memoryview is one of the built-in types and can simply be called as:
arr = np.random.rand(5,4)
view = memoryview(arr)
view
<memory at 0x12699c318>
Answer from Grr on Stack Overflow Top answer 1 of 2
11
memoryview is one of the built-in types and can simply be called as:
arr = np.random.rand(5,4)
view = memoryview(arr)
view
<memory at 0x12699c318>
2 of 2
6
Additionally to accepted answer providing another simple method to get memoryview out of Numpy array:
Try it online!
a = np.arange(1, 9)
view = a.data
print(type(view)) # <class 'memoryview'>
In other words .data attribute of array variable gives exactly memoryview.
Cython
cython.readthedocs.io › en › latest › src › userguide › memoryviews.html
Typed Memoryviews — Cython 3.3.0 documentation
Of course, you are not restricted to using NumPy’s type (such as cnp.int32_t here), you can use any usable type. Although memoryview slices are not objects they can be set to None and they can be checked for being None as well:
python - Cython: Convert memory view to NumPy array - Stack Overflow
How to convert a typed memoryview to an NumPy array in cython? The docs have cimport numpy as np import numpy as np numpy_array = np.asarray( my_pointer) I took this f... More on stackoverflow.com
Allow numpy to copy internal buffer into memoryview
Hello, I have a C extension which maps memory address to a pointer. In this extension, I return this pointer through a memoryview PyMemoryView_FromMemory(char *mem, Py_ssize_t size, int flags). In my python code, I created a numpy array ... More on github.com
Assembling a cython memoryview from numpy arrays - Stack Overflow
I have a bunch of numpy arrays as attributes of an array of python objects, in cython, in preparation for prange processing (which requires nogil), I wanted to create a memory view that was "indire... More on stackoverflow.com
ndarray does not accept memoryview for buffer= under python2
I've discovered that ndarray(buffer=memoryview) fails on python 2: In [1]: sys.version Out[1]: '2.7.10 (default, May 26 2015, 13:16:40) \n[GCC 4.9.2]' In [2]: numpy.__version__ Out[2]: '1.10.0.dev0+9e7a0b2' In [3]: b = bytearray([1,2,3]) In [4]: b Out[4]: bytearray(b'\x01\x02\x03') In [5]: ... More on github.com
Python Data Science Handbook
jakevdp.github.io › blog › 2012 › 08 › 08 › memoryview-benchmarks
Memoryview Benchmarks | Pythonic Perambulations
The only change is that instead of using the np.ndarray[...] type specifier, we use the typed memoryview double[:, ::1] specifier. The ::1 in the second position means that we are passing a two-dimensional array, which is contiguous and C-ordered. We time the results and see the following: In [1]: import numpy as np In [2]: from memview_bench_v3 import pairwise In [3]: X = np.random.random((500, 3)) In [4]: timeit pairwise(X) 10 loops, best of 3: 22 ms per loop
w3resource
w3resource.com › python-exercises › extended-data-types › python-extended-data-types-index-memory-views-exercise-3.php
NumPy memory views in Python: 1D and 3D array examples
August 11, 2025 - import numpy as np def main(): try: # Create a NumPy array array_1_d = np.array([1, 2, 3, 4, 5, 6]) array_3_d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) # Create memory views from the NumPy arrays print("Memory views:") mem_view_1_d = memoryview(array_1_d) print(mem_view_1_d) mem_view_3_d = memoryview(array_3_d) print(mem_view_3_d) # Print the memory views print("\n1-D Memory View:") print(mem_view_1_d.tolist()) print("\n3-D Memory View:") print(mem_view_3_d.tolist()) except Exception as e: print("An error occurred:", e) if __name__ == "__main__": main() Output: Memory views: <memory at 0x00000266DBB4E108> <memory at 0x00000266DBB085E8> 1-D Memory View: [1, 2, 3, 4, 5, 6] 3-D Memory View: [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]] The above exercise demonstrates how to create memory views from NumPy arrays and print their contents.
