import pandas as pd

class SimpleDataAnalyser:
    def __init__(self):
        pass

    # extract property information
    def extract_property_info(self, file_path):
        return pd.read_csv(file_path, encoding='utf-8')

    def currency_exchange(self, dataframe, exchange_rate):
        trans_price = dataframe['price'].values * [exchange_rate]
        return trans_price

    def suburb_summary(self, dataframe, suburb):
        frameGroup = dataframe.groupby(['suburb'])
        if suburb == 'all':
            print('>>> bedrooms describe info: ')
            print(frameGroup.bedrooms.describe())
            print('>>> bathrooms describe info: ')
            print(frameGroup.bathrooms.describe())
            print('>>> parking_spaces describe info: ')
            print(frameGroup.parking_spaces.describe())
        else:
            if suburb not in frameGroup.suburb.groups.keys():
                print('suburb is not exist')
            else:
                print('>>> suburb <{}> describe info: '.format(suburb))
                print('>>> bedrooms describe info: ')
                print(frameGroup.bedrooms.describe().loc[[suburb]])
                print('>>> bathrooms describe info: ')
                print(frameGroup.bathrooms.describe().loc[[suburb]])
                print('>>> parking_spaces describe info: ')
                print(frameGroup.parking_spaces.describe().loc[[suburb]])

    def avg_land_size(self, dataframe, suburb):
        frameGroup = dataframe.groupby(['suburb'])
        if suburb == 'all':
            dataframe = dataframe[dataframe['land_size'].apply(lambda x: x!=-1) & dataframe['land_size_unit'].apply(lambda x:x=='m²')]
            frameGroup = dataframe.groupby(['suburb'])
            print('>>> avg land size: ')
            print(frameGroup.land_size.mean())
        else:
            if suburb not in frameGroup.suburb.groups.keys():
                print('suburb is not exit.')
                return None
            else:
                dataframe = dataframe[
                    dataframe['land_size'].apply(lambda x: x != -1) & dataframe['land_size_unit'].apply(
                        lambda x: x == 'm²')]
                frameGroup = dataframe.groupby(['suburb'])
                print('>>> suburb <{}> avg land size: {}'.format(suburb, frameGroup.land_size.mean().get(suburb)))
        pass

if __name__ == '__main__':
    simpleDataAna = SimpleDataAnalyser()
    dataframe = simpleDataAna.extract_property_info('./property_information.csv')
    print(simpleDataAna.currency_exchange(dataframe, 0.6))
    # simpleDataAna.avg_land_size(dataframe, 'Balwyn')