To filter the rows based on such a function, use the conditional function inside the selection brackets []. Selecting rows in pandas DataFrame based on conditions Step 1: Data Setup. This tutorial will introduce how we can create new columns in Pandas DataFrame based on the values of other columns in the DataFrame by applying a function to each element of a column or using the DataFrame.apply () method. Pandas creates data frames to process the data in a python program. Create new row in Pandas DataFrame based on conditions 2. gapminder ['gdpPercap_ind'] = gapminder.gdpPercap.apply(lambda x: 1 if x >= 1000 else 0) gapminder.head () 1. Otherwise, if the number is greater than 4, then assign the value of 'False'. There could be instances when we have more than two values, in that case, we can use a dictionary to map new values onto the keys. The method works by using split, transform, and apply operations. Example 4: add a value to an existing field in pandas dataframe after checking conditions gapminder['gdpPercap_ind'] = gapminder.gdpPercap.apply(lambda x: 1 if x >= 1000 else 0 . loc[ data ['x3']. Actually we don't have to rely on NumPy to create new column using condition on another column. In this article, I will explain several ways of how to create a conditional DataFrame column (new) with examples . 1. 10. . Pandas' loc creates a boolean mask, based on a condition. For example, if we have a function f that sum an iterable of numbers (i.e. How To Create a Column Using Condition on Another Column in Pandas? With examples. Changing boolean value in pandas dataframe through function Create a new column using if else python pandas code snippet