assign name to column pandas

Getting started

> basic concepts, > installations, > dataframe, > data types, > cheat sheet, dataframe attributes, > multiindex, > read_csv(), > read_excel(), > read_xml(), > read_json(), > to_dict(), > to_json(), modify dataframe, > groupby(), string operation, > replace(), special operation, > data validation, > data cleaning, > duplicate, > time series, > pandas error, visualization, > dataisbeautiful, projects & challenges, > beginners, > data science projects, > newsletter, how to rename column in pandas.

In this short guide, I'll show you how to rename column names in Pandas DataFrame .

(1) rename single column

(2) rename multiple columns

(3) rename multi-index columns

(4) rename all columns

In the next sections, I'll review the steps to apply the above syntax in practice and a few exceptional cases.

Let's say that you have the following DataFrame with random numbers generated by:

If you like to understand more about how to create DataFrame with random numbers please check: How to Create a Pandas DataFrame of Random Integers

Step 1: Rename all column names in Pandas DataFrame

Column names in Pandas DataFrame can be accessed by attribute: .columns :

The same attribute can be used to rename all columns in Pandas.

Step 2: Rename specific column names in Pandas

If you like to rename specific columns in Pandas you can use method - .rename . Let's work with the first DataFrame with names - A, B etc.

To rename two columns - A, B to First, Second we can use the following code:

will result in:

Note : If any of the column names are missing they will be skipped without any error or warning because of default parameter errors='ignore' Note 2: Instead of syntax: df = df.rename(columns=column_map) you can use df.rename(columns=column_map, inplace=False)

Step 3: Rename column names in Pandas with lambda

Sometimes you may like to replace a character or apply other functions to DataFrame columns. In this example we will change all columns names from upper to lowercase :

the result will be:

This step is suitable for complex transformations and logic.

Step 4: Rename column names in Pandas with str methods

You can apply str methods to Pandas columns. For example we can add extra character for each column name with a regex:

Working with the original DataFrame will give us:

Step 5: Rename multi-level column names in DataFrame

Finally let's check how to rename columns when you have MultiIndex. Let's have a DataFrame like:

If we check the column names we will get:

Renaming of the MultiIndex columns can be done by:

and will result into:

• Notebook - pandas-rename-column-names.ipynb
• pandas.DataFrame.rename — pandas
• pandas.Series.str.replace
• pandas.DataFrame.columns

By using DataScientYst - Data Science Simplified , you agree to our Cookie Policy.

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

Published by Zach

Leave a reply cancel reply.

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

How to Use the Pandas Assign Method to Add New Variables

In this tutorial, I’ll explain how to use the Pandas assign method to add new variables to a Pandas dataframe.

In this tutorial, I’ll explain what the assign method does and how it works. I’ll explain the syntax, and I’ll show you step-by-step examples of how to use it.

If you need something specific, you can click on one of the following links and it will take you to the right section in the tutorial.

Table of Contents:

• Introduction to Pandas Assign
• The syntax of Pandas Assign
• Pandas Assign examples
• Pandas Assign FAQ

Having said that, if you really want to understand Pandas assign, I recommend that you read the whole article.

A quick introduction to Pandas Assign

So what does the assign method do?

Put simply, the assign method adds new variables to Pandas dataframes.

Quickly, I’ll explain that in a little more depth.

Pandas is a toolkit for working with data in Python

You’re probably aware of this, but just to clarify: Pandas is a toolkit for working with data in the Python programming language.

In Pandas, we typically work with a data structure called a dataframe.

A dataframe is a collection of data stored in a rows and column format .

Pandas gives us a toolkit for creating these Dataframes, and it also provides tools for modifying dataframes.

Pandas has tools for sorting dataframes , aggregating dataframes, reshaping dataframes, and a lot more.

And one of the most important things we need to be able to do, is add new columns to a dataframe .

Pandas Assign Adds New Columns to a Dataframe

The Pandas assign method enables us to add new columns to a dataframe.

We provide the input dataframe, tell assign how to calculate the new column, and it creates a new dataframe with the additional new column.

It’s fairly straightforward, but as the saying goes, the devil is in the details.

So with that said, let’s take a look at the syntax so we can see how the assign method works.

The syntax of the assign method

The syntax for the assign method is fairly simple.

You type the name of your dataframe, then a “dot”, and then type assign() .

Remember, the assign method is a Python method that’s associated with dataframe objects, so we can use so-called “dot syntax” to call the method.

Next, inside the parenthesis, we need to provide a “name value pair.”

What does that mean?

We simply provide the name of the new variable and the value that we want to assign to that variable. The value that we assign can be simple (like an integer constant), but it can also be a complicated value that we calculate.

I’ll show you examples of exactly how we use it in the examples section of this tutorial .

Syntax to add multiple variables to a dataframe

One quick note on the syntax:

If you want to add multiple variables, you can do this with a single call to the assign method.

Just type the name of your dataframe, call the method, and then provide the name-value pairs for each new variable, separated by commas.

Honestly, adding multiple variables to a Pandas dataframe is really easy. I’ll show you how in the examples section .

The Output of the Assign Method

Before we look at the examples, let’s quickly talk about the output of the assign method.

This is really important, so you need to pay attention …

The output of the assign method is a new dataframe .

Read that again. It’s really important.

So if you use the assign method, you need to save the output in some way, or else the output will go to the console (if you’re working in an IDE).

The implication of this, is that if you just run the method, your original dataframe will be left unchanged unless you store the output to the original name.

(You can obviously also store the output to a new name. This is safer, unless you’re positive that you want to overwrite your original data.)

Examples: how to add a column to a dataframe in Pandas

Ok. Now that I’ve explained how the syntax works, let’s take a look at some examples of how to use assign to add new variables to a dataframe.

• Create a new variable and assign a constant
• Add a variable that’s a computed value
• Add multiple variables to your dataframe
• Store the output of assign to a new name
• Assign a specific value conditionally, based on another column

Obviously, you can click on any of the above links, and it will take you to that example in the tutorial.

