchases
total_purchases = len(data)
temp_df["Total Purchases"] = total_purchases

# * Total Revenue
total_revenue = round(data["Price"].sum(), 2)
temp_df["Total Revenue"] = total_revenue
temp_df

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	Number of Unique Items	Average Purchase Price	Total Purchases	Total Revenue
0	183	2.93	780	2286.33

**Gender Demographics**

# Removing duplicate players from the data
data_without_duplicate_players = data.drop_duplicates(subset=["SN"])

# Seperating the no duplicate data into gender groups
gender_groups = data_without_duplicate_players["Gender"].value_counts()

# Printing Percentage of palyers and total count according to their gender
x = pd.DataFrame({"Total Count":gender_groups,
                   "Percentage of Players": round(gender_groups/unique_players * 100, 2)})
x

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	Percentage of Players	Total Count
Male	81.15	465
Female	17.45	100
Other / Non-Disclosed	1.40	8

**Purchasing Analysis (Gender)**

# Number of purchases by each gender
grouped_data = data.groupby(["Gender"])
purchases = pd.DataFrame()
for gender, info in grouped_data:
    price_column = info["Price"]
    purchase = pd.DataFrame({
        "Total Purchases" : [len(info)],
        "Average Purchase Price" : [round(price_column.mean(), 2)],
        "Total Purchase Value" : [price_column.sum()]
    }, index=[gender])
    purchases = purchases.append(purchase)
#   TODO : Figure out how to calculate Normalized Totals
purchases

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	Average Purchase Price	Total Purchase Value	Total Purchases
Female	2.82	382.91	136
Male	2.95	1867.68	633
Other / Non-Disclosed	3.25	35.74	11

**Age Demographics**

# TODO:  Need to Redo - Incorrect
# Number of purchases by each gender
grouped_data = data.groupby(["Age"])
purchases = pd.DataFrame()
for age, info in grouped_data:
    price_column = info["Price"]
    purchase = pd.DataFrame({
        "Total Purchases" : [len(info)],
        "Average Purchase Price" : [round(price_column.mean(), 2)],
        "Total Purchase Value" : [price_column.sum()]
    }, index=[age])
    purchases = purchases.append(purchase)
#   TODO : Figure out how to calculate Normalized Totals
purchases

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	Average Purchase Price	Total Purchase Value	Total Purchases
7	2.92	55.47	19
8	1.96	5.87	3
9	3.69	22.12	6
10	3.29	13.16	4
11	2.97	26.76	9
12	3.84	19.21	5
13	2.14	23.52	11
14	2.38	14.30	6
15	2.99	140.36	47
16	2.64	60.71	23
17	3.01	51.11	17
18	2.80	67.14	24
19	3.05	67.10	22
20	2.88	282.68	98
21	2.80	120.37	43
22	3.03	206.05	68
23	2.74	156.21	57
24	3.05	213.46	70
25	3.08	206.52	67
26	2.45	31.83	13
27	3.06	58.21	19
28	2.56	12.81	5
29	2.90	60.96	21
30	3.11	56.01	18
31	2.98	47.62	16
32	3.41	37.50	11
33	2.89	31.80	11
34	3.04	24.32	8
35	3.08	37.02	12
36	2.88	20.14	7
37	2.35	21.11	9
38	2.87	25.79	9
39	3.07	15.34	5
40	3.22	45.11	14
42	2.11	2.11	1
43	3.81	3.81	1
45	2.72	2.72	1

**Top Spenders**

# Grouping the players and their purchases. 
grouped_players = data.groupby(["SN"])

players_info = pd.DataFrame()

# Going through every player and storing important values
for name, info in grouped_players:
    price_column = info["Price"]
    spender_info = pd.DataFrame({
        'Purchase Count': [len(price_column)],
        'Average Purchase Price' : [price_column.mean()],
        'Total Purchase Value' : [price_column.sum()]
    }, index=[name])
    # Need to store append value back into players_info because the person who wrote append was retarted
    players_info = players_info.append(spender_info, ignore_index=False)

top_5_spenders = players_info.sort_values(by="Total Purchase Value", ascending=False).head()
top_5_spenders.style.format({'Total Purchase Value': '${:.2f}', 'Average Purchase Price': '${:.2f}'})

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	Average Purchase Price	Purchase Count	Total Purchase Value
Undirrala66	$3.41	5	$17.06
Saedue76	$3.39	4	$13.56
Mindimnya67	$3.18	4	$12.74
Haellysu29	$4.24	3	$12.73
Eoda93	$3.86	3	$11.58

Most Popular Items

grouped_items = data.groupby(["Item ID"])
items = pd.DataFrame()


# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  Using mean to get just 1 value instead of all the values #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
for item_id, info in grouped_items:
    item = pd.DataFrame({
        "Item ID": [item_id],
        "Purchase Count" : [len(info)],
        "Item Price": [info["Price"].mean()],
        "Total Purchase Value": [info["Price"].sum()]
    }, index=[[data.loc[item_id]["Item Name"]]])
    items = items.append(item, ignore_index=False)

top_5_items = items.sort_values(by="Purchase Count", ascending=False).head()
top_5_items

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	Item ID	Item Price	Purchase Count	Total Purchase Value
Stormfury Mace	39	2.35	11	25.85
Thorn, Satchel of Dark Souls	84	2.23	11	24.53
Shadow Strike, Glory of Ending Hope	31	2.07	9	18.63
Retribution Axe	175	1.24	9	11.16
Piety, Guardian of Riddles	13	1.49	9	13.41

Most Profitable Items

top_5_profitable_items = items.sort_values(by="Total Purchase Value", ascending=False).head()
top_5_profitable_items

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	Item ID	Item Price	Purchase Count	Total Purchase Value
Alpha, Reach of Ending Hope	34	4.14	9	37.26
Thorn, Conqueror of the Corrupted	115	4.25	7	29.75
Rage, Legacy of the Lone Victor	32	4.95	6	29.70
Mercy, Katana of Dismay	103	4.87	6	29.22
Spectral Diamond Doomblade	107	3.61	8	28.88