[jira] [Created] (ARROW-14767) Categorical int8 index types written as int32 in parquet files

["Gavin (Jira)" <ji...@apache.org>]

Gavin created ARROW-14767:
-----------------------------

             Summary: Categorical int8 index types written as int32 in parquet files
                 Key: ARROW-14767
                 URL: https://issues.apache.org/jira/browse/ARROW-14767
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 5.0.0
         Environment: NAME="CentOS Linux"
VERSION="7 (Core)"
            Reporter: Gavin


When converting from a pandas dataframe to a table, categorical variables are by default given an index type int8 (presumably because there are fewer than 128 categories) in the schema. When this is written to a parquet file, the schema changes such that the index type is int32 instead. This causes an inconsistency between the schemas of tables derived from pandas and those read from disk.

A minimal recreation of the issue is as follows:
{code:java}
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

df = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": ["a", "a", "b", "c", "b"]})
dtypes = {
    "A": np.dtype("int8"),
    "B": pd.CategoricalDtype(categories=["a", "b", "c"], ordered=None),
}
df = df.astype(dtypes)

tbl = pa.Table.from_pandas(
    df, 
)  
where = "tmp.parquet"
filesystem = pa.fs.LocalFileSystem()

pq.write_table(
    tbl,
    filesystem.open_output_stream(
        where,
        compression=None,
    ),
    version="2.0",
)

schema = tbl.schema

read_schema = pq.ParquetFile(
    filesystem.open_input_file(where), #buffer_size=_BUFFER_SIZE, pre_buffer=True
).schema_arrow{code}
By printing schema and read_schema, you can the inconsistency.

I have workarounds in place for this, but am raising the issue anyway so that you can resolve it properly.



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