# library doc string


# import libraries




def import_data(pth):
    '''
    returns dataframe for the csv found at pth

    input:
            pth: a path to the csv
    output:
            df: pandas dataframe
    '''	
	pass


def perform_eda(df):
    '''
    perform eda on df and save figures to images folder
    input:
            df: pandas dataframe

    output:
            None
    '''
	pass


def encoder_helper(df, category_lst, response):
    '''
    helper function to turn each categorical column into a new column with
    propotion of churn for each category - associated with cell 15 from the notebook

    input:
            df: pandas dataframe
            category_lst: list of columns that contain categorical features
            response: string of response name

    output:
            df: pandas dataframe with new columns for
    '''
    pass


def perform_feature_engineering(df, response):
    '''
    input:
              df: pandas dataframe
              response: string of response name

    output:
              X_train: X training data
              X_test: X testing data
              y_train: y training data
              y_test: y testing data
    '''

def classification_report_image(y_train,
                                y_test,
                                y_train_preds_lr,
                                y_train_preds_rf,
                                y_test_preds_lr,
                                y_test_preds_rf):
    '''
    produces classification report for training and testing results and stores report as image
    in images folder
    input:
            y_train: training response values
            y_test:  test response values
            y_train_preds_lr: training predictions from logistic regression
            y_train_preds_rf: training predictions from random forest
            y_test_preds_lr: test predictions from logistic regression
            y_test_preds_rf: test predictions from random forest

    output:
             None
    '''
    pass


def feature_importance_plot(model, X_data, output_pth):
    '''
    creates and stores the feature importances in pth
    input:
            model: model object containing feature_importances_
            X_data: pandas dataframe of X values
            output_pth: path to store the figure

    output:
             None
    '''
    pass

def train_models(X_train, X_test, y_train, y_test):
    '''
    train, store model results: images + scores, and store models
    input:
              X_train: X training data
              X_test: X testing data
              y_train: y training data
              y_test: y testing data
    output:
              None
    '''
    pass