How to Check for NaN Values when Applying a Function on a DataFrame

Nan values in a Pandas DataFrame can be tricky to work with. In this post, I’ll show you how to correctly account for them when writing a function with conditionals.

To check if the value in the column is not null, you can wrap the column in pd.notnull(x['column_name']) or pd.isnull(x['column_name'])

Here is an example function:

def some_function(x):
  
  if pd.notnull(x['column_name']):
    return 'this value is not null'
  
  elif pd.isnull(x['column_name']):
    return 'this value is null'
  
  else:
    pass
  

Further Explanation

Here’s an example of how you can run the above function on a DataFrame.

There are many ways to check if a value is NaN in Python but it’s important to wrap the cell value in the pd.notnull() or pd.isnull() operators to get your desired results when using Pandas. I thought you could treat the value as any variable and try the test of == None or == pd.nan, but these just return False in every case.

Thanks for reading!