Pandas DataFrame Transpose

Transposing a Pandas DataFrame involves swapping its rows with columns and vice versa, effectively rotating the DataFrame. This guide covers the syntax, example, output, explanation, use cases, important points, and a summary of transposing a Pandas DataFrame.

Syntax

import pandas as pd

# Transpose a DataFrame
transposed_df = original_df.T

• original_df: The original DataFrame that you want to transpose.

Example

import pandas as pd

# Creating a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'San Francisco', 'Los Angeles']}

df = pd.DataFrame(data)

# Transposing the DataFrame
transposed_df = df.T

# Displaying the transposed DataFrame
print(transposed_df)

Output

         0            1              2
Name   Alice          Bob        Charlie
Age       25           30             35
City  New York  San Francisco  Los Angeles

Explanation

• The .T attribute is used to transpose the original DataFrame, swapping rows and columns.
• Column labels become index labels, and vice versa.

Use

• Transposing is useful when you want to switch between viewing data by rows and columns.
• It can be helpful in certain data analysis and visualization scenarios.

Important Points

• Transposing does not modify the original DataFrame; it returns a new transposed DataFrame.
• Be cautious when transposing large datasets, as it creates a copy of the data.

Summary

Transposing a Pandas DataFrame is a simple operation that provides a different perspective on the data. It can be particularly useful when you want to inspect or present data in a different format. Keep in mind that transposing creates a new DataFrame, and the original data structure remains unchanged.