Run this code first

Before you run any of these examples, you need to do two things:

• import pandas
• create the dataframe we’ll use

Import Pandas

You can run this code to import Pandas:

Create DataFrame

Next, let’s create our dataframe.

We’ve called this DataFrame sales_data .

This dataframe contains mock sales data for 11 people and it has variables for both sales and expenses .

From here, we can use the assign() method to add some new variables.

EXAMPLE 1: Create a new variable and assign a constant

In this first example, we’re going to add a new variable to the datafame and assign a constant value for every row.

Let’s think about something specific.

Say that you’re working with this dataset, and all of these people work for the same company. You might have some other dataframes that have records for salespeople who work for different companies, but everyone in sales_data works for the same company.

What if we want to create a variable that contains the company name for the people in this dataframe?

We can do that with assign as follows:

Explanation

So what did we do in this example?

Here, we created a new variable called company .

For every row in the data, the value for the company variable is the same. The value is “Vandelay Industries.”

In technical terms, the value is a constant for every row. More specifically, it’s a string value.

Having said that, when we create variables with constant values, we can add string values like this example, but we can also assign a new variable with a constant numeric value. For example, try the code sales_data.assign(newvar = 1) .

EXAMPLE 2: Add a variable that’s a computed value

Here, we’re going to assign a new variable that’s a computed value.

Specifically, we’re going to create a new variable called profit that equals sales minus expenses. (Finance and accounting geeks will know that this is not a precise way to compute profit, but we’ll use this simplified calculation for purposes of example.)

Let’s run the code, and I’ll explain below.

Here, we created a new computed column called profit .

As you can see, profit is simply sales minus expenses .

Notice though, that when we reference the sales and expenses variables inside of assign() , we need to call them as sales_data.sales and sales_data.expenses .

Alternatively, we could call them as sales_data['sales'] and sales_data['expenses'] .

I prefer the former because they’re much easier to read, but you can choose.

EXAMPLE 3: Add multiple variables to your dataframe

In the previous two examples, we were adding only one new variable at a time.

Here in this example, we’ll add two variables at the same time.

We’re going to add the profit variable and the company variable.

Let’s take a look.

Here in this example, we added two variables at the same time: profit and company .

Notice that syntactically, I actually put the second variable on a new line of code. This is mostly for readability. If you want, you can keep all of your code on the same line, but I don’t necessarily recommend it. I personally think that your code is much easier to read and debug if each different variable assignment is on a separate line.

That said, the two new variable assignments must be separated by a comma. Here, the comma that separates the two variable assignments comes before the assignment of the company variable. This is important, so don’t forget the comma.

EXAMPLE 4: Store the output of assign to a new name

Finally, let’s do one more example.

Here, we’re going to store the output to a new name.

Notice that in the previous examples, the code did not modify the original dataframe.

When we use assign, it produces a new dataframe as an output and leaves your original dataframe unchanged. This is very important to remember! Many beginner data science students get frustrated when they first use this technique, because they can’t figure out why their dataframe stays the same, even after they run assign() . Always remember: assign produces a new dataframe.

Having said that, we can store the new output dataframe to a new name.

If we want, we can store it to a new name, like sales_data_revised .

Or, we can store it to the original dataframe name, sales_data , and overwrite the original!

So it is possible to directly modify your original dataframe, but you need to do it with an equal sign to store the output of the assign method.

Ok, with all that said, let’s look at an example.

Here, we’ll take the output of assign and store it to a new name called sales_data_revised .

Now, the new dataframe is stored in sales_data_revised .

Let’s print it out.

When we run the code in this example, assign() is creating a new dataframe with the newly assigned variables, profit and company .

But instead of letting that new output be passed to the console, we’re storing it with a new name so we can access it later.

Remember: assign produces a new dataframe as an output and leaves the original unchanged. If you want to store the output, you need to use the equal sign to pass the output to a new name.

How to Overwrite your Original Data

One last comment on this.

You can actually overwrite your original data directly. To do this, just run the assign method and pass the output to the original dataframe name, sales_data .

This is totally appropriate to do in some circumstances. Sometimes, you really do want to overwrite your data.

But be careful!

Test your code before you do this, otherwise you might overwrite your data with incorrect values!

EXAMPLE 5: Assign a specific value conditionally, based on another column

Ok, one more example to answer a new question in the comments section.

Here, we’re going to answer the question “What if the company name is different for each individual, how do you assign that?”

So, we’re going to assume that every person is from a different company, and we’re going to assign a specific company name based on the “name” of the sales agent.

To do this, we’re going to create a mapping from name to company name, and then create the new company variable based on that mapping.

Create Mapping from Name to Company

First, we’re going to create a mapping from the name variable to the company name we want to assign.

To do this, we’ll simply use a dictionary. The “keys” of the dictionary will be the names of our sales reps, and the corresponding “values” of our dictionary will be the company name we want to assign.

Assign Company Name

Next, we’ll use the map() method to map from the name variable to company name, and assign the output to a new variable called company .

Here, we’re using the Pandas series method, map() .

To do this, we’re retrieving the name variable and calling map() .

We’re sending the output of this operation to a variable called company , and this whole expression is happening inside of a call to Pandas assign.

The output creates a new variable called company , where the value of company is conditionally assigned according to the mapping we created in our dictionary, company_mapping .

Frequently Asked Questions about the Pandas Assign Method

Let’s very quickly address one common question about the Pandas assign method.

Question 1: Why is my dataframe unchanged, after using assign?

This is a very common question, and the answer is very straightforward.

As I mentioned several times in this tutorial, the assign method returns a new dataframe that contains the newly assigned variables, and it leaves your input dataframe unchanged.

If you want to overwrite your dataframe, and add the new variables, you need to take the output and use the equal sign to re-store the output into the original name.

