Comments (4)
I believe this is caused by this change.
cc: @ritchie46
from polars.
We forked arrow for our needs. We don't want to support mutable dictionary keys we don't use. We want to keep our binary size in check.
from polars.
We forked arrow for our needs. We don't want to support mutable dictionary keys we don't use. We want to keep our binary size in check.
Ah, fair enough. We use polars most of the time, but we migrated from arrow2 to polars-arrow for converting/normalizing some JSON to arrow files.
Would you accept a PR which addresses our use case, but doesn't affect polars' performance/binary size or is the plan to eventually get rid of MutableDictionaryArray entirely?
from polars.
But it likely will affect the binary size as it needs to compile the u16
branch in that case.
from polars.
Related Issues (20)
- exception thrown if converting arrow Table with struct and dictionary columns to polar dataframe
- converting pandas to Polars drops column if its name, when converted to string, matches another column's name
- pl.format should be clear it will return null when one of the arguments is null
- Off-by-one error when casting to Decimal with set precision
- Importing pyarrow after polars causes `SIGSEGV` HOT 4
- Polars assumes microseconds instead of reading numpy timedelta units
- Cannot create Array column containing large u64 value
- Multipling a Decimal by Int returns Int type HOT 2
- Split out `Expr.top_k` from `Expr.top_k_by`
- `pl.Datetime` `time_zone` parameter has no type or value check HOT 5
- Cast from `pl.Date` to `pl.Datetime` silently returns incorrect value when new dtype cannot hold value HOT 2
- exception thrown if converting chunked arrow Table with struct and dictionary columns to polar Dataframe
- Panic when constructing Series with dtype `Duration('ms')` with large `timedelta` objects
- Can the separator of the read csv function support regular splitting? HOT 5
- Casting float to Decimal fails silently HOT 2
- Use parquet statistics when collecting column statistics from scanned parquet
- Excessive Memory Consumption During Rolling Operations on Large DataFrames
- write_database() - Insert many rows with sql server using fast_executemany HOT 3
- fill_null doesn't support expr HOT 6
- `dt.total_nanoseconds` and `dt.total_microseconds` may overflow silently
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from polars.