Data Cleaning with Pandas

What is Pandas Data Cleaning?

Pandas Data Cleaning means data cleaning fixes missing values, duplicate rows, wrong data types and inconsistent entries.

In real programs, this topic helps in removing duplicates. Learn the idea first, then type the program yourself and compare the output.

Why should you learn this?

• It is useful for removing duplicates.
• It connects with filling missing values.
• It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

import pandas as pd
df = pd.DataFrame({"Name": ["Aarav", "Riya", "Riya"], "Marks": [82, None, None]})
df = df.drop_duplicates()
df["Marks"] = df["Marks"].fillna(0)
print(df)

Complete Example Program

import pandas as pd

df = pd.DataFrame({"Name": ["Aarav", "Riya", "Riya"], "Marks": [82, None, None]})

df = df.drop_duplicates()
df["Marks"] = df["Marks"].fillna(0)

print(df)

Expected Output

Program Explanation

• import pandas as pd imports ready-made features from a module/library.
• df = pd.DataFrame({"Name": ["Aarav", "Riya", "Riya"], "Marks": [82, None, None]} stores a value in df.
• df = df.drop_duplicates() stores a value in df.
• df["Marks"] = df["Marks"].fillna(0) stores a value in df["Marks"].
• print(df) displays information or calculated result on the screen.

Where will you use it?

• Removing duplicates.
• Filling missing values.
• Fixing column data types.

Common Mistakes

• Analysing data before checking missing values, duplicates and data types.
• Changing original data without keeping a clean copy.
• Creating charts without title, labels or explanation.

Practice Tasks

1. Type the program in pandas-data-cleaning.py and run it.
2. Change input values or sample data and observe the new output.
3. Create one example related to removing duplicates.
4. Write 5 lines explaining the logic in your own words.

Summary

Pandas Data Cleaning is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.