Panda lib

Data analysis and manipulation

Use case: Analyze user behavior, product metrics and financial data. This is used to help clean data, manipulate and analyze large datasets quickly.

Breaking down the below code:

• import pandas as pd - This imports the pandas library which is used for working with tables, datasets and spreadsheets in python. as pd gives pandas a short name (pd)

• df = pd.read_csv("user_data.csv") - .read_csv() is a pandas function that reads a csv file and ocnvert s it into a data frame. this reads csv file named user_data.csv. df =.... stores the data from the CSV file to the variable called df(stands for DataFrame) which is like a table (similar to excel spreadsheet) that organizes data in rows and columns.

• print(df.head()) - df.head() is a pandas function that returns the first 5 rows of the dataframe and print outputs on the screen. example print(df.head(10)) to show first 10 rows

import pandas as pd  # Load user engagement data

df = pd.read_csv("user_data.csv")  # Read the CSV file into a DataFrame

print(df.head())  # View the first few rows of the dataset

Table of Pandas Functions generally used 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.