Boolean Indexing

Boolean indexing is how you ask a DataFrame a question and keep only the rows that answer yes. It's the heart of real data analysis — "show me everyone who…".

1Filter by a condition

A comparison like df['IP Marks'] > 85 produces a True/False for every row. Put that back inside df[...] and only the True rows survive. Slide the threshold:

Boolean filtering

A condition turns into a True/False test for every row. Feed it back into df[...] and only the True rows survive.

Keep rows where IP Marks >85

df[df['IP Marks'] > 85]

	Name	English	IP	Maths
ID 1	Rinku	65	89	72
ID 2	Ritu	77	83	69
ID 3	Pankaj	72	95	87
ID 4	Ajay	81	99	94
ID 5	Aditya	92	87	96

TrueFalseTrueTrueTrue

4 of 5 rows have IP Marks above 85. Only those come back — the True ones.

2One condition

df_filter.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

# Step 1: the condition -> a True/False Series
print(df['IP Marks'] > 88)

# Step 2: use it to keep only the True rows
print("\n--- students with IP Marks > 88 ---")
print(df[df['IP Marks'] > 88])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

# Step 1: the condition -> a True/False Series
print(df['IP Marks'] > 88)

# Step 2: use it to keep only the True rows
print("\n--- students with IP Marks > 88 ---")
print(df[df['IP Marks'] > 88])

3Combining conditions with & and |

Join conditions with & (and) or | (or). Each condition must be wrapped in its own parentheses — this trips up almost everyone.

df_filter_combined.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

# Maths > 75 AND English > 70 — note the ( ) around each part!
print(df[(df['Maths Marks'] > 75) & (df['English Marks'] > 70)])

# Name is Rinku OR Ajay
print("\n--- Rinku or Ajay ---")
print(df[(df['Name'] == 'Rinku') | (df['Name'] == 'Ajay')])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

# Maths > 75 AND English > 70 — note the ( ) around each part!
print(df[(df['Maths Marks'] > 75) & (df['English Marks'] > 70)])

# Name is Rinku OR Ajay
print("\n--- Rinku or Ajay ---")
print(df[(df['Name'] == 'Rinku') | (df['Name'] == 'Ajay')])

Watch Out

Use & and |, not the Python words and/or — and wrap every condition in parentheses. df[df.a > 1 & df.b < 2] without brackets is a common bug.

Key Takeaway

Boolean indexing = build a True/False mask from a condition, then df[mask] keeps the True rows. Combine masks with &/| and parenthesise each part.

Quick Check

What does df[df['Maths Marks'] > 90] return?

Quick Check

Which correctly filters rows where a > 1 AND b < 5?

← Previous

✏️ Deleting & Renaming

Iterating over a DataFrame 🔁