If you are on sklearn>0.20.dev0 In [11]: from sklearn.preprocessing import OneHotEncoder …: cat = OneHotEncoder() …: X = np.array([[‘a’, ‘b’, ‘a’, ‘c’], [0, 1, 0, 1]], dtype=object).T …: cat.fit_transform(X).toarray() …: Out[11]: array([[1., 0., 0., 1., 0.], [0., 1., 0., 0., 1.], [1., 0., 0., 1., 0.], [0., 0., 1., 0., 1.]]) If you are on … Read more
In [77]: df = pd.concat([df, pd.get_dummies(df[‘YEAR’])], axis=1); df Out[77]: JOINED_CO GENDER EXEC_FULLNAME GVKEY YEAR CONAME BECAMECEO \ 5622 NaN MALE Ira A. Eichner 1004 1992 AAR CORP 19550101 5622 NaN MALE Ira A. Eichner 1004 1993 AAR CORP 19550101 5622 NaN MALE Ira A. Eichner 1004 1994 AAR CORP 19550101 5622 NaN MALE Ira A. … Read more
A list with the original column names can be passed to get_feature_names. >>> encoder.get_feature_names([‘Sex’, ‘AgeGroup’]) array([‘Sex_female’, ‘Sex_male’, ‘AgeGroup_0’, ‘AgeGroup_15’, ‘AgeGroup_30’, ‘AgeGroup_45’, ‘AgeGroup_60’, ‘AgeGroup_75’], dtype=object) DEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead. As per sklearn.preprocessing.OneHotEncoder. >>> encoder.get_feature_names_out([‘Sex’, ‘AgeGroup’]) array([‘Sex_female’, ‘Sex_male’, ‘AgeGroup_0’, ‘AgeGroup_15’, ‘AgeGroup_30’, ‘AgeGroup_45’, ‘AgeGroup_60’, ‘AgeGroup_75’], dtype=object)
Usually, when you want to get a one-hot encoding for classification in machine learning, you have an array of indices. import numpy as np nb_classes = 6 targets = np.array([[2, 3, 4, 0]]).reshape(-1) one_hot_targets = np.eye(nb_classes)[targets] The one_hot_targets is now array([[[ 0., 0., 1., 0., 0., 0.], [ 0., 0., 0., 1., 0., 0.], [ … Read more
With pandas 0.19, you can do that in a single line : pd.get_dummies(data=df, columns=[‘A’, ‘B’]) Columns specifies where to do the One Hot Encoding. >>> df A B C 0 a c 1 1 b c 2 2 a b 3 >>> pd.get_dummies(data=df, columns=[‘A’, ‘B’]) C A_a A_b B_b B_c 0 1 1.0 0.0 0.0 … Read more
No, there isn’t. Somebody’s working on this and the patch might be merged into mainline some day, but right now there’s no support for categorical variables in scikit-learn except dummy (one-hot) encoding.
Approach 1: You can use pandas’ pd.get_dummies. Example 1: import pandas as pd s = pd.Series(list(‘abca’)) pd.get_dummies(s) Out[]: a b c 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0 3 1.0 0.0 0.0 Example 2: The following will transform a given column into one hot. Use prefix to have multiple dummies. … Read more
Create a zeroed array b with enough columns, i.e. a.max() + 1. Then, for each row i, set the a[i]th column to 1. >>> a = np.array([1, 0, 3]) >>> b = np.zeros((a.size, a.max() + 1)) >>> b[np.arange(a.size), a] = 1 >>> b array([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., … Read more