Python uses Features engine's MeanMedianImputer, CategoricalInput, EndTailInput, and other functions to handle missing values

Preparation work: 1. Install Python: Ensure that Python is installed and can be run from the command line. 2. Install the Feature engine library: Run the following command from the command line to install the Feature engine library. pip install feature_engine 3. Prepare data: You can use any dataset that contains missing values, such as a Pandas DataFrame object. Dependent class libraries: -Feature_ Engine library: provides a series of classes and functions for Feature engineering, including MeanMedianImputer, CategoricalInput, EndTailInput and other functions. The example code is as follows: python import pandas as pd from feature_engine.imputation import MeanMedianImputer, CategoricalImputer, EndTailImputer #Prepare data samples data = {'col1': [1, 2, None, 4, 5], 'col2': ['A', 'B', None, 'D', 'E'], 'col3': [1.0, 2.0, None, 4.0, None]} df = pd.DataFrame(data) #Using the MeanMedianImputer function to handle numerical missing values num_imputer = MeanMedianImputer(imputation_method='median') df['col1_imputed'] = num_imputer.fit_transform(df['col1']) #Using the CategoricalInput function to handle missing values for subtypes cat_imputer = CategoricalImputer(variables=['col2']) df['col2_imputed'] = cat_imputer.fit_transform(df['col2']) #Using the EndTailInput function to handle tail missing values tail_imputer = EndTailImputer(imputation_method='linear', fold=1.5, variables=['col3']) df['col3_imputed'] = tail_imputer.fit_transform(df['col3']) #Print processed data print(df) Summary: The above example code demonstrates how to use functions such as MeanMedianImputer, CategoricalInput, and EndTailInput in the Feature engine library to handle different types of missing values. In actual use, different functions can be used to deal with different types of missing values as needed, and function parameters can be adjusted according to specific circumstances. Through the functions provided by the Feature engine library, missing values in data can be easily processed, thereby improving the quality and accuracy of the data.