## How Can I Efficiently Count the Frequency of Unique Values in a NumPy Array?

Efficient Frequency Counting for Unique Values in NumPy Arrays

For efficient frequency counting of unique values in a NumPy array, consider utilizing numpy.unique with the return_counts=True option, especially for NumPy versions 1.9 and above. This approach provides both unique values and their respective counts.

<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)

print(np.asarray((unique, counts)).T)  # Output in tuple format</code>

This approach surpasses scipy.stats.itemfreq in terms of efficiency, as demonstrated below:

<code class="python">x = np.random.random_integers(0,100,1e6)

%timeit unique, counts = np.unique(x, return_counts=True)  # 31.5 ms
%timeit scipy.stats.itemfreq(x)  # 170 ms</code>

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