diff(a, n=1, axis=-1, prepend=None, append=None)

This docstring was copied from numpy.diff.

Some inconsistencies with the Dask version may exist.

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.

Notes

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)  # doctest: +SKIP
>>> np.diff(u8_arr)  # doctest: +SKIP
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]  # doctest: +SKIP
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)  # doctest: +SKIP
>>> np.diff(i16_arr)  # doctest: +SKIP
array([-1], dtype=int16)

Parameters

a : array_like

Input array

n : int, optional

The number of times values are differenced. If zero, the input is returned as-is.

axis : int, optional

The axis along which the difference is taken, default is the last axis.

prepend, append : array_like, optional

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.

versionadded

Returns

diff : ndarray

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 :None:None:`datetime64`, which results in a :None:None:`timedelta64` output array.

Calculate the n-th discrete difference along the given axis.

See Also

cumsum

ediff1d

gradient

Examples

This example is valid syntax, but we were not able to check execution

>>> x = np.array([1, 2, 4, 7, 0])  # doctest: +SKIP
... np.diff(x)  # doctest: +SKIP
array([ 1,  2,  3, -7])

This example is valid syntax, but we were not able to check execution

>>> np.diff(x, n=2)  # doctest: +SKIP
array([  1,   1, -10])

This example is valid syntax, but we were not able to check execution

>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])  # doctest: +SKIP
... np.diff(x)  # doctest: +SKIP
array([[2, 3, 4],
       [5, 1, 2]])

This example is valid syntax, but we were not able to check execution

>>> np.diff(x, axis=0)  # doctest: +SKIP
array([[-1,  2,  0, -2]])

This example is valid syntax, but we were not able to check execution

>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)  # doctest: +SKIP
... np.diff(x)  # doctest: +SKIP
array([1, 1], dtype='timedelta64[D]')

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.routines.ediff1d dask.array.reductions.cumsum dask.array.routines.diff

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