| import pandas as pd |
| import numpy as np |
| from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder |
|
|
|
|
| class DataTransformers: |
| def normalize_column(self, df: pd.DataFrame, column: str, method: str = 'min-max') -> pd.DataFrame: |
| """Normalize numeric column using specified method""" |
| df = df.copy() |
| |
| if method == 'min-max': |
| scaler = MinMaxScaler() |
| df[column] = scaler.fit_transform(df[[column]]) |
| elif method == 'standard': |
| scaler = StandardScaler() |
| df[column] = scaler.fit_transform(df[[column]]) |
| |
| return df |
|
|
| def fix_data_types(self, df: pd.DataFrame) -> pd.DataFrame: |
| """Automatically detect and fix incorrect data types""" |
| df = df.copy() |
| |
| for col in df.columns: |
| if df[col].dtype == 'object': |
| try: |
| df[col] = pd.to_numeric(df[col]) |
| continue |
| except: |
| pass |
| try: |
| df[col] = pd.to_datetime(df[col]) |
| continue |
| except: |
| pass |
| return df |
|
|
| def encode_categorical(self, df: pd.DataFrame, column: str, method: str = 'label') -> pd.DataFrame: |
| """Encode categorical columns""" |
| df = df.copy() |
| |
| if method == 'label': |
| le = LabelEncoder() |
| df[column] = le.fit_transform(df[column].astype(str)) |
| elif method == 'onehot': |
| dummies = pd.get_dummies(df[column], prefix=column, drop_first=True) |
| df = pd.concat([df.drop(columns=[column]), dummies], axis=1) |
| |
| return df |
