Pandas DataFrame t() and transpose() Method

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

• The Pandas library enables access to/from a DataFrame.
• The Xarray library works with labeled multi-dimensional arrays and advanced analytics.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install xarray

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to Install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import xarray

DataFrame t() & transpose()

The T or transpose() method switches (transposes) the index and columns.

The syntax for this method is as follows:

DataFrame.transpose(*args, copy=False)

For this example, the countries.csv file reads in.

💡 Note: Click here to download the CSV file. Move to the current working directory.

df = pd.read_csv('countries.csv').head(3)
print(df)

result1 = df.T
print(result1)

result2 = df.transpose()
print(result2)

• Line [1] reads in the top three (3) rows of the comma-delimited CSV file. The output saves to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses the T method to transpose the DataFrame. The output saves to result1.
• Line [4] outputs result1 to the terminal.
• Line [5] uses the transpose() method to transpose the DataFrame. The output saves to result2.
• Line [6] outputs result2 to the terminal.

Output

df

result1

result2

💡 Note: The output from result1 and result2 are identical.

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.