In Spyder, I print the first row of my matrix and I get: *[3 'male' 22.0 1 7.25]*
And then I want to encode the second column in this matrix as a categorical variable. My code for that is: *from sklearn.preprocessing import LabelEncoder, OneHotEncoder* *labelencoder_X = LabelEncoder()* *X[:, 1] = labelencoder_X.fit_transform(X[:, 1])* *onehotencoder = OneHotEncoder(categorical_features = [1])* *X = onehotencoder.fit_transform(X).toarray() * This workds because when I print the first row again, I get: *[ 0. 1. 3. 22. 1. 7.25]* You can see that gender has taken over the first two columns. The first column has a 0, and second column has a 1, so this is how "males" are categorized. The first two column values would be 1, 0 if gender is female. The strange thing is when I run this exact same code in Jupyter notebook, I get a completely different output that doesn't make sense. I get: *[ 0. 0. 1. 1. 22. 1. 7.25 ]* Why is my output with the Jupyter notebook different? How can I make it the same as my output in Spyder? -- You received this message because you are subscribed to the Google Groups "spyder" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/spyderlib. For more options, visit https://groups.google.com/d/optout.
