How to check for missing values in a Pandas DataFrame using Python?

I was learning about models, how data is important for them, and how crucial it is to clean the data before giving it to a model for training. I then explored a dataset and noticed that there were some missing values in the first few rows of the dataset. Is there a way to process the complete dataset at once and find all the missing values it contains? If there are methods and techniques for finding this, please provide them and give an example code if possible.

Hi @mubashir_rizvi! This would help you:

  • The isnull() method is used to check if a value in a Pandas DataFrame or Series is null or missing. It returns a boolean array of the same shape as the input.
  • Since we only want to check if there are missing values or not, we use the any() method to check if there is at least one True value in the results provided by the isnull() method.

@mubashir_rizvi, there are many methods and approaches available for this purpose, but it depends on the data and your needs.

  • To find missing values, you can use the notna() method to return a boolean DataFrame where True values represent non-missing values and False represents missing values.
  • You can use the all() method to check if all values in the DataFrame are True, if the result is False, it means you have missing values in the data.

The info() method in Pandas DataFrame provides information such as the number of non-null values and total entries per column, making it easy to identify issues and inconsistencies in the data. Here is the code below for your better understanding: