There is actually 2 warnings : FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values. If you want the future behaviour and silence this warning, you can specify “categories=”auto””. … Read more
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
Instead of using pd.get_dummies() you need LabelEncoder + OneHotEncoder which can store the original values and then use them on the new data. Changing your code like below will give you required results. import pandas as pd from sklearn.preprocessing import OneHotEncoder, LabelEncoder input_df = pd.DataFrame(dict(fruit=[‘Apple’, ‘Orange’, ‘Pine’], color=[‘Red’, ‘Orange’,’Green’], is_sweet = [0,0,1], country=[‘USA’,’India’,’Asia’])) filtered_df = … 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
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
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