So you need to set sales_data = sales_data.assign(...) , like this:

Keep in mind that this will overwrite your data! So you need to be very careful when you do this. Test your code and make sure that it’s working exactly as expected before you do this. If you don’t you might overwrite your original data with an incorrect dataset, and you’ll have to re-start your data retrieval and data wrangling from scratch. This is sometimes a huge pain in the a**, so be careful.

Alternatively, you can store the output of assign with a new name, like this:

Storing the output with a new name, like sales_data_revised , is safer because it doesn’t overwrite the original.

You may actually want to overwrite the original, just make sure that your code works before you do.

Leave your other questions in the comments below

Do you have other questions about the assign method?

Leave your questions in the comments section near the bottom of the page.

Discover how to master data wrangling with Pandas

This tutorial should give you a taste of how to use Pandas to manipulate your data, but there’s a lot more to learn.

If you really want to master data wrangling with Pandas, you should join our premium online course, Pandas Mastery .

Pandas Mastery is our online course that will teach you these critical data manipulation tools.

Inside the course, you’ll learn all of the essentials of data manipulation in pandas, like:

• adding new variables
• filtering data by logical conditions
• subsetting data
• working with Pandas indexes
• reshaping data
• and much more …

Additionally, you’ll discover our unique practice system that will enable you to memorize all of the syntax you learn.

And, it will only take a few weeks.

Find out more here:

Learn More About Pandas Mastery

Joshua Ebner

5 thoughts on “How to Use the Pandas Assign Method to Add New Variables”

There are alternatives to the apply method but none is as clean as using apply.

Thank you so much for the clear explanation.

How can I assign at a specific column index? Every time I use assign() is appends the new column at the end of the table. Is there a way to move that column around?

I’d recommend using .loc[] after using .assign().

Ideally, you can chain these together, like this:

Notice that here, I’m using a list of column name inside .loc[] to specify the order of the columns.

What if the company name is different for each individual, how do you assign that?

I just added a new example to explain how to do this.

Check out Example 5.

Leave a Comment Cancel reply

• TutorialKart
• SAP Tutorials
• Salesforce Admin
• Salesforce Developer
• Visualforce
• Informatica
• Kafka Tutorial
• Spark Tutorial
• Tomcat Tutorial
• Python Tkinter

Programming

• Bash Script
• Julia Tutorial
• CouchDB Tutorial
• MongoDB Tutorial
• PostgreSQL Tutorial
• Android Compose
• Flutter Tutorial
• Kotlin Android

Web & Server

• Selenium Java
• Pandas Tutorial
• Pandas - Create Empty DataFrame
• Pandas - Create DataFrame from Dictionary
• Pandas - Get Column Names of DataFrame
• Pandas - Set Column Names of DataFrame
• Pandas - Get Index of DataFrame
• Pandas - Set Index of DataFrame
• Pandas - Check if DataFrame is Empty
• ❯ Pandas Tutorial

How to Change Column Names of Pandas DataFrame?

Pandas dataframe – change column names.

You can access Pandas DataFrame columns using DataFrame.columns property. We can assign an array with new column names to the DataFrame.columns property.

Note : Length of new column names arrays should match number of columns in the DataFrame.

Example 1 – Change Column Names of Pandas DataFrame

In the following example, we take a DataFrame with some initial column names and change these column names to new values.

Python Example

Example – 2 [Negative Scenario] – Column Names Array Length not same as that of Columns in DataFrame

In this example, we shall assign DataFrame.columns an array that has length greater than that of columns in the DataFrame. As the lengths are not same, the program should throw ValueError.

We get a Value Error with the message that there is a length mismatch for old column names and new column namess.

In this Pandas Tutorial , we learned how to change the column names of a DataFrame.

Popular Courses by TutorialKart

App developement, web development, online tools.

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar

PythonForBeginners.com

Learn By Example

Pandas Assign New Columns to a DataFrame

Author: Aditya Raj Last Updated: March 8, 2023

Pandas dataframes are the data structures that we use to handle tabular data in python. This article discusses different ways to assign new columns to pandas dataframe using the assign() method.

The Pandas assign() Method

Pandas assign a column to a dataframe, assign a column based on another column, assign multiple columns to a dataframe.

The assign() method is used to assign new columns to a pandas dataframe. It has the following syntax.

In the above function, the column names and the values for the columns are passed as keyword arguments. Here, column names are the keywords, and list or series objects containing the data for the column are the corresponding values.

When we invoke the assign() method on a dataframe df, it takes column names and series objects as its input argument. After execution, the assign() method adds the specified column to the dataframe df and returns the modified dataframe. 

To assign a new column to the pandas dataframe using the assign() method, we will pass the column name as its input argument and the values in the column as the value assigned to the column name parameter.

After execution, the assign() method returns the modified dataframe. You can observe this in the following example.

In the above example, we created a column "Name" in the input dataframe using a list and the a ssign() method.

Instead of a list, we can also assign a pandas series to the column name parameter to create a new column in the dataframe as shown below.

In this example, we passed a series to the assign() method as an input argument instead of a list. However, you can observe that the output dataframe in this example is the same as the previous example.

We can also create a column based on another column in a pandas dataframe. For this, we will first create a series based on another column. Then, we can use the assign() method and pass the column name with the series as its input to assign the column to the pandas dataframe.

You can observe this in the following example.

In this example, we have created the GPI column using the "Maths" column. For this, we created a series by dividing the Maths column by 10. Then, we assigned the new series to the GPI keyword as an input argument in the assign() method. You can also use the pandas apply method to create a new series in this case.

To assign multiple columns to the pandas dataframe, you can use the assign() method as shown below.

In this example, we assigned two columns to the pandas dataframe using the assign() method. For this, we passed both the column names and their values as keyword arguments to the assign() method.

