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authorAnirudh Subramanian <anirudh2290@ufl.edu>2020-04-02 19:49:56 +0000
committerAnirudh Subramanian <anirudh2290@ufl.edu>2020-04-08 00:03:31 +0000
commitf8c598a104c91d094d654cf7b98391d795f7835d (patch)
tree509f5045fe14a8f160b5dd0c74f7f3ce4521056a /doc
parent0ce156413feb21a4f2a8eae3b1cd954d04240edd (diff)
downloadnumpy-f8c598a104c91d094d654cf7b98391d795f7835d.tar.gz
DOC: Correct private function name to PyArray_AdaptFlexibleDType
Diffstat (limited to 'doc')
-rw-r--r--doc/neps/nep-0040-legacy-datatype-impl.rst2
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/neps/nep-0040-legacy-datatype-impl.rst b/doc/neps/nep-0040-legacy-datatype-impl.rst
index 86bd488fe..a768fa08c 100644
--- a/doc/neps/nep-0040-legacy-datatype-impl.rst
+++ b/doc/neps/nep-0040-legacy-datatype-impl.rst
@@ -65,7 +65,7 @@ values are unaffected by it, and it is always possible to cast them to the
native, canonical representation without any loss of information.
The concept of flexibility can be generalized to parametric datatypes.
-For example the private ``AdaptFlexibleDType`` function also accepts the
+For example the private ``PyArray_AdaptFlexibleDType`` function also accepts the
naive datetime dtype as input to find the correct time unit.
The datetime dtype is thus parametric not in the size of its storage,
but instead in what the stored value represents.