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Scientific and technical
Work with multidimensional numerical data and vectorized operations.
import numpy as np
values = np.array([1, 2, 3])
print(values.sum())COURSE CURRICULUM
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Learn how numpy arrays, dtypes, and vectorized execution works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how array creation, indexing, axes, and shapes works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how names, views, copies, and memory sharing works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how dtypes, structured arrays, missing data, and casting works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how broadcasting, ufuncs, and numerical operators works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how boolean masks, where, and conditional selection works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how vectorization, iteration, and generalized ufuncs works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how functions, signatures, and reusable array transforms works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how arrays, records, masked arrays, and stacking works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how string arrays, datetime values, and formatting works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how shape errors, nan handling, and validation works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how modules, environments, and reproducible numerical code works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how array protocols, subclassing, and interoperability works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how files, memory maps, random generators, and parallel libraries works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how testing tolerances, shapes, and numerical stability works in NumPy, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.