python assign multiple columns

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Pandas: Assigning multiple new columns simultaneously

I have a DataFrame df with a column containing labels for each row (in addition to some relevant data for each row). I have a dictionary labeldict with keys equal to the possible labels and values equal to 2-tuples of information related to that label. I'd like to tack two new columns onto my frame, one for each part of the 2-tuple corresponding to the label for each row.

Here is the setup:

I can get what I want by running:

But how can I do this if I don't want to manually type out the two columns on the left side of the assignment? I.e. how can I create multiple new columns on the fly. For example, if I had 10-tuples in labeldict instead of 2-tuples, this would be a real pain as currently written. Here are a couple things that don't work:

This does work, but seems like a hack:

Better solutions?

2 FYI your last method will work in 0.13 (without initially creating the column) – Jeff Dec 29, 2013 at 21:55
Thanks Jeff. I have a similar question inspired by your comment here. I am having a similar problem to the one stated here – Amelio Vazquez-Reina Sep 17, 2014 at 16:58
Seems like the answer is pythonically simple: stackoverflow.com/a/34074894/3494126 – Ufos Oct 8, 2019 at 9:57

6 Answers 6

Just use result_type='expand' in pandas apply

and here some copy paste code

Use of apply was what I wanted but although this answer was helpful it made the assignment and function interdependent based on the order of the columns used as input and output. I ended up using the approach from @EricNess instead to capture this mapping entirely within the helper function. – deaks Feb 12, 2020 at 23:24
Better use this anser: stackoverflow.com/a/76098907/7128154 – Markus Dutschke Apr 25 at 8:08

You can use merge instead:

4 you can get away without the reset_index using merge's left_index and right_on. – Andy Hayden Dec 30, 2013 at 0:50
Note that merge generates a new dataframe instead of adding columns to an existing one. Also if there is no index column in the dataframe of your problem, you can't use merge. – Louis Yang Dec 19, 2020 at 9:18

Instead of doing what you're doing with labeldict, you could make that information into a DataFrame and then join it with your original one:

If you want to add multiple columns to a DataFrame as part of a method chain, you can use apply . The first step is to create a function that will transform a row represented as a Series into the form you want. Then you can call apply to use this function on each row.

This should work:

pandas apply method support this out of the box

A few years after writing my first answer to this question I got a bit better overview of the mechanics in pandas.

The func argument in apply takes a function: pd.Series -> Any whereas from the type of Any the exact processing of the returned value into the DataFrame is deduced.

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function.

If you pass a lambda function: pd.Series -> pd.Series , you get exactly the required behavior.

apply can also be applied on pd.groupby outputs, achieving a more flexible alternative to .aggregate . You can find an example in the question: Python pandas groupby aggregate on multiple columns, then pivot

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Pandas Add Multiple Columns to DataFrame

Post author: Malli
Post category: Pandas / Python
Post last modified: March 6, 2023

In pandas you can add/append multiple columns to the existing DataFrame using assign() function, this function updates the existing DataFrame with new multiple columns. DataFrame.insert() is also used to insert multiple columns however, this function returns a new Dataframe after adding columns. In this article, I will explain several ways how to add multiple columns to a DataFrame with examples.

Please enable JavaScript

1. Quick Examples of Add Multiple Columns DataFrame

If you are in a hurry, below are some quick examples of how to add multiple columns to DataFrame.

Now, Let’s create Pandas DataFrame using data from a Python dictionary , where the columns are Courses , Fee , Duration and Discount .

Yields below output.

2. Pandas Add Multiple Columns to a DataFrame Using df[]

Using df[] notation we can add multiple columns to Pandas DataFrame. This is the best example when we want to add a single column or multiple columns to DataFrame. Take two columns "Tutors” , and "Percent" as a list and pass them into df[] notation which will add these columns to the given DataFrame.

3. Add Multiple Columns Using Dataframe.assign()

DataFrame.assign() is also used to add/append multiple columns to the Pandas DataFrame, this function returns a new DataFrame after adding multiple columns to the existing DataFrame. Now let’s add multiple columns "Tutors” , and "Percent" to the DataFrame. Using assign() we cannot modify the existing DataFrame in place instead it returns a new DataFrame after adding multiple columns.

Yields the same output as above.

4. Pandas Add Multiple Columns Using insert()

DataFrame.insert() function is another function to add multiple columns to Pandas DataFrame at any position. Using this you can specify the index where you would like to add multiple columns. The below example adds multiple columns at the first position (Index 0) and fifth position (Index 4).

Note that in pandas, the Index starts from zero. insert() function updates the existing DataFrame object with the new multiple columns.

5. Complete Example of Adding Multiple Columns

6. conclusion.

