conditional assignment in pandas dataframe

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5 ways to apply an IF condition in Pandas DataFrame

In this guide, you’ll see 5 different ways to apply an IF condition in Pandas DataFrame.

Specifically, you’ll see how to apply an IF condition for:

• Set of numbers
• Set of numbers and lambda
• Strings and lambda
• OR condition

Applying an IF condition in Pandas DataFrame

Let’s now review the following 5 cases:

(1) IF condition – Set of numbers

Suppose that you created a DataFrame in Python that has 10 numbers (from 1 to 10). You then want to apply the following IF conditions:

• If the number is equal or lower than 4, then assign the value of ‘ Yes ‘
• Otherwise, if the number is greater than 4, then assign the value of ‘ No ‘

This is the general structure that you may use to create the IF condition:

For our example, the Python code would look like this:

Here is the result that you’ll get in Python:

(2) IF condition – set of numbers and  lambda

You’ll now see how to get the same results as in case 1 by using lambda, where the conditions are:

Here is the generic structure that you may apply in Python:

For our example:

This is the result that you’ll get, which matches with case 1:

(3) IF condition – strings

Now, let’s create a DataFrame that contains only strings/text with 4  names : Jon, Bill, Maria and Emma.

The conditions are:

• If the name is equal to ‘Bill,’ then assign the value of ‘ Match ‘
• Otherwise, if the name is not   ‘Bill,’ then assign the value of ‘ Mismatch ‘

Once you run the above Python code, you’ll see:

(4) IF condition – strings and lambda 

You’ll get the same results as in case 3 by using lambda:

And here is the output from Python:

(5) IF condition with OR

Now let’s apply these conditions:

• If the name is ‘Bill’  or ‘Emma,’ then assign the value of ‘ Match ‘
• Otherwise, if the name is neither ‘Bill’ nor ‘Emma,’ then assign the value of ‘ Mismatch ‘

Run the Python code, and you’ll get the following result:

Applying an IF condition under an existing DataFrame column

So far you have seen how to apply an IF condition by creating a new column.

Alternatively, you may store the results under an existing DataFrame column.

For example, let’s say that you created a DataFrame that has 12 numbers, where the last two numbers are zeros :

‘set_of_numbers’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0 , 0 ]

You may then apply the following IF conditions, and then store the results under the existing ‘ set_of_numbers ‘ column:

• If the number is equal to 0 , then change the value to 999
• If the number is equal to 5 , then change the value to 555

Here are the before and after results, where the ‘5’ became ‘555’ and the 0’s became ‘999’ under the existing ‘set_of_numbers’ column:

On another instance, you may have a DataFrame that contains NaN values . You can then apply an IF condition to replace those values with zeros , as in the example below:

Before you’ll see the NaN values, and after you’ll see the zero values:

You just saw how to apply an IF condition in Pandas DataFrame . There are indeed multiple ways to apply such a condition in Python. You can achieve the same results by using either lambda, or just by sticking with Pandas.

At the end, it boils down to working with the method that is best suited to your needs.

Finally, you may want to check the following external source for additional information about Pandas DataFrame .

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Set Pandas Conditional Column Based on Values of Another Column

• August 9, 2021 February 22, 2022

There are many times when you may need to set a Pandas column value based on the condition of another column. In this post, you’ll learn all the different ways in which you can create Pandas conditional columns.

Table of Contents

Video Tutorial

If you prefer to follow along with a video tutorial, check out my video below:

Loading a Sample Dataframe

Let’s begin by loading a sample Pandas dataframe that we can use throughout this tutorial.

We’ll begin by import pandas and loading a dataframe using the .from_dict() method:

This returns the following dataframe:

Using Pandas loc to Set Pandas Conditional Column

Pandas loc is incredibly powerful! If you need a refresher on loc (or iloc), check out my tutorial here . Pandas’ loc creates a boolean mask, based on a condition. Sometimes, that condition can just be selecting rows and columns, but it can also be used to filter dataframes. These filtered dataframes can then have values applied to them.

Let’s explore the syntax a little bit:

With the syntax above, we filter the dataframe using .loc and then assign a value to any row in the column (or columns) where the condition is met.

Let’s try this out by assigning the string ‘Under 30’ to anyone with an age less than 30, and ‘Over 30’ to anyone 30 or older.

Let's take a look at what we did here:

• We assigned the string 'Over 30' to every record in the dataframe. To learn more about this, check out my post here or creating new columns.
• We then use .loc to create a boolean mask on the Age column to filter down to rows where the age is less than 30. When this condition is met, the Age Category column is assigned the new value 'Under 30'

But what happens when you have multiple conditions? You could, of course, use .loc multiple times, but this is difficult to read and fairly unpleasant to write. Let's see how we can accomplish this using numpy's .select() method.

