Pandas-HeroesOfPymoli

Inna Baloyan's Heroes Of Pymoli Game Data Analysis using Pandas Library and Jupyter Notebook

Heroes Of Pymoli Data Analysis

• Of the 780 active players, the vast majority are male (84%). There also exists, a smaller, but notable proportion of female layers (14%).

• Our peak age demographic falls between 20-24 (46.8%) with secondary groups falling between 15-19 (17.4%) and 25-29 (13%).

• The majority of purchases are also done by the age group 20-24 (46.8%) with secondary groups falling between 15-19 (17.4%) and 25-29 (13%).

• Out of 183 items offered, the most popular and profitable ones are "Oathbreaker, Last Hope of the Breaking Storm" (12 buys), brought $51 and "Nirvana" and "Fiery Glass Crusader" having (9 buys) each and brought $44 and $41 respectively. Generally, all players (780) prefer different items, there are no significantly more popular item(s) than others.

• Average purchase is about $3 per person with the top spenders paying up to $19 for their purchases. Still, 97% are paying way under $10. The total profit from the sold items is about $2400 for 780 players.

---

# Dependencies and Setup
import pandas as pd
import numpy as np

# Raw data file
file_to_load = "Resources/purchase_data.csv"

# Read purchasing file and store into pandas data frame
purchase_data = pd.read_csv(file_to_load)
purchase_data_df = pd.DataFrame(purchase_data)
purchase_data_df.head()

	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

Player Count

• Display the total number of players

total_players = purchase_data_df["SN"].count()
total_players

780

Purchasing Analysis (Total)

• Run basic calculations to obtain number of unique items, average price, etc.

• Create a summary data frame to hold the results

• Optional: give the displayed data cleaner formatting

• Display the summary data frame

unique_items = len(purchase_data_df["Item ID"].unique())
unique_items

total_purchases = purchase_data_df["Purchase ID"].count()
total_purchases

total_revenue = purchase_data_df["Price"].sum()
total_revenue

average_price1 = purchase_data_df["Price"].mean()
average_price1

average_price2 = total_revenue/total_purchases
average_price2

# average_price1 = average_price2

# Display the summary data frame
purchase_analysis_df = pd.DataFrame([{"Number of Unique Items": unique_items, "Average Price": average_price1,
                                      "Number of Purchases": total_purchases, "Total Revenue": total_revenue}])
purchase_analysis_df["Average Price"] = purchase_analysis_df["Average Price"].map("${:,.2f}".format)
purchase_analysis_df["Total Revenue"] = purchase_analysis_df["Total Revenue"].map("${:,.2f}".format)
purchase_analysis_df

# reorganazing the column order for summary dataframe
org_purchase_analysis_df = purchase_analysis_df[["Number of Unique Items","Average Price","Number of Purchases", "Total Revenue" ]]
org_purchase_analysis_df

	Number of Unique Items	Average Price	Number of Purchases	Total Revenue
0	183	$3.05	780	$2,379.77

Gender Demographics

• Run basic calculations to obtain number of unique items, average price, etc.

• Create a summary data frame to hold the results

• Optional: give the displayed data cleaner formatting

• Display the summary data frame

# The value counts method counts unique values in a column, then dataframe created to hold results
gender_demo_df = pd.DataFrame(purchase_data_df["Gender"].value_counts())
gender_demo_df

percentage_of_players = (purchase_data_df["Gender"].value_counts()/total_players)*100
percentage_of_players

# Calculations performed and added into Data Frame as a new column
gender_demo_df["Percentage of Players"] = percentage_of_players
gender_demo_df["Percentage of Players"] = gender_demo_df["Percentage of Players"].map("{:,.2f}%".format)
gender_demo_df

# Change the order of the columns 
org_gender_demo_df = gender_demo_df[["Percentage of Players", "Gender"]]
org_gender_demo_df

# Rename the column "Gender" to "Total Counts" using .rename(columns={})
fin_gender_demo_df = org_gender_demo_df.rename(columns={"Gender":"Total Count"})
fin_gender_demo_df

	Percentage of Players	Total Count
Male	83.59%	652
Female	14.49%	113
Other / Non-Disclosed	1.92%	15

Purchasing Analysis (Gender)

• Run basic calculations to obtain purchase count, avg. purchase price, etc. by gender

• For normalized purchasing, divide total purchase value by purchase count, by gender

• Create a summary data frame to hold the results

• Optional: give the displayed data cleaner formatting

• Display the summary data frame

# Using GroupBy in order to separate the data into fields according to "Gender" values
gender_grouped_purchased_data_df = purchase_data_df.groupby(["Gender"])