Google Groups
groups.google.com › g › cython-users › c › AYFMrvSTVr4
creating a memoryview from scratch
If I use regular old malloc, and copy to the memoryview, then how/when do I free that memory? Memory will be released, when the view.array is destroyed. A numpy array created with asarray will share memory and keeps a reference, so memory keeps allocated as long the numpy array exists.
Codecademy
codecademy.com › docs › python › built-in functions › memoryview()
Python | Built-in Functions | memoryview() | Codecademy
August 26, 2023 - Note: memoryview() can only be used on objects that support the buffer protocol and have a readable or writable buffer interface, such as byte, bytearray, array.array, numpy arrays, and other objects created using the struct module.
Simonwenkel
simonwenkel.com › notes › programming_languages › python › cython-memory-views-of-numpy-arrays.html
Cython Memory Views of NumPy Arrays - SimonWenkel.com
October 4, 2020 - The setup is straight forward. The following functions are evaluated on numpy arrays of dimensions (1x1), (10x10), (100x100). On larger arrays (1000x1000, 10000x10000) only numba and cython memory functions were evaluated.
Cython
cython.readthedocs.io › en › 0.29.x › src › userguide › memoryviews.html
Typed Memoryviews — Cython 0.29.37 documentation
Of course, you are not restricted to using NumPy’s type (such as np.int32_t here), you can use any usable type. Although memoryview slices are not objects they can be set to None and they can be checked for being None as well:
GitHub
github.com › numpy › numpy › issues › 8408
Allow numpy to copy internal buffer into memoryview · Issue #8408 · numpy/numpy
December 21, 2016 - mymemoryview = MyMemoryView() # pointer on a 24 bytes buffer vertices = np.array([0, -0.5, 0.5, 0.5, -0.5, 0.5], dtype=np.float32) wrapper = np.array(mymemoryview, copy=False, dtype=vertices.dtype) # Wrap my memoryview into a numpy array without copy np.copyto(wrapper, vertices, casting='no')
Author: numpy
Chilkat
chilkatsoft.com › python_memoryview.asp
Explaining the Python memoryview
A "memoryview" supports multi-dimensional data structures (like NumPy arrays).
NumPy
numpy.org › doc › 2.2 › user › basics.copies.html
Copies and views — NumPy v2.2 Manual
When operating on NumPy arrays, it is possible to access the internal data buffer directly using a view without copying data around. This ensures good performance but can also cause unwanted problems if the user is not aware of how this works.
NumPy
numpy.org › doc › stable › reference › generated › numpy.memmap.view.html
numpy.memmap.view — NumPy v2.5 Manual
New view of array with the same data · Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype(np.float64)
Hacker News
news.ycombinator.com › item
Second this. Cython's memory view for numpy array is great. | Hacker News
August 4, 2018 - No such item
GitHub
github.com › numpy › numpy › issues › 5935
ndarray does not accept memoryview for buffer= under python2 · Issue #5935 · numpy/numpy
June 2, 2015 - I've discovered that ndarray(buffer=memoryview) fails on python 2: In [1]: sys.version Out[1]: '2.7.10 (default, May 26 2015, 13:16:40) \n[GCC 4.9.2]' In [2]: numpy.__version__ Out[2]: '1.10.0.dev0+9e7a0b2' In [3]: b = bytearray([1,2,3]) In [4]: b Out[4]: bytearray(b'\x01\x02\x03') In [5]: m = memoryview(b) In [6]: m Out[6]: <memory at 0x7fa1f4e59b50> Creating ndarray backed by memoryview fails: In [7]: a = ndarray((3,), buffer=m, dtype=uint8) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-32-1528204f5358> in <module>() ----> 1 a = ndarray((3,), buffer=m, dtype=uint8) TypeError: expected a readable buffer object ·
Author: numpy