Using Numpy Select to Set Values using Multiple Conditions

Similar to the method above to use .loc to create a conditional column in Pandas, we can use the numpy .select() method.

Let's begin by importing numpy and we'll give it the conventional alias np :

Now, say we wanted to apply a number of different age groups, as below:

• <20 years old,
• 20-39 years old,
• 40-59 years old,
• 60+ years old

In order to do this, we'll create a list of conditions and corresponding values to fill:

Running this returns the following dataframe:

Let's break down what happens here:

• We first define a list of conditions in which the criteria are specified. Recall that lists are ordered meaning that they should be in the order in which you would like the corresponding values to appear.
• We then define a list of values to use , which corresponds to the values you'd like applied in your new column.

Something to consider here is that this can be a bit counterintuitive to write. You can similarly define a function to apply different values. We'll cover this off in the section of using the Pandas .apply() method below .

One of the key benefits is that using numpy as is very fast, especially when compared to using the .apply() method.

Using Pandas Map to Set Values in Another Column

The Pandas .map() method is very helpful when you're applying labels to another column. In order to use this method, you define a dictionary to apply to the column.

For our sample dataframe, let's imagine that we have offices in America, Canada, and France. We want to map the cities to their corresponding countries and apply and "Other" value for any other city.

When we print this out, we get the following dataframe returned:

What we can see here, is that there is a NaN value associated with any City that doesn't have a corresponding country. If we want to apply "Other" to any missing values, we can chain the .fillna() method:

Using Pandas Apply to Apply a function to a column

Finally, you can apply built-in or custom functions to a dataframe using the Pandas .apply() method.

Let's take a look at both applying built-in functions such as len() and even applying custom functions.

Applying Python Built-in Functions to a Column

We can easily apply a built-in function using the .apply() method. Let's see how we can use the len() function to count how long a string of a given column.

Take note of a few things here:

• We apply the .apply() method to a particular column,
• We omit the parentheses "()"

Using Third-Party Packages in Pandas Apply

Similarly, you can use functions from using packages. Let's use numpy to apply the .sqrt() method to find the scare root of a person's age.

Using Custom Functions with Pandas Apply

Something that makes the .apply() method extremely powerful is the ability to define and apply your own functions.

Let's revisit how we could use an if-else statement to create age categories as in our earlier example:

In this post, you learned a number of ways in which you can apply values to a dataframe column to create a Pandas conditional column, including using .loc , .np.select() , Pandas .map() and Pandas .apply() . Each of these methods has a different use case that we explored throughout this post.

Learn more about Pandas methods covered here by checking out their official documentation:

• Pandas Apply
• Numpy Select

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

2 thoughts on “Set Pandas Conditional Column Based on Values of Another Column”

Thank you so much! Brilliantly explained!!!

Thanks Aisha!

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How to Apply the If-Else Condition in a Pandas DataFrame

• Python Pandas Howtos
• How to Apply the If-Else Condition in a …

Use DataFrame.loc[] to Apply the if-else Condition in a Pandas DataFrame in Python

Use dataframe.apply() to apply the if-else condition in a pandas dataframe in python, use numpy.select() to apply the if-else condition in a pandas dataframe in python, use lambda with apply() to apply the if-else condition in a pandas dataframe in python.

Pandas is an open-source data analysis library in Python. It provides many built-in methods to perform operations on numerical data.

In some cases, we want to apply the if-else conditions on a Pandas dataframe to filter the records or perform computations according to some conditions. Python provides many ways to use if-else on a Pandas dataframe.

loc[] is a property of the Pandas data frame used to select or filter a group of rows or columns. In the following example, we will employ this property to filter the records that meet a given condition.

Here, we have a Pandas data frame consisting of the students’ data. Using loc[] , we can only apply a single condition at a time.

We will filter those students having marks greater than or equal to 60 in the first condition and assign their result as Pass in the new column Result . Similarly, we will set Fail for the rest of the student’s results in another condition.

Example Code:

The apply() method uses the data frame’s axis (row or column) to apply a function. We can make our defined function that consists of if-else conditions and apply it to the Pandas dataframe.

Here, we have defined a function assign_Result() and applied it to the Marks column. The function consists of if-else conditions that assign the result based on the Marks and invoke this for every column row.

We can define multiple conditions for a column in a list and their corresponding values in another list if the condition is True . The select() method takes the list of conditions and their corresponding list of values as arguments and assigns them to the Result column.

A lambda is a small anonymous function consisting of a single expression. We will use lambda with apply() on the Marks column.

The x contains the marks in the lambda expression. We applied the if-else condition to the x and assigned the result accordingly in the Result column.

