Using Python to handle missing data in a dataset usually requires multiple stages. Import the required libraries first, like pandas. Take advantage of pd.read_csv() or a comparable tool to load your dataset into a DataFrame.
Identifying Missing Data: To find missing values in each column, use functions like df.isnull().sum().
Drop Missing Data: Use df.dropna() to eliminate rows or columns that have missing values. This works well when there aren't many missing data points.
Fill Missing Data: Use df.fillna(value) to substitute a specified value for any missing values. Typical tactics involve utilizing the column's mean, median, or mode for filling.
Interpolate Missing Data: For linear interpolation of missing values—useful in time series data—use df.interpolate().
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