Comments (2)
Yes! Also benchmarking could be then common across implementations.
And perhaps there could be equivalent CSVs (with some type information in the header, e.g. colname (float64)
)? Or do you think the roundtrip tests are sufficient? I'm just thinking that the format may evolve and it would be helpful to have something human readable at hand.
I still don't have feather on my Mac (issue already reported), so I could certainly use some reference data.
Thanks.
from feather.
I think it would be best to add a data generation script for benchmarking purposes. We can also host sample feather files outside of the git repo.
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Related Issues (20)
- Incompatibility with tibble>=2.0.0 HOT 4
- Read file in "feather" format by command "View" HOT 2
- Disable test for UTF-8 filenames on windows
- Reading/writing fails for large data.frames HOT 5
- Support for set/list columns in python HOT 2
- "cannot serialize duplicate column names" HOT 2
- Is it possible to save feather file in compressed zip format? HOT 6
- Should feather::read_feather respect options(stringsAsFactors = FALSE)? HOT 3
- In R, Feather cannot read dataframe back if missing or having empty column header HOT 2
- [Feature request] allow datetime index HOT 3
- python feather-format 0.4.0 is incompatible with pyarrow 0.17.0: ImportError FeatherError FeatherReader FeatherWriter HOT 3
- feather installation failed with "big endian" issue HOT 3
- Reading feather HOT 1
- Another CRAN release needed: not compatible with arrow HOT 2
- where are the docs? HOT 1
- Strange error HOT 1
- how to inspect metadata?
- pyarrow.lib.ArrowTypeError: Expected bytes, got a 'dict' object
- Support for Big-endian?
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