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How to Rename a Column in Pandas Dataframe?

Mayank Jain

Mayank Jain

Last updated on 16 Feb 2024

Renaming a column in a Pandas DataFrame involves altering the label of a specific column. Renaming a column in Pandas is essential for clarity and consistency in data analysis tasks. By following a few simple steps, you can efficiently rename columns to suit your analysis needs. Let's delve into the process of renaming columns in a Pandas DataFrame.

Rename a Column in Pandas Dataframe by Using rename() function

To rename a column in a Pandas DataFrame, the rename () function can be employed. This function allows for a straightforward alteration of column names, providing flexibility and ease in data manipulation tasks.

Here's a basic syntax example:

In this example, the column 'A' is renamed to 'New_Column_Name' using the rename () function. The inplace = True parameter ensures that the change is made directly to the original DataFrame.

Rename a Column in Pandas Dataframe by Using a List

Another method to rename a column in a Pandas DataFrame is by utilizing a list. This approach allows for renaming multiple columns simultaneously, providing efficiency in handling bulk column renaming tasks.

Here's an example of how to rename columns using a list:

In this example, the columns 'A' and 'B' are renamed to 'New_Column_A' and 'New_Column_B', respectively, by assigning a list of new column names to the DataFrame's columns attribute. This method offers a convenient way to rename columns in bulk.

Rename a Column in Pandas Dataframe by using DataFrame set_axis() function

The set_axis () function in Pandas DataFrame provides an alternative method to rename columns efficiently. This function allows you to specify new column labels, replacing the existing ones seamlessly.

Here's an example demonstrating the usage of set_axis () to rename columns:

In this example, the columns of the DataFrame are renamed to 'New_Column_A' and 'New_Column_B' using the set_axis () function. The axis = 1 parameter specifies that column labels are being altered, while inplace = True ensures the changes are made directly to the original DataFrame.

Rename a Column in Pandas Dataframe by assigning a list of new column names

An efficient way to rename columns in a Pandas DataFrame is by directly assigning a list of new column names to the DataFrame's columns attribute. This method allows for quick and simultaneous renaming of multiple columns.

Here's an example illustrating the process of renaming columns using a list of new column names:

In this example, the columns 'A' and 'B' are renamed to 'New_Column_A' and 'New_Column_B', respectively, by assigning a list of new column names directly to the DataFrame's columns attribute. This approach provides a straightforward way to rename columns in Pandas DataFrame.

Rename column names using DataFrame add_prefix() and add_suffix() functions

In Pandas DataFrame, you can rename column names by using the add_prefix () and add_suffix () functions. These functions allow you to prepend or append a specified string to the existing column names, effectively renaming them in a systematic manner.

Here's how you can utilize these functions to rename columns:

In this example, the add_prefix () function adds the prefix 'Prefix_' to all existing column names, while the add_suffix () function appends the suffix '_Suffix' to each column name. These functions are useful for renaming columns in a systematic and uniform manner.

Replace specific texts of column names using Dataframe.columns.str.replace function

The DataFrame .columns .str .replace function in Pandas allows you to replace specific texts within column names efficiently. This method is particularly useful when you want to update certain parts of column names while keeping the rest intact.

Here's an example demonstrating the usage of DataFrame .columns .str .replace to replace specific texts in column names:

In this example, the str. replace function is applied to the DataFrame's columns attribute to replace '_' with '-', 'A' with 'X', and 'E' with 'Y' in column names. This method offers flexibility in modifying specific parts of column names according to your requirements.

We explored various methods to rename columns in a Pandas DataFrame. From using functions like rename () and set_axis () to leveraging add_prefix () and add_suffix () for systematic renaming, Pandas offers versatile tools for this task. Additionally, we learned how to replace specific texts within column names using DataFrame .columns .str .replace . With these techniques, you can efficiently rename columns according to your analysis needs, ensuring clarity and consistency in your data manipulation workflows.

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Set Column Names for DataFrame in Pandas

Set column names for dataframe.

To set column names of DataFrame in Pandas, use pandas.DataFrame.columns attribute. Assign required column names as a list to this attribute.

In this tutorial, we will learn how to set column names of DataFrame in Pandas using DataFrame.columns attribute.

The syntax to access value/item at given row and column in DataFrame is

where new_column_names is a list of new column names for this DataFrame.

In the following program, we take a DataFrame with some initial column names, and update the column names using DataFrame.columns.

