Python is a much more readable and efficient language, especially compared to languages like Java, PHP, and C, both of which have made it very popular with developers. Python is an excellent language that allows you to do a lot of things in a short amount of time with very little code. Not only that, it easily supports multitasking, such as multi-processing.
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People who don’t like Python often complain that Python is running too slow. However, this is not the case. Try these six tips to speed up your Python applications.
Tip 1: Use external feature packs for critical code
Python simplifies many programming tasks, but for some time-sensitive tasks, its performance is often disappointing. Using external feature packs in C/C++ or machine language to handle time-sensitive tasks can effectively improve the performance of your application. These feature packs tend to be platformer specific, so you need to choose the right feature pack depending on the platform you’re using. In short, the trick is to sacrifice application portability for performance that can only be achieved through direct programming of the underlying host. Here are some feature packs you can choose to use to increase your productivity:
- Cython
- Pylnlne
- PyPy
- Pyrex
These feature packs have different uses. Using C data types, for example, can make tasks involving memory operations more efficient or intuitive. Pyrex can help Python extend this functionality. Pylnline allows you to use C code directly in Python applications. Inline code is compiled independently, but it keeps all compiled files somewhere and takes full advantage of the efficiency provided by the C language.
Tip 2: Use keys when sorting
Python has a lot of ancient collation rules that can take a lot of time to create custom collations, which can also slow down the actual execution of the program. The best way to sort is to use as many keys and the built-in sort() method as possible. For example, take the following code:
importoperator
,5,8 somelist = [(1), (6, 4-trichlorobenzene), (9,7,5)]
somelist.sort(key=operator.itemgetter(0))
somelist
Output = [(1, 5, 8), (6, 2, 4), (9, 7, 5)]
somelist.sort(key=operator.itemgetter(1))
somelist
Output = [(6, 2, 4), (1, 5, 8), (9, 7, 5)]
somelist.sort(key=operator.itemgetter(2))
somelist
Output = [(6, 2, 4), (9, 7, 5), (1, 5, 8)],
In each example, the list is sorted by the index you selected as the key parameter. This method works not only for numeric types, but also for string types.
Tip three: optimization for the cycle
Every programming language emphasizes optimal looping. When using Python, there are plenty of tricks you can use to make loops run faster. However, one trick that developers often forget is to avoid accessing variable properties in a loop. For example, take the following code:
lowerlist = [‘this’,’is’,’lowercase’]
upper = str.upper
upperlist = []
append = upperlist.append
forword inlowerlist:
append(upper(word))
print(upperlist)
Output = [‘THIS’, ‘IS’, ‘LOWERCASE’]
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Python evaluates this formula every time you call STR. Upper. However, if you assign this evaluation to a variable, the result of the evaluation is known in advance, and the Python program runs faster. Therefore, the key is to keep Python’s loop work as small as possible. Because Python interprets the nature of execution, it slows it down considerably in the above example.
(Note: There are many ways to optimize a loop, and this is just one of them. For example, many programmers would argue that list derivations are the best way to increase loop speed. The point is that optimizing the loop is a great way to speed up your application.)
Tip 4: Use a newer version of Python
If you search the web for Python, you’ll find countless pieces of information on how to upgrade the Python version. Typically, each version of Python includes optimizations that make it run faster than the previous version. The limiting factor, however, is whether or not your favorite library has been synchronized to support the new Python version. Rather than debating whether the library should be updated, the key is whether the new Python version is efficient enough to support this update.
You need to make sure that your code will still work in the new version. You’ll need a new library to experience the new version of Python, and then you’ll need to check your application before you make any critical changes. Only after you have made the necessary corrections will you feel the difference in the new version.
However, if you just make sure your application works in the new version, you may miss out on the new features that the new version offers. Once you’ve decided to update, analyze how your app will perform under the new version and check for areas that might be problematic, and then apply the new version’s features to those areas first. Only in this way will users notice the improvement in application performance at the start of the update.
Tip 5: Try a variety of coding methods
Using the same coding method every time you create an application almost invariably results in a less efficient application. You can try some experimental methods during program analysis. For example, when dealing with items in a dictionary, you can either use the safe method of making sure they already exist before updating them, or you can update them directly and treat the items that don’t exist as special cases. Look at the first piece of code below:
n = 16
myDict = {}
foriinrange(0,n):
char = ‘abcd'[i%4]
ifchar notinmyDict:
myDict[char] = 0
myDict[char] += 1
print(myDict)
When MyDict is empty at first, this code will run faster. More often than not, however, MyDict fills up with data, or at least most of it, and it’s more efficient to switch to another approach.
n = 16
myDict = {}
foriinrange(0,n):
char = ‘abcd'[i%4]
try:
myDict[char] += 1
exceptKeyError:
myDict[char] = 1
print(myDict)
The output is the same in both methods. The difference is how the output is obtained. Thinking outside the box and creating new programming techniques can make your application more efficient.
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Tip 6: Cross-compile your application
Developers sometimes forget that computers don’t really understand the programming languages used to create modern applications. Computers understand machine language. To run your application, you use an application to convert your human-readable code into machine-readable code. Sometimes, you write an application in a language like Python, and then you run your application in a language like C++, and that makes sense from a run point of view. The key is what you want your application to accomplish and what resources your host system can provide.
Nuitka is an interesting cross-compiler that converts your Python code into C++ code. In this way, you can execute your application in native mode without relying on the interpreter program. You will find that your application runs much more efficiently, but this will vary by platform and task.
(Note: Nuitka is still in beta, so be careful when it comes to practical applications. In fact, it’s best to experiment with it for now. In addition, there is room for debate as to whether cross-compilation is the best way to improve performance. Developers have been using cross-compilation for years to speed up applications. Remember that each solution has pros and cons, so weigh them carefully before using them in a production environment.)
When using a cross-compiler, make sure it supports the version of Python you’re using. Nuitka supports Python2.6, 2.7, 3.2, and 3.3. In order for the solution to work, you need a Python interpreter and a C++ compiler. Nuitka supports many C++ compilers, including Microsoft Visual Studio, MinGW, and Clang/LLVM.
Cross-compilation can cause some serious problems. For example, with Nuitka, you’ll find that even a small program consumes a lot of driver space. Nuitka uses a series of dynamically linked libraries (DDLs) to perform Python functions. Therefore, if you are working on a system with limited resources, this approach may not be feasible.