I am Fariba Laiq from Pakistan. An android app developer, technical content writer, and coding instructor. Writing has always been one of my passions. I love to learn, implement and convey my knowledge to others.

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Generally on a Pandas DataFrame the if condition can be applied either column-wise, row-wise, or on an individual cell basis. The further document illustrates each of these with examples.

First of all we shall create the following DataFrame : 

Output : 

Example 1 : if condition on column values (tuples) : The if condition can be applied on column values like when someone asks for all the items with the MRP <=2000 and Discount >0 the following code does that. Similarly, any number of conditions can be applied on any number of attributes of the DataFrame. 

Example 2 : if condition on row values (tuples) : This can be taken as a special case for the condition on column values. If a tuple is given (Sofa, 5000, 20) and finding it in the DataFrame can be done like :

Example 3 : Using Lambda function : Lambda function takes an input and returns a result based on a certain condition. It can be used to apply a certain function on each of the elements of a column in Pandas DataFrame. The below example uses the Lambda function to set an upper limit of 20 on the discount value i.e. if the value of discount > 20 in any cell it sets it to 20.

Example 4 : Using iloc() or loc() function : Both iloc() and loc() function are used to extract the sub DataFrame from a DataFrame. The sub DataFrame can be anything spanning from a single cell to the whole table. iloc() is generally used when we know the index range for the row and column whereas loc() is used on a label search.

The below example shows the use of both of the functions for imparting conditions on the Dataframe. Here a cell with index [2, 1] is taken which is the Badminton product’s MRP. 

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Pandas: How to create new column using multiple if-else conditions (4 examples)

Introduction.

When working with data in Python, the Pandas library stands out for its powerful data manipulation capabilities. One frequent need is to create new columns based on conditions applied to existing ones. In this tutorial, we’ll explore four examples of how to use multiple if-else conditions to create new columns in a Pandas DataFrame, ranging from basic to more advanced scenarios. These techniques are essential for data preprocessing, feature engineering, and data analysis tasks.

Setup: Import Pandas and Create a Sample DataFrame

First, let’s import the Pandas library and create a sample DataFrame to work with:

Example 1: Basic If-Else Condition

Let’s start with a simple scenario where we create a new column, ‘Adult’ , to indicate whether each person is an adult (18 or over) or not:

The output should show our DataFrame with the new column:

Example 2: Advanced If-Else with Multiple Conditions

Next, let’s create a new column, ‘Financial Status’ , based on multiple conditions conditioned on the ‘Salary’ and ‘Age’ columns:

The output would look like this:

Example 3: Using np.where

Now, for a more concise way to implement conditional logic, we turn to np.where from the NumPy library. Here, we’ll use it to add a ‘Student’ column, indicating whether the individual is likely a student.

The resulting DataFrame:

Example 4: Using pd.cut for Categorical Variables

For our final example, we’ll categorize the ‘Age’ column into bins to create a new ‘Age Group’ column. This is particularly useful when working with continuous data that you’d like to analyze categorically.

The updated DataFrame would look like:

Creating new columns based on multiple if-else conditions is a fundamental technique in data manipulation with Pandas. Through these examples, we’ve explored various approaches from basic to advanced, including logical operations, np.where , and pd.cut . Mastering these techniques allows for efficient and effective data analysis, enabling data scientists to gain deeper insights from their datasets.

Next Article: Pandas: How to import a CSV file into a DataFrame

Previous Article: Pandas DataFrame: How to group rows by ranges of values

Series: DateFrames in Pandas

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Home » Python » Python Programs

Vectorize conditional assignment in pandas dataframe

Given a pandas dataframe, we have to vectorize conditional assignment in pandas dataframe. By Pranit Sharma Last updated : October 03, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Problem statement

We are given a DataFrame df with some columns and we want to create a new column based on some previous columns.

We want to apply some conditions like if the value of a column is less then some specific value then the value of a new column is some new specific value. If the value of that column is some other specific value then the value of the new column would be some new specific value and so on.

Vectorize conditional assignment

We will use pandas.DataFrame.loc property of pandas so that we can access the exact element that fits the condition and we can set the value of a new column for each value of the old column.

The pandas.DataFrame.loc property is a type of data selection method which takes the name of a row or column as a parameter. To perform various operations using the pandas.DataFrame.loc property, we need to pass the required condition of rows and columns in order to get the filtered data.

Let us understand with the help of an example,

Python program to vectorize conditional assignment in pandas dataframe

The output of the above program is:

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Popular Tutorials

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

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

assign() Syntax

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

assign() Argument

The assign() method takes the following argument:

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

assign() Return Value

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

Example 1: Basic Column Assignment

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

Example 2: Assignment Using Functions

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

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

Example 3: Multiple Column Assignments

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

Example 4: Chaining Assignments

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

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