# The object returned is a "GroupBy" object and cannot be viewed normally...
# print(gender_grouped_purchased_data_df)

# In order to be visualized, a data function must be used...
gender_grouped_purchased_data_df["Purchase ID"].count().head(10)

# Get total purchase value by gender
total_purchase_value = gender_grouped_purchased_data_df["Price"].sum()
total_purchase_value.head()
dlr_total_purchase_value = total_purchase_value.map("${:,.2f}".format)
dlr_total_purchase_value.head()

# Average purchase price by gender
avg_purchase_price = gender_grouped_purchased_data_df["Price"].mean()
avg_purchase_price.head()
dlr_avg_purchase_price = avg_purchase_price.map("${:,.2f}".format)
dlr_avg_purchase_price.head()

# Normalized totals, total purchase value divided by purchase count by gender
normalized_totals = total_purchase_value/gender_grouped_purchased_data_df["Purchase ID"].count()
dlr_normalized_totals = normalized_totals.map("${:,.2f}".format)
dlr_normalized_totals.head()

# Organize summary gender data, get all columns to organized Data Frame, add needed columns to it
org_gender_purchased_data_df = pd.DataFrame(gender_grouped_purchased_data_df["Purchase ID"].count())
org_gender_purchased_data_df["Average Purchase Price"] = dlr_avg_purchase_price  
org_gender_purchased_data_df["Total Purchase Value"] = dlr_total_purchase_value 
org_gender_purchased_data_df["Normalized Totals"] = dlr_normalized_totals 
org_gender_purchased_data_df

# Summary purchasing analysis DF grouped by gender, rename "Purchase ID" column, using .rename(columns={})
summary_gender_purchased_data_df = org_gender_purchased_data_df.rename(columns={"Purchase ID":"Purchase Count"})
summary_gender_purchased_data_df

	Purchase Count	Average Purchase Price	Total Purchase Value	Normalized Totals
Gender
Female	113	$3.20	$361.94	$3.20
Male	652	$3.02	$1,967.64	$3.02
Other / Non-Disclosed	15	$3.35	$50.19	$3.35

Age Demographics

• Establish bins for ages

• Categorize the existing players using the age bins. Hint: use pd.cut()

• Calculate the numbers and percentages by age group

• Create a summary data frame to hold the results

• Optional: round the percentage column to two decimal points

• Display Age Demographics Table

# Create the bins in which Purchse Data will be held
# Bins are 0, 9.90, 14.90, 19.90, 24.90, 29.90, 34.90, 39.90, 99999
# Establish bins for ages
age_bins = [0, 9.90, 14.90, 19.90, 24.90, 29.90, 34.90, 39.90, 99999]

# # Create the names for the four bins
group_names = ["<10", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40+"]

grp_by_age_purchase_data_df = purchase_data_df
grp_by_age_purchase_data_df["Age Summary"] = pd.cut(grp_by_age_purchase_data_df["Age"], age_bins, labels=group_names)
grp_by_age_purchase_data_df

# Creating a group based off of the bins and saving it
grp_by_age_purchase_data_df = grp_by_age_purchase_data_df.groupby("Age Summary")
grp_by_age_purchase_data_df.count()

# Let's work with this new dataframe
summary_by_age_df = pd.DataFrame(grp_by_age_purchase_data_df.count())
summary_by_age_df 

# Calculations performed on "Purchase Id"" column of summary df
summary_by_age_df["Purchase ID"] = (summary_by_age_df["Purchase ID"]/total_players)*100
summary_by_age_df 

# format percentages 
summary_by_age_df["Purchase ID"] = summary_by_age_df["Purchase ID"].map("{:,.2f}%".format)
summary_by_age_df

#  Reduce number of columns to "Age Summary", "Purcahe ID", format percentages and write into org Data Frame
org_summary_by_age_df = summary_by_age_df[["Purchase ID","SN"]]
org_summary_by_age_df

# Rename the columns for the final age dempgraphic df using .rename(columns={})
fin_grp_by_age_summary_df = org_summary_by_age_df.rename(columns={"Purchase ID":"Percentage of Players", "SN":"Total Count"})
fin_grp_by_age_summary_df

	Percentage of Players	Total Count
Age Summary
<10	2.95%	23
10-14	3.59%	28
15-19	17.44%	136
20-24	46.79%	365
25-29	12.95%	101
30-34	9.36%	73
35-39	5.26%	41
40+	1.67%	13

Purchasing Analysis (Age)

• Bin the purchase_data data frame by age

• Run basic calculations to obtain purchase count, avg. purchase price, etc. in the table below