The column names have been set to [‘fruit’, ‘quantity’].

In this Pandas Tutorial , we learned how to set column names of DataFrame using DataFrame().columns attribute.

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How to Rename a Column in Pandas DataFrame: Rename Column Names (With Examples)

While working with data you may require to change the names of some or all the columns of a dataframe. In this tutorial, we’ll cover three different ways in Pandas to rename column names along with examples.

How to Rename a Column in Pandas

You can use the Pandas dataframe rename() function to rename column names in Pandas. There are other methods for renaming columns as well. The following are some easy, useful, and effective ways to change the name of columns in Pandas –

Table of Contents

Top 3 Methods to Rename Column Names in Panda

Example: Change the name of a specific column

Example: apply rename() function to column names, example: change the name of a column using set_axis().

  • Example: Update the columns attribute to change column names

Summary: Changing Column Names in Pandas (Key Takeaways)

Top 3 methods to rename column names in pandas.

  • Method 1: Use the Pandas dataframe rename() function to modify specific column names.
  • Method 2: Use the Pandas dataframe set_axis() method to change all your column names.
  • Method 3: Set the dataframe’s columns attribute to your new list of column names.

Let’s explore in more detail by examing practical examples of using Pandas rename column names methods to manipulate and manage columns for data.

Method 1: Use Pandas rename() function to rename columns

The Pandas dataframe rename() function is a quite versatile function used not only to rename column names but also row indices.

You can use this function to rename specific columns. Note that we can also rename a single column or multiple columns. The following is the syntax to change column names using the Pandas rename() function.

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The rename() function returns a new dataframe with renamed axis labels (i.e. the renamed columns or rows depending on usage). To modify the dataframe in place set the argument inplace to True .

Let’s now look at some examples.

Here, we will create a dataframe storing the category and color information of some pets in the columns “Category” and “Color” respectively.

pets dataframe with "Category" and "Color" columns

We now have a dataframe containing the “Category” and “Color” information of some pets.

Let’s now use the rename() function to change the name of the “Category” column to “Pet”.

pets dataframe with "Category" column name changed to "Pet"

You can see that the column name has been changed.

Note that you can also just get the column names of a Pandas dataframe using df.columns .

The rename() function also accepts a function that can be applied to each column name. For example, you can write a function to replace special characters like underscore _ with a space, split the column name on a delimiter, keep only a specific portion, etc.

In the above example, we pass a function to the rename function to modify the column names. The function gets applied to each column and gives its respective new name. Here, we split each column name on _ and use the second string as our new name for that column.

Method 2: Use Pandas set_axis() function to rename column names

The Pandas dataframe set_axis() method can be used to rename a dataframe’s columns by passing a list of all columns with their new names.

Note that the length of this list must be equal to the number of columns in the dataframe. The following is the syntax:

You have to explicitly specify the axis as 1 or 'columns' to update column names since its default is 0 (which modifies the axis for rows).

It returns a new dataframe with the updated axis. To modify the dataframe in place, set the argument inplace to True .

Let’s now look at an example.

We’ll take the same use case as above, and change the column name “Category” to “Pet” in a dataframe but this time we will be using the set_axis() method.

In the above example, the set_axis() function is used to rename the column Category to Pet in the dataframe df . Note that we had to provide the list of all the columns for the dataframe even if we had to change just one column name.

Method 3: Rename columns in Pandas by changing its attribute

You can also update a dataframe’s column by setting its columns attribute to your new list of columns. The following is the syntax:

Note that new_column_list must be of the same length as the number of columns in your dataframe.

Let’s look at our final example.

Example: Update columns attribute to change column names

We’ll take the same use case as above. Create a dataframe with “Category” ad “Color” columns and then change the column name “Category” to “Pet” but this time we’ll do it by updating the columns attribute.

In the above example, we change the column names of the dataframe df by setting df.columns to a new column list. Like the set_index() function, we had to provide the list of all the columns for the dataframe even if we had to change just one column name.

