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A Primer on Scientific Programming with Python (4th Edition) by Hans Petter Langtangen

By Hans Petter Langtangen

The ebook serves as a primary advent to desktop programming of medical 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 publication teaches "Matlab-style" and procedural programming in addition to object-oriented programming. highschool arithmetic is a required history and it truly is constructive to check classical and numerical one-variable calculus in parallel with analyzing this booklet. in addition to studying tips to application pcs, the reader also will the best way to clear up mathematical difficulties, bobbing up in numerous branches of technological know-how and engineering, by way of numerical tools and programming. via mixing programming, arithmetic and medical functions, the ebook lays an outstanding starting place for practising computational technological know-how.

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

Sample text

However, the convention for importing it as a two-letter name is similar. api as sm 2. Define the x (exogenous) and y (endogenous) variables. The exogenous variable is really a vector that contains the regressors for the endogenous variable. fit() #looking at the summary in IPython Notebook 28 Instant Data-intensive Apps with pandas How-to 3. Select the different t-values from the regression. 68521999, 4. Get a printout of the confidence intervals of the betas. 051323 29 Instant Data-intensive Apps with pandas How-to There's more As you play more with pandas, StatsModels is a great place to go and get data sets to practice with.

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.

Each comment will be the filename, then the subsequent code will be the contents of that file. How to do it... 1. py and populate it with the following text. If you do not have Flask installed, run the pip install flask setup. run() 2. py was created. html. DOCTYPE html>

My Great Report

#This is the only fancy part… we're clearing the html #from the to_html() method {{ df|safe }} 31 Instant Data-intensive Apps with pandas How-to 3. py, then go to localhost:5000/report in the browser.

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