How to Select Rows with Multiple Conditions in pandas

To select rows with multiple conditions in pandas, use the DataFrame.loc accessor combined with conditions inside parentheses and joined by logical operators like & (and) or | (or). Each condition must be enclosed in parentheses to ensure correct evaluation.

Syntax

Use df.loc[condition] where condition combines multiple boolean expressions with & (and) or | (or). Each condition must be wrapped in parentheses.

• df: your DataFrame
• condition1, condition2: boolean expressions like df['col'] > 5
• &: logical AND
• |: logical OR

python

df.loc[(condition1) & (condition2)]  # AND condition

df.loc[(condition1) | (condition2)]  # OR condition

Example

This example shows how to select rows where the 'age' is greater than 25 and 'score' is at least 80.

python

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [24, 30, 22, 35],
        'score': [85, 78, 90, 88]}

df = pd.DataFrame(data)

# Select rows where age > 25 AND score >= 80
filtered_df = df.loc[(df['age'] > 25) & (df['score'] >= 80)]

print(filtered_df)

Output

    name  age  score
3  David   35     88

Common Pitfalls

Common mistakes include forgetting to wrap each condition in parentheses or using and/or instead of &/|. This causes errors or unexpected results.

Wrong example:

df.loc[(df['age'] > 25) and (df['score'] >= 80)]

Right example:

df.loc[(df['age'] > 25) & (df['score'] >= 80)]

python

import pandas as pd

data = {'age': [20, 30], 'score': [90, 85]}
df = pd.DataFrame(data)

# Wrong: causes error
# df.loc[(df['age'] > 25) and (df['score'] >= 80)]

# Correct:
filtered = df.loc[(df['age'] > 25) & (df['score'] >= 80)]
print(filtered)

Output

   age  score
1   30     85

Quick Reference

Operator	Meaning	Example
&	Logical AND	df.loc[(df['A'] > 5) & (df['B'] < 10)]
|	Logical OR	df.loc[(df['A'] > 5) | (df['B'] < 10)]
~	Logical NOT	df.loc[~(df['A'] > 5)]

Key Takeaways

Use parentheses around each condition when combining multiple conditions in pandas.

Use & for AND and | for OR between conditions, not and/or keywords.

Use df.loc[...] to filter rows based on conditions.

Logical NOT can be done with ~ before a condition.

Incorrect syntax causes errors or wrong filtering results.