In this article, I have explained how to add/append multiple columns to the existing Pandas DataFrame by using df[] , DataFrame.assing(), and DataFrame.insert() e.t.c. Also learned insert() is used to add multiple columns at any position of the DataFrame.

Happy Learning !!

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https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.insert.html

Add multiple columns to dataframe in Pandas

In Pandas, we have the freedom to add columns in the data frame whenever needed. There are multiple ways to add columns to pandas dataframe.

Add multiple columns to a DataFrame using Lists

Add DataFrame columns using Lists

Add multiple columns to a data frame using Dataframe.assign() method

Using DataFrame.assign() method, we can set column names as parameters and pass values as list to replace/create the columns.

Added multiple columns using DataFrame assign() Method

Add multiple columns to a data frame using Dataframe.insert() method

Using DataFrame.insert() method, we can add new columns at specific position of the column name sequence. Although insert takes single column name, value as input, but we can use it repeatedly to add multiple columns to the DataFrame.

Added multiple columns using DataFrame insert() Method

Add multiple columns to a data frame using Dictionary and zip()

Using Dict and zip() we can create a mapping of key values, which can be assigned to a new column name.

Added multiple columns using Dictionary and zip()

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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!

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How to add multiple columns to pandas dataframe in one assignment

I'm new to pandas and trying to figure out how to add multiple columns to pandas simultaneously. Any help here is appreciated. Ideally I would like to do this in one step rather than multiple repeated steps.

python-programming

Your comment on this question:

1 answer to this question., your answer.

Hello @kartik,

You could use assign with a dict of column names and values.

Hope it helps!!

ask related question

Your comment on this answer:

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Using loc with assignment and multiple columns fails #16187

cancan101 commented May 1, 2017

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jreback commented May 1, 2017

Sorry, something went wrong.

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Select Multiple Columns of Pandas DataFrame in Python (4 Examples)

In this Python article you’ll learn how to extract certain columns of a pandas DataFrame .

The article will consist of four examples for the selection of DataFrame variables. To be more precise, the article is structured as follows:

Here’s how to do it:

pandas Library Creation of Example Data

As the first step, we have to import the pandas library to Python:

import pandas as pd # Load pandas

Next, we can create an example pandas DataFrame by running the following Python syntax:

As you can see based on the previous output, we have created a pandas DataFrame with five rows and five variables called x1, x2, x3, x4, and x5.

In the following examples, I’ll explain how to select some of these variables and how to store them in a new data set.

Keep on reading!

Example 1: Extract DataFrame Columns Using Column Names & Square Brackets

This example shows how to use the names of our variables and square brackets to subset our pandas DataFrame.

Have a look at the following Python code:

As you can see, we have created a new pandas DataFrame called data_new1 that contains only the variables x1, x3, and x5. The columns x2 and x4 have been dropped.

Looks good!

However, the Python programming language provides many alternative ways on how to select and remove DataFrame columns. In the following examples I’ll show some of these alternatives!

Example 2: Extract DataFrame Columns Using Column Names & DataFrame Function

In this example, I’ll illustrate how to use the column names and the DataFrame() function of the pandas library to get a new DataFrame with specific variables.

Check out the following syntax and its output:

We have created another pandas DataFrame called data_new2, which contains exactly the same variables and values as the DataFrame that we have created in Example 1. However, this time we have used the DataFrame() function.

Example 3: Extract DataFrame Columns Using Indices & iloc Attribute

So far, we have subsetted our DataFrame using the names of our columns. However, it is also possible to use the column indices to select certain variables from a DataFrame.

The following Python syntax demonstrates how to use the iloc command in combination with the column index to retain only some variables of our input DataFrame:

Again, we have created the same output as in the previous examples.

Example 4: Extract DataFrame Columns Using Indices & columns Attribute

In Example 4, I’ll illustrate another alternative on how to use column indices to keep only particular columns.

More precisely, we are using the columns argument to retain certain variables:

Even though we have used a different code, the output is again the same as in the previous examples. So as you have seen, we have many alternatives when we want to remove unnecessary variables from a data matrix.

Video & Further Resources on the Topic

Any questions left? I have recently released a video on my YouTube channel , which shows the Python syntax of this article. You can find the video below:

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Have a look at the following video that was published by Corey Schafer on his YouTube channel. He’s illustrating some examples on how to select rows and columns of a pandas DataFrame in the video.

In addition to the video, you may want to read some of the related articles of my website:

Select Rows of pandas DataFrame by Index in Python
Extract Top & Bottom N Rows from pandas DataFrame
pandas DataFrames Operations in Python
Modify & Edit pandas DataFrames in Python
Python Programming Overview

In this Python tutorial you have learned how to subset a DataFrame . In case you have any further questions, let me know in the comments.

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