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Python Pandas: How to Select Rows from a DataFrame Based on Column Values in Python

Learn how to filter Pandas DataFrames by column values using boolean indexing, loc, query, and isin methods with practical Python examples.

When working with tabular data in Python, one of the most common tasks is filtering rows based on the values in one or more columns. Whether you need to isolate records that meet a specific threshold, match a category, or satisfy a combination of conditions, Pandas offers several powerful and expressive ways to accomplish this.

This guide walks you through the most practical methods for selecting rows from a Pandas DataFrame based on column values, from simple boolean indexing to SQL-like queries, complete with examples and outputs.

Setting Up the Sample DataFrame

All examples in this article use the following DataFrame:

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}

df = pd.DataFrame(data)
print(df)

Output:

      Name  Age         City
0    Alice   24     New York
1      Bob   27  Los Angeles
2  Charlie   22      Chicago
3    David   32      Houston
4      Eva   29     New York

Boolean Indexing (The Most Common Approach)

Boolean indexing is the most widely used technique for filtering rows in Pandas. You pass a condition - which evaluates to a Series of True/False values - directly into the bracket operator of the DataFrame.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

selected = df[df["Age"] > 25]
print(selected)

Output:

    Name  Age         City
1    Bob   27  Los Angeles
3  David   32      Houston
4    Eva   29     New York

Every row where the condition df["Age"] > 25 is True is included in the result.

Combining Multiple Conditions

You can chain multiple conditions using the bitwise operators & (AND), | (OR), and ~ (NOT). Each individual condition must be wrapped in parentheses; otherwise Python's operator precedence will raise an error.

The correct way is to wrap each condition in parentheses:

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df[(df["Age"] > 25) & (df["City"] == "New York")]
print(result)

Output:

  Name  Age      City
4  Eva   29  New York

You can also use | for OR logic:

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df[(df["Age"] < 24) | (df["City"] == "Houston")]
print(result)

Output:

      Name  Age     City
2  Charlie   22  Chicago
3    David   32  Houston

Using loc for Conditional Row Selection

The loc accessor selects rows by label or boolean array. It works just like bracket-based boolean indexing but offers an important advantage: you can simultaneously filter rows and select specific columns.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df.loc[df["City"] == "Chicago"]
print(result)

Output:

      Name  Age     City
2  Charlie   22  Chicago

Filtering Rows and Selecting Columns at the Same Time

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df.loc[df["Age"] >= 27, ["Name", "City"]]
print(result)

Output:

    Name         City
1    Bob  Los Angeles
3  David      Houston
4    Eva     New York

Using the query Method for SQL-Like Filtering

The query method lets you write filter conditions as a string expression, which can feel more natural - especially if you have a SQL background. It also tends to produce cleaner code when conditions are complex.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df.query("Age < 30")
print(result)

Output:

      Name  Age         City
0    Alice   24     New York
1      Bob   27  Los Angeles
2  Charlie   22      Chicago
4      Eva   29     New York

Referencing External Variables with @

You can reference Python variables inside a query string by prefixing them with @:

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)


min_age = 25
target_city = "New York"

result = df.query("Age > @min_age and City == @target_city")
print(result)

Output:

  Name  Age      City
4  Eva   29  New York

Using isin for Membership-Based Selection

When you need to check whether column values belong to a set of allowed values, the isin method is the cleanest option. It replaces long chains of OR conditions with a single, readable call.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df[df["City"].isin(["New York", "Chicago"])]
print(result)

Output:

      Name  Age      City
0    Alice   24  New York
2  Charlie   22   Chicago
4      Eva   29  New York

To exclude certain values, negate the condition with ~:

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David", "Eva"],
    "Age": [24, 27, 22, 32, 29],
    "City": ["New York", "Los Angeles", "Chicago", "Houston", "New York"],
}
df = pd.DataFrame(data)

result = df[~df["City"].isin(["Los Angeles", "Houston"])]
print(result)

Output:

      Name  Age      City
0    Alice   24  New York
2  Charlie   22   Chicago
4      Eva   29  New York

Quick Comparison of Methods

Method Best For Supports Column Selection String Syntax
df[condition] Simple, fast filtering No No
df.loc[condition] Filtering + column selection Yes No
df.query("...") Complex or SQL-like conditions No (directly) Yes
df[df["col"].isin([...])] Matching against a list of values No No

Summary

Selecting rows based on column values is a foundational skill for any data analysis workflow in Python. Here is a quick recap of when to use each method:

  • Boolean indexing (df[condition]) - your go-to for straightforward filters.
  • loc - ideal when you need to filter rows and pick specific columns in one step.
  • query - great for readability, especially with multiple conditions or external variables.
  • isin - the best choice when checking membership against a list of values.

All four approaches are performant for typical dataset sizes. Choose the one that makes your code the most readable and maintainable for the task at hand.

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