NumPy pad(): Constant, Edge, Reflect, and Array Padding
Use NumPy pad to add values around arrays with constant, edge, reflect, and statistic modes while controlling pad widths and axes.
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Use NumPy pad to add values around arrays with constant, edge, reflect, and statistic modes while controlling pad widths and axes.
Implement bitonic sort in Python, understand its power-of-two constraint, validate ascending and descending phases, and choose it for parallel sorting networks when appropriate.
Use NumPy memmap for large array files with the right mode, dtype, shape, flush behavior, and memory expectations without loading the whole file at once.
Build a NumPy Kronecker delta with eye(), equal(), or where(), and distinguish the elementwise delta from np.kron().
Convert integers to floats in Python with float(), mixed arithmetic, division, precision notes, and safe handling of strings and user input.
Fix break outside loop by moving break into a for or while loop, using return inside functions, or choosing sys.exit for program termination.
Use NumPy quantile() with q probabilities, axes, interpolation methods, nanquantile, keepdims, weights, and distribution summaries.
Use Python str.zfill() to add leading zeros, preserve plus and minus signs, and choose the right width for IDs and reports.
Use numpy.choose for indexed selection with compatible arrays, modes, broadcasting, bounds, dtypes, and clearer alternatives.
Understand process forking in Python with multiprocessing start methods, copy-on-write, threads, file descriptors, Windows differences, and safe entry points.