tableau /
TabPy
| Total Complexity | 3 |
| Total Lines | 26 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | import scipy.stats as stats |
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| 2 | from tabpy.models.utils import setup_utils |
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| 3 | |||
| 4 | |||
| 5 | def anova(_arg1, _arg2, *_argN): |
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| 6 | ''' |
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| 7 | ANOVA is a statistical hypothesis test that is used to compare |
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| 8 | two or more group means for equality.For more information on |
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| 9 | the function and how to use it please refer to tabpy-tools.md |
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| 10 | ''' |
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| 11 | |||
| 12 | cols = [_arg1, _arg2] + list(_argN) |
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| 13 | for col in cols: |
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| 14 | if not isinstance(col[0], (int, float)): |
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| 15 | print("values must be numeric") |
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| 16 | raise ValueError |
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| 17 | _, p_value = stats.f_oneway(_arg1, _arg2, *_argN) |
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| 18 | return p_value |
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| 19 | |||
| 20 | |||
| 21 | if __name__ == '__main__': |
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| 22 | setup_utils.deploy_model( |
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| 23 | 'anova', |
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| 24 | anova, |
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| 25 | 'Returns the p-value form an ANOVA test') |
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| 26 |
