🤜🏻How to eliminate missing values in datasets for ML: 3 Python-functions easy to use
• fillna() - function is available in the pandas package. It is used to fill null (NA/NaN) values. It returns an object as output in which null/missing values are filled. Series.fillna(value=None, method=None, axis=None, inplace=False, **kwargs)
• dropna() - function to remove or drop null values from the data in different ways. This function analyzes and drops the rows/columns from the data that contain missing/NaN values. The parameter axis=0 indicates to drop rows that contain missing values and axis=1 is used to drop the columns. DataFrame.dropna(axis=0, how=’any’, thresh=None, subset=None, inplace=False)
• interpolate() - function to fill missing/NaN values using different interpolation techniques to fill the missing data. DataFrame.interpolate(method=’linear’, axis=0, limit=None, inplace=False, limit_direction=’forward’, limit_area=None, downcast=None, **kwargs)