How Can I Efficiently Group NumPy Arrays?

Efficient Array Grouping with NumPy

While NumPy may not offer an out-of-the-box function specifically designed for grouping arrays, there are versatile techniques that can effectively achieve similar outcomes.

Inspired by Eelco's Library

One approach is inspired by Eelco Hoogendoorn's library, simplifying it by exploiting the assumption that the first column of the input array is monotonically increasing. If not, it can be sorted first using a = a[a[:, 0].argsort()].

np.split(a[:, 1], np.unique(a[:, 0], return_index=True)[1][1:])

Uniquely Identifying Groups

This snippet leverages np.unique() to identify unique values in the first column, returning their indices. These indices are used to split the second column into separate subarrays representing each group.

Time Complexity and Performance

This method exhibits O(n) complexity, making it highly efficient. Empirical timeit measurements on arrays with different group sizes confirm its performance advantages over other approached like pandas, numpy-indexed, and defaultdict.

Alternative Solutions

Beyond the presented approach, NumPy-based techniques such as numpy_groupies can also be explored for grouping operations.

Additional Considerations

If the first column of the input array is not sorted, it is recommended to sort it prior to grouping to ensure accurate results. Keep in mind that certain sorting algorithms, such as argsort, have a time complexity of O(n log(n)).

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