• Calculate Normalized Purchasing

• Create a summary data frame to hold the results

• Optional: give the displayed data cleaner formatting

• Display the summary data frame

# Let's work with data sorted by age new dataframe
analysis_by_age_df = pd.DataFrame(grp_by_age_purchase_data_df["Purchase ID"].count())
analysis_by_age_df

# Get Total purchase value by age
total_purchase_value_age = grp_by_age_purchase_data_df["Price"].sum()
total_purchase_value_age
dlr_total_purchase_value_age = total_purchase_value_age.map("${:,.2f}".format)
dlr_total_purchase_value_age

# Get Average purchase price by age
avg_purchase_price_age = grp_by_age_purchase_data_df["Price"].mean()
avg_purchase_price_age
dlr_avg_purchase_price_age = avg_purchase_price_age.map("${:,.2f}".format)
dlr_avg_purchase_price_age

# Get Normalized totals by age, total purchase value divided by purchase count by age
normalized_totals_age = total_purchase_value_age/grp_by_age_purchase_data_df["Purchase ID"].count()
dlr_normalized_totals_age = normalized_totals_age.map("${:,.2f}".format)
dlr_normalized_totals_age

# Organize summary gender data, get all columns to organized Data Frame, add needed columns to it
analysis_by_age_df["Average Purchase Price"] = dlr_avg_purchase_price_age  
analysis_by_age_df["Total Purchase Value"] = dlr_total_purchase_value_age 
analysis_by_age_df["Normalized Totals"] = dlr_normalized_totals_age 
analysis_by_age_df

# Summary purchasing analysis DF grouped by age, rename "Purchase ID" column, using .rename(columns={})summary_gender_purchased_data_df = org_gender_purchased_data_df.rename(columns={"Purchase ID":"Purchase Count"})
summary_age_purchased_data_df = analysis_by_age_df.rename(columns={"Purchase ID":"Purchase Count"})
summary_age_purchased_data_df

	Purchase Count	Average Purchase Price	Total Purchase Value	Normalized Totals
Age Summary
<10	23	$3.35	$77.13	$3.35
10-14	28	$2.96	$82.78	$2.96
15-19	136	$3.04	$412.89	$3.04
20-24	365	$3.05	$1,114.06	$3.05
25-29	101	$2.90	$293.00	$2.90
30-34	73	$2.93	$214.00	$2.93
35-39	41	$3.60	$147.67	$3.60
40+	13	$2.94	$38.24	$2.94

Top Spenders

• Run basic calculations to obtain the results in the table below

• Create a summary data frame to hold the results

• Sort the total purchase value column in descending order

• Optional: give the displayed data cleaner formatting

• Display a preview of the summary data frame

# Let's work with original purchase data grouped by player("SN") 
orig_purchase_data_df = pd.DataFrame(purchase_data)
orig_purchase_data_df.head()

# Group by Spendors ( "SN" )
grp_SN_top_spendor_df = orig_purchase_data_df.groupby("SN")
grp_SN_top_spendor_df.count()

# Let's work with data sorted by SN new dataframe
analysis_by_SPENDOR_df = pd.DataFrame(grp_SN_top_spendor_df["Purchase ID"].count())
analysis_by_SPENDOR_df

# Get Total purchase value by SN
total_purchase_value_SN = grp_SN_top_spendor_df["Price"].sum()
total_purchase_value_SN

# Get Average purchase price by SN
avg_purchase_price_SN = grp_SN_top_spendor_df["Price"].mean()
avg_purchase_price_SN
dlr_avg_purchase_price_SN = avg_purchase_price_SN.map("${:,.2f}".format)
dlr_avg_purchase_price_SN

# Organize summary Top Spender data, get all columns to organized Data Frame, add needed columns to it
analysis_by_SPENDOR_df["Average Purchase Price"] = dlr_avg_purchase_price_SN
analysis_by_SPENDOR_df["Total Purchase Value"] = total_purchase_value_SN 
analysis_by_SPENDOR_df

# Summary Top Spendor analysis grouped by SN, rename "Purchase ID" column, using .rename(columns={})
SUM_SN_purchased_data_df = analysis_by_SPENDOR_df.rename(columns={"Purchase ID":"Purchase Count"})
TOP5_spendors_df=SUM_SN_purchased_data_df.sort_values("Total Purchase Value", ascending=False)
TOP5_spendors_df.head()

# Format Total Purchase Price to be in dollar form
dlr_total_purchase_value_SN = total_purchase_value_SN.map("${:,.2f}".format)
TOP5_spendors_df["Total Purchase Value"] = dlr_total_purchase_value_SN
TOP5_spendors_df.head()

	Purchase Count	Average Purchase Price	Total Purchase Value
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

