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| author | Matti Picus <matti.picus@gmail.com> | 2019-07-14 09:27:45 -0500 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-07-14 09:27:45 -0500 |
| commit | a5e2e4d5b5d01a43133d03bf0c401cf9516efabe (patch) | |
| tree | ba233baae564e2f611d1a78e3455311f2f36196c | |
| parent | e55bcb2f2c40852be7d9f0e6e262c332dd2b277d (diff) | |
| parent | de9ab5fc1f360afe94d8a2cce5a5e58addfc0bb2 (diff) | |
| download | numpy-a5e2e4d5b5d01a43133d03bf0c401cf9516efabe.tar.gz | |
Merge pull request #13970 from BBQuercus/patch-1
DOC: spellcheck numpy/doc/broadcasting.py
| -rw-r--r-- | numpy/doc/broadcasting.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/numpy/doc/broadcasting.py b/numpy/doc/broadcasting.py index 0bdb6ae7d..f7bd2515b 100644 --- a/numpy/doc/broadcasting.py +++ b/numpy/doc/broadcasting.py @@ -42,7 +42,7 @@ We can think of the scalar ``b`` being *stretched* during the arithmetic operation into an array with the same shape as ``a``. The new elements in ``b`` are simply copies of the original scalar. The stretching analogy is only conceptual. NumPy is smart enough to use the original scalar value -without actually making copies, so that broadcasting operations are as +without actually making copies so that broadcasting operations are as memory and computationally efficient as possible. The code in the second example is more efficient than that in the first @@ -52,7 +52,7 @@ because broadcasting moves less memory around during the multiplication General Broadcasting Rules ========================== When operating on two arrays, NumPy compares their shapes element-wise. -It starts with the trailing dimensions, and works its way forward. Two +It starts with the trailing dimensions and works its way forward. Two dimensions are compatible when 1) they are equal, or |