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# numpy.memmap.astype

method

memmap.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

  Copy of the array, cast to a specified type.

  Parameters:
    dtype str or dtype

      Typecode or data-type to which the array is cast.

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

      Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

    casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘same_value’, ‘unsafe’}, optional

      Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

      - ‘no’ means the data types should not be cast at all.

      - ‘equiv’ means only byte-order changes are allowed.

      - ‘safe’ means only casts which can preserve values are allowed.

      - ‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

      - ‘unsafe’ means any data conversions may be done.

      - ‘same_value’ means any data conversions may be done, but the values must not change, including rounding of floats or overflow of ints

      New in version 2.4: Support for `'same_value'` was added.

    subok bool, optional

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

    copy bool, optional

      By default, astype always returns a newly allocated array. If this is set to false, and the `dtype`, order, and subok requirements are satisfied, the input array is returned instead of a copy.

  Returns:
    arr_t ndarray

      Unless `copy` is False and the other conditions for returning the input array are satisfied (see description for `copy` input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by `dtype`, order.

  Raises:
    ComplexWarning

      When casting from complex to float or int. To avoid this, one should use `a.real.astype(t)`.

    ValueError

      When casting using `'same_value'` and the values change or would overflow

  Examples

      >>> import numpy as np
      >>> x = np.array([1, 2, 2.5])
      >>> x
      array([1. ,  2. ,  2.5])

      >>> x.astype(int)
      array([1, 2, 2])

      >>> x.astype(int, casting="same_value")
      Traceback (most recent call last):
      ...
      ValueError: could not cast 'same_value' double to long

      >>> x[:2].astype(int, casting="same_value")
      array([1, 2])