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This content is taken from the Partnership for Advanced Computing in Europe (PRACE)'s online course, Python in High Performance Computing. Join the course to learn more.

Skip to 0 minutes and 11 secondsHello, and welcome to the second week in Python in high-performance computing. As discussed last week, Python is very flexible programming language with many bells and whistles bolted in. But, unfortuntately, the flexibility of Python comes with a cost. Python's built-in data structures are not very suitable for numerical computing, especially when dealing with a large number of similar data such as numbers. Especially, dealing with multidimensional arrays can be very cumbersome. In this week, we will present NumPy which is the de-facto third-party library for doing numerical computing in Python. NumPy provides a static multidimensional array which is very suitable for numerical data. It also provides tools and utilities to efficiently operate on large amounts of data.

Skip to 1 minute and 7 secondsWe will discuss how to construct NumPy arrays and how to efficiently use them in your program. The week is packed with exercises to get your hands dirty on using NumPy arrays and to illustrate the main concepts.

Welcome to week 2

Python’s built-in features and standard library are not always enough for numerical computations.

In this week, we discuss how NumPy package can be used for more efficient number crunching.

What kind of numerical problems you are dealing with? Please comment!

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This video is from the free online course:

Python in High Performance Computing

Partnership for Advanced Computing in Europe (PRACE)