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| author | Tyler Reddy <tyler.je.reddy@gmail.com> | 2021-02-02 20:43:09 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-02-02 20:43:09 -0700 |
| commit | 6c141d0663e2d0da30cf601c5726cbc44830d15f (patch) | |
| tree | f08b9e487b2b0b7f601107fd21a039f89c00c4d8 | |
| parent | 78cf92c19a7cfe9a07b970a2156d177a9ba627b0 (diff) | |
| parent | b3aeb8391f3cfd0a10292780b481d0bd1539a0c4 (diff) | |
| download | numpy-6c141d0663e2d0da30cf601c5726cbc44830d15f.tar.gz | |
Merge pull request #18307 from sleeplessinseattledev/bug18256
DOC: Corrected numpy.power example.
| -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) |