How to perform OneHotEncoding in Sklearn, getting value error

Question

I just started learning machine learning, when practicing one of the task, I am getting value error, but I followed the same steps as the instructor does.

I am getting value error, please help.

dff

     Country    Name
 0     AUS      Sri
 1     USA      Vignesh
 2     IND      Pechi
 3     USA      Raj

First I performed labelencoding,

X=dff.values
label_encoder=LabelEncoder()
X[:,0]=label_encoder.fit_transform(X[:,0])

out:
X
array([[0, 'Sri'],
       [2, 'Vignesh'],
       [1, 'Pechi'],
       [2, 'Raj']], dtype=object)

then performed One hot encoding for the same X

onehotencoder=OneHotEncoder( categorical_features=[0])
X=onehotencoder.fit_transform(X).toarray()

I am getting the below error:

ValueError                                Traceback (most recent call last)
<ipython-input-472-be8c3472db63> in <module>()
----> 1 X=onehotencoder.fit_transform(X).toarray()

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit_transform(self, X, y)
   1900         """
   1901         return _transform_selected(X, self._fit_transform,
-> 1902                                    self.categorical_features, copy=True)
   1903 
   1904     def _transform(self, X):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in _transform_selected(X, transform, selected, copy)
   1695     X : array or sparse matrix, shape=(n_samples, n_features_new)
   1696     """
-> 1697     X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
   1698 
   1699     if isinstance(selected, six.string_types) and selected == "all":

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    380                                       force_all_finite)
    381     else:
--> 382         array = np.array(array, dtype=dtype, order=order, copy=copy)
    383 
    384         if ensure_2d:

ValueError: could not convert string to float: 'Raj'

Please edit my question is anything wrong, thanks in advance!

Answer 1

You can go directly to OneHotEncoding now without using the LabelEncoder, and as we move toward version 0.22 many might want to do things this way to avoid warnings and potential errors (see DOCS and EXAMPLES).

---

Example code 1 where ALL columns are encoded and where the categories are explicitly specified:

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder

data= [["AUS", "Sri"],["USA","Vignesh"],["IND", "Pechi"],["USA","Raj"]]

df = pd.DataFrame(data, columns=['Country', 'Name'])
X = df.values

countries = np.unique(X[:,0])
names = np.unique(X[:,1])

ohe = OneHotEncoder(categories=[countries, names])
X = ohe.fit_transform(X).toarray()

print (X)

---

Output for code example 1:

[[1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0.]]

---

Example code 2 showing the 'auto' option for specification of categories:

The first 3 columns encode the country names, the last four the personal names.

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder

data= [["AUS", "Sri"],["USA","Vignesh"],["IND", "Pechi"],["USA","Raj"]]

df = pd.DataFrame(data, columns=['Country', 'Name'])
X = df.values

ohe = OneHotEncoder(categories='auto')
X = ohe.fit_transform(X).toarray()

print (X)

Output for code example 2 (same as for 1):

[[1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0.]]

---

Example code 3 where only the first column is one hot encoded:

Now, here's the unique part. What if you only need to One Hot Encode a specific column for your data?

(Note: I've left the last column as strings for easier illustration. In reality it makes more sense to do this WHEN the last column was already numerical).

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder

data= [["AUS", "Sri"],["USA","Vignesh"],["IND", "Pechi"],["USA","Raj"]]

df = pd.DataFrame(data, columns=['Country', 'Name'])
X = df.values

countries = np.unique(X[:,0])
names = np.unique(X[:,1])

ohe = OneHotEncoder(categories=[countries]) # specify ONLY unique country names
tmp = ohe.fit_transform(X[:,0].reshape(-1, 1)).toarray()

X = np.append(tmp, names.reshape(-1,1), axis=1)

print (X)

---

Output for code example 3:

[[1.0 0.0 0.0 'Pechi']
 [0.0 0.0 1.0 'Raj']
 [0.0 1.0 0.0 'Sri']
 [0.0 0.0 1.0 'Vignesh']]