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fenago
42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
import unittest
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import import_ipynb
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import pandas as pd
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import pandas.testing as pd_testing
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import numpy.testing as np_testing
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from sklearn.cluster import KMeans
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class Test(unittest.TestCase):
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def setUp(self):
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import Exercise12_04
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self.exercises = Exercise12_04
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self.file_url = '../Dataset/ames_iowa_housing.csv'
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self.df = pd.read_csv(self.file_url)
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self.df_agg = self.df.groupby(['Neighborhood', 'YrSold']).agg({'SalePrice': 'max'}).reset_index()
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self.df_agg.columns = ['Neighborhood', 'YrSold', 'SalePriceMax']
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self.df_new = pd.merge(self.df, self.df_agg, how='left', on=['Neighborhood', 'YrSold'])
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self.df_new['SalePriceRatio'] = self.df_new['SalePrice'] / self.df_new['SalePriceMax']
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self.df_agg2 = self.df.groupby(['Neighborhood', 'YrSold']).agg({'LotArea': 'max'}).reset_index()
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self.df_agg2.columns = ['Neighborhood', 'YrSold', 'LotAreaMax']
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self.df_final = pd.merge(self.df_new, self.df_agg2, how='left', on=['Neighborhood', 'YrSold'])
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self.df_final['LotAreaRatio'] = self.df_final['LotArea'] / self.df_final['LotAreaMax']
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def test_file_url(self):
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self.assertEqual(self.exercises.file_url, self.file_url)
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def test_df(self):
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pd_testing.assert_frame_equal(self.exercises.df, self.df)
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def test_df_agg(self):
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pd_testing.assert_frame_equal(self.exercises.df_agg, self.df_agg)
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def test_df_new(self):
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pd_testing.assert_frame_equal(self.exercises.df_new, self.df_new)
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def test_df_agg2(self):
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pd_testing.assert_frame_equal(self.exercises.df_agg2, self.df_agg2)
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if __name__ == '__main__':
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unittest.main()
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