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- numpy.memmap.any
- numpy.memmap.any (Python method, in numpy.memmap.any)
- NumPy 1.12.0 Release Notes
...an. In particular, this means calls which returned a scalar may return a 0-d subclass object instead. Multiple-field manipulation of structured arrays In 1.13 the behavior of structured arrays involving multiple fields will change in two...
- NumPy 1.13.0 Release Notes
...e length of the array must match the size of the corresponding dimension Any combination of N scalars/arrays with the meaning of 2. and 3. This means that, e.g., it is now possible to do the following: >>> f = np.array([[1, 2, 6], [3, 4, 5...
- NumPy 1.25.0 Release Notes
...or is a duck type that defines numpy.class.__array_ufunc__ it can override the behavior of the ufunc using the same mechanism as the input and output arguments. Note that for this to work properly, the where.__array_ufunc__ implementation w...
- NumPy 2.0.0 Release Notes
...t. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API. See Custom Data Types for more details about the API. As always with a new feature,...
- NumPy 2.2.0 Release Notes
...th DTypes using the string ufuncs should now generate much more uniform results. (gh-27636) Improved support for empty memmap. Previously an empty memmap would fail unless a non-zero offset was set. Now a zero-size memmap is supported even...
- numpy.lib.Arrayterator
...nto memory. See also numpy.ndenumerateMultidimensional array iterator. numpy.flatiterFlat array iterator. numpy.memmapCreate a memory-map to an array stored in a binary file on disk. Notes The algorithm works by first finding a “...
- numpy.load
...mmap_mode{None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optionalIf not None, then memory-map the file, using the given mode (see numpy.memmap for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and...
- numpy.memmap
- numpy.memmap.any
- numpy.memmap.astype
...numpy.memmap.astype...
- numpy.memmap.flags
...numpy.memmap.flags...
- numpy.memmap.flush
- numpy.memmap.tobytes
- Reading and writing files
...s generated by any of numpy.save, numpy.savez, or numpy.savez_compressed. Use memory mapping. See numpy.lib.format.open_memmap. Write to a file to be read back by NumPy Binary Use numpy.save, or to store multiple arrays numpy.savez or n...
- Standard array subclasses
- Structured arrays
- Subclassing ndarray