Python Pandas loc Function

last modified February 25, 2025

Pandas is a powerful Python library for data manipulation. The loc function is used for label-based indexing, allowing you to select and manipulate data in DataFrames. This tutorial covers how to use loc with practical examples.

The loc function is versatile and supports selecting rows, columns, and specific cells. It is essential for working with labeled data in Pandas.

Selecting Rows by Label

This example shows how to select rows using the loc function.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_row = df.loc['b']
print(selected_row)

The loc['b'] selects the row with the label 'b'. This is useful for accessing specific rows in a DataFrame.

Selecting Columns by Label

This example demonstrates selecting columns using the loc function.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_column = df.loc[:, 'Name']
print(selected_column)

The loc[:, 'Name'] selects the 'Name' column for all rows. This is useful for accessing specific columns.

Selecting Specific Cells

This example shows how to select specific cells using the loc function.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_cell = df.loc['b', 'Age']
print(selected_cell)

The loc['b', 'Age'] selects the cell at row 'b' and column 'Age'. This is useful for accessing individual values.

Selecting Multiple Rows and Columns

This example demonstrates selecting multiple rows and columns using loc.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_data = df.loc[['a', 'c'], ['Name', 'City']]
print(selected_data)

The loc[['a', 'c'], ['Name', 'City']] selects rows 'a' and 'c' and columns 'Name' and 'City'. This is useful for extracting subsets of data.

Conditional Selection with loc

This example shows how to use loc for conditional selection.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_data = df.loc[df['Age'] > 30]
print(selected_data)

The loc[df['Age'] > 30] selects rows where the 'Age' column is greater than 30. This is useful for filtering data based on conditions.

Updating Data with loc

This example demonstrates updating data using the loc function.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
df.loc['b', 'Age'] = 31
print(df)

The loc['b', 'Age'] = 31 updates the 'Age' value for row 'b'. This is useful for modifying specific data points.

Selecting Rows with Slicing

This example shows how to use slicing with loc to select rows.

import pandas as pd
data = {
 'Name': ['Alice', 'Bob', 'Charlie'],
 'Age': [25, 30, 35],
 'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data, index=['a', 'b', 'c'])
selected_data = df.loc['a':'c']
print(selected_data)

The loc['a':'c'] selects rows from 'a' to 'c'. This is useful for selecting a range of rows.

Best Practices for Using loc

• Understand Labels: Ensure row and column labels are known before using loc.
• Use Conditional Selection: Leverage conditions for filtering data.
• Update Data Carefully: Use loc to update specific data points.
• Validate Results: Check selected or updated data for accuracy.

Source

Pandas loc Documentation

In this article, we have explored how to use the loc function in Pandas.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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