Split dataframe in Pandas based on values in multiple columns

In this article, we are going to see how to divide a dataframe by various methods and based on various parameters using Python. To divide a dataframe into two or more separate dataframes based on the values present in the column we first create a data frame.

Creating a DataFrame for demonestration

Output:

Split dataframe based on values By Boolean Indexing

We can create multiple dataframes from a given dataframe based on a certain column value by using the boolean indexing method and by mentioning the required criteria.

Example 1: Creating a dataframe for the students with Score >= 80

Output:

Example 2: Creating a dataframe for the students with Last_Name as Mishra

Output:

We can do the same for other columns as well by putting the appropriate condition

Split dataframe based on values Boolean Indexing with mask variable

We create a mask variable for the condition of the column in the previous method

Example 1: To get dataframe of students with Degree as MBA

Output :

Example 2: To get a dataframe for the rest of the students

To get the rest of the values in a dataframe we can simply invert the mask variable by adding a ~(tilde) after it.

Output :

Split dataframe based on values Using groupby() function

Using groupby() we can group the rows using a specific column value and then display it as a separate dataframe.

Example 1: Group all Students according to their Degree and display as required

Output: dataframe of students with Degree as MBA

Example 2: Group all Students according to their Score and display as required

Output: dataframe of students with Score = 90.