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author | Amrit K <amrit110@gmail.com> | 2021-11-15 19:23:34 -0500 |
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committer | Amrit K <amrit110@gmail.com> | 2021-11-15 19:23:34 -0500 |
commit | 76e6409d2d7cbcc88a804560cfb765f365531858 (patch) | |
tree | 800e75270a0eefb3d14990ae707ec0754736977f | |
parent | c87959a0095ff52f1fdfc6423f29c42be46cdf40 (diff) | |
download | numpy-76e6409d2d7cbcc88a804560cfb765f365531858.tar.gz |
Update doc with better wording
-rw-r--r-- | doc/source/user/basics.creation.rst | 9 |
1 files changed, 4 insertions, 5 deletions
diff --git a/doc/source/user/basics.creation.rst b/doc/source/user/basics.creation.rst index f54ac5c71..a02cc8889 100644 --- a/doc/source/user/basics.creation.rst +++ b/doc/source/user/basics.creation.rst @@ -75,11 +75,10 @@ assign a new type that satisfies all of the array elements involved in the computation, here ``uint32`` and ``int32`` can both be represented in as ``int64``. -The default NumPy behavior is to create arrays in either 64-bit signed -integers (32-bit signed integers on Windows platform) or double precision -floating point numbers, ``int64`` (``int32`` on Windows platform) and -``float``, respectively. If you expect your arrays to be a certain type, -then you need to specify the ``dtype`` while you create the array. +The default NumPy behavior is to create arrays in either 32 or 64-bit signed +integers (platform dependent) or double precision floating point numbers, +int32/int64 and float, respectively. If you expect your arrays to be a +certain type, then you need to specify the dtype while you create the array. 2) Intrinsic NumPy array creation functions =========================================== |