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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
adefine 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
ais Fortran contiguous, ?C? otherwise. ?K? means match the layout ofaas 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.
Returns: out : ndarray
Array of
fill_valuewith the same shape and type asa.See also
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zeros_like - Return an array of zeros with shape and type of input.
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ones_like - Return an array of ones with shape and type of input.
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empty_like - Return an empty array with shape and type of input.
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zeros - Return a new array setting values to zero.
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ones - Return a new array setting values to one.
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empty - Return a new uninitialized array.
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full - 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])
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numpy.full_like()
2017-01-10 18:14:11
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