Pandas DataFrame take() Method

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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 take()

The take() method returns the elements (data) across the selected axis. The indexing performs on the actual position of the DataFrame element.

🛑 Note: This method has been deprecated (since version 1.0.0).

The syntax for this method is as follows:

DataFrame.take(indices, axis=0, is_copy=None, **kwargs)
indicesList (array) of integers that specify locations to take.
axisIf zero (0) or index is selected, apply to each column. Default 0.
If one (1) apply to each row.
is_copyAs of pandas v1.0, this parameter always returns a copy.
**kwargsTo be compatible with numpy.take(), the take() method does not affect the output.

For this example, the finxters.csv data saves to a DataFrame to manipulate the data.

df   = pd.read_csv('finxters.csv')
result = df.take([30, 31], axis=0)
  • Line [1] reads in the comma-separated CSV file and saves it to df.
  • Line [2] takes the 30th and 31st row of the CSV file and saves it to the result variable.
  • Line [3] outputs the result to the terminal.


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.