assignment pandas python

pandas.DataFrame.assign #

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

A new DataFrame with the new columns in addition to all the existing columns.

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Where the value is a callable, evaluated on df :

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries

Pandas DataFrame assign() Method | Create new Columns in DataFrame

• Create a column using for loop in Pandas Dataframe
• Change Data Type for one or more columns in Pandas Dataframe
• Create a new column in Pandas DataFrame based on the existing columns
• Pandas DataFrame to_dict() Method | Convert DataFrame to Dictionary
• Create a Pandas DataFrame from List of Dicts
• How to Delete a column from Pandas DataFrame
• How to Find & Drop duplicate columns in a Pandas DataFrame?
• Adding new column to existing DataFrame in Pandas
• Concatenate two columns of Pandas dataframe
• Count number of columns of a Pandas DataFrame
• Create a Pandas DataFrame from Lists
• Create a list from rows in Pandas dataframe
• Convert a Dataframe Column to Integer in Pandas
• Add multiple columns to dataframe in Pandas
• Convert given Pandas series into a dataframe with its index as another column on the dataframe
• How to Concatenate Column Values in Pandas DataFrame?
• How to add column from another DataFrame in Pandas ?
• How to drop one or multiple columns in Pandas Dataframe
• Create empty dataframe in Pandas

Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, making importing and analyzing data much easier.

The Dataframe.assign() method assigns new columns to a DataFrame , returning a new object (a copy) with the new columns added to the original ones. 

Existing columns that are re-assigned will be overwritten . The length of the newly assigned column must match the number of rows in the DataFrame.

Syntax: DataFrame.assign(**kwargs)  Parameters kwargs : keywords are the column names. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas don’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.  Returns: A new DataFrame with the new columns in addition to all the existing columns.

Let’s look at some Python programs and learn how to use the assign() method of the Pandas library to create new columns in DataFrame with these examples.

Assign a new column called Revised_Salary with a 10% increment of the original Salary.

Assigning more than one column at a time

Please Login to comment...

Similar reads.

• AI-ML-DS With Python
• Python pandas-dataFrame
• Python pandas-dataFrame-methods
• Python-pandas
• Technical Scripter

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Statistics Made Easy

How to Use the assign() Method in Pandas (With Examples)

The assign() method can be used to add new columns to a pandas DataFrame.

This method uses the following basic syntax:

It’s important to note that this method will only output the new DataFrame to the console, but it won’t actually modify the original DataFrame.

To modify the original DataFrame, you would need to store the results of the assign() method in a new variable.

The following examples show how to use the assign() method in different ways with the following pandas DataFrame:

Example 1: Assign New Variable to DataFrame

The following code shows how to use the assign() method to add a new variable to the DataFrame called points2 whose values are equal to the values in the points column multiplied by two:

Note that this assign() method doesn’t change the original DataFrame.

If we print the original DataFrame, we’ll see that it remains unchanged:

To save the results of the assign() method, we can store the results in a new DataFrame:

The new DataFrame called df_new now contains the points2 column that we created.

Example 2: Assign Multiple New Variables to DataFrame

The following code shows how to use the assign() method to add three new variables to the DataFrame:

Notice that three new columns have been added to the DataFrame.

Note : You can find the complete documentation for the pandas assign() method here .

Additional Resources

The following tutorials explain how to use other common functions in pandas:

How to Use describe() Function in Pandas How to Use idxmax() Function in Pandas How to Apply a Function to Selected Columns in Pandas

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Join the Statology Community

Sign up to receive Statology's exclusive study resource: 100 practice problems with step-by-step solutions. Plus, get our latest insights, tutorials, and data analysis tips straight to your inbox!

By subscribing you accept Statology's Privacy Policy.

• Stack Overflow Public questions & answers
• Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers
• Talent Build your employer brand
• Advertising Reach developers & technologists worldwide
• Labs The future of collective knowledge sharing
• About the company

Collectives™ on Stack Overflow

Find centralized, trusted content and collaborate around the technologies you use most.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

Get early access and see previews of new features.

vectorize conditional assignment in pandas dataframe

If I have a dataframe df with column x and want to create column y based on values of x using this in pseudo code:

How would I achieve this? I assume np.where is the best way to do this but not sure how to code it correctly.

