I have a dataframe X with integer, float and string columns. I'd like to one-hot encode every column that is of "Object" type, so I'm trying to do this:
encoding_needed = X.select_dtypes(include='object').columns
ohe = preprocessing.OneHotEncoder()
X[encoding_needed] = ohe.fit_transform(X[encoding_needed].astype(str)) #need astype bc I imputed with 0, so some rows have a mix of zeroes and strings.
However, I end up with IndexError: tuple index out of range. I don't quite understand this as per the documentation the encoder expects X: array-like, shape [n_samples, n_features], so I should be OK passing a dataframe. How can I one-hot encode the list of columns specifically marked in encoding_needed?
EDIT:
The data is confidential so I cannot share it and I cannot create a dummy as it has 123 columns as is.
I can provide the following:
X.shape: (40755, 123)
encoding_needed.shape: (81,) and is a subset of columns.
Full stack:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-90-6b3e9fdb6f91> in <module>()
1 encoding_needed = X.select_dtypes(include='object').columns
2 ohe = preprocessing.OneHotEncoder()
----> 3 X[encoding_needed] = ohe.fit_transform(X[encoding_needed].astype(str))
~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
3365 self._setitem_frame(key, value)
3366 elif isinstance(key, (Series, np.ndarray, list, Index)):
-> 3367 self._setitem_array(key, value)
3368 else:
3369 # set column
~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py in _setitem_array(self, key, value)
3393 indexer = self.loc._convert_to_indexer(key, axis=1)
3394 self._check_setitem_copy()
-> 3395 self.loc._setitem_with_indexer((slice(None), indexer), value)
3396
3397 def _setitem_frame(self, key, value):
~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
592 # GH 7551
593 value = np.array(value, dtype=object)
--> 594 if len(labels) != value.shape[1]:
595 raise ValueError('Must have equal len keys and value '
596 'when setting with an ndarray')
IndexError: tuple index out of range
# example data
X = pd.DataFrame({'int':[0,1,2,3],
'float':[4.0, 5.0, 6.0, 7.0],
'string1':list('abcd'),
'string2':list('efgh')})
int float string1 string2
0 0 4.0 a e
1 1 5.0 b f
2 2 6.0 c g
3 3 7.0 d h
pandasWith pandas.get_dummies, it will automatically select your object columns and drop these columns while appenind the one-hot-encoded columns:
pd.get_dummies(X)
int float string1_a string1_b string1_c string1_d string2_e \
0 0 4.0 1 0 0 0 1
1 1 5.0 0 1 0 0 0
2 2 6.0 0 0 1 0 0
3 3 7.0 0 0 0 1 0
string2_f string2_g string2_h
0 0 0 0
1 1 0 0
2 0 1 0
3 0 0 1
sklearnHere we have to specify that we only need the object columns:
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
X_object = X.select_dtypes('object')
ohe.fit(X_object)
codes = ohe.transform(X_object).toarray()
feature_names = ohe.get_feature_names(['string1', 'string2'])
X = pd.concat([df.select_dtypes(exclude='object'),
pd.DataFrame(codes,columns=feature_names).astype(int)], axis=1)
int float string1_a string1_b string1_c string1_d string2_e \
0 0 4.0 1 0 0 0 1
1 1 5.0 0 1 0 0 0
2 2 6.0 0 0 1 0 0
3 3 7.0 0 0 0 1 0
string2_f string2_g string2_h
0 0 0 0
1 1 0 0
2 0 1 0
3 0 0 1