91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
from sklearn import preprocessing
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import numpy as np
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import pandas as pd
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import kaggle
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import pandas
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import os
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path_to_data = './data'
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def download_data():
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('thedevastator/airbnb-prices-in-european-cities', path=path_to_data, unzip=True)
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def read_data_from_file(filename):
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return pandas.read_csv(filename)
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def split_data(data):
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return np.split(data.sample(frac=1, random_state=42), [int(.6 * len(data)), int(.8 * len(data))])
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def calculate_stats(data, col_name):
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col_values = data[col_name]
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return [
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len(data),
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np.min(col_values),
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np.max(col_values),
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np.std(col_values),
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np.median(col_values)
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]
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def calculate_value_counts(data, col_name):
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return data[col_name].value_counts()
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def normalize_data(data):
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data = data.iloc[:, 1:]
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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']
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non_numeric_columns = ['room_type', 'room_shared', 'room_private', 'host_is_superhost', ]
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numeric_data = data[numeric_columns]
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non_numeric_data = data[non_numeric_columns]
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enc = preprocessing.OrdinalEncoder()
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non_numeric_data_norm = enc.fit_transform(non_numeric_data)
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scaler = preprocessing.MinMaxScaler()
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numeric_data_norm = scaler.fit_transform(numeric_data)
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data[numeric_columns] = numeric_data_norm
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data[non_numeric_columns] = non_numeric_data_norm
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return data
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if __name__ == '__main__':
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# 1.1
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if not os.path.isdir('data'):
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download_data()
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whole_set = read_data_from_file(path_to_data + '/barcelona_weekends.csv')
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print(whole_set.head())
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# 1.2
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train_set, dev_set, test_set = split_data(whole_set)
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# 1.3
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whole_set_stats = calculate_stats(whole_set, 'realSum')
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train_set_stats = calculate_stats(train_set, 'realSum')
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dev_set_stats = calculate_stats(dev_set, 'realSum')
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test_set_stats = calculate_stats(test_set, 'realSum')
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columns = ['size', 'minimum', 'maximum', 'standard deviation', 'median']
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rows = ['whole set', 'train', 'dev', 'test']
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print(pd.DataFrame(
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data=np.array([whole_set_stats, train_set_stats, dev_set_stats, test_set_stats]),
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index=rows,
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columns=columns),
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end='\n\n')
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print('Whole set', calculate_value_counts(whole_set, 'person_capacity'), end='\n\n')
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print('Train', calculate_value_counts(train_set, 'person_capacity'), end='\n\n')
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print('Dev', calculate_value_counts(dev_set, 'person_capacity'), end='\n\n')
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print('Test', calculate_value_counts(test_set, 'person_capacity'), end='\n\n')
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# 1.4 & 1.5
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normalized_data = normalize_data(whole_set)
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print(normalized_data, '\n\n')
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