diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
The first difference is given by out[i] = a[i+1] - a[i]
along the given axis, higher differences are calculated by using diff
recursively.
Type is preserved for boolean arrays, so the result will contain :None:None:`False`
when consecutive elements are the same and :None:None:`True`
when they differ.
For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly:
>>> u8_arr = np.array([1, 0], dtype=np.uint8) >>> np.diff(u8_arr) array([255], dtype=uint8) >>> u8_arr[1,...] - u8_arr[0,...] 255
If this is not desirable, then the array should be cast to a larger integer type first:
>>> i16_arr = u8_arr.astype(np.int16) >>> np.diff(i16_arr) array([-1], dtype=int16)
Input array
The number of times values are differenced. If zero, the input is returned as-is.
The axis along which the difference is taken, default is the last axis.
Values to prepend or append to a
along axis prior to performing the difference. Scalar values are expanded to arrays with length 1 in the direction of axis and the shape of the input array in along all other axes. Otherwise the dimension and shape must match a
except along axis.
The n-th differences. The shape of the output is the same as a
except along :None:None:`axis`
where the dimension is smaller by n
. The type of the output is the same as the type of the difference between any two elements of a
. This is the same as the type of a
in most cases. A notable exception is datetime64
, which results in a timedelta64
output array.
Calculate the n-th discrete difference along the given axis.
>>> x = np.array([1, 2, 4, 7, 0])
... np.diff(x) array([ 1, 2, 3, -7])
>>> np.diff(x, n=2) array([ 1, 1, -10])
>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
... np.diff(x) array([[2, 3, 4], [5, 1, 2]])
>>> np.diff(x, axis=0) array([[-1, 2, 0, -2]])
>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)See :
... np.diff(x) array([1, 1], dtype='timedelta64[D]')
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.random.RandomState.power
numpy.ediff1d
numpy.diff
scipy.signal._peak_finding.find_peaks
scipy.signal._filter_design.bessel
numpy.cumsum
dask.array.routines.diff
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