One Hot Encoding a single column

Question

I am trying to use one hot encoder on the target column('Species') in the Iris dataset.

But I am getting the following errors:

ValueError: Expected 2D array, got 1D array instead:

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm    Species
0   1   5.1 3.5 1.4         0.2     Iris-setosa
1   2   4.9 3.0 1.4         0.2     Iris-setosa
2   3   4.7 3.2 1.3         0.2     Iris-setosa
3   4   4.6 3.1 1.5         0.2     Iris-setosa
4   5   5.0 3.6 1.4         0.2     Iris-setosa

I did google the issue and i found that most of the scikit learn estimators need a 2D array rather than a 1D array.

At the same time, I also found that we can try passing the dataframe with its index to encode single columns, but it didn't work

onehotencoder = OneHotEncoder(categorical_features=[df.columns.tolist().index('pattern_id')
X = dataset.iloc[:,1:5].values
y = dataset.iloc[:, 5].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder= LabelEncoder()
y = labelencoder.fit_transform(y)


onehotencoder = OneHotEncoder(categorical_features=[0])
y = onehotencoder.fit_transform(y)

I am trying to encode a single categorical column and split into multiple columns (the way the encoding usually works)

Answer 1

ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Says that you need to convert your array to a vector. You can do that by:

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import numpy as np

# load iris dataset 
>>> iris = datasets.load_iris()
>>> iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']], columns= iris['feature_names'] + ['target'])
>>> y = iris.target.values
>>> onehotencoder = OneHotEncoder(categories='auto')
>>> y = onehotencoder.fit_transform(y.reshape(-1,1))
# y - will be sparse matrix of type '<class 'numpy.float64'>
# if you want it to be a array you need to 
>>> print(y.toarray())
[[1. 0. 0.]
 [1. 0. 0.]
    . . . . 
 [0. 0. 1.]
 [0. 0. 1.]]

Also you can use get_dummies function (docs)

>>> pd.get_dummies(iris.target).head()
   0.0  1.0  2.0
0    1    0    0
1    1    0    0
2    1    0    0
3    1    0    0
4    1    0    0

Hope that helps!