NumPy: Difference between revisions
Created page with "NumPy is a python library for working with arrays. ==Memory Usage== To optimize memory usage, try to use views instead of actual arrays where possible. Indexing into an arr..." |
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To output to an existing array <code>c</code>, you can use <code>np.add(a, b, out=c)</code>. | To output to an existing array <code>c</code>, you can use <code>np.add(a, b, out=c)</code>. | ||
If you need to perform an operation over multiple arrays, you can use [https://numpy.org/doc/stable/reference/generated/numpy.ufunc.reduce.html np.ufunc.reduce]. E.g. <code>np.logical_and.reduce((a, b, c))</code>. | If you need to perform an operation over multiple arrays, you can use [https://numpy.org/doc/stable/reference/generated/numpy.ufunc.reduce.html np.ufunc.reduce]. E.g. <code>np.logical_and.reduce((a, b, c))</code>. | ||
===Batched Multiplication=== | |||
If you need batched matrix multiplication, use [https://numpy.org/doc/stable/reference/generated/numpy.tensordot.html <code>np.tensordot</code>] and transpose the result. | |||
Latest revision as of 14:56, 6 February 2021
NumPy is a python library for working with arrays.
Memory Usage
To optimize memory usage, try to use views instead of actual arrays where possible.
Indexing into an array will return a view. See numpy indexing for details.
Fancy indexing, using a list of indices, will not return a view.
The following functions also return views
- np.broadcast_to
- np.reshape - usually returns a view
- ndarray.view
Most function calls will return a new array. E.g. a+b will return a new array with the broadcasted size which requires memory allocation.
To output to an existing array c, you can use np.add(a, b, out=c).
If you need to perform an operation over multiple arrays, you can use np.ufunc.reduce. E.g. np.logical_and.reduce((a, b, c)).
Batched Multiplication
If you need batched matrix multiplication, use np.tensordot and transpose the result.