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Suggest to use SimpleImputer for str categorical data with None values #16739

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Suggest to use SimpleImputer for str categorical data with None values #16739

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@ogrisel ogrisel commented Mar 21, 2020

This is a follow up to #16713 to better tackle #16702 and #16703.

I found the this specific case when loading the Ames housing dataset from fetch_openml:

X, y = fetch_openml("house_prices", as_frame=True, return_X_y=True)

Note that #11996 also discusses how to natively handle missing values in categorical encoders which could be an alternative to this PR, assuming we support both None and np.nan as missing value markers in object-dtyped columns.

@ogrisel ogrisel requested a review from jnothman March 21, 2020 14:06
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ogrisel commented Mar 21, 2020
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Do we want this to raise this error for LabelEncoder?

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
le.fit_transform([None, 'a', 'b', 'a'])

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ogrisel commented May 2, 2020

Indeed, displaying this message in the context of LabelEncoder is weird.

Base automatically changed from master to main January 22, 2021 10:52
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Now that we have #19069 and #17317 merged this is less of an issue?

OneHotEncoder encodes None and np.nan separately. OrdinalEncoder passes np.nan through and encodes None. (OrdinalEncoder encoding of None was on main before #19069)

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ogrisel commented Apr 13, 2021

Maybe yes. Let's close.

@ogrisel ogrisel closed this Apr 13, 2021
@ogrisel ogrisel deleted the more-helpful-error-message-encoders branch April 13, 2021 13:25
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