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How to use ColumnTransformer to deal with different types of data in Python, I believe that many inexperienced people do not know what to do. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

Applying data transformations, such as scaling or encoding classification variables, is simple when all input variables are of the same type. The Python machine learning library scikit-learn provides ColumnTransformer to optionally apply data transformations to different columns in the dataset.

There are many data transformations commonly used in your work, such as the SimpleImputer class can be used to replace missing values, the MinMaxScaler class can be used to scale values, and OneHotEncoder can be used to encode classification variables.

... # prepare transformscaler = MinMaxScaler () # fit transform on training datascaler.fit (train_X) # transform training datatrain_X = scaler.transform (train_X) how to use ColumnTransformer?

ColumnTransformer can optionally perform data conversion in Python's machine learning library scikit-learn. For example, it allows specific transformations or transformation sequences to be applied only to numeric columns, while separate transformation sequences are applied only to category columns.

To use ColumnTransformer, you must specify a list of converters. Each converter is a three-element tuple that defines the name of the converter, the transformation to apply, and the column index to be applied to it, for example: (name, object, column).

For example, the following ColumnTransformer applies OneHotEncoder to columns 0 and 1.

Transformer = ColumnTransformer (transformers= [('cat', OneHotEncoder (), [0,1])])

The following example applies SimpleImputer with median interpolation to numeric columns 0 and 1, and SimpleImputer with the most frequent interpolation to classification columns 2 and 3.

T = [('num', SimpleImputer (strategy='median'), [0,1]), (' cat', SimpleImputer (strategy='most_frequent'), [2,3])] transformer = ColumnTransformer (transformers=t)

For example, if columns 0 and 1 are numeric columns, and columns 2 and 3 are classified columns, and we only want to transform the classified data without changing the numeric columns, we can define ColumnTransformer as follows:

Transformer = ColumnTransformer (transformers= [('cat', OneHotEncoder (), [2,3])], remainder='passthrough')

ColumnTransformer can also be used in pipes to selectively prepare the columns of the dataset before fitting the model to the transformed data. This is the most likely use case because it ensures that the transformation of the original data is performed automatically when fitting the model and making predictions, such as evaluating the model on the test dataset through cross-validation or predicting new data.

. # define modelmodel = LogisticRegression () # define transformtransformer = ColumnTransformer (transformers= [(('cat', OneHotEncoder (), [0,1])]) # define pipelinepipeline = Pipeline (steps= [((' tweets, transformer), ('massively transformer)]) # fit the model on the transformed datamodel.fit (train_X, train_y) # make predictionsyhat = model.predict (test_X) after reading the above, have you mastered how to use ColumnTransformer to process different types of data in Python? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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