add homework [2.Dane]

main.py

@@ -1,7 +1,6 @@
-import jupyter
+from sklearn import preprocessing
 import numpy as np
 import pandas as pd
-from sklearn.model_selection import train_test_split
 import kaggle
 import pandas
 import os
@@ -18,11 +17,74 @@ def read_data_from_file(filename):
     return pandas.read_csv(filename)
 
 
+def split_data(data):
+    return np.split(data.sample(frac=1, random_state=42), [int(.6 * len(data)), int(.8 * len(data))])
+
+
+def calculate_stats(data, col_name):
+    col_values = data[col_name]
+    return [
+        len(data),
+        np.min(col_values),
+        np.max(col_values),
+        np.std(col_values),
+        np.median(col_values)
+    ]
+
+
+def calculate_value_counts(data, col_name):
+    return data[col_name].value_counts()
+
+
+def normalize_data(data):
+    data = data.iloc[:, 1:]
+
+    numeric_columns = ['realSum', 'person_capacity', 'multi', 'biz', 'cleanliness_rating', 'guest_satisfaction_overall', 'bedrooms', 'dist', 'metro_dist', 'attr_index', 'attr_index_norm', 'rest_index', 'rest_index_norm', 'lng', 'lat']
+    non_numeric_columns = ['room_type', 'room_shared', 'room_private', 'host_is_superhost', ]
+    numeric_data = data[numeric_columns]
+    non_numeric_data = data[non_numeric_columns]
+
+    enc = preprocessing.OrdinalEncoder()
+    non_numeric_data_norm = enc.fit_transform(non_numeric_data)
+
+    scaler = preprocessing.MinMaxScaler()
+    numeric_data_norm = scaler.fit_transform(numeric_data)
+
+    data[numeric_columns] = numeric_data_norm
+    data[non_numeric_columns] = non_numeric_data_norm
+
+    return data
+
+
 if __name__ == '__main__':
+    # 1.1
     if not os.path.isdir('data'):
         download_data()
-    df = read_data_from_file(path_to_data + '/barcelona_weekends.csv')
-    print(df.head())
+    whole_set = read_data_from_file(path_to_data + '/barcelona_weekends.csv')
+    print(whole_set.head())
 
+    # 1.2
+    train_set, dev_set, test_set = split_data(whole_set)
 
+    # 1.3
+    whole_set_stats = calculate_stats(whole_set, 'realSum')
+    train_set_stats = calculate_stats(train_set, 'realSum')
+    dev_set_stats = calculate_stats(dev_set, 'realSum')
+    test_set_stats = calculate_stats(test_set, 'realSum')
 
+    columns = ['size', 'minimum', 'maximum', 'standard deviation', 'median']
+    rows = ['whole set', 'train', 'dev', 'test']
+    print(pd.DataFrame(
+        data=np.array([whole_set_stats, train_set_stats, dev_set_stats, test_set_stats]),
+        index=rows,
+        columns=columns),
+        end='\n\n')
+
+    print('Whole set', calculate_value_counts(whole_set, 'person_capacity'), end='\n\n')
+    print('Train', calculate_value_counts(train_set, 'person_capacity'), end='\n\n')
+    print('Dev', calculate_value_counts(dev_set, 'person_capacity'), end='\n\n')
+    print('Test', calculate_value_counts(test_set, 'person_capacity'), end='\n\n')
+
+    # 1.4 & 1.5
+    normalized_data = normalize_data(whole_set)
+    print(normalized_data, '\n\n')