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NumPy | put_along_axis method

schedule May 20, 2023
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Numpy's `put_along_axis(~)` sets a specific value in the input array. This is done in-place, that is, no new Numpy array is created.

Parameters

1. `a` | `array-like`

The input array. All input arrays are treated as a flattened array.

2. `indices` | `Numpy array`

The indices in the specified axis where the values will be set. For 2D arrays, indices would represent the column/row index. The dimensions must be the same as that of `a`.

3. `values` | `array-like`

The values to set. If `values` is shorter than `a`, then `values` will be repeated to ensure the shape matches up.

4. `axis`link | `int`

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

Value

Description

0

Values are set row-wise.

1

Values are set column-wise.

By default, `mode="raise"`.

Return value

None - the setting is done in-place.

Examples

Basic usage

``` a = np.array([5,6,7,8])np.put_along_axis(a, np.array([1]), [9], axis=0)a array([5, 9, 7, 8]) ```

2D arrays

Consider the following 2D array:

``` a = np.array([[5,6],[7,8]])a array([[5, 6],       [7, 8]]) ```

Setting values row-wise

Set `axis=0`, like so:

``` a = np.array([[5,6],[7,8]])np.put_along_axis(a, np.array([[1]]), [9], axis=0)a array([[5, 6],       [9, 9]]) ```

Notice how the dimensions of the indices matches that of the input array `a`.

Setting values column-wise

Set `axis=1`, like so:

``` a = np.array([[5,6],[7,8]])np.put_along_axis(a, np.array([[1]]), [9], axis=1)a array([[5, 9],       [7, 9]]) ```

Here, note how `[1,0]` does not represent the 2nd row 1st column. Instead, it represents the 1st and 0th index of the flattened version of a. Therefore, we could have equivalently used `[0,1]` instead.

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