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NumPy | diff method

schedule Aug 12, 2023
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Numpy's `diff(~)` method computes the difference between each value and its adjacent value in the input array.

Parameters

1. `a` | `array-like`

The input array.

2. `n` | `int` | `optional`

The number of differences you want to compute recursively. By default, `n=1`. Check out the examples below for clarification.

3. `axis` | `int` | `optional`

The axis along which to compute the differences. For 2D arrays, the allowed values are as follows:

Axis

Meaning

0

Differences will be computed column-wise

1

Differences will be computed row-wise

By default, the axis is equal to the last axis. This means that, for 2D arrays, `axis=1`.

4. `prepend` | `array-like` | `optional`

Values you wish to prepend to the input array a prior to computing the differences.

Return value

A Numpy array that contains the difference between each value and its adjacent value in the input array.

Examples

Basic usage

``` a = np.array([1, 3, 8, 15, 30])np.diff(a) array([ 2, 5, 7, 15]) ```

Recursively computing differences

Suppose we wanted to compute the differences twice recursively, that is, `n=2`. The `diff(~)` first computes the case when `n=1`, and then performs yet another `diff(~)` on its output.

The case when `n=1`:

``` a = np.array([1, 3, 8, 15, 30])np.diff(a, n=1) array([ 2, 5, 7, 15]) ```

The case when `n=2`:

``` a = np.array([1, 3, 8, 15, 30])np.diff(a, n=2) array([3, 2, 8]) ```

Observe that `n=2` is simply applying the diff method on the output of `n=1`.

Computing differences for 2D arrays

Consider the following 2D array

``` a = np.array([[1, 3], [8, 15]])a array([[ 1, 3], [ 8, 15]]) ```

Row-wise

``` np.diff(a) # or axis=1 array([[2], [7]]) ```

Column-wise

``` np.diff(a, axis=0) array([[ 7, 12]]) ```

Prepending values before computation

``` a = np.array([3, 8, 15, 30])np.diff(a, prepend=1) array([ 2, 5, 7, 15]) ```

Here, we've prepended the value 1 to the `a`, so essentially we're computing the differences of `[1,3,8,15,30]`.

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