Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

9.3.2 Iterators are disposable

One salient feature of iterators is that they may be used only once. In order to use the iterator again, you will have to create a new iterator object. Note that an iterable object is able to create new iterators as many times as necessary. Let's examine the case of a list:

L = ['a', 'b', 'c']
iterator = iter(L)
list(iterator) # ['a', 'b', 'c']
list(iterator) # [] empty list, because the iterator is exhausted


new_iterator = iter(L) # new iterator, ready to be used list(new_iterator) # ['a', 'b', 'c']

Each time a generator object is called, it creates a new iterator. Hence, when that iterator is exhausted, you have to call the generator again to obtain a new iterator:

g = odd_numbers(10)
for k in g:
    ... # do something with k

# now the iterator is exhausted:
for k in g: # nothing will happen!!
    ...

# to loop through it again, create a new one:...