numpy.full_like — NumPy v1.11 Manual (original) (raw)
numpy.full_like(a, fill_value, dtype=None, order='K', subok=True)[source]¶
Return a full array with the same shape and type as a given array.
| Parameters: | a : array_like The shape and data-type of a define these same attributes of the returned array. fill_value : scalar Fill value. dtype : data-type, optional Overrides the data type of the result. order : {‘C’, ‘F’, ‘A’, or ‘K’}, optional Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. subok : bool, optional. If True, then the newly created array will use the sub-class type of ‘a’, otherwise it will be a base-class array. Defaults to True. |
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| Returns: | out : ndarray Array of fill_value with the same shape and type as a. |
See also
Return an array of zeros with shape and type of input.
Return an array of ones with shape and type of input.
Return an empty array with shape and type of input.
Return a new array setting values to zero.
Return a new array setting values to one.
Return a new uninitialized array.
Fill a new array.
Examples
x = np.arange(6, dtype=np.int) np.full_like(x, 1) array([1, 1, 1, 1, 1, 1]) np.full_like(x, 0.1) array([0, 0, 0, 0, 0, 0]) np.full_like(x, 0.1, dtype=np.double) array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) np.full_like(x, np.nan, dtype=np.double) array([ nan, nan, nan, nan, nan, nan])
y = np.arange(6, dtype=np.double) np.full_like(y, 0.1) array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])