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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, 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)
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About Packt
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References

2.2.2 Floating-point numbers

If you execute the statement a = 3.0 in Python, you create a floating-point number (Python type: float). These numbers form a finite subset of rational numbers, ℚ.

Alternatively, the constant could have been given in exponent notation as a = 30.0e-1 or simply a = 30.e-1. The symbol e separates the exponent from the mantissa, and the expression reads in mathematical notation as . The name floating-point number refers to the internal representation of these numbers and reflects the floating position of the decimal point when considering numbers over a wide range.

Applying elementary mathematical operations, such as +-*, and /to two floating-point numbers, or to an integer and a floating-point number, returns a floating-point number.

Operations between floating-point numbers rarely return the exact result expected from rational number operations:

0.4 - 0.3 # returns 0.10000000000000003

This fact matters when comparing floating-point numbers:

0.4 - 0.3 == 0.1 # returns False

The reason for this becomes apparent when looking at the internal representation of floating-point numbers; see also Section 15.2.6, Float comparisons.

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