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Pandas Selecting Data

Columns, rows, loc and iloc.

✍️ 3 exercises + code challenge in this lesson
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  • One column: df["name"] (a Series); several: df[["name", "city"]]
  • df.loc[row_label, column]: select by labels.
  • df.iloc[row_pos, col_pos]: select by positions.

Key Takeaways: Pandas Selecting Data

Mastering Pandas Selecting Data helps you write clean and reliable Pandas code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.

Pandas Selecting Data Code Example

Pandas 
import pandas as pd

df = pd.DataFrame({
    "name": ["Ali", "Sara", "Usman"],
    "city": ["Lahore", "Karachi", "Islamabad"],
    "marks": [81, 94, 67],
})
print(df[["name", "marks"]])
print(df.loc[1, "city"])
print(df.iloc[2, 0])
Output
name  marks
0    Ali     81
1   Sara     94
2  Usman     67
Karachi
Usman
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Pandas Selecting Data Practice Exercises

Check what you learned in “Pandas Selecting Data”.

/ 3 solved
Fill in the blank

Select by row label and column name.

df.[1, "name"]
✓ Correct! Not quite. Try again.
Multiple choice

Which accessor selects by integer position?

✓ Correct! Not quite. Try again.
Code challenge

Print the name in the third row (position 2) using iloc.

Write the code in the live editor. Your answer is checked automatically.

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Pandas Filtering & Sorting

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