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import numpy as np |
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2
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import pandas as pd |
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3
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import unittest |
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4
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from ..clean import (drop_missing, |
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5
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convert_datatypes, |
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6
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pool_duplicate_subsets) |
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7
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8
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9
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class Test_drop_missing(unittest.TestCase): |
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10
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11
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@classmethod |
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12
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def setUpClass(cls): |
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13
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cls.df_data_drop = pd.DataFrame([[np.nan, np.nan, np.nan, np.nan, np.nan], |
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14
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[pd.NA, pd.NA, pd.NA, pd.NA, pd.NA], |
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15
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[pd.NA, 'b', 'c', 'd', 'e'], |
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[pd.NA, 6, 7, 8, 9], |
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[pd.NA, 2, 3, 4, pd.NA], |
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18
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[pd.NA, 6, 7, pd.NA, pd.NA]]) |
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19
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20
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def test_drop_missing(self): |
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self.assertEqual(drop_missing(self.df_data_drop).shape, (4, 4)) |
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23
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# Drop further columns based on threshold |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_cols=0.5).shape, (4, 4)) |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_cols=0.49).shape, (4, 3)) |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_cols=0).shape, (4, 2)) |
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27
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28
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# Drop further rows based on threshold |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_rows=0.5).shape, (4, 4)) |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_rows=0.49).shape, (3, 4)) |
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self.assertEqual(drop_missing(self.df_data_drop, drop_threshold_rows=0).shape, (2, 4)) |
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32
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33
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34
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class Test_convert_dtypes(unittest.TestCase): |
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35
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36
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@classmethod |
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37
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def setUpClass(cls): |
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38
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cls.df_data_convert = pd.DataFrame([[1, 7.0, 'y', 'x', pd.NA, 'v'], |
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[3, 8.0, 'd', 'e', pd.NA, 'v'], |
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[5, 7.0, 'o', 'z', pd.NA, 'v'], |
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[1, 7.0, 'u', 'f', pd.NA, 'p'], |
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[1, 7.0, 'u', 'f', pd.NA, 'p'], |
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[2, 7.0, 'g', 'a', pd.NA, 'p']]) |
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45
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def test_convert_dtypes(self): |
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expected_results = ['Int8', 'Float32', 'string', 'string', 'category', 'category'] |
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for i, _ in enumerate(expected_results): |
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self.assertEqual(convert_datatypes(self.df_data_convert, cat_threshold=0.4).dtypes[i], expected_results[i]) |
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49
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50
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expected_results = ['Int8', 'Float32', 'string', 'string', 'object', 'string'] |
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for i, _ in enumerate(expected_results): |
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self.assertEqual(convert_datatypes(self.df_data_convert).dtypes[i], expected_results[i]) |
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54
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expected_results = ['Int8', 'Float32', 'string', 'string', 'object', 'category'] |
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for i, _ in enumerate(expected_results): |
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self.assertEqual(convert_datatypes(self.df_data_convert, cat_threshold=0.5, |
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cat_exclude=[4]).dtypes[i], expected_results[i]) |
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58
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59
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expected_results = ['Int8', 'Float32', 'string', 'category', 'object', 'category'] |
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60
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for i, _ in enumerate(expected_results): |
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self.assertEqual(convert_datatypes(self.df_data_convert, cat_threshold=0.95, |
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cat_exclude=[2, 4]).dtypes[i], expected_results[i]) |
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63
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64
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expected_results = ['Int8', 'Float32', 'string', 'string', 'object', 'string'] |
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65
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for i, _ in enumerate(expected_results): |
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self.assertEqual(convert_datatypes(self.df_data_convert, category=False, |
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cat_threshold=0.95, cat_exclude=[2, 4]).dtypes[i], expected_results[i]) |
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68
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69
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70
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class Test_pool_duplicate_subsets(unittest.TestCase): |
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71
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72
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@classmethod |
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73
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def setUpClass(cls): |
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74
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cls.df_data_subsets = pd.DataFrame([[1, 7, 'd', 'x', pd.NA, 'v'], |
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[1, 8, 'd', 'e', pd.NA, 'v'], |
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[2, 7, 'g', 'z', pd.NA, 'v'], |
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[1, 7, 'u', 'f', pd.NA, 'p'], |
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[1, 7, 'u', 'z', pd.NA, 'p'], |
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[2, 7, 'g', 'z', pd.NA, 'p']]) |
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80
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81
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def test_pool_duplicate_subsets(self): |
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self.assertEqual(pool_duplicate_subsets(self.df_data_subsets).shape, (6, 3)) |
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self.assertEqual(pool_duplicate_subsets(self.df_data_subsets, col_dupl_thresh=1).shape, (6, 6)) |
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self.assertEqual(pool_duplicate_subsets(self.df_data_subsets, subset_thresh=0).shape, (6, 2)) |
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85
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akanz1 /
klib
