Pandas DataFrame stack() Method

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Preparation

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

  • The Pandas library enables access to/from a DataFrame.
  • The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

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 numpy

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 libraries.


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 numpy as np 

DataFrame stack()

The stack() method returns a re-shaped Multi-Level index DataFrame/Series containing at minimum one (1) or more inner levels. A pivot occurs on the new levels using the columns of the DataFrame/Series.

💡 Note: If a single level, the output returns as a Series. If multi-level, the new level(s) are retrieved from the said levels and return a DataFrame.

The syntax for this method is as follows:

DataFrame.stack(level=- 1, dropna=True)
levelThis parameter is the level(s) to stack on the selected axis. Levels can be a string, integer, or list. By default, -1 (last level).
dropnaThis parameter determines if rows containing missing values drop. True, by default.

We have two (2) students with relevant details that save to a DataFrame. The code below displays the original DataFrame and the DataFrame using the stack() method.

df = pd.DataFrame([[8, 7], [7, 5]],
                  index=['Micah', 'Philip'],
                  columns=['Age', 'Grade'])
print(df)

result = df.stack()
print(result)
  • Line [1] creates a DataFrame with index labels and columns specified. This output saves to df.
  • Line [2] outputs the DataFrame to the terminal.
  • Line [3] stacks the DataFrame and saves the output to result.
  • Line [4] outputs the result to the terminal (stacked format).

Output

df

 AgeGrade
Micah87
Philip75

result

MicahAge8
 Grade7
PhilipAge7
 Grade5
dtype: int64  

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.