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Handle int when unexpectedly included in the lables e.g. CD07 has 0 i… #171

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Oct 3, 2025
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Handle int when unexpectedly included in the lables e.g. CD07 has 0 i… #171

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Handle int when unexpectedly included in the lables e.g. CD07 has 0 i…
girumb Oct 2, 2025
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Add tests and address PR feedback see HEA-751
girumb Oct 3, 2025
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10 changes: 6 additions & 4 deletions pipelines/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,12 +93,14 @@ def prepare_lookup(data: str | list[str] | pd.Series | pd.DataFrame) -> pd.Serie
elif isinstance(data, (list, pd.Series)):
result = pd.DataFrame(data)
else:
result = data
# Handle unexpected types (like int, float)
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@rhunwicks rhunwicks Oct 2, 2025

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I am not sure that this will work. The current else is covering the situation where data has type pd.DataFrame. We want to end up in a situation where we have a regular dataframe to work on. What does pd.DataFrame(pd.DataFrame(data)) actually do.

Also, if the int label is passed as part of a list or series then it will still be part of the resulting dataframe.

Can't we solve this by just reversing the order of the if clauses and leaving everything else untouched:

    if isinstance(data, pd.DataFrame):
        result = data
    elif isinstance(data, (list, pd.Series)):
        result = pd.DataFrame(data)
    else:
        # Handle other types (like str, int, float)
        result = pd.DataFrame([data])

Then the result.map(str) will apply to a dataframe containing a column that contains an int, which will convert it to a str.

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@rhunwicks rhunwicks Oct 2, 2025

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And similarly reverse the if statements for extracting the result from the dataframe to return.

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@rhunwicks rhunwicks Oct 2, 2025

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@girum-air you might also want to add a pipelines_tests.test_utils.PrepareLookupTestCase that tests each of these input types (str, int, list, Series, DataFrame)

result = pd.DataFrame([str(data)])

result = result.map(str).map(str.strip).map(str.lower).replace(r"\s+", " ", regex=True)
if isinstance(data, str):
result = result.iloc[0, 0]
if isinstance(data, str) or (not isinstance(data, (list, pd.Series))):
return result.iloc[0, 0]
elif isinstance(data, (list, pd.Series)):
result = result.iloc[:, 0]
return result.iloc[:, 0]
return result


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