How to sort pandas dataframe based on a column and put missing values first?

Python provides various modules and function to sort Dataframe. Sort_values in Pandas helps in sorting Pandas Dataframe. One key challenge with sorting is presence of missing or NA values. Na values are grouped into one category and placed in the bottom of the Dataframe. In case the size of Dataframe is huge, one cannot know if the variable has NA values present while printing top rows of the file. We will discuss step by step guide on How to sort pandas dataframe based on a column and put missing values first?

Amy has employee data available with her for her company. She is looking to sort the dataset by Gender column. During sorting she wants to keep all the missing Gender rows at the top.

Below are the key steps Amy has to follow:

• Step 1: Import all the necessary modules in Python.

import pandas as pd

• Step 2: Use sort_values function to sort Dataframe with changed na_position. na_position is by default set to ‘last’. Therefore by default NA values comes in the last of the Dataframe. To change this default behaviour, we will update the syntax as below.

Employee_Data2=Employee_Data.sort_values(['gender'],na_position='first')

• Step 3: Check the quality of output data. Use Dataframe.head to look top rows in the Dataframe.

Employee_Data2.head(20)

Thus, Amy is able to create single Dataframe as per her requirement in Python. How to sort pandas dataframe based on a column and put missing values first is no more a challenge for her.

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