A Primer on Scientific Programming with Python (4th Edition) - download pdf or read online
By Hans Petter Langtangen
The e-book serves as a primary advent to desktop programming of clinical functions, utilizing the high-level Python language. The exposition is instance and problem-oriented, the place the purposes are taken from arithmetic, numerical calculus, records, physics, biology and finance. The ebook teaches "Matlab-style" and procedural programming in addition to object-oriented programming. highschool arithmetic is a required historical past and it truly is effective to check classical and numerical one-variable calculus in parallel with studying this publication. in addition to studying easy methods to application pcs, the reader also will tips on how to resolve mathematical difficulties, bobbing up in quite a few branches of technological know-how and engineering, by using numerical equipment and programming. via mixing programming, arithmetic and medical functions, the e-book lays an effective starting place for working towards computational technological know-how.
Read Online 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 info for A Primer on Scientific Programming with Python (4th Edition) (Texts in Computational Science and Engineering, Volume 6)
We can also use environments such as equation, eqnarray, or align. These features are very useful to scientific users. By combining code cells and Markdown cells, we can create a standalone interactive document that combines computations (code), text, and graphics. 9. IPython also comes with a sophisticated display system that lets us insert rich web elements in the notebook. Here, we show how to add HTML, SVG (Scalable Vector Graphics), and even YouTube videos in a notebook. *float(i) ) for i in range(10)]) + '''''') SVG in the notebook Finally, we display a YouTube video by giving its identifier to YoutubeVideo: In : YouTubeVideo('j9YpkSX7NNM') YouTube in the notebook 10.
We can now display this information in a figure. We first need to reorder the table by the weekday using ix (indexing operation). ylim(0); # Set the bottom axis to 0. 10. 0. We will plot a smoothed version of the track attendance as a function of time (rolling mean). The idea is to compute the mean value in the neighborhood of any day. The larger the neighborhood, the smoother the curve. We will create an interactive slider in the notebook to vary this parameter in real time in the plot. show() 26 Chapter 1 Interactive widget in the notebook There's more...
Head(2) First rows of the DataFrame Here, every row contains the number of bicycles on every track of the city, for every day of the year. 23 A Tour of Interactive Computing with IPython 5. describe() Summary statistics of the DataFrame 6. Let's display some figures. We will plot the daily attendance of two tracks. First, we select the two columns, Berri1 and PierDup. plot() 24 Chapter 1 7. Now, we move to a slightly more advanced analysis. We will look at the attendance of all tracks as a function of the weekday.
A Primer on Scientific Programming with Python (4th Edition) (Texts in Computational Science and Engineering, Volume 6) by Hans Petter Langtangen