Numpy np.unique() method

Introduction

In data analysis and scientific computing, identifying unique elements in datasets is a fundamental task. NumPy, a powerful library for numerical computations in Python, provides the np.unique() method to efficiently find unique elements in an array. This method is versatile and can be applied to both one-dimensional and multi-dimensional arrays.

Syntax of np.unique()

The basic syntax of the np.unique() method is:

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, equal_nan=True)

Where:

• ar: Input array.
• return_index: If True, returns the indices of the unique elements in the original array.
• return_inverse: If True, returns the indices to reconstruct the original array from the unique array.
• return_counts: If True, returns the number of times each unique element appears in the original array.
• axis: The axis along which to operate. If None, the array is flattened before operation.
• equal_nan: If True, treats NaNs as equal during comparison.

Basic Example

Let's start with a simple example to find unique elements in a one-dimensional array:

import numpy as np

arr = np.array([1, 2, 2, 4, 3, 6, 4, 8])
unique_elements = np.unique(arr)
print("Unique elements:", unique_elements)

Unique elements: [1 2 3 4 6 8]

Handling Multi-dimensional Arrays

For multi-dimensional arrays, np.unique() can be used to find unique elements across the entire array or along a specific axis:

arr_2d = np.array([[10.2, 21.4, 3.6, 14.8], [1.0, 5.0, 10.0, 15.0]])
unique_elements_2d = np.unique(arr_2d)
print("Unique elements in 2D array:", unique_elements_2d)

Unique elements in 2D array: [ 1.   3.6  5.  10.  10.2 14.8 15.  21.4]

Advanced Features

The np.unique() method also offers advanced features:

• return_index=True: Returns the indices of the unique elements in the original array.
• return_inverse=True: Returns the indices to reconstruct the original array from the unique array.
• return_counts=True: Returns the number of times each unique element appears in the original array.

Example:

arr = np.array([1, 2, 2, 3, 3, 3, 4])
unique_elements, indices, inverse_indices, counts = np.unique(arr, return_index=True, return_inverse=True, return_counts=True)
print("Unique elements:", unique_elements)
print("Indices:", indices)
print("Inverse indices:", inverse_indices)
print("Counts:", counts)

Unique elements: [1 2 3 4]
Indices: [0 1 3 6]
Inverse indices: [0 1 1 2 2 2 3]
Counts: [1 2 3 1]

Using the axis Parameter

To find unique rows or columns in a multi-dimensional array, use the axis parameter:

arr_2d = np.array([[1, 2], [2, 3], [1, 2], [2, 3]])
unique_rows = np.unique(arr_2d, axis=0)
print("Unique rows:", unique_rows)

Unique rows: [[1 2]
 [2 3]]

Conclusion

The np.unique() method is a powerful tool in NumPy for identifying unique elements in arrays. Whether working with one-dimensional or multi-dimensional data.