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TypeError: "iteration over a 0-d array" when trying to preprocessing.scale a pandas.Series #12622

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Andi144 opened this issue Nov 20, 2018 · 4 comments · Fixed by #12625
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TypeError: "iteration over a 0-d array" when trying to preprocessing.scale a pandas.Series #12622

Andi144 opened this issue Nov 20, 2018 · 4 comments · Fixed by #12625
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Blocker Bug help wanted Regression
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0.20.1

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@Andi144
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Andi144 commented Nov 20, 2018

Description

When trying to call preprocessing.scale on a pandas.Series instance, an error is thrown with scikit-learn version 0.20.0. Version 0.19.1. works just fine. The documentation states that the input to preprocessing.scale can be "array-like", and pandas.Series should fulfill this requirement since it is a "one-dimensional ndarray".

Steps/Code to Reproduce

import pandas as pd
from sklearn import preprocessing

s = pd.Series([1.0, 2.0, 3.0])
preprocessing.scale(s)

Expected Results

This should be the output (as it is in version 0.19.1):

[-1.22474487,  0.        ,  1.22474487]

A workaround is replacing preprocessing.scale(s) with preprocessing.scale([i for i in s]), which also yields this output.

Actual Results

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-ef1d298414c3> in <module>
      3 
      4 s = pd.Series([1.0, 2.0, 3.0])
----> 5 preprocessing.scale(s)

~\anaconda3\envs\tensorflow\lib\site-packages\sklearn\preprocessing\data.py in scale(X, axis, with_mean, with_std, copy)
    143     X = check_array(X, accept_sparse='csc', copy=copy, ensure_2d=False,
    144                     warn_on_dtype=True, estimator='the scale function',
--> 145                     dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
    146     if sparse.issparse(X):
    147         if with_mean:

~\anaconda3\envs\tensorflow\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    594 
    595     if (warn_on_dtype and dtypes_orig is not None and
--> 596             {array.dtype} != set(dtypes_orig)):
    597         # if there was at the beginning some other types than the final one
    598         # (for instance in a DataFrame that can contain several dtypes) then

TypeError: iteration over a 0-d array

Versions

System
------
    python: 3.6.7 |Anaconda, Inc.| (default, Oct 28 2018, 19:44:12) [MSC v.1915 64 bit (AMD64)]
executable: C:\Users\...\anaconda3\envs\tensorflow\python.exe
   machine: Windows-10-10.0.17134-SP0

Python deps
-----------
       pip: 18.1
setuptools: 40.6.2
   sklearn: 0.20.0
     numpy: 1.15.4
     scipy: 1.1.0
    Cython: None
    pandas: 0.23.4
@qinhanmin2014
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qinhanmin2014 commented Nov 20, 2018 •
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Minimal script to reproduce:

import pandas as pd
from sklearn.utils.validation import check_array
check_array(pd.Series([1, 2, 3]), ensure_2d=False, warn_on_dtype=True)

Related PR #10949

@qinhanmin2014 qinhanmin2014 added this to the 0.20.1 milestone Nov 20, 2018
@amueller
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Thanks for reporting. Yeah would be good to get this into 0.20.1, hrm...

@qinhanmin2014
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Heading to bed, seems that an easy solution will be:
change

if hasattr(array, "dtypes") and hasattr(array, "__array__"):
        dtypes_orig = np.array(array.dtypes)

to something like

if hasattr(array, "dtypes") and hasattr(array, "__array__") and hasattr(array.dtypes, "__array__"):
        dtypes_orig = np.array(array.dtypes)

@qinhanmin2014 qinhanmin2014 mentioned this issue Nov 20, 2018
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@amueller amueller mentioned this issue Nov 20, 2018
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@amueller
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Wow I think this must have been our quickest turn-around for a user-reported bug so far ;)

@qinhanmin2014 qinhanmin2014 mentioned this issue Dec 1, 2018
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fix check_array dtype check for pandas series amueller/scikit-learn
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@amueller @qinhanmin2014 @Andi144