In this article, we have discussed different ways to assign columns to a pandas dataframe. To learn more about python programming, you can read this article on how to use the insert() method to insert a column into a dataframe . You might also like this article on how to convert epoch to datetime in python .

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!

Recommended Python Training

Course: Python 3 For Beginners

Over 15 hours of video content with guided instruction for beginners. Learn how to create real world applications and master the basics.

More Python Topics

• Change Password

Change column names in Pandas DataFrame | rename multiple column names in pandas

In this chapter of Pandas tutorial, we will learn about:

• change column names in pandas dataframe using rename method,
• rename multiple column names in pandas using rename method,
• how to change all column names in pandas dataframe  using columns method, 
• change multiple column names in pandas using set_axis method
• change column names in pandas using method str.replace (single, double or multiple)

In previous chapters, we have already explained about DataFrame and how to create one. Here, we will check one more example and then begin with change column names in pandas dataframe or rename column names in pandas dataframe and rename multiple column names in pandas using different functions.

What is DataFrame? DataFrame is an two dimensional data structure that will store data in two dimensional format. One dimension refers to a row and second dimension refers to a column, So It will store the data in rows and columns.

We can able to create this DataFrame using DataFrame() method . But this is available in pandas module, so we have to import pandas module.

Where, data is the input dataframe.  The data can be a dictionary that stores list of values with specified key.

In this example, we will create a dataframe with 4 rows and 4 columns with college data and assign index labels using index parameter.

Output : Given below is the output dataframe

Now, we will work on this dataframe in upcoming explanation.

Method 1 : change column names in pandas Using rename()

The function rename() is used to change single or multiple columns at a time.

It will take columns parameter with a dictionary and take new column names as values and old column names as keys. So based on the key – it will set the new column names.

So this function can easily help rename column names in pandas dataframe .

1.  dataframe is the input dataframe.

2.  old_column refers to the old column name

3.  new_column refers to the new column name

Example 1: change column names in pandas dataframe

In this example, we are going to change college_id column to college_ID

Output :Given below is output of rename column names in pandas dataframe  from  college_id  to  college_ID

Since we have already seen how to change column names in pandas dataframe  lets see how to change all column names in pandas dataframe , or change multiple column names in pandas .

Example 2 :  change multiple column names in pandas

In this example, we are going to  rename multiple column names in pandas . So here we will change :

• college_id column to college_ID ,
• college_name to name ,
• college_address to address and
• Total Staff to staff .

Given below is code snippet to how to change multiple column names in pandas :

Output : After change in multiple column names in pandas  for above example we obtain below:

We have used rename function to  change column names in pandas dataframe  or rename column names in pandas dataframe , and rename multiple column names in pandas .

Now lets see other way using columns method.

Method 2 :  change column names in pandas using columns

Here , we are going to use a list of columns and assign it to the columns method . So it will assign the new columns to the dataframe .

where, dataframe is the input dataframe.

Example :  change multiple column names in pandas

In this example, we are going to change college_id column to college_ID , college_name to name , college_address to address and Total Staff to staff .

Output: Given below is the result obtained after we rename multiple column names in pandas  as required.  

We have seen  how to change all column names in pandas dataframe  using columns. Lets move ahead to third method.

Method 3 :  change column names in pandas using set_axis()

Here , we are going to use a list of columns and assign it to the set_axis method. So it will assign the new columns to the dataframe.

1.      dataframe is the input dataframe.

2.      axis=1 represents column

Example :  rename multiple column names in pandas

In this example, we are going to change college_id column to college_ID ,  college_name to name , college_address to address and Total Staff to staff .

Lets see  how to change all column names in pandas dataframe using below code snippet:

Output :  change multiple column names in pandas is done and result is presented below:

Lets see the other method to  change column names in pandas dataframe .

Method 4 :   change column names in pandas using str.replace()

This method str.replace will replace each column name at a time using columns method

1.      data is the input dataframe

2.      old_column is the old column name

3.      new_column is the new column name

Example :  change multiple column names in pandas using str.replace

Output : We get following result after rename multiple column names in pandas as per above code:

As you can see, we can  change multiple column names in pandas or single column name using str.replace easily.

Conclusion:

In this article, we discussed about change column names in pandas dataframe \ rename column names in pandas dataframe and how to change all column names in pandas dataframe with four methods - rename() , set_axis() , str.replace() and columns methods .

Related Articles

You might be interested in.

• 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.

Naming returned columns in Pandas aggregate function? [duplicate]

I'm having trouble with Pandas' groupby functionality. I've read the documentation , but I can't see to figure out how to apply aggregate functions to multiple columns and have custom names for those columns.

This comes very close, but the data structure returned has nested column headings:

(ie. I want to take the mean and std of column2, but return those columns as "mean" and "std")

What am I missing?

• aggregate-functions

• hi @david_chouinard I have the same question actually. Would you think the proposed solution is still best as of today with pandas 17.1? –  ℕʘʘḆḽḘ Feb 2, 2016 at 18:07

6 Answers 6

For pandas >= 0.25.

The functionality to name returned aggregate columns has been reintroduced in the master branch and is targeted for pandas 0.25. The new syntax is .agg(new_col_name=('col_name', 'agg_func') . Detailed example from the PR linked above:

It will also be possible to use multiple lambda expressions with this syntax and the two-step rename syntax I suggested earlier (below) as per this PR . Again, copying from the example in the PR:

and then .rename() , or in one go:

For pandas < 0.25

The currently accepted answer by unutbu describes are great way of doing this in pandas versions <= 0.20. However, as of pandas 0.20, using this method raises a warning indicating that the syntax will not be available in future versions of pandas.

FutureWarning: using a dict on a Series for aggregation is deprecated and will be removed in a future version

DataFrames:

FutureWarning: using a dict with renaming is deprecated and will be removed in a future version

According to the pandas 0.20 changelog , the recommended way of renaming columns while aggregating is as follows.

Please see the 0.20 changelog for additional details.

