Pandas DataFrame assign() Method – Be on the Right Side of Change (original) (raw)


Preparation

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

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.


FeFeel 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


The assign() method adds (assigns) column(s) to an existing DataFrame.

The syntax for this method is as follows:

DataFrame.assign(**kwargs)

Parameter Description
**kwargs The column(s) name(s) is assigned as keywords.

For this example, a new column (accessed) adds to the DataFrame df_custs. The column fills in with random integer values. In a real-life, this column would keep track of how many times the user logged in to their account.

df_custs = pd.DataFrame({('jkende', 'Vzs*@4:kNq%)'), ('sarahJ', '{M$*3zB~-a-W'), ('AmyKerr', '*7#<bSt?Y_Z<')}, columns=['username', 'password'], index=['user-a', 'user-b', 'user-c'])

result = df_custs.assign(accessed=pd.Series(np.random.randint(0,500,size=3)).values) print(result)

Output

df_custs

| | username | password | accessed | | | ---------- | -------- | -------------- | --- | | user-a | sarahJ | {M$*3zB~-a-W | 155 | | user-b | jkende | Vzs*@4:kNq%) | 472 | | user-c | AmyKerr | *7#<bSt?Y_Z< | 354 |

💡 Note: The order of the records changes.

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.