numpy.ndarray.astype#
method
- ndarray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)#
Copy of the array, cast to a specified type.
- Parameters:
- dtypestr 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.- subokbool, optional
If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
- copybool, 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:
- 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])