Update 2017-01-03 in response to @JunkMechanic's comment.

With the old style dictionary syntax, it was possible to pass multiple lambda functions to .agg , since these would be renamed with the key in the passed dictionary:

Multiple functions can also be passed to a single column as a list:

However, this does not work with lambda functions, since they are anonymous and all return <lambda> , which causes a name collision:

To avoid the SpecificationError , named functions can be defined a priori instead of using lambda . Suitable function names also avoid calling .rename on the data frame afterwards. These functions can be passed with the same list syntax as above:

• 4 Thanks for the update. I use this pattern quite often: df = df.groupby('col_to_grpd_by').agg({'quantity': { 'mu': lambda series: stats.norm.fit(series)[0], 'sigma': lambda series: stats.norm.fit(series)[1], 'active': 'count', }}) . How would this be handled going forward. Only way I can think of is to define 2 separate functions that return the corresponding elements of the tuple from stats.norm.fit . Ignore the fact that I am using norm . It could have been a different distribution. –  JunkMechanic Jun 3, 2017 at 10:02
• @JunkMechanic I believe you need to define them first and then pass them as a list. I updated the answer with an example. –  joelostblom Jun 3, 2017 at 15:07
• 5 Why are they doing away with the .agg({'B': {'min': lambda x: x.min(), 'max': lambda x: x.max()}}) syntax? It seems very useful and less messy than having to define named functions if only used for this purpose. –  sheridp Jun 30, 2017 at 19:54
• @sheridp From the 0.20 changelog linked above: "However, .agg(..) can also accept a dict that allows ‘renaming’ of the result columns. This is a complicated and confusing syntax, as well as not consistent between Series and DataFrame. We are deprecating this ‘renaming’ functionaility." –  joelostblom Jun 30, 2017 at 23:32
• 1 @joelostblom - it looks like this option from your Update section: df.groupby('A').agg({'B': [lambda x: x.min(), lambda x: x.max()]}) now works by automatically adding suffixes to the lambda column names. @JunkMechanic, @sheridp - an option similar to this old behaviour can be got from defining the named aggregations in a dictionary and then unpacking in the agg method. E.g.: df.groupby('A').agg(**{"min": ("B", lambda x: x.min()),"max": ("B", lambda x: x.max())}) –  Mad Aug 5, 2022 at 8:57

This will drop the outermost level from the hierarchical column index:

If you'd like to keep the outermost level, you can use the ravel() function on the multi-level column to form new labels:

update : in newer pandas instead of .ravel() use .tolist() or .to_numpy() use df.columns = ["_".join(x) for x in df.columns.tolist()]

For example:

Alternatively, to keep the first level of the index:

• 2 Works, but gets rid of the grouped by column since its in level 0 :( –  Mugen Feb 5, 2017 at 6:09
• 4 Note that this syntax will be deprecated in future versions of pandas. Details are in the 0.20 changelog , which I summarized in my answer. –  joelostblom May 10, 2017 at 15:43
• 2 @Mugen sorry for the (very) late response, but this is an easy fix, you can just do df.columns = ['_'.join(x) if isinstance(x,tuple) else x for x in df.columns.ravel()] which takes advantage of the fact that only the aggregated columns will be tuples, so if you have other tuples in your column names, then exercise caution here. –  Lucas H Jul 18, 2019 at 16:10

I agree with the OP that it seems more natural and consistent to name and define the output columns in the same place (e.g. as is done with tidyverse's summarize in R ), but a work-around in pandas for now is to create the new columns with desired names via assign before doing the aggregation:

(Using reset_index turns 'Country' , 'f' , 'mean' , and 'std' all into regular columns with a separate integer index.)

If you want to have a behavior similar to JMP, creating column titles that keep all info from the multi index you can use:

It will change your dataframe from:

• You can also add a conditional check to get the same column name if there is no 2nd row ` if n2 == '': new_col_name.append("%s" % n1) else: new_col_name.append("%s_%s" % (n1, n2))` –  Adarsh Madrecha Dec 13, 2018 at 11:48
• u don't need .ravel() , i think df.columns is enough! –  Yas Dec 10, 2022 at 19:18

With the inspiration of @Joel Ostblom

For those who already have a workable dictionary for merely aggregation, you can use/modify the following code for the newer version aggregation, separating aggregation and renaming part. Please be aware of the nested dictionary if there are more than 1 item.

Please let me know if there is a smarter way to do it. Thanks.

• seems like the only viable solution 4 years later. –  Ufos Oct 27, 2021 at 18:54
• 1 or also this should work df.columns = ['_'.join(a) for a in df.columns.to_flat_index()] –  Ufos Oct 27, 2021 at 19:17

such as this kind of dataframe, there are two levels of thecolumn name:

we can use this code：

df.columns = [col[0] if col[-1]=='' else col[-1] for col in df.columns.values]

• I like this method the most. With a little modification I got the following:[col[0] if col[-1]=='' else col[-1]+'_'+col[0] for col in path_info.columns.values] –  Moein Nov 4, 2020 at 1:18

Not the answer you're looking for? Browse other questions tagged python group-by pandas aggregate-functions or ask your own question .

