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Bug fix for float precision calculation using categorical data with trailing 0s #1125

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merged 1 commit into from
Apr 15, 2024
Merged

Bug fix for float precision calculation using categorical data with trailing 0s #1125

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5 changes: 4 additions & 1 deletion dataprofiler/profilers/float_column_profile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,10 @@ def _get_float_precision(

# length of sampled cells after all punctuation removed
len_per_float = (
df_series_clean.sample(sample_size).replace(to_replace=r, value="").map(len)
df_series_clean.sample(sample_size)
.astype(object)
.replace(to_replace=r, value="")
.map(len)
).astype(float)

# Determine statistics precision
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7 changes: 7 additions & 0 deletions dataprofiler/tests/profilers/test_float_column_profile.py
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Expand Up @@ -211,6 +211,13 @@ def test_profiled_precision(self):
msg=f"Errored for: {sample[0]}",
)

# Validate categorical series with trailing zeros supported
categorical_series = pd.Series(
[202209, 202210, 202211], dtype="category"
).apply(str)
float_profiler = FloatColumn("Name")
float_profiler.update(categorical_series)
Comment on lines +215 to +219
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testing this locally so may have some additional comments, but initial thought is that there should definitely be some assert statements here to validate this is actually working as intended post-change @SchadtJ @scottiegarcia

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thoughts @scottiegarcia @SchadtJ?


def test_profiled_min(self):
# test with multiple values
data = np.linspace(-5, 5, 11)
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