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# numpy.asarray

numpy.asarray(a, dtype=None, order=None, *, device=None, copy=None, like=None)

  Convert the input to an array.

  Parameters:
    a array_like

      Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.

    dtype data-type, optional

      By default, the data-type is inferred from the input data.

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

      The memory layout of the output. ‘C’ gives a row-major layout (C-style), ‘F’ gives a column-major layout (Fortran-style). ‘C’ and ‘F’ will copy if needed to ensure the output format. ‘A’ (any) is equivalent to ‘F’ if input a is non-contiguous or Fortran-contiguous, otherwise, it is equivalent to ‘C’. Unlike ‘C’ or ‘F’, ‘A’ does not ensure that the result is contiguous. ‘K’ (keep) is the default and preserves the input order for the output.

    device str, optional

      The device on which to place the created array. Default: `None`. For Array-API interoperability only, so must be `"cpu"` if passed.

      Added in version 2.0.0.

    copy bool, optional

      If `True`, then the object is copied. If `None` then the object is copied only if needed, i.e. 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.). For `False` it raises a `ValueError` if a copy cannot be avoided. Default: `None`.

      Added in version 2.0.0.

    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.

      Added in version 1.20.0.

  Returns:
    out ndarray

      Array interpretation of `a`. No copy is performed if the input is already an ndarray with matching dtype and order. If `a` is a subclass of ndarray, a base class ndarray is returned.

  See also

  `asanyarray`

    Similar function which passes through subclasses.

  `ascontiguousarray`

    Convert input to a contiguous array.

  `asfortranarray`

    Convert input to an ndarray with column-major memory order.

  `asarray_chkfinite`

    Similar function which checks input for NaNs and Infs.

  `fromiter`

    Create an array from an iterator.

  `fromfunction`

    Construct an array by executing a function on grid positions.

  Examples

  Convert a list into an array:

      >>> a = [1, 2]
      >>> import numpy as np
      >>> np.asarray(a)
      array([1, 2])

  Existing arrays are not copied:

      >>> a = np.array([1, 2])
      >>> np.asarray(a) is a
      True

  If `dtype` is set, array is copied only if dtype does not match:

      >>> a = np.array([1, 2], dtype=np.float32)
      >>> np.shares_memory(np.asarray(a, dtype=np.float32), a)
      True
      >>> np.shares_memory(np.asarray(a, dtype=np.float64), a)
      False

  Contrary to `asanyarray`, ndarray subclasses are not passed through:

      >>> issubclass(np.recarray, np.ndarray)
      True
      >>> a = np.array([(1., 2), (3., 4)], dtype='f4,i4').view(np.recarray)
      >>> np.asarray(a) is a
      False
      >>> np.asanyarray(a) is a
      True