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# numpy.array

numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, ndmax=0, like=None)

  Create an array.

  Parameters:
    object array_like

      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.

    dtype data-type, optional

      The desired data-type for the array. If not given, NumPy will try to use a default `dtype` that can represent the values (by applying promotion rules when necessary.)

    copy bool, optional

      If `True` (default), then the array data is copied. If `None`, 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 (`dtype`, `order`, etc.). Note that any copy of the data is shallow, i.e., for arrays with object dtype, the new array will point to the same objects. See Examples for `ndarray.copy`. For `False` it raises a `ValueError` if a copy cannot be avoided. Default: `True`.

    order{‘K’, ‘A’, ‘C’, ‘F’}, optional

      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=None` 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’.

    subok bool, optional

      If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).

    ndmin int, optional

      Specifies the minimum number of dimensions that the resulting array should have. Ones will be prepended to the shape as needed to meet this requirement.

    ndmax int, optional

      Specifies the maximum number of dimensions to create when inferring shape from nested sequences. By default (ndmax=0), NumPy recurses through all nesting levels (up to the compile-time constant `NPY_MAXDIMS`). Setting `ndmax` stops recursion at the specified depth, preserving deeper nested structures as objects instead of promoting them to higher-dimensional arrays. In this case, `dtype=object` is required.

      New in version 2.4.0.

    like array_like, optional

      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.

      New in version 1.20.0.

  Returns:
    out ndarray

      An array object satisfying the specified requirements.

  See also

  `empty_like`

    Return an empty array with shape and type of input.

  `ones_like`

    Return an array of ones with shape and type of input.

  `zeros_like`

    Return an array of zeros with shape and type of input.

  `full_like`

    Return a new array with shape of input filled with value.

  `empty`

    Return a new uninitialized array.

  `ones`

    Return a new array setting values to one.

  `zeros`

    Return a new array setting values to zero.

  `full`

    Return a new array of given shape filled with value.

  `copy`

    Return an array copy of the given object.

  Notes

  When order is ‘A’ and `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.

  Examples

      >>> import numpy as np
      >>> np.array([1, 2, 3])
      array([1, 2, 3])

  Upcasting:

      >>> np.array([1, 2, 3.0])
      array([ 1.,  2.,  3.])

  More than one dimension:

      >>> np.array([[1, 2], [3, 4]])
      array([[1, 2],
             [3, 4]])

  Minimum dimensions 2:

      >>> np.array([1, 2, 3], ndmin=2)
      array([[1, 2, 3]])

  Type provided:

      >>> np.array([1, 2, 3], dtype=complex)
      array([ 1.+0.j,  2.+0.j,  3.+0.j])

  Data-type consisting of more than one element:

      >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
      >>> x['a']
      array([1, 3], dtype=int32)

  Creating an array from sub-classes:

      >>> np.array(np.asmatrix('1 2; 3 4'))
      array([[1, 2],
             [3, 4]])

      >>> np.array(np.asmatrix('1 2; 3 4'), subok=True)
      matrix([[1, 2],
              [3, 4]])

  Limiting the maximum dimensions with `ndmax`:

      >>> a = np.array([[1, 2], [3, 4]], dtype=object, ndmax=2)
      >>> a
      array([[1, 2],
             [3, 4]], dtype=object)
      >>> a.shape
      (2, 2)

      >>> b = np.array([[1, 2], [3, 4]], dtype=object, ndmax=1)
      >>> b
      array([list([1, 2]), list([3, 4])], dtype=object)
      >>> b.shape
      (2,)