# numpy.array

numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=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, then the type will be determined as the minimum type required to hold the objects in the sequence.

    copy bool, optional

      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 (`dtype`, order, etc.).

    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=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’.

    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.

    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.

  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

      >>> 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])

  Creating an array from sub-classes:

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

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

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