A free-to-play fantasy game known as Heroes of Pymoli is run by a small independent gaming company where players are given the option to purchase in-game items to enhance their player experience. The task of this analysis is to analyze and generate a statistical report that breaks down the in-game purchasing data into insightful information.
Of the 576 active players, the majority of the player base is 84% male while the remainder is 16% female.
The age demographic presented that the age peak falls between ages 20--24 (44.79%). Secondary groups that fall behind are ages between 15--19 (18.58%) and 25--29 (13.37%).
The age group that spends the most money falls between ages 20--24 with $1114.06 as the total purchase value with an average purchase per individual of $4.32. However, the age group that has the highest average purchase per individual is ages 35--39 with $4.76.
#Dependencies and Setup
import pandas as pd
#File name to load
file = "Resources/purchase_data.csv"
#Read file and store into Pandas df
purchase_data = pd .read_csv (file )
purchase_data .head ()#Get total count of players
unique_names = purchase_data ["SN" ].nunique ()
count = pd .DataFrame ({"Player Count" :[unique_names ]})
count #Purchasing analysis
item_count = purchase_data ["Item ID" ].nunique ()
avg_price = purchase_data ["Price" ].mean ()
purchase_count = purchase_data ["Purchase ID" ].count ()
total_revenue = purchase_data ["Price" ].sum ()
summary = pd .DataFrame ({"Number of Unique Items" :[item_count ],
"Average Price" :[avg_price ],
"Number of Purchases" :[purchase_count ],
"Total Revenue" :[total_revenue ]})
summary [["Average Price" ,"Total Revenue" ]] = summary [["Average Price" ,"Total Revenue" ]].applymap ("${:,.2f}" .format )
summary
Number of Unique Items
Average Price
Number of Purchases
Total Revenue
0
179
$3.05
780
$2379.77
#Gender Demographics
SN = purchase_data .drop_duplicates (subset = "SN" )
gender_count = SN ["Gender" ].value_counts ()
gender_percentage = gender_count / SN ["Gender" ].count () * 100
gender_summary_df = pd .DataFrame ({"Total Count" : gender_count ,
"Percentage of Players" : gender_percentage })
gender_summary_df ["Percentage of Players" ] = gender_summary_df ["Percentage of Players" ].map ("{0:.2f}" .format )
gender_summary_df
Total Count
Percentage of Players
Male
484
84.03
Female
81
14.06
Other / Disclosed
11
1.91
Purchase and Gender Analysis #Purchasing analysis with gender
gender_purchase_count = purchase_data .groupby ("Gender" )["Item Name" ].count ()
gender_avg_purchase_price = purchase_data .groupby ("Gender" )["Price" ].mean ()
gender_total_purchase_price = purchase_data .groupby ("Gender" )["Price" ].sum ()
gender_avg_purchase_total = gender_total_purchase_price / gender_count
gender_purchase_summary_df = pd .DataFrame ({"Purchase Count" : gender_purchase_count ,
"Average Purchase Price" : gender_avg_purchase_price ,
"Total Purchase Value" : gender_total_purchase_price ,
"Avg Total Purchase per Person" : gender_avg_purchase_total })
gender_purchase_summary_df [["Average Purchase Price" ,"Total Purchase Value" ,"Avg Total Purchase per Person" ]] = gender_purchase_summary_df [["Average Purchase Price" ,"Total Purchase Value" ,"Avg Total Purchase per Person" ]].applymap ("${:.2f}" .format )
gender_purchase_summary_df
Purchase Count
Average Purchase Price
Total Purchase Value
Avg Total Purchase per Person
Female
113
$3.20
$361.94
$4.47
Male
652
$3.02
$1967.64
$4.07
Other / Disclosed
113
$3.35
$50.19
$4.56
#Age Demographics
#Create Bins for age groups
bins = [0 ,9 ,14 ,19 ,24 ,29 ,34 ,39 ,100 ]
age_groups = ["0-9" ,"10-14" ,"15-19" ,"20-24" ,"25-29" ,"30-34" ,"35-39" ,"40 and over" ]
SN ["Age Range" ] = pd .cut (SN ["Age" ],bins , labels = age_groups )
age_purchase_count = SN ["Age Range" ].value_counts ()
age_percentage_player = (age_purchase_count / SN ["SN" ].count ()) * 100
age_summary = pd .DataFrame ({"Total Count" : age_purchase_count ,
"Percentage of Players" : age_percentage_player })
age_summary = age_summary .sort_index ()
age_summary ["Percentage of Players" ] = age_summary ["Percentage of Players" ].map ("{:.2f}%" .format )
