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8d6e02d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | 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 |