Polynomial features labeled in a dataframe

poly.py

def PolynomialFeatures_labeled(input_df,power):
    '''Basically this is a cover for the sklearn preprocessing function. 
    The problem with that function is if you give it a labeled dataframe, it ouputs an unlabeled dataframe with potentially
    a whole bunch of unlabeled columns. 

    Inputs:
    input_df = Your labeled pandas dataframe (list of x's not raised to any power) 
    power = what order polynomial you want variables up to. (use the same power as you want entered into pp.PolynomialFeatures(power) directly)

    Ouput:
    Output: This function relies on the powers_ matrix which is one of the preprocessing function's outputs to create logical labels and 
    outputs a labeled pandas dataframe   
    '''
    poly = pp.PolynomialFeatures(power)
    output_nparray = poly.fit_transform(input_df)
    powers_nparray = poly.powers_

    input_feature_names = list(input_df.columns)
    target_feature_names = ["Constant Term"]
    for feature_distillation in powers_nparray[1:]:
        intermediary_label = ""
        final_label = ""
        for i in range(len(input_feature_names)):
            if feature_distillation[i] == 0:
                continue
            else:
                variable = input_feature_names[i]
                power = feature_distillation[i]
                intermediary_label = "%s^%d" % (variable,power)
                if final_label == "":         #If the final label isn't yet specified
                    final_label = intermediary_label
                else:
                    final_label = final_label + " x " + intermediary_label
        target_feature_names.append(final_label)
    output_df = pd.DataFrame(output_nparray, columns = target_feature_names)
    return output_df

output_df = PolynomialFeatures_labeled(input_df,2)

Awesome, works perfectly!