In this tutorial, we looked at three different methods along with examples of application to rename columns in a Pandas dataframe. The following are the key takeaways –

  • You can change Pandas dataframe column names by using the rename() function to change the name of a single column or to rename multiple columns. Pass a dictionary {"OldName":"NewName"} to the columns parameter. You can also pass a function to the columns parameter to dynamically change the column names.
  • You can set the column names of the dataframe to new column names using the Pandas dataframe setaxis() function. Explicitly specify axis=1 or axis='columns' to indicate that you’re setting the columns’ axis.
  • Alternatively, you can change the column names of a dataframe by changing the .columns attribute of the dataframe.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a  Jupyter Notebook  with a python (version 3.8.3) kernel having pandas version 1.0.5

More on Pandas DataFrames –

  • Pandas – Sort a DataFrame
  • Change Order of Columns of a Pandas DataFrame
  • Pandas DataFrame to a List in Python
  • Pandas – Count of Unique Values in Each Column
  • Pandas – Replace Values in a DataFrame
  • Pandas – Filter DataFrame for multiple conditions
  • Pandas – Random Sample of Rows
  • Pandas – Random Sample of Columns
  • Save Pandas DataFrame to a CSV file
  • Pandas – Save DataFrame to an Excel file
  • Create a Pandas DataFrame from Dictionary
  • Convert Pandas DataFrame to a Dictionary
  • Drop Duplicates from a Pandas DataFrame
  • Concat DataFrames in Pandas
  • Append Rows to a Pandas DataFrame
  • Compare Two DataFrames for Equality in Pandas
  • Get Column Names as List in Pandas DataFrame
  • Select One or More Columns in Pandas
  • Pandas – Rename Column Names
  • Pandas – Drop one or more Columns from a Dataframe
  • Pandas – Iterate over Rows of a Dataframe
  • How to Reset Index of a Pandas DataFrame?
  • Read CSV files using Pandas – With Examples
  • Apply a Function to a Pandas DataFrame

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Piyush Raj

Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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5 Best Ways to Rename Columns in a Pandas DataFrame

💡 Problem Formulation: When working with Pandas DataFrames, you might encounter scenarios where the column names are not descriptive or suitable for the analyses you intend to perform. For example, suppose you have a DataFrame with columns named ‘A’, ‘B’, and ‘C’, and you want to rename them to ‘Product’, ‘Category’, and ‘Price’ respectively for better readability and understanding. This article will guide you through different methods to achieve such column renaming.

Method 1: Using DataFrame’s rename() Method

The rename() method in Pandas allows you to rename specific columns by passing a dictionary where keys are the current column names and values are the new column names. This method is particularly flexible as it provides the opportunity to rename a subset of columns without changing the entire structure.

Here’s an example:

In the example above, we first create a sample DataFrame with columns ‘A’, ‘B’, and ‘C’. We then use the rename() method, passing a dictionary that maps the old column names to the new ones. Finally, we print the DataFrame with the renamed columns.

Method 2: Assigning to DataFrame.columns Attribute

You can directly assign a new list of column names to the DataFrame.columns attribute. This method is very straightforward and is suitable when you want to rename all the columns at once.

In the code snippet above, we replace the current column names by setting the DataFrame.columns attribute with a new list of column names. Note that the length of the list must match the number of columns in the DataFrame.

Method 3: Using the In-Place Renaming Feature

The rename() method can be used with its inplace=True argument to apply the renaming operation directly to the original DataFrame without the need to create a new DataFrame.

By setting inplace=True in the rename() method, we tell pandas to modify the DataFrame in-place. This means that no new DataFrame is returned and instead, the existing DataFrame is altered.

Method 4: Using a List Comprehension for Partial Renaming

If you need to modify only part of the column names, for example, adding a prefix or suffix to each name, you can use a list comprehension to automate the process.

The list comprehension iterates over each column name in df.columns and prepends the string ‘Product_’ to each one. The resulting list is then assigned back to df.columns .

Bonus One-Liner Method 5: Using the lambda Function

It’s possible to rename DataFrame columns using a lambda function to apply any transformation you wish. This is a flexible one-liner approach that can be helpful for quick and simple column name changes.

This one-liner uses the rename() method with a lambda function that takes each column name (represented by x ) and adds the suffix ‘_value’. We also use inplace=True to modify the DataFrame directly.

