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author | Marten van Kerkwijk <mhvk@astro.utoronto.ca> | 2021-09-14 13:37:59 -0400 |
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committer | Marten van Kerkwijk <mhvk@astro.utoronto.ca> | 2021-09-14 13:38:07 -0400 |
commit | 0dbc9ad1454aab5044ab0a14b9094db1a3c7c027 (patch) | |
tree | 58ca9e0c3e0dbe6012071fc1e45a51f806463911 /numpy/lib/function_base.py | |
parent | 6ba48721e22622403a60b7f9d3ec5cae308ba3a9 (diff) | |
download | numpy-0dbc9ad1454aab5044ab0a14b9094db1a3c7c027.tar.gz |
MAINT: remove unused argument in private function
Diffstat (limited to 'numpy/lib/function_base.py')
-rw-r--r-- | numpy/lib/function_base.py | 15 |
1 files changed, 7 insertions, 8 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index d875a00ae..e33d55056 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3714,16 +3714,15 @@ def _median(a, axis=None, out=None, overwrite_input=False): indexer[axis] = slice(index-1, index+1) indexer = tuple(indexer) + # Use mean in both odd and even case to coerce data type, + # using out array if needed. + rout = mean(part[indexer], axis=axis, out=out) # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact) and sz > 0: - # warn and return nans like mean would - rout = mean(part[indexer], axis=axis, out=out) - return np.lib.utils._median_nancheck(part, rout, axis, out) - else: - # if there are no nans - # Use mean in odd and even case to coerce data type - # and check, use out array. - return mean(part[indexer], axis=axis, out=out) + # If nans are possible, warn and replace by nans like mean would. + rout = np.lib.utils._median_nancheck(part, rout, axis) + + return rout def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, |