sklearn-pandas

I am trying to do cross validation and I am running into an error that says: 'Found input variables with inconsistent numbers of samples: [18, 1]' I am using different columns in a pandas data frame (df) as the features, with the last column as the label. This is derived from the machine learning repository for UC Irvine. When importing the cross-validation package that I have used in the past, I am getting an error that it may have depreciated. I am going to be running a decision tree, SVM, and K-NN. My code is as such: feature = [df['age'], df['job'], df['marital'], df['education'], df[

I have a list of names like: names = ['A', 'B', 'C', 'D'] and a list of documents, that in each documents some of these names are mentioned. document =[['A', 'B'], ['C', 'B', 'K'],['A', 'B', 'C', 'D', 'Z']] I would like to get an output as a matrix of co-occurrences like: A B C D A 0 2 1 1 B 2 0 2 1 C 1 2 0 1 D 1 1 1 0 There is a solution ( Creating co-occurrence matrix ) for this problem in R, but I couldn't do it in Python. I am thinking of doing it in Pandas, but yet no progress! Obviously this can be extended for your purposes, but it performs the general operation in mind: import math for

I would like to break down a pandas column consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence). For example, taking dataframe df Col1 Col2 Col3 C 33 [Apple, Orange, Banana] A 2.5 [Apple, Grape] B 42 [Banana] I would like to convert this to: df Col1 Col2 Apple Orange Banana Grape C 33 1 1 1 0 A 2.5 1 0 0 1 B 42 0 0 1 0 How can I use pandas/sklearn to achieve this? We can also use sklearn.preprocessing.MultiLabelBinarizer : from sklearn.preprocessing