How NumPy speeds array math in Python. A big part of NumPy’s speed comes from using machine-native datatypes, instead of Python’s object types.

Learn how to create, index, slice, reshape, and perform arithmetic operations on arrays using NumPy, the most popular Python library for data science.

Learn how to use PuLP and SciPy libraries to define and solve linear programming problems in Python. Compare the advantages and disadvantages of each approach.

Arrays in Python give you a huge amount of flexibility for storing, organizing, and accessing data. This is crucial, not least because of Python’s popularity for use in data science.

An application can consist of multiple components. Add a new component by choosing from one of the available templates. To see the list of available templates, run: Currently all imports must happen ...

Using Golem's command line interface provides a set of predefined, Golem-specific examples to choose from as a starting point. To see the available examples for Python, run: $ golem-cli list-examples ...