Summary/Discussion

  • Method 1: rename() Method . Highly versatile. Allows selective renaming without affecting untouched columns. Requires the creation of a dictionary mapping which could be verbose for a large number of columns.
  • Method 2: DataFrame.columns Attribute . Very straightforward. Best for renaming all columns at once. Not ideal for partial renaming or when column names are not known beforehand.
  • Method 3: In-Place Renaming . Avoids creating a new DataFrame, saving memory. The original DataFrame is altered, which might not be desirable if you need to retain the original structure.
  • Method 4: List Comprehension for Partial Renaming . Great for systematic renaming patterns. It’s concise but less readable for those not familiar with list comprehensions.
  • Method 5: Using lambda Function . Offers maximum flexibility in a one-liner format. Useful for applying simple transformations to column names.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

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4 Different Ways to Rename Columns in Pandas

Feb 04, 2024 by Harsh S. Leave a Comment

Searching for ways to rename a column using Pandas? This is a common operation you might have to perform while using a data frame. This tutorial will walk you through several methods to rename one or more columns in Pandas, providing examples and a comparison of each method to help you choose the most suitable approach for your data manipulation needs.

How to Rename One or More Columns in Pandas

In this Python tutorial, we’ll cover the following topics. Please make sure you first go through the brief description in the examples and then check out the code. It will ensure you understand the code and its purpose clearly.

  • Renaming a Single Column
  • Renaming More than One Column
  • Renaming Columns with a Dictionary
  • In-place vs. Non-Inplace Renaming
  • Comparison of Methods

By the way, once you finish with this tutorial, you might like to check up on the 3 ways to read a CSV file in Python using Pandas including multiple examples .

1. Rename a Single Column

You can rename a single column in a Pandas DataFrame using the rename() API. Let’s suppose we have a data frame with a column named “old_col,” and we want to rename it to “new_col.”

This code will rename the ‘old_col’ to ‘new_col’ in the data frame. The inplace=True parameter modifies the original data frame. If you omit it or set it to False, the original data frame will remain unchanged.

2. Renaming More than One Column Using Pandas

To rename more than one column in a Pandas DataFrame, pass a dictionary using the current column names as keys and the new names as values . Here’s an example:

This code will rename both ‘old_col1’ and ‘old_col2’ to ‘new_col1’ and ‘new_col2,’ respectively. Again, you can choose to modify the original data frame in place by setting inplace=True .

3. Renaming Columns with a Dictionary

You can also use a dictionary for the purpose of renaming columns in a more dynamic way. This is useful when you want to rename specific columns based on a mapping. Here’s an example:

Must Read: Convert Python Dictionary to DataFrame

In this example, we create a dictionary column_mapping that specifies the mapping of old column names to new names. Using this dictionary, we rename the columns in the Pandas data frame accordingly.

4. Inplace vs. Non-Inplace Renaming

As mentioned earlier, you can choose between in-place and non in place renaming by setting the inplace option in the rename API.

  • In place, renaming modifies your original data frame and does not return a new one.
  • The non-inplace renaming returns a new data frame with the renamed columns, leaving the original one unchanged.

Here’s an example to illustrate the difference:

In this example, df.rename(...) does not modify the original data frame df . It returns a new data frame object, new_df with the renamed columns. This allows you to keep both the original and the renamed versions.

If you want to modify the original data frame in place, you would set the value of the "inplace" option to True as demonstrated in previous examples.

5. Comparison of the Above Approaches

Now, let’s compare the different approaches used to rename one or more columns in Pandas:

Furthermore, a common misconception is that the Pandas set_axis() function also renames columns in a data frame. However, this is not true as it only changes the labels of rows or columns but does not assign new names to columns.

In this tutorial, you’ve learned various examples for renaming columns in a Pandas data frame. Each method has its own benefits and use cases, so the choice depends on your specific requirements:

  • For renaming a single column, use the “Single Column Renaming” method.
  • When renaming more than two or more columns, the “Multi-Column Renaming” method is efficient.
  • If you need dynamic and complex renaming, the “Dictionary Mapping” method is the most suitable.

Remember to consider whether you want to modify the original data frame in place or create a new one with the renamed columns. Your choice should be based on your data manipulation workflow and requirements.

With these methods at your disposal, you can easily rename columns in your Pandas DataFrames. We hope doing all this makes your data analysis and processing tasks more efficient and clear.

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Sharp Sight

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 .

A simple image of a Pandas dataframe, storing data in a row-and-column structure.

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.

A simple example of how we use the assign method to add a new variable to a dataframe.

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.”

An image that shows how the Pandas assign syntax works.

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.

An image that explains the syntax for adding multiple variables using Pandas assign.

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.

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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

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How to change column names in pandas, understanding dataframes in pandas.