• The Overflow Blog
• If you want to address tech debt, quantify it first
• Fighting comment spam at Facebook scale (Ep. 602)
• Featured on Meta
• Moderation strike: Results of negotiations
• Our Design Vision for Stack Overflow and the Stack Exchange network
• Call for volunteer reviewers for an updated search experience: OverflowAI Search
• Temporary policy: Generative AI (e.g., ChatGPT) is banned
• Discussions experiment launching on NLP Collective

Hot Network Questions

• Do interspecies couples exist in Zootopia?
• Probability approximation and computation given Compound Poisson random variable
• Is there any clear evidence that private universities tend to be better than public ones?
• Enthalpy change of a solution of of 1-pentanol in 1-butanol
• Placement of surd with unicode-math is incorrect
• move the instance collection to the point of contact with the plane?
• What are Non-GNU versions of terminal commands?
• What does в сложн. сл. mean in Russian dictionary?
• What adhesive solution should use for walls with powdery surface?
• Correcting how a student writes symbols
• Missing transactions from full node
• Sustainable eating habits as a pilot
• Is there a socially-optimal way to drive on a busy interstate?
• Should all method hashes be found in a smart contract's opcodes?
• Is there a (proposed) name for Coatlicue's progenitor?
• What type of security measure/contingent conditions could make jumping into a portal impossible inside a laboratory?
• Is it safe to create a public ID by hashing a private key?
• Express a "not equal to A or B" criterion in Select
• The complexity of checking for short cycles in a graph
• Why is there a voltage difference between these two grounds?
• Calculate the Distance to a Line Segment
• How do I select an array to loop through from an array of arrays?
• How can I make Manipulate faster?
• How can a country force an ambassador to leave

Your privacy

By clicking “Accept all cookies”, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy .

How to Assign Column Names to a Pandas Series

As a data scientist or software engineer, you are likely to work with Pandas , one of the most popular data manipulation libraries in Python . Pandas provides various data structures, such as Series and DataFrame, to handle and manipulate data efficiently. In this article, we will focus on how to assign column names to a Pandas Series.

What is a Pandas Series?

A Pandas Series is a one-dimensional labeled array that can hold data of any type, such as integers, floats, strings, and so on. Each element in the series has a label or an index that identifies it uniquely. You can think of a Pandas Series as a column in a spreadsheet, where each row corresponds to an index label and contains a value.

To create a Pandas Series, you can use the Series constructor and pass a list or an array of values as an argument. Here’s an example:

In the above example, we created a Pandas Series my_series from a list of integers my_data . The output shows the series values and their corresponding index labels, which are integers from 0 to 4.

Why Assign Column Names to a Pandas Series?

While the default integer index labels are useful, they may not be meaningful or informative in some cases. For instance, if you are working with a dataset that has multiple variables, you may want to assign meaningful names to each column to make it easier to understand and analyze the data.

Assigning column names to a Pandas Series can also help you to manipulate and select data more easily. You can use the column name instead of the index label to access a specific column, which is more intuitive and less error-prone. Additionally, when you combine multiple series into a dataframe, the column names become essential to align the data properly.

To assign column names to a Pandas Series, you can use the name attribute. The name attribute is a string that represents the column name of the series. Here’s an example:

In the above example, we assigned the column name 'my_column' to the Pandas Series my_series using the name attribute. The output shows the series values and their corresponding index labels, as well as the column name.

You can also assign column names to multiple series at once by creating a Pandas DataFrame and specifying the column names as a list. Here’s an example:

In the above example, we created a Pandas DataFrame df from a dictionary data that contains two series with column names 'column1' and 'column2' . The columns parameter in the pd.DataFrame constructor specifies the column names as a list.

In this article, we have learned how to assign column names to a Pandas Series, which can be useful to make the data more informative and easier to manipulate. We have seen that you can assign column names to a series using the name attribute or create a DataFrame with column names by specifying the columns parameter in the constructor. These techniques can help you to work more efficiently with Pandas and manipulate your data more effectively.

• Write For US

Different Ways to Change Data Type in pandas

• Post author: Naveen (NNK)
• Post category: Pandas / Python
• Post last modified: January 22, 2023

While working in Pandas DataFrame or any table-like data structures we are often required to chang the data type(dtype) of a column also called type casting, for example, convert from int to string, string to int e.t.c, In pandas, you can do this by using several methods like astype() , to_numeric() , covert_dttypes() , infer_objects() and e.t.c. In this article, I will explain different examples of how to change or convert the data type in Pandas DataFrame – convert all columns to a specific type, convert single or multiple column types – convert to numeric types e.t.c.

Please enable JavaScript

1. Quick Examples of Changing Data Type

Below are some quick examples of converting column data type on Pandas DataFrame.

Now let’s see with an example. first, create a Pandas DataFrame with columns names Courses , Fee , Duration , Discount .

Yields below output.

2. DataFrame.convert_dtypes() to Convert Data Type in Pandas

convert_dtypes() is available in Pandas DataFrame since version 1.0.0, this is the most used method as it automatically converts the column types to best possible types.

Below is the Syntax of the pandas.DataFrame.convert_dtypes() .

Now, let’s see a simple example.

Yields below output. Note that it converted columns with object type to string type.

3. DataFrame.astype() to Change Data Type in Pandas

In pandas DataFrame use dataframe.astype() function to convert one type to another type of single or multiple columns at a time, you can also use it to change all column types to the same type. When you perform astype() on a DataFrame without specifying a column name, it changes all columns to a specific type. To convert a specific column, you need to explicitly specify the column.

Below is the syntax of pandas.DataFrame.astype()

3.1 Change All Columns to Same type in Pandas

df.astype(str) converts all columns of Pandas DataFrame to string type.

3.2 Change Type For One or Multiple Columns in Pandas

On astype() Specify the param as JSON notation with column name as key and type you wanted to convert as a value to change one or multiple columns. Below example cast DataFrame column Fee to int type and Discount to float type.

3.3 Convert Data Type for All Columns in a List

Sometimes you may need to convert a list of DataFrame columns to a specific type, you can achieve this in several ways. Below are 3 different ways that coverts columns Fee and Discount to float type.

3.4 Raise or Ignore Error when Convert Column type Fails

By default, when you are trying to change a column to a type that is not supported with the data, Pandas generates an error, in order to ignore error use errors param; this takes either ignore or error as value. In the below example I am converting a column that has string value to int which is not supported hence it generates an error, I used errors='ignore' to ignore the error.

4. DataFrame.infer_objects() to Change Data Type in Pandas

Use DataFrame.infer_objects() method to automatically convert object columns to a type of data it holding. It checks the data of each object column and automatically converts it to data type. Note that it converts only object types. For example, if a column with object type is holding int or float types, using infer_object() converts it to respective types.

