import sklearn
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn_pandas import DataFrameMapper

def normalize_encode_dataframe(df, encoder=OrdinalEncoder):
    """Normalise numeric data, encode categorical data."""
    columns_input_numeric = list(df._get_numeric_data().columns)
    columns_categorical = list(set(df.columns)-set(columns_input_numeric))
    
    transformers= [([n], StandardScaler()) for n in columns_input_numeric] + \
                  [([n], encoder()) for n in columns_categorical]
    scaler = DataFrameMapper(transformers, df_out=True)
    df_norm = scaler.fit_transform(df)
    return df_norm, scaler
    
def timeseries_split(df, test_fraction=0.2):
    """Split timeseries data with test in the future"""
    i = int(len(df)*test_fraction)
    return df.iloc[:i], df.iloc[i:]