2. Pandas

Adding the pandas functions in the code created in the "1." on the last page.

import pandas as pd
from faker import Faker
import random

#Initailize Faker
fake = Faker()

#set a random seed for reproducibility
random.seed(42)
fake.seed_instance(42) #to make sure Faker generates the same random values each time as it also had its own randomness stored somewhere in the module 


#Generate and print a fake name
#We only need a placeholder like _ to run the loop and didn't necessarily need it for generating sequential user IDs.
users = []
for i in range(1, 21):
    user = {
        "user_id": "PWR" + str(100 + i),
        "name": fake.name(),
        "age": random.randint(18,60),
        "country": fake.country(),
        "group": random.choice(["A", "B"]),
        "clicks": random.randint(0,20),
        "converted": random.choice([0, 1])
    }
    users.append(user)

#Create DataFrame
df = pd.DataFrame(users)

#to shift the default index from 0 to 1 in pandas
df.index = df.index + 1

print(df)

#below is done to save the data in a csv file 
#df.to_csv("ab_testing_data.csv", index=False)

print("="*50)
print("Wohoo! Data saved successfully, 1/3rd of your project is complete!")

## ==================== For Pandas Functions ==================================#

# ==================== Dataset Overview ====================
print("\n" + "="*50)
print("Dataset Overview")
print("="*50)

print("\nLoading the dataset...")
df = pd.read_csv("ab_testing_data.csv") #loading the dataset

# ==================== Summary of Dataset ====================
print("\n" + "="*50)
print("Summary of dataset (Column types and missing values)")
print("="*50)
print(df.info()) #summary of dataset(column types, missing values to help understand the structure of data)
print("="*50)

# ==================== Summary Statistics ====================
print("\n" + "="*50)
print("Summary Stats (Numerical Data)")
print(df.describe()) #shows statistics for numerical columns useful for understanding age distribution, clicks and conversion rates
print("="*50)

# ==================== A/B Test Group Distribution ====================
print("\n" + "="*50)
print("User Distribution in A/B test groups")
print(df["group"].value_counts()) #counts users in Group A & B to ensure balanced users in A/B testing groups
print("="*50)

# ==================== A/B Test Analysis ====================
#calculated average clicks and conversion rate for each group to help compare Group A and Group B on engagement and conversions
print("\n" + "="*50)
print("A/B test analysis results (Average clicks and conversion rates)")
group_analysis = df.groupby("group").agg({
    "clicks": "mean",
    "converted": "mean"
})
print(group_analysis)
print("="*50)

# ==================== Saving Cleaned Dataset ====================
df.to_csv("cleaned_ab_testing_data_pandas.csv", index=False) #save cleaned daatset for NumPy analysis or deeper Pandas analysis

print("\n" + "="*50)
print("Wohoo! You just learnt how to use basic panda functions and completed 2/3rd of your overall project!")
print("="*50)

Table of Pandas Functions used in the above code and Real-Life case scenarios

Pandas Function	Purpose in This Code	Other Real-Life Use Cases
pd.read_csv("file.csv")	Load the A/B testing dataset	Reading sales reports, customer databases, or stock market data.
df.info()	Show dataset structure (columns, data types, missing values)	Checking data quality in survey responses or product inventory.
df.describe()	Summary statistics for numerical data	Analyzing website traffic, marketing campaign performance, or test scores.
df["group"].value_counts()	Count users in A/B test groups	Counting product categories in e-commerce, tracking survey responses.
df.groupby("group").agg({"clicks": "mean", "converted": "mean"})	Calculate average clicks and conversion rates	Finding average customer spending per region, analyzing user engagement per platform.
df.index = df.index + 1	Shift DataFrame index to start from 1	Reindexing rows after filtering or merging datasets (e.g., employee records).
df.to_csv("cleaned_data.csv", index=False)	Save cleaned dataset for further analysis	Exporting processed data for reporting, saving filtered customer lists.