By Hans Petter Langtangen

The e-book serves as a primary advent to computing device programming of clinical purposes, utilizing the high-level Python language. The exposition is instance and problem-oriented, the place the purposes are taken from arithmetic, numerical calculus, statistics, physics, biology and finance. The e-book teaches "Matlab-style" and procedural programming in addition to object-oriented programming. highschool arithmetic is a required history and it truly is beneficial to review classical and numerical one-variable calculus in parallel with analyzing this booklet. along with studying how you can application desktops, the reader also will how you can remedy mathematical difficulties, bobbing up in a number of branches of technological know-how and engineering, through numerical equipment and programming. by means of mixing programming, arithmetic and clinical purposes, the publication lays an excellent beginning for practising computational technological know-how.

Show description

Read or Download A Primer on Scientific Programming with Python (4th Edition) (Texts in Computational Science and Engineering, Volume 6) PDF

Similar python books

Learning BeagleBone Python Programming

BeagleBone is a barebone machine that may be configured and customised for various purposes and is sort of part the cost of a customary desktop. This booklet will conceal the fundamentals of the way BeagleBone Black's interface subsystems paintings, and will be managed utilizing renowned Python libraries for BeagleBone Black.

Instant OpenCV Starter

Start with OpenCV utilizing sensible, hands-on tasks with this booklet and e-book evaluate study whatever new straight away! a quick, quick, targeted advisor offering rapid effects step-by-step set up of OpenCV in home windows and Linux Examples and code according to real-life implementation of OpenCV to aid the reader comprehend the significance of this expertise Codes and algorithms with precise causes intimately within the electronic age of photo and video sharing, the necessity for desktop imaginative and prescient is at an all time excessive.

Data Analysis with Open Source Tools: A hands-on guide for programmers and data scientists

Accumulating info is comparatively effortless, yet turning uncooked info into whatever important calls for that you just know the way to extract accurately what you would like. With this insightful booklet, intermediate to skilled programmers attracted to information research will study suggestions for operating with facts in a company atmosphere.

Data Structures and Algorithms Using Python and C++

THIS booklet is meant to be used in a standard college-level facts buildings path (commonly referred to as CS2). This e-book assumes that scholars have discovered the fundamental syntax of Python and been uncovered to using latest periods. most standard CS1 classes that use Python may have lined all of the worthwhile subject matters, and a few could have coated a number of the themes coated during this e-book.

Extra resources for A Primer on Scientific Programming with Python (4th Edition) (Texts in Computational Science and Engineering, Volume 6)

Example text

Build your own Python web applications from scratch 2. Follow the examples to create a number of different Python-based web applications, including a task list, book database, and wiki application 3. com for information on our titles MySQL for Python ISBN: 978-1-84951-018-9 Paperback: 440 pages Integrate the flexibility of Python and the power of MySQL to boost the productivity of your Python applications 1. Implement the outstanding features of Python's MySQL library to their full potential 2.

Ix['gs':'ibm'] > df['Open'] How it works... The previous example was certainly contrived, but when indexing and statistical techniques are incorporated, the power of pandas begins to come through. Statistics will be covered in an upcoming recipe. pandas' indexes by themselves can be thought of as descriptors of a certain point in the DataFrame. When ticker and timestamp are the only indexes in a DataFrame, then the point is individualized by the ticker, timestamp, and column name. After the point is individualized, it's more convenient for aggregation and analysis.

Therefore it is also subjected to the same commands for formatting, which will be discussed later. For the plotting sections it is recommended that you use IPython Notebook. 33 Instant Data-intensive Apps with pandas How-to How to do it... 1. plot method. plot(kind='bar') 2. Create a boxplot method, which is another method that is directly accessible from the DataFrame object. boxplot() 3. Earlier we were trying to determine the relationship of closing prices between stocks—a scatter matrix is a good choice.

Download PDF sample

Rated 4.23 of 5 – based on 8 votes