age_summary
Total Count
Percentage of Players
0-9
17
2.59%
10-14
22
3.82%
15-19
107
18.58%
20-24
258
44.79%
25-29
77
13.37%
30-34
52
9.03%
35-39
31
5.38%
40 and over
12
2.08%
Purchase and Age Analysis #Purchasing analysis with Age
purchase_data ["Age Ranges" ] = pd .cut (purchase_data ["Age" ], bins , labels = age_groups )
purchase_age_count = purchase_data ["Age Ranges" ].value_counts ()
avg_age_price = purchase_data .groupby ("Age Ranges" )["Price" ].mean ()
total_age_purchase = purchase_data .groupby ("Age Ranges" )["Price" ].sum ()
avg_age_purchase = total_age_purchase / age_purchase_count
purchase_age_summary = pd .DataFrame ({"Purchase Count" : purchase_age_count ,
"Average Purchase Price" : avg_age_price ,
"Total Purchase Value" : total_age_purchase ,
"Avg Total Purchase per Person" : avg_age_purchase })
purchase_age_summary [["Average Purchase Price" ,"Total Purchase Value" ,"Avg Total Purchase per Person" ]] = purchase_age_summary [["Average Purchase Price" ,"Total Purchase Value" ,"Avg Total Purchase per Person" ]].applymap ("${:.2f}" .format )
purchase_age_summary = purchase_age_summary .rename_axis ("Age Ranges" )
purchase_age_summary
Total Count
Average Purchase Price
Total Purchase Value
Avg Total Purchase per Person
0-9
23
$3.35
$77.13
$4.54
10-14
28
$2.96
$82.78
$3.76
15-19
136
$3.04
$412.89
$3.86
20-24
365
$3.05
$1114.06
$4.32
25-29
101
$2.90
$293.00
$3.81
30-34
73
$2.93
$214.00
$4.12
35-39
41
$3.60
$147.67
$4.76
40 and over
13
$2.94
$38.24
$3.19
#Who Spends the most
user_purchase_count = purchase_data .groupby ("SN" )["Price" ].count ()
user_purchase_price = purchase_data .groupby ("SN" )["Price" ].mean ()
user_purchase_total = purchase_data .groupby ("SN" )["Price" ].sum ()
user_summary = pd .DataFrame ({"Purchase Count" : user_purchase_count ,
"Average Purchase Price" : user_purchase_price ,
"Total Purchase per Person" : user_purchase_total })
user_summary = user_summary .sort_values (by = "Total Purchase per Person" , ascending = False )
user_summary [["Average Purchase Price" ,"Total Purchase per Person" ]] = user_summary [["Average Purchase Price" ,"Total Purchase per Person" ]].applymap ("${:.2f}" .format )
user_summary
Purchase Count
Average Purchase Price
Total Purchase per Person
SN
Lisosia93
5
$3.79
$18.96
Idastidru52
4
$3.86
$15.45
Chamjask73
3
$4.61
$13.83
Iral74
4
$3.40
$13.62
Iskadarya95
3
$4.37
$13.10
...
...
...
...
Ililsasya43
1
$1.02
$1.02
Irilis75
1
$1.02
$1.02
Aidai61
1
$1.01
$1.01
Chanirra79
1
$1.01
$1.01
Alo38
1
$1.00
$1.00
Most Popular Item Choices #Most popular item purchases
most_purchased = purchase_data .groupby (["Item ID" ,"Item Name" ])["Price" ].count ()
popular_item = purchase_data .groupby (["Item ID" ,"Item Name" ])["Price" ].sum ()
item_price = popular_item / most_purchased
item_summary = pd .DataFrame ({"Purchase Count" : most_purchased ,
"Item Price" : item_price ,
"Total Purchase Value" : popular_item })
item_summary = item_summary .sort_values (["Purchase Count" ], ascending = False )
item_summary [["Item Price" , "Total Purchase Value" ]] = item_summary [["Item Price" , "Total Purchase Value" ]].applymap ("${:.2f}" .format )
item_summary .head ()
Purchase Count
Item Price
Total Purchase Value
Item ID
Item Name
92
Final Critic
13
$4.61
$59.99
178
Oathbreaker, Last Hope of the Breaking Storm
12
$4.23
$50.76
145
Fiery Glass Crusader
9
$4.58
$41.22
132
Persuasion
9
$3.22
$28.99
108
Extraction, Quickblade Of Trembling Hands
9
$3.53
$31.77
#Most profitable items
most_profitable = item_summary .sort_values (["Total Purchase Value" ], ascending = False )
most_profitable .head (10 )
Purchase Count
Item Price
Total Purchase Value
Item ID
Item Name
63
Stormfury Mace
2
$4.99
$9.98
29
Chaos, Ender of the End
5
$1.98
$9.90
173
Stormfury Longsword
2
$4.93
$9.86
38
The Void, Vengeance of Dark Magic
4
$2.37
$9.48
143
Frenzied Scimitar
6
$1.56
$9.36
7
Thorn, Satchel of Dark Souls
7
$1.33
$9.31
18
Torchlight, Bond of Storms
2
$4.65
$9.30
129
Fate, Vengeance of Eternal Justice
6
$1.54
$9.24
166
Thirsty Iron Reaver
3
$3.07
$9.21
123
Twilight's Carver
4
$2.28
$9.12