Search
Search Results
Search finished, found 86 page(s) matching the search query.
- numpy.ndarray.ndim
...NumPy reference Array objects The N-dimensional array (ndarray) numpy.ndarray.ndim...
- numpy.ndarray.ndim (Python attribute, in numpy.ndarray.ndim)
- Array iterator API
...S_OK, NPY_KEEPORDER, NPY_NO_CASTING, NULL); if (iter == NULL) { return -1; } if (NpyIter_GetNDim(iter) != 2) { NpyIter_Deallocate(iter); PyErr_SetString(PyExc_ValueError, "Array must be 2-D");...
- ctypes foreign function interface (
numpy.ctypeslib)...the expected extension, or the library is defective and cannot be loaded. numpy.ctypeslib.ndpointer(dtype=None, ndim=None, shape=None, flags=None)[source] Array-checking restype/argtypes. An ndpointer instance is used to describe an...
- Indexing on
ndarrays...selection tuple to index all dimensions. In most cases, this means that the length of the expanded selection tuple is x.ndim. There may only be a single ellipsis present. From the above example: >>> x[..., 0] array([[1, 2, 3], [4, 5,...
- NumPy 1.13.0 Release Notes
...tch the behavior of np.conjugate, which throws an error). Calling expand_dims when the axis keyword does not satisfy -a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped, is deprecated. Future Changes Assignment between st...
- NumPy 1.22.0 Release Notes
...nt.bit_count or popcount in C++. >>> np.uint32(1023).bit_count() 10 >>> np.int32(-127).bit_count() 7 (gh-19355) The ndim and axis attributes have been added to numpy.AxisError The ndim and axis parameters are now also stored as attribut...
- NumPy 1.25.0 Release Notes
....sometrue is deprecated. Use np.any instead. (gh-23314) np.alltrue is deprecated. Use np.all instead. (gh-23314) Only ndim-0 arrays are treated as scalars. NumPy used to treat all arrays of size 1 (e.g., np.array([3.14])) as scalars. In...
- NumPy 1.9.0 Release Notes
...nctions can now be specified using the dtype parameter. More general np.triu and np.tril broadcasting For arrays with ndim exceeding 2, these functions will now apply to the final two axes instead of raising an exception. tobytes alias...
- NumPy for MATLAB users
...te.solve_ivp(f, method='BDF') integrate an ODE with BDF method Linear algebra equivalents MATLAB NumPy Notes ndims(a) np.ndim(a) or a.ndim number of dimensions of array a numel(a) np.size(a) or a.size number of elements of array...
- NumPy quickstart
...one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndimthe number of axes (dimensions) of the array. ndarray.shapethe dimensions of the array. This is a tuple of integers...
- numpy.argwhere
...rray elements that are non-zero, grouped by element. Parameters: aarray_likeInput data. Returns: index_array(N, a.ndim) ndarrayIndices of elements that are non-zero. Indices are grouped by element. This array will have shape (N, a.ndi...
- numpy.ascontiguousarray
...numpy.ascontiguousarray numpy.ascontiguousarray(a, dtype=None, *, like=None) Return a contiguous array (ndim >= 1) in memory (C order). Parameters: aarray_likeInput array. dtypestr or dtype object, optionalData-type of re...
- numpy.asfortranarray
...numpy.asfortranarray numpy.asfortranarray(a, dtype=None, *, like=None) Return an array (ndim >= 1) laid out in Fortran order in memory. Parameters: aarray_likeInput array. dtypestr or dtype object, optiona...
- numpy.atleast_1d
...s: arys1, arys2, …array_likeOne or more input arrays. Returns: retndarrayAn array, or tuple of arrays, each with a.ndim >= 1. Copies are made only if necessary. See also atleast_2d, atleast_3d Examples >>> import numpy as np >>...
- numpy.atleast_2d
...dy have two or more dimensions are preserved. Returns: res, res2, …ndarrayAn array, or tuple of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned. See also atleast...
