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88 lines
2.5 KiB
88 lines
2.5 KiB
array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)
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Create an array.
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Parameters
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----------
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object : array_like
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An array, any object exposing the array interface, an
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object whose __array__ method returns an array, or any
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(nested) sequence.
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dtype : data-type, optional
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The desired data-type for the array. If not given, then
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the type will be determined as the minimum type required
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to hold the objects in the sequence. This argument can only
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be used to 'upcast' the array. For downcasting, use the
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.astype(t) method.
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copy : bool, optional
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If true (default), then the object is copied. Otherwise, a copy
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will only be made if __array__ returns a copy, if obj is a
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nested sequence, or if a copy is needed to satisfy any of the other
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requirements (`dtype`, `order`, etc.).
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order : {'C', 'F', 'A'}, optional
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Specify the order of the array. If order is 'C', then the array
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will be in C-contiguous order (last-index varies the fastest).
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If order is 'F', then the returned array will be in
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Fortran-contiguous order (first-index varies the fastest).
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If order is 'A' (default), then the returned array may be
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in any order (either C-, Fortran-contiguous, or even discontiguous),
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unless a copy is required, in which case it will be C-contiguous.
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subok : bool, optional
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If True, then sub-classes will be passed-through, otherwise
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the returned array will be forced to be a base-class array (default).
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ndmin : int, optional
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Specifies the minimum number of dimensions that the resulting
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array should have. Ones will be pre-pended to the shape as
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needed to meet this requirement.
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Returns
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-------
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out : ndarray
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An array object satisfying the specified requirements.
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See Also
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--------
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empty, empty_like, zeros, zeros_like, ones, ones_like, fill
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Examples
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--------
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>>> np.array([1, 2, 3])
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array([1, 2, 3])
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Upcasting:
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>>> np.array([1, 2, 3.0])
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array([ 1., 2., 3.])
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More than one dimension:
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>>> np.array([[1, 2], [3, 4]])
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array([[1, 2],
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[3, 4]])
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Minimum dimensions 2:
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>>> np.array([1, 2, 3], ndmin=2)
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array([[1, 2, 3]])
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Type provided:
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>>> np.array([1, 2, 3], dtype=complex)
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array([ 1.+0.j, 2.+0.j, 3.+0.j])
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Data-type consisting of more than one element:
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>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
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>>> x['a']
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array([1, 3])
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Creating an array from sub-classes:
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>>> np.array(np.mat('1 2; 3 4'))
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array([[1, 2],
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[3, 4]])
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>>> np.array(np.mat('1 2; 3 4'), subok=True)
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matrix([[1, 2],
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[3, 4]])
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