Scikit-learn Imputer with multiple values

Question

Is there a way for a Scikit-learn Imputer to look for and replace multiple values which are considered "missing values"?

For example, I would like to do something like

imp = Imputer(missing_values=(7,8,9))

But according to the docs, the missing_values parameter only accepts a single integer:

missing_values : integer or “NaN”, optional (default=”NaN”)

The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.

First replace all the values you want to impute to Nan, as given in the answer below. — Vivek Kumar
– Vivek Kumar, Commented Jun 12, 2018 at 6:20

Jan K · Accepted Answer · 2018-06-11 22:38:28Z

Why not to do this manually in your original dataset? Assuming you are using pd.DataFrame you can do the following:

import numpy as np import pandas as pd from sklearn.preprocessing import Imputer df = pd.DataFrame({'A': [1, 2, 3, 8], 'B': [1, 2, 5, 3]}) df_new = df.replace([1, 2], np.nan) df_imp = Imputer().fit_transform(df_new)

This results in df_imp:

array([[ 5.5, 4. ], [ 5.5, 4. ], [ 3. , 5. ], [ 8. , 3. ]])

If you want to make this a part of a pipeline, you would just need to implement a custom transformer with a similar logic.

Looked at the documentation for replace(). It also accepts regex, so even if there were another variable 'C' in your example which I don't want to change, that can be done. Thank you

Asad Dhorajiwala · Accepted Answer · 2022-03-14 14:13:06Z

You could chain multiple imputers in a pipeline, but that might become hectic pretty soon and I'm not sure how efficient that is.

pipeline = make_pipeline( SimpleImputer(missing_values=7, strategy='constant', fill_value=10), SimpleImputer(missing_values=8, strategy='constant', fill_value=10), SimpleImputer(missing_values=9, strategy='constant', fill_value=10) )

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