Comments (2)
alexander-beedie
commented on July 1, 2024
Two solutions can be envisaged: either
Dasksupport withinPolars, orSASsupport to guaranteePolars' autonomous operation
There's another solution; Arrow export from the existing SAS libraries - with that in place we could simply zero-copy the output into Polars without having to write an entire (complicated) SAS-parsing i/o stack (which I suspect there is little appetite for). Could be worth adding an Issue to the various projects, requesting efficient Arrow export 😉 Otherwise some intermediate conversions are likely the way to go for now...
Out of curiosity, what are the major domains that use these files? I've never come across them in finance; are they somewhat domain-specific?
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krz
commented on July 1, 2024
SAS files are integral within the health sector, especially while dealing with health authorities and regulators. SAS facilitates regulatory compliance, thereby it's a common choice among health professionals.
Polars support would be very much appreciated.
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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 HOT 2
- 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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