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Related: pandas-dev/pandas#20521
What the general thoughts are to use DataFrame.attrs and Series.attrs for reading and writing metadata to/from parquet?
For example, here is how the metadata would be written:
pdf = pandas.DataFrame({"a": [1]}) pdf.attrs = {"name": "my custom dataset"} pdf.a.attrs = {"long_name": "Description about data", "nodata": -1, "units": "metre"} pdf.to_parquet("file.parquet")
Then, when loading in the data:
pdf = pandas.read_parquet("file.parquet") pdf.attrs
{"name": "my custom dataset"}
pdf.a.attrs
{"long_name": "Description about data", "nodata": -1, "units": "metre"}
Reporter: Alan Snow
Note: This issue was originally created as ARROW-12823. Please see the migration documentation for further details.
The text was updated successfully, but these errors were encountered:
Alan Snow: Seems like writing metadata could happen in get_column_metadata
Possibly add an "attrs" item so it doesn't conflict with "metadata".
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feat: add attribute for pandas column attrs for apache#28558
18e77a1
pandas
attrs
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Related: pandas-dev/pandas#20521
What the general thoughts are to use DataFrame.attrs and Series.attrs for reading and writing metadata to/from parquet?
For example, here is how the metadata would be written:
Then, when loading in the data:
{"name": "my custom dataset"}
{"long_name": "Description about data", "nodata": -1, "units": "metre"}
Reporter: Alan Snow
Note: This issue was originally created as ARROW-12823. Please see the migration documentation for further details.
The text was updated successfully, but these errors were encountered: