Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> 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
I recommend using the dummyVars function in the caret package: customers <- data.frame( id=c(10, 20, 30, 40, 50), gender=c(‘male’, ‘female’, ‘female’, ‘male’, ‘female’), mood=c(‘happy’, ‘sad’, ‘happy’, ‘sad’,’happy’), outcome=c(1, 1, 0, 0, 0)) customers id gender mood outcome 1 10 male happy 1 2 20 female sad 1 3 30 female happy 0 4 40 male … 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
Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> 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
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