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Numerical Computing With NumPy
Arrays, Broadcasting, Vectorization, Indexing, Linear Algebra, Random Numbers and Memory-Efficient Data Handling
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Your array code runs, so you assume it is right. Then an 8-bit counter wraps without a warning, a slice edit changes the original data, or a row normalisation is wrong only on square tables. This book gives you the working model behind those surprises and shows how to avoid them. You will learn how dtypes decide results, when indexing returns a view or a copy, how broadcasting and strides work, how to replace loops with vectorised operations, how to solve linear systems and least-squares problems, how to generate reproducible random numbers, how to handle missing values, and how to save, memory-map and chunk large arrays. Every example was run with a current release on Python 3.14, and the real output is shown, including the version 2 behaviour that older tutorials get wrong.
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