from pandas.core.frame import DataFrame

from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder

class DataPreprocessor:
    def __init__(self, data : DataFrame) -> None:
        self._data = data
        self._transformers = {}

    def remove_outliers_by(self, property : str, lower : float, upper: float) -> None:
        q_low = self._data[property].quantile(lower)
        q_hi  = self._data[property].quantile(upper)
        self._data = self._data[(self._data[property] < q_hi) & (self._data[property] > q_low)]

    def transform_column_with_standard_scaler(self, column_name : str):
        scaler =  StandardScaler()
        scaler.fit(self._data[[column_name]])
        self._data[[column_name]] = scaler.transform(self._data[[column_name]])
        self._transformers[column_name] = scaler

    def encode_labels(self, column_name: str) -> None:
        encoder = LabelEncoder()
        encoder.fit(self._data[column_name])
        self._data[column_name] = encoder.transform(self._data[column_name])
        self._transformers[column_name] = encoder

    def get_value(self, column_name, original_data):
        return self._transformers[column_name].transform(original_data)

    def transorm_address_to_district(self, row):
        elements = row.split(',')
        if len(elements) < 4:
            return ""
        return elements[-4].lstrip()

    def trasform_column(self, column_name, function):
        self._data[column_name] = self._data[column_name].apply(function)

    def preprocess_data(self) -> None:
        self.transform_column_with_standard_scaler("Area")
        self.transform_column_with_standard_scaler("Construction year")
        self.encode_labels("Property form")
        self.encode_labels("State")
        self.trasform_column("Location", self.transorm_address_to_district)
        self._data = self._data[self._data['Location'] != ""]
        self.encode_labels("Location")

        self.remove_outliers_by("Price", 0.1, 0.9)
        self.remove_outliers_by("Area", 0.1, 0.9)
        
    def get_preprocessed_data(self) -> DataFrame:
        return self._data