5. Using DataFrame.to_numeric() to Convert Numeric Types

pandas.DataFrame.to_numeric()  is used to convert columns with non-numeric  dtypes  to the most suitable numeric type.

5.1 Convert Numeric Types

The below example just converts Fee column to the numeric type.

5.2 Convert multiple Numeric Types using apply() Method

Use to_numeric() along with DataFrame.apply() method to convert multiple columns into a numeric type. Below example converts column Fee and Discount to numeric types.

In this article, you have learned how to convert/change all columns of the DataFrame to a specific type, case one or multiple columns and finally converting columns to numeric type using astype() , to_numeric() , covert_dttypes() , infer_objects() methods.

Happy Learning !!

Related Articles

• How to Get All Column Names as List in Pandas?
• Pandas Drop Single & Multiple Columns From DataFrame
• Pandas Add New Column to Existing DataFrame
• How to rename column on pandas DataFrame
• Pandas Get DataFrame Columns by Data Type
• Pandas Empty DataFrame with Column Names & Types
• Pandas Get Row Number of DataFrame
• Apply Multiple Filters to Pandas DataFrame or Series
• How to Rename a Pandas Series
• https://stackoverflow.com/questions/21291259/convert-floats-to-ints-in-pandas

You may also like reading:

• Pandas Change String to Date in DataFrame
• Pandas Convert Integer to Datetime Type
• Pandas Replace Values based on Condition
• Pandas – Drop List of Rows From DataFrame
• Pandas Count The Frequency of a Value in Column
• How to Combine Two Series into pandas DataFrame
• Pandas Remap Values in Column with a Dictionary (Dict)
• Install Python Pandas on Windows, Linux & Mac OS
• Pandas – Create DataFrame From Multiple Series
• Pandas Drop Last N Rows From DataFrame

Naveen (NNK)

Leave a reply cancel reply.

Save my name, email, and website in this browser for the next time I comment.

This Post Has One Comment

Excellent. Thankyou

• Data Structure In Pandas
• Data preprocessing
• Data Manipulation
• Data Analysis using Pandas
• Pandas Exercise
• Data Analysis
• Machine Learning Math
• Machin Learning
• Deep Learning
• Deep Learning Projects
• Computer vision
• Data science
• Deep learning interview question
• Machin Learning Interview question
• Write an Interview Experience
• Share Your Campus Experience
• Pandas Exercises and Programs
• Different ways to create Pandas Dataframe
• Create a Pandas DataFrame from Lists
• Make a Pandas DataFrame with two-dimensional list | Python
• Python | Creating DataFrame from dict of narray/lists
• Creating Pandas dataframe using list of lists
• Creating a Pandas dataframe using list of tuples
• Create a Pandas DataFrame from List of Dicts
• Python | Convert list of nested dictionary into Pandas dataframe
• Replace values in Pandas dataframe using regex
• Creating a dataframe from Pandas series
• Construct a DataFrame in Pandas using string data
• Clean the string data in the given Pandas Dataframe
• Reindexing in Pandas DataFrame
• Mapping external values to dataframe values in Pandas
• Reshape a pandas DataFrame using stack,unstack and melt method
• Reset Index in Pandas Dataframe

Change column names and row indexes in Pandas DataFrame

• How to print an entire Pandas DataFrame in Python?
• Working with Missing Data in Pandas

Pandas Dataframe Rows Practice Exercise

• How to iterate over rows in Pandas Dataframe
• Different ways to iterate over rows in Pandas Dataframe
• Selecting rows in pandas DataFrame based on conditions
• Select any row from a Dataframe using iloc[] and iat[] in Pandas
• Limited rows selection with given column in Pandas | Python
• Drop rows from the dataframe based on certain condition applied on a column
• Insert row at given position in Pandas Dataframe
• Create a list from rows in Pandas dataframe
• Create a list from rows in Pandas DataFrame | Set 2
• Ranking Rows of Pandas DataFrame
• Sorting rows in pandas DataFrame
• Select row with maximum and minimum value in Pandas dataframe
• Get all rows in a Pandas DataFrame containing given substring
• Convert a column to row name/index in Pandas
• How to randomly select rows from Pandas DataFrame

Pandas Dataframe Columns Practice Exercise

• Create a pandas column using for loop
• How to get column names in Pandas dataframe
• How to rename columns in Pandas DataFrame
• Collapse multiple Columns in Pandas
• Get unique values from a column in Pandas DataFrame
• Conditional operation on Pandas DataFrame columns
• Return the Index label if some condition is satisfied over a column in Pandas Dataframe
• Using dictionary to remap values in Pandas DataFrame columns
• Formatting float column of Dataframe in Pandas
• Create a new column in Pandas DataFrame based on the existing columns
• Python | Creating a Pandas dataframe column based on a given condition
• Split a column in Pandas dataframe and get part of it
• Getting Unique values from a column in Pandas dataframe
• Split a String into columns using regex in pandas DataFrame
• Getting frequency counts of a columns in Pandas DataFrame
• Change Data Type for one or more columns in Pandas Dataframe
• Split a text column into two columns in Pandas DataFrame
• Difference of two columns in Pandas dataframe
• Get the index of maximum value in DataFrame column
• Get the index of minimum value in DataFrame column
• Get n-largest values from a particular column in Pandas DataFrame
• Get n-smallest values from a particular column in Pandas DataFrame
• How to drop one or multiple columns in Pandas Dataframe
• How to lowercase strings in a column in Pandas dataframe
• Capitalize first letter of a column in Pandas dataframe
• Apply uppercase to a column in Pandas dataframe

Pandas Series Practice Exercise

• Create a Pandas Series from array
• Creating a Pandas Series from Dictionary
• Creating a Pandas Series from Lists
• Create Pandas Series using NumPy functions
• Access the elements of a Series in Pandas

Pandas Date and Time Practice Exercise

• Pandas | Basic of Time Series Manipulation
• Using Timedelta and Period to create DateTime based indexes in Pandas
• Convert the column type from string to datetime format in Pandas dataframe

DataFrame String Manipulation

• Extract punctuation from the specified column of Dataframe using Regex
• Replace missing white spaces in a string with the least frequent character using Pandas
• How to Convert Floats to Strings in Pandas DataFrame?

