Python Module Quotes

Linux has a forking process to create new processes by cloning the parent process. Windows lacks fork, so the multiprocessing module imposes some Windows-specific restrictions that we urge you to review if you’re using that platform.

One downfall of numpy’s optimization of vector operations is that it only occurs on one operation at a time. That is to say, when we are doing the operation A * B + C with numpy vectors, first the entire A * B operation completes, and the data is stored in a temporary vector; then this new vector is added with C. The in-place version of the diffusion code in Example 6-14 shows this quite explicitly. However, there are many modules that can help with this. numexpr is a module that can take an entire vector expression and compile it into very efficient code that is optimized to minimize cache misses and temporary space used. In addition, the expressions can utilize multiple CPU cores (see Chapter 9 for more information) and specialized instructions for Intel chips to maximize the speedup. It is very easy to change code to use numexpr: all that’s required is to rewrite the expressions as strings with references to local variables. The expressions are compiled behind the scenes (and cached so that calls to the same expression don’t incur the same cost of compilation) and run using optimized code. Example 6-19 shows the simplicity of changing the evolve function to use numexpr. In this case, we chose to use the out parameter of the evaluate function so that numexpr doesn’t allocate a new vector to which to return the result of the calculation.

test3 defines the sin function as a keyword argument, with its default value being a reference to the sin function within the math module. While we still do need to find a reference to this function within the module, this is only necessary when the test3 function is first defined. After this, the reference to the sin function is stored within the function definition as a local variable in the form of a default keyword argument. As mentioned previously, local variables do not need a dictionary lookup to be found; they are stored in a very slim array that has very fast lookup times. Because of this, finding the function is quite fast! While these effects are an interesting result of the way namespaces in Python are managed, test3 is definitely not “Pythonic.” Luckily, these extra dictionary lookups only start to degrade performance when they are called a lot (i.e., in the innermost block of a very fast loop, such as in the Julia set example). With this in mind, a more readable solution would be to set a local variable with the global reference before the loop is started. We’ll still have to do the global lookup once whenever the function is called, but all the calls to that function in the loop will be made faster. This speaks to the fact that even minute slowdowns in code can be amplified if that code is being run millions of times. Even though a dictionary lookup may only take several hundred nanoseconds, if we are looping millions of times over this lookup it can quickly add up. In fact, looking at Example 4-10 we see a 9.4% speedup simply by making the sin function local to the tight loop that calls it.

To initiate the debugger, all you have to do is import the pdb built-in module and run its set_trace function. You’ll often see this done in a single line so programmers can comment it out with a single # character.

Just like how Python comes with several modules like random, math, or time that provide additional functions for your programs, the Pygame framework includes several modules with functions for drawing graphics, playing sounds, handling mouse input, and other things.

In Pythons 3.3 and 2.7, you can get help for a module you have not imported by quoting the module’s name as a string — for example, help('re'), help('email.message') — but support for this and other modes may differ across Python versions.

In Python, the built-in logging module provides the functionality you need to easily create logfiles by using just three lines of code:

Python is a mainstream programming language that is commonly used to solve cognitive and mathematical problems. Many Python modules and useful Python libraries, such as IPython, Pandas, SciPy, and others, are most commonly used for these tasks. Usage of Business Applications Python is used by many engineers to assemble and maintain their commercial programs or apps. Python is used by many designers to maintain their web-based company sites. An application that runs on the console Python can be used to create help-based software. IPython, for example, can be used to create a variety of support-based applications. Audio or Video-based Application Programming Python is an excellent programming language for a variety of video and audio projects. Python is used by many professionals to create a variety of media applications. You can do this with the help of cplay, another Python compiler. 3D based Computer-Aided Drafting Applications Python is used by many designers to create 3D-based Computer-Aided Drafting systems. Fandango is a very useful Python-based application that allows you to see all of the capabilities of CAD to expand these types of applications. Applications for Business Python is used by many Python experts to create a variety of apps that can be used in a business. Tryton and Picalo are the most famous applications in this regard.

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One such usage is the turtle module (which is also part of the standard library). To quote the Python docs: Turtle graphics is a popular way for introducing programming to kids. It was part of the original Logo programming language developed by Wally Feurzig and Seymour Papert in 1966. Programmers

The sqlalchemy module is to SQL geeks what requests is to web geeks: indispensable. The

In general, you can define collector modules that import all the names from other modules so they’re available in a single convenience module.

By coding functions and classes in module files, we’ve ensured that they naturally support reuse. And by coding our software as classes, we’ve ensured that it naturally supports extension.

mix-in classes are the class equivalent of modules

In a sense, a module is like a single-instance class, without inheritance, which corresponds to an entire file of code.

Best practice in all Pythons recommends listing all your imports at the top of a module file; it’s not required, but makes them easier to spot.

Python’s bytecode disassembler lives in the dis module that’s part of the standard library. So we can just import it and call dis.dis()

In a sense, mix-in classes are similar to modules: they provide packages of methods for use in their client subclasses.