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| author | Mary <sleeplessinseattle.dev@gmail.com> | 2021-02-02 13:55:23 -0800 |
|---|---|---|
| committer | Mary <sleeplessinseattle.dev@gmail.com> | 2021-02-02 13:55:23 -0800 |
| commit | b3aeb8391f3cfd0a10292780b481d0bd1539a0c4 (patch) | |
| tree | 3d1cb72e03613a20e4aa4044faaf071268a4a092 | |
| parent | b4491833d636344ef295f1609bcf4742c1d94d10 (diff) | |
| download | numpy-b3aeb8391f3cfd0a10292780b481d0bd1539a0c4.tar.gz | |
DOC: Corrected numpy.power example.
See #18256
| -rw-r--r-- | doc/source/user/basics.types.rst | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/basics.types.rst b/doc/source/user/basics.types.rst index 64b6dcf50..a9e210c3a 100644 --- a/doc/source/user/basics.types.rst +++ b/doc/source/user/basics.types.rst @@ -270,7 +270,7 @@ Overflow Errors The fixed size of NumPy numeric types may cause overflow errors when a value requires more memory than available in the data type. For example, -`numpy.power` evaluates ``100 * 10 ** 8`` correctly for 64-bit integers, +`numpy.power` evaluates ``100 ** 8`` correctly for 64-bit integers, but gives 1874919424 (incorrect) for a 32-bit integer. >>> np.power(100, 8, dtype=np.int64) |