5 essential Python functions for handling missing data:

🔹 isna(): Detects missing values in your DataFrame. Identifies NaNs

🔹 notna(): Detects non-missing values. Filters out the NaNs.

🔹 interpolate(): Fills missing values using interpolation

🔹 bfill(): Backward fill. Fills missing values with the next valid observation.

🔹 ffill(): Forward fill. Fills missing values with the previous valid observation.