add parameter to coerce non-numeric values to NaN during validation #4
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@TMiguelT what do you think about this? |
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This seems reasonable. Could you add a test that currently breaks? ie a column containing non-numeric data? |
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Sure I will during the week, if that's okay with you |
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this is really old, but I ran into this again: Out[85]: df["my_column"].unique()
Out[85]:
array(['nan', '2008', '2016', '2015', '2014', '2013', '2012', '2010',
'2011', '2009', '2017'], dtype=object)Say we have a simple dictionary dictionary = ps.Schema(
[
ps.Column('my_column', [ps.validations.InRangeValidation(1900, 3000)]),
])In [86]: errors = dictionary.validate(df, columns=["my_column"])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
pandas/_libs/src/inference.pyx in pandas._libs.lib.maybe_convert_numeric()
ValueError: Unable to parse string "nan"
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-86-2a1c78e8916b> in <module>()
----> 1 errors = dictionary.validate(df, columns=["my_column"])
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas_schema/schema.py in validate(self, df, columns)
83 # Iterate over each pair of schema columns and data frame series and run validations
84 for series, column in column_pairs:
---> 85 errors += column.validate(series)
86
87 return sorted(errors, key=lambda e: e.row)
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas_schema/column.py in validate(self, series)
25 :return: An iterable of ValidationError instances generated by the validation
26 """
---> 27 return [error for validation in self.validations for error in validation.get_errors(series, self)]
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas_schema/column.py in <listcomp>(.0)
25 :return: An iterable of ValidationError instances generated by the validation
26 """
---> 27 return [error for validation in self.validations for error in validation.get_errors(series, self)]
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas_schema/validation.py in get_errors(self, series, column)
82 # Calculate which columns are valid using the child class's validate function, skipping empty entries if the
83 # column specifies to do so
---> 84 simple_validation = ~self.validate(series)
85 if column.allow_empty:
86 # Failing results are those that are not empty, and fail the validation
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas_schema/validation.py in validate(self, series)
205
206 def validate(self, series: pd.Series) -> pd.Series:
--> 207 series = pd.to_numeric(series)
208 return (series >= self.min) & (series < self.max)
209
~/Code/lap-etl-project/venv/lib/python3.7/site-packages/pandas/core/tools/numeric.py in to_numeric(arg, errors, downcast)
131 coerce_numeric = False if errors in ('ignore', 'raise') else True
132 values = lib.maybe_convert_numeric(values, set(),
--> 133 coerce_numeric=coerce_numeric)
134
135 except Exception:
pandas/_libs/src/inference.pyx in pandas._libs.lib.maybe_convert_numeric()
ValueError: Unable to parse string "nan" at position 0I believe line 207 is the responsible, as it raises an error if it can't convert to numeric values. This is true for NaN values. |
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Please just make a test case out of your example and I'll be happy to accept the PR |
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Maybe this parameter should be exposed, but I set it to coerce by default. All non numeric values are converted into np.NaN elements. Without this setting validation raises an error if a string is found in a column of ints or floats.
Please let me know what you think