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Tutorial Reference

Python Pandas: How to Select a Subset of a DataFrame in Pandas

Learn how to efficiently extract columns, filter rows by conditions, and use loc[], iloc[], and boolean indexing to select subsets in Pandas DataFrames.

Selecting subsets of data is one of the most fundamental operations in data analysis. Whether you need to extract specific columns for a report, filter rows based on conditions, or isolate a precise block of data, Pandas provides powerful and flexible tools to accomplish this.

In this guide, you'll learn how to select subsets by columns, rows, or both, using methods like bracket notation, loc[], iloc[], and conditional filtering.

Setting Up the Sample Data

All examples use a sample dataset. You can follow along with any CSV file or create one:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

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

Output:

      Name  Age     Team    Salary  Experience
0    Alice   28   Lakers   8500000           4
1      Bob   35  Celtics  12000000          10
2  Charlie   42   Lakers  15000000          18
3    Diana   31     Heat   9500000           7
4      Eve   26  Celtics   7000000           3

Selecting Columns

Selecting a Single Column

Use bracket notation with the column name to extract a single column as a Series:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

ages = df['Age']
print(ages)
print(f"\nType: {type(ages)}")

Output:

0    28
1    35
2    42
3    31
4    26
Name: Age, dtype: int64

Type: <class 'pandas.core.series.Series'>

Selecting Multiple Columns

Pass a list of column names inside brackets to select multiple columns. The result is a DataFrame:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

subset = df[['Name', 'Age', 'Salary']]
print(subset)
print(f"\nType: {type(subset)}")

Output:

      Name  Age    Salary
0    Alice   28   8500000
1      Bob   35  12000000
2  Charlie   42  15000000
3    Diana   31   9500000
4      Eve   26   7000000

Type: <class 'pandas.core.frame.DataFrame'>

Selecting Rows by Condition

Single Condition

Use boolean indexing to filter rows where a condition is true:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

result = df[df['Age'] > 30]
print(result)

Output:

      Name  Age     Team    Salary  Experience
1      Bob   35  Celtics  12000000          10
2  Charlie   42   Lakers  15000000          18
3    Diana   31     Heat   9500000           7

Multiple Conditions

Combine conditions using & (AND) and | (OR). Each condition must be wrapped in parentheses:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

result = df[(df['Age'] > 30) & (df['Salary'] > 10000000)]
print(result)

Output:

      Name  Age     Team    Salary  Experience
1      Bob   35  Celtics  12000000          10
2  Charlie   42   Lakers  15000000          18
import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

result = df[(df['Team'] == 'Lakers') | (df['Age'] < 28)]
print(result)

Output:

      Name  Age     Team    Salary  Experience
0    Alice   28   Lakers   8500000           4
2  Charlie   42   Lakers  15000000          18
4      Eve   26  Celtics   7000000           3

Using isin() for Multiple Values

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

teams = ['Lakers', 'Heat']
result = df[df['Team'].isin(teams)]
print(result)

Output:

      Name  Age    Team    Salary  Experience
0    Alice   28  Lakers   8500000           4
2  Charlie   42  Lakers  15000000          18
3    Diana   31    Heat   9500000           7

Selecting Rows and Columns Together

Using loc[] (Label-Based Selection)

loc[] selects data by labels - column names and row conditions or index labels:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

result = df.loc[df['Age'] > 30, 'Name']
print(result)

Output:

1        Bob
2    Charlie
3      Diana
Name: Name, dtype: object
import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)


result = df.loc[df['Team'] == 'Lakers', ['Name', 'Salary']]
print(result)

Output:

      Name    Salary
0    Alice   8500000
2  Charlie  15000000

Using iloc[] (Position-Based Selection)

iloc[] selects data by integer positions - row and column indices:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

result = df.iloc[:3, :2]
print(result)

Output:

      Name  Age
0    Alice   28
1      Bob   35
2  Charlie   42
import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)


result = df.iloc[[0, 2, 4], [0, 3]]
print(result)

Output:

      Name    Salary
0    Alice   8500000
2  Charlie  15000000
4      Eve   7000000

Quick Subset Methods

head() - First N Rows

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

print(df.head(3))

Output:

      Name  Age     Team    Salary  Experience
0    Alice   28   Lakers   8500000           4
1      Bob   35  Celtics  12000000          10
2  Charlie   42   Lakers  15000000          18

tail() - Last N Rows

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

print(df.tail(2))

Output:

    Name  Age     Team   Salary  Experience
3  Diana   31     Heat  9500000           7
4    Eve   26  Celtics  7000000           3

sample() - Random Rows

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

print(df.sample(3, random_state=42))

Output:

      Name  Age     Team    Salary  Experience
1      Bob   35  Celtics  12000000          10
4      Eve   26  Celtics   7000000           3
2  Charlie   42   Lakers  15000000          18

nlargest() and nsmallest() - Top/Bottom by Value

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

print(df.nlargest(3, 'Salary'))

Output:

      Name  Age     Team    Salary  Experience
2  Charlie   42   Lakers  15000000          18
1      Bob   35  Celtics  12000000          10
3    Diana   31     Heat   9500000           7
import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

print(df.nsmallest(2, 'Age'))

Output:

    Name  Age     Team   Salary  Experience
4    Eve   26  Celtics  7000000           3
0  Alice   28   Lakers  8500000           4

Using query() for Readable Filtering

The query() method lets you write filter conditions as strings, which can be more readable for complex selections:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)


result = df.query('Age > 30 and Team == "Lakers"')
print(result)

Output:

      Name  Age    Team    Salary  Experience
2  Charlie   42  Lakers  15000000          18

You can reference variables with @:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [28, 35, 42, 31, 26],
    'Team': ['Lakers', 'Celtics', 'Lakers', 'Heat', 'Celtics'],
    'Salary': [8500000, 12000000, 15000000, 9500000, 7000000],
    'Experience': [4, 10, 18, 7, 3]
}

df = pd.DataFrame(data)

min_salary = 9000000
result = df.query('Salary >= @min_salary')
print(result)

Output:

      Name  Age     Team    Salary  Experience
1      Bob   35  Celtics  12000000          10
2  Charlie   42   Lakers  15000000          18
3    Diana   31     Heat   9500000           7

Summary

Task Method Example
Select one column df['col'] df['Age']
Select multiple columns df[['col1', 'col2']] df[['Name', 'Age']]
Filter rows by condition df[condition] df[df['Age'] > 30]
Multiple conditions df[(cond1) & (cond2)] df[(df['Age'] > 30) & (df['Salary'] > 1e7)]
Rows + columns by label df.loc[condition, cols] df.loc[df['Age'] > 30, ['Name', 'Salary']]
Rows + columns by position df.iloc[rows, cols] df.iloc[:3, :2]
Match from a list df[df['col'].isin(list)] df[df['Team'].isin(['Lakers', 'Heat'])]
String-based query df.query('expression') df.query('Age > 30')
First N rows df.head(n) df.head(10)
Last N rows df.tail(n) df.tail(5)
Random rows df.sample(n) df.sample(3)
Top N by value df.nlargest(n, 'col') df.nlargest(3, 'Salary')

Pandas offers multiple ways to select subsets of a DataFrame.

  • Use bracket notation for quick column or row filtering.
  • Use loc[] for label-based selection combining rows and columns
  • Use iloc[] for integer-position-based selection
  • Use query() for readable string-based filtering.

Choose the method that makes your code clearest for the task at hand.

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