Pandas Series and DataFrame

What is Pandas Series and DataFrame?

Pandas Series and DataFrame means pandas provides Series and DataFrame for handling tabular data like Excel sheets and CSV files.

In real programs, this topic helps in reading CSV/Excel data. Learn the idea first, then type the program yourself and compare the output.

Why should you learn this?

• It is useful for reading CSV/Excel data.
• It connects with handling result tables.
• 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({
    "Student": ["Aarav", "Riya", "Kabir"],
    "Marks": [82, 94, 76]
})
print(df)
print("Average:", df["Marks"].mean())

Complete Example Program

import pandas as pd

df = pd.DataFrame({
    "Student": ["Aarav", "Riya", "Kabir"],
    "Marks": [82, 94, 76]
})

print(df)
print("Average:", df["Marks"].mean())

Expected Output

Program Explanation

• import pandas as pd imports ready-made features from a module/library.
• df = pd.DataFrame({ stores a value in df.
• "Student": ["Aarav", "Riya", "Kabir"], performs the next step of the program logic.
• "Marks": [82, 94, 76] performs the next step of the program logic.
• }) performs the next step of the program logic.
• print(df) displays information or calculated result on the screen.
• print("Average:", df["Marks"].mean()) displays information or calculated result on the screen.

Where will you use it?

• Reading csv/excel data.
• Handling result tables.
• Building reports.

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-series-dataframe.py and run it.
2. Change input values or sample data and observe the new output.
3. Create one example related to reading CSV/Excel data.
4. Write 5 lines explaining the logic in your own words.

Summary

Pandas Series and DataFrame 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.