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PyPI · #1294 most downloaded on PyPI
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Last release 3 years ago
no release in 18 months
Release timing varies
gaps range from 3 weeks to 10 months
Most releases are documented
notes for 44 of the last 60 stable releases
1 version withdrawn
withdrawn after publishing
8 years old
86 releases · first in 2018
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One column per quarter.
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Added a groupby_apply function to utilize dask for groupby apply when its faster. Simply use as df.swifter.groupby_apply(groupby_col, func). I would'v
Added a groupby_apply function to utilize dask for groupby apply when its faster. Simply use as df.swifter.groupby_apply(groupby_col, func). I would've extended the Pandas DataFrameGroupBy object, but he hasn't added support for that kind of extension yet. Also, removed the str_object limitation to utilizing dask. Now it will simply determine whether to use dask v pandas based on the dask_threshold (default 1 second).
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Completely refactored the package as an extension to pandas, rather than an independent function call. This will allow for increased flexibility of th
Completely refactored the package as an extension to pandas, rather than an independent function call. This will allow for increased flexibility of the user and simplicity of using swiftapply. This new update changed the way to use swiftapply. Now the format is df.swifter.apply(func)
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Fixed bug that would allow certain functions to be applied to the entire series/dataframe, rather than to each element. For example, len(x) returned t
Fixed bug that would allow certain functions to be applied to the entire series/dataframe, rather than to each element. For example, len(x) returned the length of the series, rather than the length of each string within the series. A special thanks to @bharatvem for pointing this out.
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Added support for vectorized and pandas applies to dataframes. Converted all string manipulations to pandas apply (unless vectorizable) because dask p
Added support for vectorized and pandas applies to dataframes. Converted all string manipulations to pandas apply (unless vectorizable) because dask processes string manipulations slowly.
Nothing published for this version
Removed numba jit function, because this was adding to the total runtime. Will do some experiments and consider readding later.
Removed numba jit function, because this was adding to the total runtime. Will do some experiments and consider readding later.
Nothing published for this version
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Currently works very well with pandas series, needs some work to optimize dask multiprocessing for pandas dataframes. For now, it is probably best to
Currently works very well with pandas series, needs some work to optimize dask multiprocessing for pandas dataframes. For now, it is probably best to apply to each series independently, rather than multiple columns of a dataframe at once.
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