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