Pandas is a powerful and widely-used Python library for data manipulation and analysis, particularly for structured data, such as tables. When you work with Pandas, you'll often deal with DataFrames, which you can think of as big spreadsheets in your Python code. Each column in a DataFrame represents a variable, and each row represents an observation.

Imagine a DataFrame as a guest list for a party, where each column could represent information like the guest's name, age, and favorite song. The rows would then correspond to each individual guest and their respective information.

Why Change Column Names?

As you become more familiar with your data, you may realize that the original column names are not as descriptive or as clear as they could be. For example, a column named 'n' might be better named 'name', or 'age' might be more descriptive if renamed to 'guest_age'. Clear column names make your data easier to understand and your code easier to read, which is especially important when sharing your work with others.

Renaming Columns with the rename Method

One way to change column names in Pandas is by using the rename method. This method allows you to change a selection of column names while keeping the others intact. It's like updating the labels on your party guest list without having to rewrite the entire list.

Here's how you can use it:

After running this code, the DataFrame df will have columns named 'name', 'age', and 'favorite_song' instead of 'n', 'a', and 'fs'.

Changing All Column Names with columns Attribute

If you want to change all the column names in your DataFrame, you can directly assign a new list of column names to the columns attribute of the DataFrame. It's like erasing all the headers from your guest list and writing new ones.

Here's an example:

This will replace all the column names in one go. It's important to note that the order of the new column names in the list should match the order of the existing columns.

Using the str.replace Method for Column Names

Sometimes you might want to make a systematic change to column names. For instance, maybe all your columns start with 'col_' and you want to remove that prefix. This is where the str.replace method comes in handy, acting like a find-and-replace for text.

After running this code, the 'col_' prefix will be removed from all the column names.

Using a Dictionary for Conditional Renaming

Sometimes, you may want to rename only a few columns based on certain conditions. You can create a dictionary where the keys are the old column names and the values are the new column names. This method is similar to having a cheat sheet that helps you remember which guest's name has changed recently.

This code snippet will only rename the 'AgeYears' column to 'Age' and 'FavSong' to 'Favorite Song', leaving 'Name' as is.

Common Pitfalls and How to Avoid Them

When renaming columns in Pandas, it's easy to make mistakes. One common error is attempting to rename a column that doesn't exist. This will not change your DataFrame, but it also won't alert you to the fact that nothing has happened. It's like trying to change the name of a guest who isn't on your list; nothing changes.

To avoid this, always double-check your column names before attempting to rename them. You can print out the current column names using print(df.columns) .

Another pitfall is trying to assign a new list of column names that doesn't match the number of columns in the DataFrame. This will result in an error because Pandas won't know how to match the list of new names to the columns. It's like trying to give names to more guests than you have on your list; it just doesn't work.

To prevent this, ensure that the list of new column names is the same length as the number of columns in the DataFrame.

Renaming columns in Pandas is an essential skill that makes your data more readable and your analyses more understandable. Whether you're using the rename method, directly setting the columns attribute, utilizing str.replace , or employing a dictionary for conditional renaming, you now have the tools to ensure your data speaks clearly and accurately.

Think of your DataFrame as a canvas and the column names as titles for your artwork. By choosing the right titles, you can convey the message of your data more effectively, making it a masterpiece of clarity and insight. With this newfound knowledge, you're ready to dive deeper into the world of data analysis with Pandas, transforming raw data into meaningful narratives one column name at a time.

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Change column names and row indexes in Pandas DataFrame

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Pandas Dataframe Rows Practice Exercise

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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, method #2: using rename() function with dictionary, change multiple column names simultaneously, method #3: using lambda function to rename the columns, method #4: using values attribute to rename the columns..

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.

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Pandas Add Column Names to DataFrame

  • Post author: Naveen (NNK)
  • Post category: Pandas
  • Post last modified: January 9, 2024
  • Reading time: 13 mins read

You can add column names to pandas at the time of creating DataFrame or assign them after creating. Sometimes you might receive a CSV file that doesn’t have names and you would need to add after reading CSV data into DataFrame.

In this article, I will explain how to add/set/assign column names to DataFrame with several examples. The column names on DataFrame are used to identify what type of data each column holds.

Please enable JavaScript

1. Quick Examples of Pandas Add Column Names

If you are in a hurry, below are some quick examples of how to add/assign or set column labels to DataFrame.

