DataFrames often contain missing values, represented as None or np.NaN. When working with dataframes, it's essential to identify these missing values. For this purpose, pandas offers specific functions.

The first of these is isna(), which returns a boolean dataframe. In this context, a True value indicates a missing value within the dataframe, while a False value suggests the value is present.

For clarity, let's review an example. Run the code provided to see a dataframe filled with True and False values, offering a clearer understanding.

The second function is isnull(). It behaves identically to the previous one, with no discernible difference between them. Let's examine its application. Running the accompanying code will show if the resulting DataFrame matches the one from the prior example.