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NumPy | put method

schedule Aug 12, 2023
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Numpy's `put(~)` 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. `ind` | `array-like`

The indices where the values will be set. Note that the indices apply to a flattened version of the input array. See examples below for clarification.

3. `v` | `array-like`

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

4. `mode` | `string` | `optional`

Dictate what happens when `ind` parameter you specify is out of bounds:

Value

Description

"raise"

Out of bounds error will be thrown.

"wrap"

Cycles around the array.

"clip"

Last element of the array is targeted.

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(a, 1, 9)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 a single value

``` a = np.array([[5,6],[7,8]])a.put(3, 9)a array([[5, 6], [7, 9]]) ```

Setting multiple values

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

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.

Different modes

Consider the following 2D array:

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

Raise

``` a = np.array([[5,6],[7,8]])np.put(a, [5], 9, mode="raise")a IndexError: index 5 is out of bounds for axis 0 with size 4 ```

This error is raise because index 5 does not exist in the flattened array.

Wrap

``` a = np.array([[5,6],[7,8]])np.put(a, [5], 9, mode="wrap")a array([[5, 9], [7, 8]]) ```

Here, we went one cycle around `a`, which has a size of 4, so the index 5 is converted to index 5-4=1.

Clip

``` a = np.array([[5,6],[7,8]])np.put(a, [5], 9, mode="clip")a array([[5, 6], [7, 9]]) ```

Here, our specified index has exceeded the bounds, so the last index is targeted instead, which in this case is index 3.

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