array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None)
Some inconsistencies with the Dask version may exist.
Create an array.
When order is 'A' and :None:None:`object` is an array in neither 'C' nor 'F' order, and a copy is forced by a change in dtype, then the order of the result is not necessarily 'C' as expected. This is likely a bug.
An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. If object is a scalar, a 0-dimensional array containing object is returned.
The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence.
If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (:None:None:`dtype`, order
, etc.).
Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless 'F' is specified, in which case it will be in Fortran order (column major). If object is an array the following holds.
===== ========= =================================================== order no copy copy=True ===== ========= =================================================== 'K' unchanged F & C order preserved, otherwise most similar order 'A' unchanged F order if input is F and not C, otherwise C order 'C' C order C order 'F' F order F order ===== ========= ===================================================
When copy=False
and a copy is made for other reasons, the result is the same as if copy=True
, with some exceptions for 'A', see the Notes section. The default order is 'K'.
If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).
Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like
supports the __array_function__
protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.
An array object satisfying the specified requirements.
This docstring was copied from numpy.array.
empty
Return a new uninitialized array.
empty_like
Return an empty array with shape and type of input.
full
Return a new array of given shape filled with value.
full_like
Return a new array with shape of input filled with value.
ones
Return a new array setting values to one.
ones_like
Return an array of ones with shape and type of input.
zeros
Return a new array setting values to zero.
zeros_like
Return an array of zeros with shape and type of input.
>>> np.array([1, 2, 3]) # doctest: +SKIP array([1, 2, 3])
Upcasting:
This example is valid syntax, but we were not able to check execution>>> np.array([1, 2, 3.0]) # doctest: +SKIP array([ 1., 2., 3.])
More than one dimension:
This example is valid syntax, but we were not able to check execution>>> np.array([[1, 2], [3, 4]]) # doctest: +SKIP array([[1, 2], [3, 4]])
Minimum dimensions 2:
This example is valid syntax, but we were not able to check execution>>> np.array([1, 2, 3], ndmin=2) # doctest: +SKIP array([[1, 2, 3]])
Type provided:
This example is valid syntax, but we were not able to check execution>>> np.array([1, 2, 3], dtype=complex) # doctest: +SKIP array([ 1.+0.j, 2.+0.j, 3.+0.j])
Data-type consisting of more than one element:
This example is valid syntax, but we were not able to check execution>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')]) # doctest: +SKIP
... x['a'] # doctest: +SKIP array([1, 3])
Creating an array from sub-classes:
This example is valid syntax, but we were not able to check execution>>> np.array(np.mat('1 2; 3 4')) # doctest: +SKIP array([[1, 2], [3, 4]])This example is valid syntax, but we were not able to check execution
>>> np.array(np.mat('1 2; 3 4'), subok=True) # doctest: +SKIP matrix([[1, 2], [3, 4]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.routines.histogram2d
dask.array.routines.histogramdd
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