bad input shape when using labelEncoder

Question

I have a fairly large dataframe with both numerical and categorical values. I'm trying to encode the categorical values but am getting the above error.

Here's a simple version of the code:

from collections import defaultdict
d = defaultdict(LabelEncoder)
# Encoding the variable
fit = df[catgoricalValues].apply(lambda x: d[x.name].fit_transform(df[catgoricalValues]))

I'm using the approach described here, except instead of applying it on the entire dataframe, I specified the columns to encode.

I get this error:

ValueError: bad input shape (490546, 11)

Does this answer your question? Label encoding across multiple columns in scikit-learn — Ben Reiniger
– Ben Reiniger, Commented Aug 28, 2020 at 0:29

Sy Ker · Accepted Answer · 2020-08-27 19:21:50Z

0

Update

Seems like you are trying to apply the LabelEncoder to multiple columns; While you can apply the same LabelEncoder to all columns;

from sklearn.preprocessing import LabelEncoder

encoded = df[categoricalVal].apply(LabelEncoder().fit_transform)

It it better to use a new encoder for each columns. The link above should provide you with the solution.

edited Aug 27, 2020 at 19:21

answered Aug 27, 2020 at 19:04

Sy Ker

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3 Comments

Lostsoul Over a year ago

I received the same error - ValueError: bad input shape (490546, 11)

Lostsoul Over a year ago

Thank you for the update. Is there a better way to do what I'm trying to do? I just have a list of categorical values and I want to encode them all(then reverse the encoding later). When I ran your code I got this error - TypeError: argument must be a string or number

Sy Ker Over a year ago

Okay, can you provide a small example of your dataset?

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