Python for Finance Cookbook

How it works...

In Step 1, we imported the required libraries. The list can look a bit daunting, but that is due to the fact that we need to combine multiple functions/classes used in the previous recipes.

In Step 2, we loaded the data from a CSV file, separated the target variable and created the stratified train-test split. Next, we also created two lists containing the names of the numerical/categorical features—we will apply different transformations depending on the type of the feature. To select the appropriate columns, we used the select_dtypes method.

In Step 4, we started preparing the separate Pipelines. For the numerical one, we only wanted to impute the missing values of the features using the column median. For the Pipeline