Most Popular Items

• Retrieve the Item ID, Item Name, and Item Price columns

• Group by Item ID and Item Name. Perform calculations to obtain purchase count, item price, and total purchase value

• Create a summary data frame to hold the results

• Sort the purchase count column in descending order

• Optional: give the displayed data cleaner formatting

• Display a preview of the summary data frame

# Let's work again with original purchase data grouped by "Item ID" and "Item Name"

# Group by Item ( "Item ID" and "Item Name" )
grp_top_item_df = orig_purchase_data_df.groupby(["Item ID", "Item Name"])
grp_top_item_df.count()

# Let's work with data sorted by Item new dataframe
analysis_by_ITEM_df = pd.DataFrame(grp_top_item_df["Purchase ID"].count())
analysis_by_ITEM_df

# Get Total purchase value by Item
total_purchase_value_ITEM = grp_top_item_df["Price"].sum()
total_purchase_value_ITEM
dlr_total_purchase_value_ITEM = total_purchase_value_ITEM.map("${:,.2f}".format)
dlr_total_purchase_value_ITEM

# Get purchase price by Item
purchase_price_ITEM = grp_top_item_df["Price"].mean()
purchase_price_ITEM
dlr_purchase_price_ITEM = purchase_price_ITEM.map("${:,.2f}".format)
dlr_purchase_price_ITEM

# Organize summary Item data, get all columns to organized Data Frame, add needed columns to it
analysis_by_ITEM_df["Item Price"] = dlr_purchase_price_ITEM
analysis_by_ITEM_df["Total Purchase Value"] = dlr_total_purchase_value_ITEM
analysis_by_ITEM_df

# Summary Most Popular Item analysis grouped by Item, rename "Purchase ID" column, using .rename(columns={})
SUM_ITEM_purchased_data_df = analysis_by_ITEM_df.rename(columns={"Purchase ID":"Purchase Count"})
TOP5_ITEMS_df=SUM_ITEM_purchased_data_df.sort_values("Purchase Count", ascending=False)
TOP5_ITEMS_df.head()

		Purchase Count	Item Price	Total Purchase Value
Item ID	Item Name
178	Oathbreaker, Last Hope of the Breaking Storm	12	$4.23	$50.76
145	Fiery Glass Crusader	9	$4.58	$41.22
108	Extraction, Quickblade Of Trembling Hands	9	$3.53	$31.77
82	Nirvana	9	$4.90	$44.10
19	Pursuit, Cudgel of Necromancy	8	$1.02	$8.16

Most Profitable Items

• Sort the above table by total purchase value in descending order

• Optional: give the displayed data cleaner formatting

• Display a preview of the data frame

# Alter analysis by Item_df to have total purchase price column in numeric form to be able to sort it numerically
SUM_ITEM_purchased_data_df["Total Purchase Value"] = grp_top_item_df["Price"].sum()
SUM_ITEM_purchased_data_df

# Summary Most Popular Item analysis grouped by Item, rename "Purchase ID" column, using .rename(columns={})
TOP5_ITEMS_df=SUM_ITEM_purchased_data_df.sort_values("Total Purchase Value", ascending=False)

# Format Total Purchase Price to be in dollar form
dlr_total_purchase_value_ITEM = total_purchase_value_ITEM.map("${:,.2f}".format)
TOP5_ITEMS_df["Total Purchase Value"] = dlr_total_purchase_value_ITEM
TOP5_ITEMS_df.head()

		Purchase Count	Item Price	Total Purchase Value
Item ID	Item Name
178	Oathbreaker, Last Hope of the Breaking Storm	12	$4.23	$50.76
82	Nirvana	9	$4.90	$44.10
145	Fiery Glass Crusader	9	$4.58	$41.22
92	Final Critic	8	$4.88	$39.04
103	Singed Scalpel	8	$4.35	$34.80

Conclusions:

• Of the 780 active players, the vast majority are male (84%). There also exists, a smaller, but notable proportion of female layers (14%).

• Our peak age demographic falls between 20-24 (46.8%) with secondary groups falling between 15-19 (17.4%) and 25-29 (13%).

• The majority of purchases are also done by the age group 20-24 (46.8%) with secondary groups falling between 15-19 (17.4%) and 25-29 (13%).

• Out of 183 items offered, the most popular and profitable ones are "Oathbreaker, Last Hope of the Breaking Storm" (12 buys), brought $51 and "Nirvana" and "Fiery Glass Crusader" having (9 buys) each and brought $44 and $41 respectively. Generally, all players (780) prefer different items, there are no significantly more popular item(s) than others.

• Average purchase is about $3 per person with the top spenders paying up to $19 for their purchases. Still, 97% are paying way under $10. The total profit from the sold items is about $2400 for 780 players.