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| author | Matti Picus <matti.picus@gmail.com> | 2023-04-01 01:22:01 +0300 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-03-31 15:22:01 -0700 |
| commit | 7131db4b4b98f9251cf5932fd052d8cdffddeff2 (patch) | |
| tree | 33343ae822dd047a1320976eed2452b4f7c3250e | |
| parent | 700211a7af88afa225ef0d8332c575c8aae813de (diff) | |
| download | numpy-7131db4b4b98f9251cf5932fd052d8cdffddeff2.tar.gz | |
DOC: default integer dtype reflect C long size (#23507)
| -rw-r--r-- | doc/source/user/basics.creation.rst | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/doc/source/user/basics.creation.rst b/doc/source/user/basics.creation.rst index 40b4e378d..3ee501889 100644 --- a/doc/source/user/basics.creation.rst +++ b/doc/source/user/basics.creation.rst @@ -75,8 +75,8 @@ the computation, here ``uint32`` and ``int32`` can both be represented in as ``int64``. The default NumPy behavior is to create arrays in either 32 or 64-bit signed -integers (platform dependent and matches C int size) or double precision -floating point numbers, int32/int64 and float, respectively. If you expect your +integers (platform dependent and matches C ``long`` size) or double precision +floating point numbers. If you expect your integer arrays to be a specific type, then you need to specify the dtype while you create the array. |