Dealing with Missing Data
Explore methods to manage missing data in datasets using Python's Pandas library. Learn to detect NaN values, remove incomplete data with dropna, and replace missing entries with fillna for cleaner, more accurate predictive analysis.
We'll cover the following...
We'll cover the following...
Missing data
Until now, we have encountered various scenarios where NaN values were obtained and identified. This lesson focuses on removing or replacing these values based on the requirements. The ffill() method is already discussed here; it deals with NaN values resulting from reindexing. Some more additional functions on how to handle NaN values are discussed below.
isnull(): This function returns an object with all