{"id":13369,"date":"2021-06-12T07:47:13","date_gmt":"2021-06-12T02:17:13","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=13369"},"modified":"2026-07-13T12:34:54","modified_gmt":"2026-07-13T07:04:54","slug":"numpy-searchsorted","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/numpy-searchsorted\/","title":{"rendered":"NumPy searchsorted: Find Insertion Indices in Sorted Arrays"},"content":{"rendered":"

Quick answer:<\/strong> np.searchsorted() returns insertion indexes for values in a sorted one-dimensional array. side=’left’ chooses the first valid position before equal values, while side=’right’ chooses the position after them. The function searches; it does not sort the input, so preserve or verify the sorted invariant first.<\/p>\n

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searchsorted returns positions that preserve sorted order; side controls duplicate placement and sorter supports an indirectly sorted array.<\/figcaption><\/figure>\n

np.searchsorted()<\/code> returns the index where a value should be inserted to keep a sorted NumPy array in order. It is a fast way to locate insertion points without scanning manually.<\/p>\n

The official NumPy searchsorted documentation<\/a> explains the full API, and NumPy sort<\/a> covers sorting arrays before searching.<\/p>\n

The input array should already be sorted in ascending order unless you pass a sorter. The function uses binary search internally, so it is useful for lookup tables, bins, thresholds, and inserting new values into ordered data.<\/p>\n

Because the function returns positions rather than changing data, it fits well in pipelines. You can calculate insertion points first, inspect them, and then decide whether to insert values, assign labels, or slice existing ranges.<\/p>\n

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