data_import_csv.py

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder

#Read Data
df = pd.read_csv("original_data.csv")

#Data-Cleaning
df.columns = df.columns.str.strip().str.replace(' ', '_').str.replace('[^\w\s]', '').str.lower()
for column in ["size_horizontal_[m]", "size_vertical_[m]", "frame_depth_[cm]"]:
    df[column] = df[column].str.replace(',', '.').astype(float)

#Droping Columns
columns_to_drop = ['recordid', 'images', 'renamedimages', 'newname', 'isspecial', 'a1',
       'a2', 'a3', 'a4', 'b1', 'b2', 'b3', 'b4', 'c1', 'c2', 'c3', 'c4', 'd1',
       'd2', 'd3', 'd4', 'outside_different_?', 'frame_colour_outside', 'frame_surface_outside',
       'frame_material_outside', 'glazing_type']
df.drop(columns=columns_to_drop, inplace=True)

#columns_to_drop2 = ['frame_colour', 'frame_surface', 'frame_material']
#df.drop(columns=columns_to_drop2, inplace=True)

# Replace NaN with Mean
#df.replace(0, np.nan, inplace=True)
#mean_values = df[["size_horizontal_[m]", "size_vertical_[m]", "frame_depth_[cm]"]].mean()
#df.fillna(mean_values, inplace=True)

# Replace NaN with -1
#df.replace(0, np.nan, inplace=True)
#df.fillna(-1, inplace=True)

# Dropping Rows with Missing Values
df.replace(0, np.nan, inplace=True)
df.dropna(inplace=True)

#One-Hot-Encoding
df_encoded = pd.get_dummies(df, columns=["frame_colour", "frame_surface", "frame_material"], drop_first=True)
df_encoded.replace({True: 1, False: 0}, inplace=True)

#Label Encoding
#df_encoded = df.copy()
#label_encoder = LabelEncoder()
#for column in ["frame_colour", "frame_surface", "frame_material"]:
 #   df_encoded[column] = label_encoder.fit_transform(df_encoded[column])

# Exporting to new CSV 
df_encoded.to_csv("cleaned_data.csv", index=False, float_format='%.2f')