Accessing and Manipulating Data in DataFrame

• Access Index of Last Element in pandas DataFrame in Python
• Replace Characters in Strings in Pandas DataFrame
• Replace values of a DataFrame with the value of another DataFrame in Pandas
• Replace negative values with latest preceding positive value in Pandas DataFrame
• How to add column from another DataFrame in Pandas ?

DataFrame Visualization and Exporting

• How to render Pandas DataFrame as HTML Table?
• Exporting Pandas DataFrame to JSON File
• Create and display a one-dimensional array-like object using Pandas in Python
• Export Pandas dataframe to a CSV file
• Display the Pandas DataFrame in Heatmap style

Data Aggregation and Grouping

• How to sum negative and positive values using GroupBy in Pandas?
• Pandas – Groupby value counts on the DataFrame
• How to count unique values in a Pandas Groupby object?
• How to Add Group-Level Summary Statistic as a New Column in Pandas?
• Find the profit and loss in the given Excel sheet using Pandas

Merging and Joining

• Prevent duplicated columns when joining two Pandas DataFrames
• How to Merge DataFrames of different length in Pandas ?
• Join Pandas DataFrames matching by substring
• Merge two Pandas DataFrames based on closest DateTime
• Merge two Pandas DataFrames on certain columns
• Merge two Pandas dataframes by matched ID number
• Merge two dataframes with same column names

Filtering and Selecting Data

• Drop specific rows from multiindex Pandas Dataframe
• Select rows that contain specific text using Pandas

Select Rows With Multiple Filters in Pandas

• Select Pandas dataframe rows between two dates
• Filter Pandas Dataframe with multiple conditions

Selection and Slicing

• How to take column-slices of DataFrame in Pandas?
• Extract all capital words from Dataframe in Pandas
• How to reverse the column order of the Pandas DataFrame?
• Check if a column starts with given string in Pandas DataFrame?

Miscellaneous DataFrame Operations

• How to display most frequent value in a Pandas series?
• Set Pandas dataframe background Color and font color in Python
• How to widen output display to see more columns in Pandas dataframe?
• Get the day from a date in Pandas
• Get the Hour from timestamp in Pandas

Data Cleaning and Manipulation

• How to fill NAN values with mean in Pandas?
• Fillna in multiple columns in place in Python Pandas
• How to remove random symbols in a dataframe in Pandas?
• Replace Negative Number by Zeros in Pandas DataFrame
• Align columns to Left in Pandas – Python

Concatenation and Manipulation

• Read multiple CSV files into separate DataFrames in Python
• Append list of dictionary and series to a existing Pandas DataFrame in Python
• Concatenate multiIndex into single index in Pandas Series
• Concatenate strings from several rows using Pandas groupby
• Split large Pandas Dataframe into list of smaller Dataframes

DataFrame Sorting and Reordering

• How to Sort a Pandas DataFrame by Date?
• Rename specific column(s) in Pandas
• How to rename multiple column headers in a Pandas DataFrame?

DataFrame Transformation and Conversion

• Get the first 3 rows of a given DataFrame
• How to Convert Pandas DataFrame columns to a Series?
• How to convert index in a column of the Pandas dataframe?
• How to add header row to a Pandas Dataframe?

DataFrame Filtering and Selection

• Select a single column of data as a Series in Pandas
• How to Select single column of a Pandas Dataframe?
• Ways to filter Pandas DataFrame by column values
• How to Filter DataFrame Rows Based on the Date in Pandas?

DataFrame Conversion and Reshaping

• Convert a series of date strings to a time series in Pandas Dataframe
• Split Pandas Dataframe by Rows
• How to convert a dictionary to a Pandas series?
• Flatten a list of DataFrames
• Convert birth date to age in Pandas

Given a Pandas DataFrame, let’s see how to change its column names and row indexes.

About Pandas DataFrame

Pandas DataFrame are rectangular grids which are used to store data. It is easy to visualize and work with data when stored in dataFrame.

• It consists of rows and columns.
• Each row is a measurement of some instance while column is a vector which contains data for some specific attribute/variable.
• Each dataframe column has a homogeneous data throughout any specific column but dataframe rows can contain homogeneous or heterogeneous data throughout any specific row.
• Unlike two dimensional array, pandas dataframe axes are labeled.

Pandas Dataframe type has two attributes called ‘columns’ and ‘index’ which can be used to change the column names as well as the row indexes.

Create a DataFrame using dictionary.

Method #1: using df.columns and df.index.

Changing the column name and row index using df.columns and df.index attribute. In order to change the column names, we provide a Python list containing the names of the column df.columns= ['First_col', 'Second_col', 'Third_col', .....] . In order to change the row indexes, we also provide a Python list for it df.index=['row1', 'row2', 'row3', ......] .

Method #2: Using rename() function with dictionary

Let’s use the pandas rename function to change a single column.

Change multiple column names simultaneously

Method #3: using lambda function to rename the columns.

A lambda function is a small anonymous function which can take any number of arguments, but can only have one expression. Using the lambda function we can modify all of the column names at once. Let’s add ‘x’ at the end of each column name using lambda function

Method #4: Using values attribute to rename the columns.

We can use the values attribute directly on the column whose name we want to change.

Let’s change the row index using the Lambda function.

Now, if we want to change the row indexes and column names simultaneously, then it can be achieved using

function and passing both column and index attribute as the parameter.

Please Login to comment...

Improve your coding skills with practice.