2. Add Column Names to DataFrame

You can add column names to pandas DataFrame while creating manually from the data object. In order to create a DataFrame , you would use a DataFrame constructor which takes a columns param to assign the names. It takes a list as a value and the number of values in a list should not exceed the number of columns in DataFrame.

Yields below output.

pandas add column names

2. Add Names While Reading CSV

pandas read_csv() method has an option to identify the column names that are presented in a CSV file, In case your CSV file doesn’t have on the first row then you can add custom names while reading a CSV into Pandas DataFrame.

3. Add Column Names to Existing DataFrame

In the above sections, you have seen how to add while creating a DataFrame. Sometimes it’s not possible to know the column names up-front and you may need to add names to the existing DataFrame.

In this example, the columns attribute is used to assign a new list of column names ( column_names ) to the DataFrame.

4. Complete Examples

Below is the complete example of how to assign column names to DataFram.

Frequently Asked Questions on Pandas Add Column Names to DataFrame

To add column names to a Pandas DataFrame while creating it from a dictionary, you can use the columns parameter of the pd.DataFrame constructor.

If you already have a DataFrame and want to change the column names, you can directly assign a new list of column names to the columns attribute of the DataFrame.

To add a new column to an existing DataFrame with a specific name and values, you can use the assign method. For example, the assign method is used to add a new column named ‘Column3’ with the specified values to the existing DataFrame. The resulting DataFrame will have the original columns along with the new one.

If your CSV file doesn’t have a header, and you want to specify column names while reading it into a Pandas DataFrame, you can use the names parameter of the read_csv function.

If your CSV file has a header (i.e., the first row contains column names), you can simply use the read_csv function without specifying the names parameter. The read_csv function automatically uses the first row of the CSV file as column names.

In this article, you have learned to assign column names to pandas DataFrame, while creating, when reading a CSV and to an existing DataFrame.

Happy Learning!!

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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.

assign name to column pandas

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!

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COMMENTS

  1. Add column names to dataframe in Pandas

    There are several ways in Pandas to add column names to your DataFrame: python3 import pandas as pd l1 =["Amar", "Barsha", "Carlos", "Tanmay", "Misbah"] l2 =["Alpha", "Bravo", "Charlie", "Tango", "Mike"] l3 =[23, 25, 22, 27, 29] l4 =[69, 54, 73, 70, 74] team = pd.DataFrame (list(zip(l1, l2, l3, l4))) print(team) Output 0 1 2 3 0 Amar Alpha 23 69

  2. Changing a specific column name in pandas DataFrame

    Renaming column names in Pandas (37 answers) Closed 2 years ago. I was looking for an elegant way to change a specified column name in a DataFrame. play data ... import pandas as pd d = { 'one': [1, 2, 3, 4, 5], 'two': [9, 8, 7, 6, 5], 'three': ['a', 'b', 'c', 'd', 'e'] } df = pd.DataFrame (d) The most elegant solution I have found so far ...

  3. pandas.DataFrame.assign

    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. Examples >>> df = pd.DataFrame( {'temp_c': [17.0, 25.0]}, ... index=['Portland', 'Berkeley']) >>> df temp_c Portland 17.0 Berkeley 25.0

  4. How to Rename a Column in Pandas Dataframe?

    Rename a Column in Pandas Dataframe by assigning a list of new column names. An efficient way to rename columns in a Pandas DataFrame is by directly assigning a list of new column names to the DataFrame's columns attribute. This method allows for quick and simultaneous renaming of multiple columns.

  5. How to set Column Names for DataFrame in Pandas?

    Assign required column names as a list to this attribute. In this tutorial, we will learn how to set column names of DataFrame in Pandas using DataFrame.columns attribute. Syntax The syntax to access value/item at given row and column in DataFrame is DataFrame.columns = new_column_names

  6. How to Rename a Column in Pandas DataFrame: Rename Column Names (With

    Here, we split each column name on _ and use the second string as our new name for that column. Method 2: Use Pandas set_axis() function to rename column names. The Pandas dataframe set_axis() method can be used to rename a dataframe's columns by passing a list of all columns with their new names.

  7. How to rename columns in Pandas DataFrame

    Method 1: Using rename () function One way of renaming the columns in a Pandas Dataframe is by using the rename () function. This method is quite useful when we need to rename some selected columns because we need to specify information only for the columns which are to be renamed. Example 1: Rename a single column. Python3 import pandas as pd

  8. pandas.DataFrame.rename

    Note. The copy keyword will change behavior in pandas 3..Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.. You can already get the future behavior and improvements through enabling copy on write pd.options.mode ...

