📌 What is Pandas?

Pandas is a Python library used to work with tabular data like Excel sheets or database tables. It helps with:

• Reading & writing data
• Analyzing & cleaning data
• Performing calculations on rows and columns

✅ Step 1: Installing and Importing Pandas

pip install pandas

import pandas as pd

✅ pd is a short name (alias) we use for pandas.

✅ Step 2: Creating a DataFrame (Table-like data)

data = {
    'Name': ['Arun', 'Priya', 'Kumar'],
    'Marks': [85, 90, 78]
}

df = pd.DataFrame(data)
print(df)

🖨 Output:

    Name  Marks
0   Arun     85
1  Priya     90
2  Kumar     78

🎯 Real-Life Use: Represent student marks or sales reports as a table.

✅ Step 3: Reading Data from Files

df = pd.read_csv("students.csv")

🎯 Real-Life Use: Read data from Excel, CSV, or Google Sheets.

✅ Step 4: Basic Info About Data

print(df.head())         # First 5 rows
print(df.tail())         # Last 5 rows
print(df.shape)          # (rows, columns)
print(df.columns)        # List of column names
print(df.info())         # Column details

✅ Step 5: Selecting Columns and Rows

print(df['Name'])        # Select one column
print(df[['Name', 'Marks']])  # Select multiple columns

print(df.iloc[0])        # First row (by index)
print(df.loc[1])         # Row with index label 1

🎯 Real-Life Use: Get details of a student by ID or name.

✅ Step 6: Filtering Data (Conditional Selection)

print(df[df['Marks'] > 80])

🖨 Output:

    Name  Marks
0   Arun     85
1  Priya     90

🎯 Real-Life Use: Find students who passed or scored more than 80.

✅ Step 7: Adding and Modifying Columns

df['Result'] = df['Marks'] >= 80
print(df)

🎯 Real-Life Use: Add “Pass/Fail” status based on marks.

✅ Step 8: Sorting Data

print(df.sort_values('Marks'))
print(df.sort_values('Marks', ascending=False))

🎯 Real-Life Use: Rank top scorers or sort products by price.

✅ Step 9: Grouping and Aggregating

group = df.groupby('Result').mean()
print(group)

🎯 Real-Life Use: Find average marks of passed vs failed students.

✅ Step 10: Handling Missing Data

df.isnull()            # Check missing values
df.dropna()            # Remove rows with missing values
df.fillna(0)           # Replace missing values with 0

🎯 Real-Life Use: Fill missing prices or names in sales data.

✅ Step 11: Exporting Data to File

df.to_csv("updated_data.csv", index=False)

🎯 Real-Life Use: Save cleaned or updated student/sales report.

✅ Step 12: Useful Pandas Functions

df.describe()      # Summary (mean, std, min, max)
df['Marks'].max()  # Maximum marks
df['Marks'].min()  # Minimum marks
df['Marks'].mean() # Average marks
df['Marks'].sum()  # Total marks

🎯 Real-Life Use: Get summary of any report like sales or performance.

📚 Interview Q&A (Pandas)

1. What is Pandas in Python?

Answer: A library for data manipulation and analysis using tables (DataFrames).

2. What is a DataFrame?

Answer: A 2D table with rows and columns (like Excel).

3. How do you read data in Pandas?

pd.read_csv('filename.csv')

4. How to filter rows where salary > 50000?

df[df['Salary'] > 50000]

5. How to handle missing values?

• df.dropna()
• df.fillna(0)

6. How to group data?

df.groupby('Department').mean()

7. Difference between loc[] and iloc[]?

• loc[]: Uses label (row name/index)
• iloc[]: Uses position (row number)

8. How to sort data?

df.sort_values('ColumnName')

9. How to add a new column?

df['NewCol'] = value

10. Real-Life Example?

Track student marks, employee salary, monthly sales, attendance, survey data, etc.

📌 Summary

Let me know your next step!