How Can I Efficiently Find Unique Rows in a NumPy Array?

Finding Unique Rows in a NumPy Array: An Efficient Solution

In data analysis and processing, it is often necessary to extract unique values from a given dataset. In this context, let's consider the problem of finding unique rows in a NumPy array.

Objective:

Given a NumPy array, the goal is to identify and obtain an array containing only the unique rows in the original array.

Efficient Solution:

As of NumPy version 1.13, an efficient solution for finding unique rows has been introduced. By leveraging the np.unique function and specifying the axis parameter, we can achieve this with ease:

unique_rows = np.unique(original_array, axis=0)

By setting the axis parameter to 0, we instruct NumPy to analyze each row of the original array independently. This operation compares the rows element-wise and returns a new array that contains only the unique rows.

Example:

Consider the following NumPy array a:

a = np.array([[1, 1, 1, 0, 0, 0],
               [0, 1, 1, 1, 0, 0],
               [0, 1, 1, 1, 0, 0],
               [1, 1, 1, 0, 0, 0],
               [1, 1, 1, 1, 1, 0]])

To obtain the unique rows from a, we can use the following code:

unique_rows = np.unique(a, axis=0)

This will produce a new array unique_rows that contains the following rows:

unique_rows = np.array([[1, 1, 1, 0, 0, 0],
                         [0, 1, 1, 1, 0, 0],
                         [1, 1, 1, 1, 1, 0]])

As we can see, the unique rows in the original array have been successfully extracted.

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