ldacey commented on PR #14641:
URL: https://github.com/apache/arrow/pull/14641#issuecomment-1315331226
This works for me, I can run the functools reduce (that simplifies what I
have).
Some things I want to make sure:
```
paths = [
'path/to/data/month_id=202105/part-0.parquet',
'path/to/data/month_id=202106/part-0.parquet',
'path/to/data/month_id=202107/part-0.parquet',
'path/to/data/month_id=202107/part-1.parquet',
'path/to/data/month_id=202107/part-2.parquet',
]
reduce(operator.or_, [partitioning.parse(file) for file in paths])
<pyarrow.compute.Expression (((((month_id == 202105) or (month_id ==
202106)) or
(month_id == 202107)) or (month_id == 202107)) or (month_id == 202107))>
```
1. Do I need to worry about there being multiple filters for 202107 in that
expression? In my example, there are 3 fragments in that partition.
2. There is no benefit in using `.isin()` for a single partition compared to
`or`, right? In terms of performance/efficiency they are the same?
```
<pyarrow.compute.Expression is_in(month_id, {value_set=int64:[
202105,
202106,
202107
], skip_nulls=false})>
```
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