Numpy Sorting

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name: numpy-sorting description: Sorting and searching algorithms including O(n) partitioning, binary search, and hierarchical multi-key sorting. Triggers: sort, argsort, partition, searchsorted, lexsort, nan sort order.

Overview

NumPy sorting provides efficient tools for ordering data. Beyond basic sorting, it includes partitioning for top-k selection and vectorized binary search for finding insertion points in sorted data.

When to Use

• Finding the top $k$ largest or smallest elements without a full sort ($O(N)$).
• Ordering data based on multiple criteria (e.g., sort by Date, then by Price).
• Mapping data into bins or ranges using binary search.
• Handling datasets containing NaNs where sorting order is sensitive.

Decision Tree

1. Need the indices of the sorted order (not the values)?
   • Use np.argsort.
2. Only need the $k$ smallest elements?
   • Use np.partition(arr, k). Elements to the left of index $k$ are smaller.
3. Finding where to insert a value to keep order?
   • Use np.searchsorted(sorted_arr, value).

Workflows

1. Efficiently Finding the Smallest K Elements

   • Identify an unsorted array.
   • Call np.partition(arr, kth=k).
   • Select the first k elements: result[:k].

2. Vectorized Lookup in Sorted Ranges

   • Ensure the target array 'A' is sorted.
   • Pass a list of values 'V' to np.searchsorted(A, V).
   • Use the returned indices to map values to specific bins or ranges.

3. Indirect Multi-Key Sort

   • Define primary and secondary key arrays.
   • Use np.lexsort((secondary, primary)) to get the index array.
   • Apply the indices to the data to achieve the desired hierarchical sort order.

Non-Obvious Insights

• NaN Position: np.nan is treated as larger than np.inf and is always sorted to the end of the array.
• Partition Performance: Partitioning along the last axis is significantly faster and uses less memory than partitioning along any other axis.
• Lexsort Order: lexsort takes keys in reverse order of importance; the last key in the sequence is the primary sort key.

Evidence

• "In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right." Source
• "Binary search is used to find the required insertion points." Source

Scripts

• scripts/numpy-sorting_tool.py: Implements top-k selection and hierarchical lexsort.
• scripts/numpy-sorting_tool.js: Basic sort simulation.

Dependencies

• numpy (Python)

References

• references/README.md