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Diffstat (limited to 'numpy/doc/broadcasting.py')
| -rw-r--r-- | numpy/doc/broadcasting.py | 13 |
1 files changed, 7 insertions, 6 deletions
diff --git a/numpy/doc/broadcasting.py b/numpy/doc/broadcasting.py index 95e9b67f9..7b6179663 100644 --- a/numpy/doc/broadcasting.py +++ b/numpy/doc/broadcasting.py @@ -13,8 +13,9 @@ efficient algorithm implementations. There are, however, cases where broadcasting is a bad idea because it leads to inefficient use of memory that slows computation. -NumPy operations are usually done element-by-element, which requires two -arrays to have exactly the same shape:: +NumPy operations are usually done on pairs of arrays on an +element-by-element basis. In the simplest case, the two arrays must +have exactly the same shape, as in the following example: >>> a = np.array([1.0, 2.0, 3.0]) >>> b = np.array([2.0, 2.0, 2.0]) @@ -38,9 +39,9 @@ only conceptual. NumPy is smart enough to use the original scalar value without actually making copies, so that broadcasting operations are as memory and computationally efficient as possible. -The second example is more effective than the first, since here broadcasting -moves less memory around during the multiplication (``b`` is a scalar, -not an array). +The code in the second example is more efficient than that in the first +because broadcasting moves less memory around during the multiplication +(``b`` is a scalar rather than an array). General Broadcasting Rules ========================== @@ -107,7 +108,7 @@ Here are examples of shapes that do not broadcast:: B (1d array): 4 # trailing dimensions do not match A (2d array): 2 x 1 - B (3d array): 8 x 4 x 3 # second from last dimensions mismatch + B (3d array): 8 x 4 x 3 # second from last dimensions mismatched An example of broadcasting in practice:: |