How can I efficiently count the frequency of unique values in a NumPy array?

Obtaining Frequency Counts for Unique Values in a NumPy Array

To efficiently determine the frequency of each unique value within a NumPy array, consider utilizing NumPy's unique function in conjunction with return_counts=True.

<code class="python">import numpy as np

x = np.array([1,1,1,2,2,2,5,25,1,1])
unique, counts = np.unique(x, return_counts=True)</code>

This approach yields a tuple containing two arrays: unique, which holds the unique values present in the original array, and counts, which indicates the respective frequencies of occurrence for each unique value.

For instance, executing the above code with the given input array returns:

[(1, 5), (2, 3), (5, 1), (25, 1)]

indicating that the value 1 appears 5 times, 2 appears 3 times, 5 appears once, and 25 appears once.

Using unique(return_counts=True) offers a substantial performance advantage over SciPy's scipy.stats.itemfreq function for large datasets, as demonstrated in the code snippet below:

<code class="python">In [4]: x = np.random.random_integers(0,100,1e6)

In [5]: %timeit unique, counts = np.unique(x, return_counts=True)
10 loops, best of 3: 31.5 ms per loop

In [6]: %timeit scipy.stats.itemfreq(x)
10 loops, best of 3: 170 ms per loop</code>

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