Filtering

Pandas Basics

1 min read

Published Sep 29 2025, updated Oct 24 2025

PandasPython

Filtering means selecting rows based on conditions applied to columns.

Sample data:

import pandas as pd

df = pd.DataFrame({

"Name": ["Alice", "Bob", "Charlie", "David"],

"Age": [25, 30, 35, 40],

"Salary": [50000, 60000, 75000, 80000]

})

Single Condition

Using the boolean indexing, so what its actually passing in from the condition is the array of True/False values and only displaying the ones where the value equates to True.

# Filter rows where Age > 30

filtered_df = df[df["Age"] > 30]

print(filtered_df)

Output:

Name Age Salary

2 Charlie 35 75000

3 David 40 80000

Multiple Conditions (AND / OR)

AND: &
OR: |
Brackets required around each condition

# Age > 30 AND Salary > 70000

df[(df["Age"] > 30) & (df["Salary"] > 70000)]

# Age < 35 OR Salary < 60000

df[(df["Age"] < 35) | (df["Salary"] < 60000)]

Using .query()

Cleaner syntax for filtering with column names directly.

df.query("Age > 30 & Salary > 70000")

Explanation:

Useful when you have multiple conditions.
You can use brackets to make more readable.
Use & for AND, | for OR, ~ for NOT.
Department == 'IT' : Department is IT.
Salary <= @max_salary : You can pass Python variables using the @ symbol, in this example the variable is max_salary.
Column names must be valid Python identifiers (no spaces, no punctuation).
- Workaround: rename columns or use backticks: df.query("`Column Name` > 10")
Only for row filtering, not for column selection.