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Scale Series.median(skipna=True/False)#579
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PokhodenkoSA wants to merge 1 commit intoIntelPython:masterfrom
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Scale Series.median(skipna=True/False)#579PokhodenkoSA wants to merge 1 commit intoIntelPython:masterfrom
PokhodenkoSA wants to merge 1 commit intoIntelPython:masterfrom
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| def nanmedian_impl(a): | ||
| # Create a temporary workspace with only non-NaN values | ||
| temp_arry = np.empty(a.size, a.dtype) |
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Result of median always should be float
| def np_nanmedian(a): | ||
| if not isinstance(a, types.Array): | ||
| return | ||
| isnan = get_isnan(a.dtype) |
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Looks good. Let's use it everywhere
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After some investigation, i see few issues with it:
- It return
Falsefor ints, but the function itself doesn't inline - Even if it would inline, there is no dead code elimination pass after inline pass. Which means, the condition would not be eliminated
- Even if dead code elimination would be added in previous versions it works only with conditions like
if True:andif False:and doesn't work with conditions likeif not False.
Probably llvm would be able to fix all this issues in it's own IR. Probably not. Need to check and measure
Still, let's stick to the strategy use this function
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Bad performance.