  9. How to change column name in Pandas

    One of the simplest ways to change column names in a Pandas DataFrame is by using the rename method. This method allows you to alter index labels and/or column names by providing a dictionary that maps old names to new ones. Here's an example:

  10. 5 Best Ways to Rename Columns in a Pandas DataFrame

    We then use the rename() method, passing a dictionary that maps the old column names to the new ones. Finally, we print the DataFrame with the renamed columns. Method 2: Assigning to DataFrame.columns Attribute. You can directly assign a new list of column names to the DataFrame.columns attribute. This method is very straightforward and is ...

  11. 4 Different Ways to Rename Columns in Pandas

    In this example, we create a dictionary column_mapping that specifies the mapping of old column names to new names. Using this dictionary, we rename the columns in the Pandas data frame accordingly. 4. Inplace vs. Non-Inplace Renaming

  12. How to rename column Pandas

    This method is straightforward but requires you to provide a new name for every column, even if you don't want to change all of them. Here's how you can do it: # Assigning new column names df.columns = ['X', 'Y', 'Z'] print (df) This will rename all the columns in df to 'X', 'Y', and 'Z'. It's important to match the length of the new columns ...

  13. How to Use the Pandas Assign Method to Add New Variables

    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 ...

  14. How to pandas df.assign () with variable names?

    1 Why not just df [new_column] = other_df ['Column1'].values? - Chris Sep 16, 2019 at 5:42 5 @Chris assign is useful in a longer chain of multiple operations - Paul H Sep 16, 2019 at 5:43 Add a comment 1 Answer Sorted by: 19 You can make a dict and unpack:

  15. How to change column names in Pandas

    One way to change column names in Pandas is by using the rename method. This method allows you to change a selection of column names while keeping the others intact. It's like updating the labels on your party guest list without having to rewrite the entire list. Here's how you can use it:

  16. Change column names and row indexes in Pandas DataFrame

    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', .....].

  17. Pandas Add Column Names to DataFrame

    You can add column names to pandas DataFrame while creating manually from the data object. In order to create a DataFrame, you would use a DataFrame constructor which takes a columns param to assign the names. It takes a list as a value and the number of values in a list should not exceed the number of columns in DataFrame.

  18. Pandas Assign New Columns to a DataFrame

    The assign () method is used to assign new columns to a pandas dataframe. It has the following syntax. df.assign (col_1=series_1, col_2=series2,...) 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 ...

  19. How to get/set a pandas index column title or name?

    10 Answers Sorted by: 605 You can just get/set the index via its name property In [7]: df.index.name Out [7]: 'Index Title' In [8]: df.index.name = 'foo' In [9]: df.index.name Out [9]: 'foo' In [10]: df Out [10]: Column 1 foo Apples 1 Oranges 2 Puppies 3 Ducks 4 Share Follow answered Aug 2, 2013 at 18:08 Jeff 127k 21 221 189 13

  20. PDF Advanced Data Management (CSCI 640/490)

    pandas • Contains high-level data structures and manipulation tools designed to make ... • Can reassign column names by passing columns kwarg D. Koop, CSCI 640/490, Spring 2024 7. Data Frame D. Koop, CSCI 640/490, Spring 2024 8. Data Frame ... • assign allows new columns to be created, returns "new" dataframe - df2 = df.assign(Total=df ...

  21. Rename unnamed column pandas dataframe

    9 Answers Sorted by: 59 You can view the current dataframe using data.head () if that returns 'Unnamed: 0' as the column title, you can rename it in the following way: data.rename ( columns= {'Unnamed: 0':'new column name'}, inplace=True ) Share Improve this answer Follow edited May 10, 2018 at 4:43 domwrap 443 1 4 12

  22. pandas assign with new column name as string

    pandas assign with new column name as string Ask Question Asked 7 years, 4 months ago Modified 10 months ago Viewed 40k times 70 I recently discovered pandas "assign" method which I find very elegant. My issue is that the name of the new column is assigned as keyword, so it cannot have spaces or dashes in it.

  23. python

    As such, where column names match between left and right, the values should also match, but there will be additional columns in right that may or may not be present in left. Furthermore, I have many right s and each will be merged into left , producing a new value for left , and the code I'm trying to write here will be part of a for-loop that ...