Ensure uniqueness of column names to avoid losing data during serialization #38
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svlandeg
commented
Apr 3, 2025
| doc_bin = DocBin(docs=[old_doc], store_user_data=True) | ||
| new_doc = list(doc_bin.get_docs(nlp.vocab))[0] | ||
| new_table = new_doc._.tables[0]._.data | ||
| assert list(new_table.columns) == ['Index', 'Value', 'Value (2)', 'Index (2)', 'Value (3)', 'Value (4)'] |
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on main, the result would be
['Index', 'Value']
i.e. 4 columns would have just been removed from the serialized output.
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When a table is parsed with duplicate column names, these will both be preserved in the pandas
DataFrameformat. However, when converting this to a dictionary inencode_df, pandas will raise a warning:And the "duplicate" columns will be removed, even if the values in the column are different than the other column with the same heading.
This may be somewhat surprising for users, so this PR instead ensures that columns are unique by appending
(2),(3)etc to non-unique column names, effectively making them unique, before serializing them to the dictionary.Open questions:
spacy-layoutissue a warning about the renaming of the columns?