Heroes of Pymoli

 

In [28]:

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

In [29]:

purchase = pd.read_csv("purchase_data.csv")
purchase.head(5)

Out[29]:

	Purchase ID	SN	Age	Gender	Item ID	Item Name	Price
0	0	Lisim78	20	Male	108	Extraction, Quickblade Of Trembling Hands	3.53
1	1	Lisovynya38	40	Male	143	Frenzied Scimitar	1.56
2	2	Ithergue48	24	Male	92	Final Critic	4.88
3	3	Chamassasya86	24	Male	100	Blindscythe	3.27
4	4	Iskosia90	23	Male	131	Fury	1.44

In [30]:

purchase.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 780 entries, 0 to 779
Data columns (total 7 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Purchase ID  780 non-null    int64  
 1   SN           780 non-null    object 
 2   Age          780 non-null    int64  
 3   Gender       780 non-null    object 
 4   Item ID      780 non-null    int64  
 5   Item Name    780 non-null    object 
 6   Price        780 non-null    float64
dtypes: float64(1), int64(3), object(3)
memory usage: 42.8+ KB

In [206]:

#Total Number of Players
total_players = purchase["SN"].nunique()
total_players

Out[206]:

576

In [32]:

#Number of Unique Items
unique_items = purchase["Item Name"].nunique()
unique_items

Out[32]:

179

In [33]:

#Average Purchase Price
avg_price = purchase["Price"].mean()
avg_price

Out[33]:

3.050987179487176

In [34]:

#Total Number of Purchases

total_purchase = purchase["Purchase ID"].count()
total_purchase

Out[34]:

780

In [35]:

#Total Revenue
total_revenue = purchase["Price"].sum()
total_revenue

Out[35]:

2379.77

In [36]:

'''Percentage and Count of Male Players
Percentage and Count of Female Players
Percentage and Count of Other / Non-Disclosed'''
total_male_players = purchase[purchase["Gender"]== "Male"].Gender.count()
total_female_players = purchase[purchase["Gender"]== "Female"].Gender.count()
total_other_players = total_players - (total_male_players + total_female_players)
per_of_male = ((total_male_players*100)/total_players).round(2)
per_of_female = ((total_female_players*100)/total_players).round(2)
per_of_other = ((total_other_players*100)/total_players).round(2)

y = pd.Series({"Male": per_of_male, 
               "Female": per_of_female, 
               "Others/Non Disclosed": per_of_other})
plt.title("Gender Demographic")
y.plot(kind = "pie", figsize = (12,8),autopct='%.2f%%');

In [37]:

'''below each broken by gender
Purchase Count
Average Purchase Price
Total Purchase Value
Average Purchase Total per Person by Gender'''

purchase_count_of_male = ((total_male_players*100) /total_players).round(2)
avg_purchase_price_of_male = purchase[purchase["Gender"]== "Male"].Price.mean()
total_purchase_of_male = purchase[purchase["Gender"]== "Male"].Price.sum()
average_purchase_total_per_male = total_purchase_of_male / total_male_players

In [38]:

purchase_count_of_female = ((total_female_players*100) /total_players).round(2)
avg_purchase_price_of_female = purchase[purchase["Gender"]== "Female"].Price.mean()
total_purchase_of_female = purchase[purchase["Gender"]== "Female"].Price.sum()
average_purchase_total_per_female = total_purchase_of_female / total_female_players

In [39]:

purchase_count_of_other = ((total_other_players*100) /total_players).round(2)
avg_purchase_price_of_other = purchase[purchase["Gender"]!= ("Female" and "Male")].Price.mean()
total_purchase_of_other = purchase[purchase["Gender"]!= ("Female" and "Male")].Price.sum()
average_purchase_total_per_other = total_purchase_of_other / total_other_players

In [40]:

plt.figure(figsize=(12,8))
plt.title("Purchase Count by Gender")
plt.pie(x = [purchase_count_of_male, purchase_count_of_female, purchase_count_of_other], autopct = "%.2f%%", labels = ["Male","Female","Other/Non Disclosed"]);

