boromir674 /
so-magic
| Conditions | 28 |
| Total Lines | 148 |
| Code Lines | 120 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like datapoint_files.datapoint_files_to_test() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | import pytest |
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| 19 | @pytest.fixture |
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| 20 | def datapoint_files_to_test(sample_collaped_json, sample_json): |
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| 21 | import pandas as pd |
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| 22 | import numpy as np |
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| 23 | return { |
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| 24 | 'data_1': { |
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| 25 | 'data_path': sample_collaped_json, |
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| 26 | 'nb_rows': 100, |
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| 27 | 'nb_columns': 46, |
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| 28 | 'type_distros': { |
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| 29 | 'type': {str: 100}, |
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| 30 | 'flavors': {list: 98, type(None): 2}, |
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| 31 | }, |
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| 32 | 'value_distros': { |
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| 33 | 'type': {'hybrid': 48, 'sativa': 19, 'indica': 33}, |
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| 34 | }, |
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| 35 | 'row': { |
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| 36 | 0: { |
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| 37 | 'flavors': [lambda v: v == ["Chemical", "Pine", "Diesel"], |
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| 38 | lambda v: type(v) == list, |
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| 39 | ], |
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| 40 | 'type': [lambda v: v == 'hybrid', |
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| 41 | lambda v: type(v) == str, |
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| 42 | ], |
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| 43 | }, |
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| 44 | 7: { |
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| 45 | 'flavors': [lambda v: v == ["Earthy", "Pungent", "Sweet"], |
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| 46 | lambda v: type(v) == list, |
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| 47 | ], |
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| 48 | 'type': [lambda v: v == 'hybrid', |
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| 49 | lambda v: type(v) == str, |
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| 50 | ], |
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| 51 | }, |
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| 52 | 76: { |
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| 53 | 'flavors': [lambda v: v is None, |
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| 54 | lambda v: pd.isnull(v), |
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| 55 | lambda v: isinstance(v, type(None)), |
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| 56 | ], |
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| 57 | }, |
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| 58 | 87: { |
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| 59 | 'flavors': [lambda v: v is None, |
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| 60 | lambda v: type(v) == type(None), |
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| 61 | ], |
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| 62 | }, |
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| 63 | }, |
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| 64 | 'column_names': ( |
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| 65 | 'flavors', |
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| 66 | 'name', |
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| 67 | 'description', |
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| 68 | 'image_urls', |
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| 69 | 'parents', |
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| 70 | '_id', |
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| 71 | 'type', |
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| 72 | 'image_paths', |
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| 73 | 'Aroused', |
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| 74 | 'Creative', |
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| 75 | 'Energetic', |
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| 76 | 'Euphoric', |
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| 77 | 'Focused', |
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| 78 | 'Giggly', |
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| 79 | 'Happy', |
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| 80 | 'Hungry', |
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| 81 | 'Relaxed', |
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| 82 | 'Sleepy', |
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| 83 | 'Talkative', |
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| 84 | 'Tingly', |
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| 85 | 'Uplifted', |
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| 86 | 'Cramps', |
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| 87 | 'Depression', |
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| 88 | 'Eye Pressure', |
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| 89 | 'Fatigue', |
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| 90 | 'Headaches', |
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| 91 | 'Inflammation', |
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| 92 | 'Insomnia', |
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| 93 | 'Lack of Appetite', |
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| 94 | 'Muscle Spasms', |
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| 95 | 'Nausea', |
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| 96 | 'Pain', |
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| 97 | 'Seizures', |
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| 98 | 'Spasticity', |
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| 99 | 'Stress', |
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| 100 | 'Anxious', |
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| 101 | 'Dizzy', |
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| 102 | 'Dry Eyes', |
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| 103 | 'Dry Mouth', |
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| 104 | 'Headache', |
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| 105 | 'Paranoid', |
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| 106 | 'difficulty', |
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| 107 | 'flowering', |
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| 108 | 'height', |
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| 109 | 'stretch', |
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| 110 | 'yield', |
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| 111 | ), |
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| 112 | }, |
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| 113 | 'data_2': { |
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| 114 | 'data_path': sample_json, |
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| 115 | 'nb_rows': 100, |
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| 116 | 'nb_columns': 12, |
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| 117 | 'type_distros': { |
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| 118 | 'type': {str: 100}, |
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| 119 | 'flavors': {list: 98, float: 2}, |
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| 120 | }, |
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| 121 | 'value_distros': { |
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| 122 | 'type': {'hybrid': 48, 'sativa': 19, 'indica': 33}, |
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| 123 | }, |
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| 124 | 'row': { |
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| 125 | 0: { |
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| 126 | 'flavors': [lambda v: v == ["Chemical", "Pine", "Diesel"], |
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| 127 | lambda v: type(v) == list, |
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| 128 | ], |
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| 129 | 'type': [lambda v: v == 'hybrid', |
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| 130 | lambda v: type(v) == str |
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| 131 | ], |
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| 132 | }, |
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| 133 | 7: { |
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| 134 | 'flavors': [lambda v: v == ["Earthy", "Pungent", "Sweet"], |
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| 135 | lambda v: type(v) == list, |
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| 136 | ], |
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| 137 | 'type': [lambda v: v == 'hybrid', |
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| 138 | lambda v: type(v) == str |
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| 139 | ], |
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| 140 | }, |
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| 141 | 76: { |
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| 142 | 'flavors': [lambda v: np.isnan(v), |
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| 143 | lambda v: pd.isnull(v), |
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| 144 | lambda v: type(v) == float, |
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| 145 | ], |
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| 146 | }, |
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| 147 | 87: { |
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| 148 | 'flavors': [lambda v: np.isnan(v), |
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| 149 | lambda v: pd.isnull(v), |
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| 150 | lambda v: type(v) == float, |
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| 151 | ], |
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| 152 | }, |
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| 153 | }, |
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| 154 | 'column_names': ( |
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| 155 | 'flavors', |
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| 156 | 'name', |
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| 157 | 'medical', |
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| 158 | 'description', |
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| 159 | 'image_urls', |
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| 160 | 'parents', |
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| 161 | 'negatives', |
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| 162 | 'grow_info', |
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| 163 | '_id', |
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| 164 | 'type', |
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| 165 | 'image_paths', |
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| 166 | 'effects', |
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| 167 | ), |
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| 170 |