- numpy.atleast_3d
...have three or more dimensions are preserved. Returns: res1, res2, …ndarrayAn array, or tuple of arrays, each with a.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D...
- numpy.block
...ension, but will not be broadcasted using the normal rules. Instead, leading axes of size 1 are inserted, to make block.ndim the same for all blocks. This is primarily useful for working with scalars, and means that code like np.block([v, 1...
- numpy.diagonal
...rix, in which case a 1-D array rather than a (2-D) matrix is returned in order to maintain backward compatibility. If a.ndim > 2, then the dimensions specified by axis1 and axis2 are removed, and a new axis inserted at the end corresponding...
- numpy.expand_dims
...expanded axes where the new axis (or axes) is placed. Deprecated since version 1.13.0: Passing an axis where axis > a.ndim will be treated as axis == a.ndim, and passing axis < -a.ndim - 1 will be treated as axis == 0. This behavior is de...
- numpy.inner
...s For vectors (1-D arrays) it computes the ordinary inner-product: np.inner(a, b) = sum(a[:]*b[:]) More generally, if ndim(a) = r > 0 and ndim(b) = s > 0: np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1)) or explicitly: np.inner(a, b)[i...
- numpy.kron
...c = np.kron(a,b) >>> c.shape (2, 10, 6, 20) >>> I = (1,3,0,2) >>> J = (0,2,1) >>> J1 = (0,) + J # extend to ndim=4 >>> S1 = (1,) + b.shape >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1)) >>> c[K] == a[I]*b[J] True...
- numpy.linalg.norm
- numpy.linalg.solve
- numpy.linalg.svd
- numpy.linalg.tensorsolve
- numpy.ma.atleast_1d
- numpy.ma.atleast_2d
- numpy.ma.atleast_3d
- numpy.ma.expand_dims
- numpy.ma.inner
- numpy.ma.innerproduct
- numpy.ma.ndim
- numpy.matrix
- numpy.max
- numpy.min
- numpy.ndarray
- numpy.ndarray.ctypes
- numpy.ndarray.ndim
- numpy.ndarray.tolist
- numpy.ndim
- numpy.nditer
- numpy.permute_dims
- numpy.polydiv
- numpy.polynomial.chebyshev.chebdomain
- numpy.polynomial.chebyshev.chebint
- numpy.polynomial.chebyshev.chebone
- numpy.polynomial.chebyshev.chebx
- numpy.polynomial.chebyshev.chebzero
- numpy.polynomial.hermite.hermdomain
- numpy.polynomial.hermite.hermint
- numpy.polynomial.hermite.hermone
- numpy.polynomial.hermite.hermx
- numpy.polynomial.hermite.hermzero
- numpy.polynomial.hermite_e.hermedomain
- numpy.polynomial.hermite_e.hermeint
- numpy.polynomial.hermite_e.hermeone
- numpy.polynomial.hermite_e.hermex
- numpy.polynomial.hermite_e.hermezero
- numpy.polynomial.laguerre.lagdomain
- numpy.polynomial.laguerre.lagint
- numpy.polynomial.laguerre.lagone
- numpy.polynomial.laguerre.lagx
- numpy.polynomial.laguerre.lagzero
- numpy.polynomial.legendre.legdomain
- numpy.polynomial.legendre.legint
- numpy.polynomial.legendre.legone
- numpy.polynomial.legendre.legx
- numpy.polynomial.legendre.legzero
- numpy.polynomial.polynomial.polydomain
- numpy.polynomial.polynomial.polyint
- numpy.polynomial.polynomial.polyone
- numpy.polynomial.polynomial.polyx
- numpy.polynomial.polynomial.polyzero
- numpy.random.Generator.choice
- numpy.recarray
- numpy.rollaxis
- numpy.tile
- numpy.transpose
- numpy.tril
- numpy.ufunc.outer
- NumPy: the absolute basics for beginners
- Glossary
- Iterating over arrays
- Putting the inner loop in Cython
- Using Python as glue