In [46]:

plt.title("Purchasing Analysis(Gender)")
plt.ylabel("Avg Purchasing Price")
plt.bar(x = ["Female", "Male", "Other/Non Disclosed"], height = [avg_purchase_price_of_female,avg_purchase_price_of_male,avg_purchase_price_of_other]);

In [50]:

purchase_count_of_bin1 = purchase[purchase["Age"] < 10]["Purchase ID"].count()
avg_purchase_price_of_bin1 = purchase[purchase["Age"] < 10]["Price"].mean()
total_purchase_value_of_bin1 = purchase[purchase["Age"] <10]["Price"].sum()
avg_purchase_total_per_bin1 = total_purchase_value_of_bin1 / purchase[purchase["Age"] < 10].SN.count()

In [51]:

purchase_count_of_bin2 = purchase[purchase["Age"].between(10, 14)]["Purchase ID"].count()
avg_purchase_price_of_bin2 = purchase[purchase["Age"].between(10, 14)]["Price"].mean()
total_purchase_value_of_bin2 = purchase[purchase["Age"].between(10, 14)]["Price"].sum()
avg_purchase_total_per_bin2 = total_purchase_value_of_bin2 / purchase[purchase["Age"].between(10,14)].SN.count()

In [77]:

purchase_count_of_bin3 = purchase[purchase["Age"].between(15, 19)]["Purchase ID"].count()
avg_purchase_price_of_bin3 = purchase[purchase["Age"].between(15, 19)]["Price"].mean()
total_purchase_value_of_bin3 = purchase[purchase["Age"].between(15, 19)]["Price"].sum()
avg_purchase_total_per_bin3 = total_purchase_value_of_bin3 / purchase[purchase["Age"].between(15,19)].SN.count()

In [76]:

purchase_count_of_bin4 = purchase[purchase["Age"].between(20, 24)]["Purchase ID"].count()
avg_purchase_price_of_bin4 = purchase[purchase["Age"].between(20, 24)]["Price"].mean()
total_purchase_value_of_bin4 = purchase[purchase["Age"].between(20, 24)]["Price"].sum()
avg_purchase_total_per_bin4 = total_purchase_value_of_bin4 / purchase[purchase["Age"].between(20,24)].SN.count()
total_purchase_value_of_bin4

Out[76]:

1114.06

In [64]:

purchase_count_of_bin5 = purchase[purchase["Age"].between(25, 29)]["Purchase ID"].count()
avg_purchase_price_of_bin5 = purchase[purchase["Age"].between(25, 29)]["Price"].mean()
total_purchase_value_of_bin5 = purchase[purchase["Age"].between(25, 29)]["Price"].sum()
avg_purchase_total_per_bin5 = total_purchase_value_of_bin5 / purchase[purchase["Age"].between(25,29)].SN.count()

In [65]:

purchase_count_of_bin6 = purchase[purchase["Age"].between(30, 34)]["Purchase ID"].count()
avg_purchase_price_of_bin6 = purchase[purchase["Age"].between(30, 34)]["Price"].mean()
total_purchase_value_of_bin6 = purchase[purchase["Age"].between(30, 34)]["Price"].sum()
avg_purchase_total_per_bin6 = total_purchase_value_of_bin6 / purchase[purchase["Age"].between(30,34)].SN.count()

In [66]:

purchase_count_of_bin7 = purchase[purchase["Age"].between(35, 39)]["Purchase ID"].count()
avg_purchase_price_of_bin7 = purchase[purchase["Age"].between(35, 39)]["Price"].mean()
total_purchase_value_of_bin7 = purchase[purchase["Age"].between(35, 39)]["Price"].sum()
avg_purchase_total_per_bin7 = total_purchase_value_of_bin7 / purchase[purchase["Age"].between(35,39)].SN.count()

In [70]:

purchase_count_of_bin8 = purchase[purchase["Age"] > 40]["Purchase ID"].count()
avg_purchase_price_of_bin8 = purchase[purchase["Age"] > 40]["Price"].mean()
total_purchase_value_of_bin8 = purchase[purchase["Age"] > 40]["Price"].sum()
avg_purchase_total_per_bin8 = total_purchase_value_of_bin8 / purchase[purchase["Age"] > 40].SN.count()