• vectorization

• 2 Note, there is going to be an additional way to do this with the assign() method in pandas 16.0 (due any day now?) similar to dplyr mutate: pandas-docs.github.io/pandas-docs-travis/… –  JohnE Mar 6, 2015 at 13:50
• 3 See this answer using np.where for two choices and np. select for more choices. –  Paul Rougieux May 14, 2019 at 10:01

5 Answers 5

One simple method would be to assign the default value first and then perform 2 loc calls:

If you wanted to use np.where then you could do it with a nested np.where :

So here we define the first condition as where x is less than -2, return 1, then we have another np.where which tests the other condition where x is greater than 2 and returns -1, otherwise return 0

So for this sample dataset the np.where method is twice as fast

Use np.select for multiple conditions

np.select(condlist, choicelist, default=0) Return elements in choicelist depending on the corresponding condition in condlist . The default element is used when all conditions evaluate to False .

np.select is much more readable than a nested np.where but just as fast:

df = pd.DataFrame({'x': np.random.randint(-5, 5, size=n)})

This is a good use case for pd.cut where you define ranges and based on those ranges you can assign labels :

• 2 I like this answer, but it is slower than the np.where option, based on %timeit on my machine –  Josh Friedlander Apr 22, 2021 at 12:41

set fixed value to 'c2' where the condition is met

You can do it easily using the index and 2 loc calls:

• 1 Isn't this just a more verbose and probably slower way to write the existing accepted answer from 3 years ago ? There's no need for another layer of .loc calls which then need to be .index ed. –  ggorlen Jan 28, 2023 at 21:40
• Because it works, is the way I use it, is updated, probably easier to understand. –  Alexander Martins Jan 30, 2023 at 13:08

Your Answer

Reminder: Answers generated by artificial intelligence tools are not allowed on Stack Overflow. Learn more

Sign up or log in

Post as a guest.

Required, but never shown

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy .

Not the answer you're looking for? Browse other questions tagged python pandas dataframe numpy vectorization or ask your own question .

• Featured on Meta
• The 2024 Developer Survey Is Live
• The return of Staging Ground to Stack Overflow
• The [tax] tag is being burninated
• Policy: Generative AI (e.g., ChatGPT) is banned

Hot Network Questions

• How can I use a transistor to control the segments on this 7-segment display?
• Universal PCB enclosure: what are these cylinders with holes for?
• Is it rational for heterosexuals to be proud that they were born heterosexual?
• Group with a translation invariant ultrafilter
• A particular infinite sum
• What is the meaning of the 'ride out the clock'?
• My players think they found a loophole that gives them infinite poison and XP. How can I add the proper challenges to slow them down?
• Can my Battle Smith Artificer attack twice with his musket?
• Is it allowed to use patents for new inventions?
• How to negotiate such toxic competitiveness during my masters studies
• Geometric Brownian Motion as the limit of a Binomial Tree?
• Has ever a country by its own volition refused to join United Nations, or those which havent joined it's because they aren't recognized as such by UN?
• Strict toposes as a finite limit theory
• Find characters common among all strings
• What's the difference between cryogenic and Liquid propellant?
• How to use Sum properly to avoid problems when the summating limits are undefined?
• Sum of square roots (as an algebraic number)
• is_decimal Function Implementation in C++
• Movie I saw in the 80s where a substance oozed off of movie stairs leaving a wet cat behind
• I'm looking for a series where there was a civilization in the Mediterranean basin, which got destroyed by the Atlantic breaking in
• How was damno derived from damnum?
• Selecting an opamp for a voltage follower circuit using a LTspice simulation
• Are there any jobs that are forbidden by law to convicted felons?
• Problems with coloured tables with \multirow and \multicolumn and text-wrapping for table with a lot of text. Getting blank, white areas

Learn Python practically and Get Certified .

Popular Tutorials

Popular examples, reference materials, learn python interactively, pandas assign().

The assign() method in Pandas is used to create a new column in a DataFrame or modify an existing one.

assign() Syntax

The syntax of the assign() method in Pandas is:

assign() Argument

The assign() method takes the following argument:

• **kwargs : the column names and their corresponding values or functions.

assign() Return Value

The assign() method returns a new DataFrame with the assigned columns. The original DataFrame remains unchanged.

Example 1: Basic Column Assignment

In this example, we assigned column B to df and displayed the resulting DataFrame.

Example 2: Assignment Using Functions

We can assign columns based on the values in the existing DataFrame using functions.

In this example, we assigned values to the new column B that are double the values in column A using lambda function.

Example 3: Multiple Column Assignments

We can assign multiple columns at once using the assign() method.

Example 4: Chaining Assignments

We can chain the assign() method to assign multiple columns.

In this example, we first assigned column B . In the next assign() call, we used the newly created B and existing A to assign column C .

Assignment 1 - Python Basics Practice

Data analysis with python: zero to pandas.

In this assignment, you'll get to apply and practice the following concepts covered during the first lesson :

• Solve word problems using variables & arithmetic operations
• Manipulate data types using methods & operators
• Use branching and iterations to translate ideas into code
• Explore the documentation and get help from the community