Re-coding a categorical field into one-hot vectors

decode_encode_onehot

import pandas as pd
from sklearn.preprocessing import LabelEncoder,OneHotEncoder

def decode_encode(colname):

    '''
    (str) -> (DataFrame)

    Returns a Data Frame with the column given to it transformed into a One-hot encoded set of columns

    '''

    label_encode = LabelEncoder()
    X = data[colname]
    
    X_new = label_encode.fit_transform(X)
    
    X_new = X_new.reshape(-1,1)
    encode = OneHotEncoder()
    X_new = pd.DataFrame(encode.fit_transform(X_new).toarray(),columns=label_encode.classes_)
    data_dropped = data.drop(colname,axis=1)
    mergelist = [X_new,data_dropped]
    data_onehot = pd.concat(mergelist,axis=1)
    return data_onehot