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# NumPy | unique method

schedule Aug 11, 2023
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Numpy's `unique(~)` method returns a Numpy array containing the sorted unique values of the input array.

# Parameters

1. `a` | `array-like`

The input array.

2. `return_index`link | `boolean` | `optional`

Whether to return the indexes of unique values. By default, `return_index=False`.

3. `return_inverse`link | `boolean` | `optional`

Whether to return the indexes that can be used to reconstruct our input array. Check the example below for clarification. By default, `return_inverse=False`.

4. `return_counts`link | `boolean` | `optional`

Whether to return the number of occurrences of each value. By default, `return_counts=False`.

5. `axis`link | `int` or `None` | `optional`

The axis along which to look for unique values.

Axis

Meaning

0

Find unique rows

1

Find unique columns

None

Find unique values

By default, `axis=None`.

# Return value

A Numpy array that contains the unique values of the input value. You will also get additional arrays depending on whether you flag any of the `return_` parameters as `True`.

# Examples

## Basic usage

### 1D case

To find all unique values in a 1D array:

``` a = np.array([4,5,6,5])np.unique(a) array([4, 5, 6]) ```

### 2D case

Consider the following 2D array:

``` a = np.array([[4,5],[6,5]])a array([[4, 5],       [6, 5]]) ```

To find all unique values in 2D array:

``` np.unique(a) array([4, 5, 6]) ```

## Getting the indices of the unique values

To get the indices of the unique values:

``` a = np.array([4,5,6,5])arr_unique_values, arr_index = np.unique(a, return_index=True)print(arr_unique_values)print(arr_index) [4 5 6][0 1 2] ```

## Getting the inverse indexes

To get the inverse indexes:

``` a = np.array([4,5,6,5])arr_unique_values, arr_inverse = np.unique(a, return_inverse=True)print(arr_unique_values)print(arr_inverse) [4 5 6][0 1 2 1] ```

Here, the inverse can be used to reconstruct the original values:

``` arr_unique_values[arr_inverse] array([4, 5, 6, 5]) ```

## Getting the counts

To get the counts, set `return_counts=True`:

``` a = np.array([4,5,6,5])arr_unique_values, arr_counts = np.unique(a, return_counts=True)print(arr_unique_values)print(arr_counts) [4 5 6][1 2 1] ```

Here, we see that the value 5 occurs twice in the original array.

## Finding unique rows

Consider the following 2D array:

``` a = np.array([[4,5],[6,5],[4,5]])a array([[4, 5],       [6, 5],       [4, 5]]) ```

Here, we see that rows at index 0 and index 2 are duplicates. To get all the unique rows:

``` np.unique(a, axis=0) array([[4, 5],       [6, 5]]) ```

## Finding unique columns

Consider the following 2D array:

``` a = np.array([[4,5,4],[6,8,6]])a array([[4, 5, 4],       [6, 8, 6]]) ```

Here, we see that columns at index 0 and index 2 are duplicate. To get all unique columns:

``` np.unique(a, axis=1) array([[4, 5],       [6, 8]]) ```
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