In [71]:

plt.title("Age Demographic")
plt.ylabel("Count")
plt.bar(x = ["<10", "10-14", "14-19","20-24","25-29","30-34", "35-39", ">40"], height = [purchase_count_of_bin1,purchase_count_of_bin2,purchase_count_of_bin3,purchase_count_of_bin4,purchase_count_of_bin5,purchase_count_of_bin6,purchase_count_of_bin7,purchase_count_of_bin8]);

In [86]:

plt.title("Purchasing Analysis(Age)")

plt.ylabel("Avg Total Purchase Per Person")
plt.bar(x = ["<10", "10-14", "14-19","20-24","25-29","30-34", "35-39", "40+"], height = [avg_purchase_total_per_bin1,avg_purchase_total_per_bin2,avg_purchase_total_per_bin3,avg_purchase_total_per_bin4,avg_purchase_total_per_bin5,avg_purchase_total_per_bin6,avg_purchase_total_per_bin7,avg_purchase_total_per_bin8], color = ["blue","orange","green","red","violet","brown","pink","silver"]);

In [87]:

plt.title("Purchasing Analysis(Age)")

plt.ylabel("Total Purchase Value")
plt.bar(x = ["<10", "10-14", "14-19","20-24","25-29","30-34", "35-39", "40+"], height = [total_purchase_value_of_bin1,total_purchase_value_of_bin2,total_purchase_value_of_bin3,total_purchase_value_of_bin4,total_purchase_value_of_bin5,total_purchase_value_of_bin6,total_purchase_value_of_bin7,total_purchase_value_of_bin8], color = ["blue","orange","green","red","violet","brown","pink","silver"]);

In [93]:

#TOP 5 Spender
top_five =purchase.sort_values(by = "Price", ascending = False).head(5)
top_five

Out[93]:

	Purchase ID	SN	Age	Gender	Item ID	Item Name	Price
554	554	Dyally87	22	Male	63	Stormfury Mace	4.99
189	189	Hiasri33	23	Male	63	Stormfury Mace	4.99
110	110	Ririp86	25	Male	139	Mercy, Katana of Dismay	4.94
246	246	Lirtilsa71	24	Male	139	Mercy, Katana of Dismay	4.94
493	493	Chanirrasta87	14	Male	139	Mercy, Katana of Dismay	4.94

In [159]:

#5 most popular items by purchase count
purchase["Item Name"].value_counts()

Out[159]:

Final Critic                                    13
Oathbreaker, Last Hope of the Breaking Storm    12
Persuasion                                       9
Fiery Glass Crusader                             9
Nirvana                                          9
                                                ..
Gladiator's Glaive                               1
Riddle, Tribute of Ended Dreams                  1
Undead Crusader                                  1
Alpha, Reach of Ending Hope                      1
Endbringer                                       1
Name: Item Name, Length: 179, dtype: int64

In [173]:

#5 most profitable items by total purchase value
purchase.sort_values("Price", ascending = False)

Out[173]:

	Purchase ID	SN	Age	Gender	Item ID	Item Name	Price
554	554	Dyally87	22	Male	63	Stormfury Mace	4.99
189	189	Hiasri33	23	Male	63	Stormfury Mace	4.99
110	110	Ririp86	25	Male	139	Mercy, Katana of Dismay	4.94
246	246	Lirtilsa71	24	Male	139	Mercy, Katana of Dismay	4.94
493	493	Chanirrasta87	14	Male	139	Mercy, Katana of Dismay	4.94
...	...	...	...	...	...	...	...
586	586	Chanirra79	23	Female	155	War-Forged Gold Deflector	1.01
282	282	Aidai61	21	Male	155	War-Forged Gold Deflector	1.01
371	371	Eusurdeu49	23	Male	155	War-Forged Gold Deflector	1.01
63	63	Alo38	20	Male	125	Whistling Mithril Warblade	1.00
418	418	Marim28	25	Female	125	Whistling Mithril Warblade	1.00

780 rows × 7 columns

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