Handling Missing Data

Missing data is very common in many data science applications, and pandas is well equipped to handle such situations. Most of the time, the missing data is also referred to as NA or NaN. Let’s learn some convenient methods to deal with missing data in Pandas.

Let’s create a DataFrame with missing data. We can use NumPy’s functionality to add missing data.