Optimise Python with closures

Wrong Side of Memphis

This blog post by Dan Crosta is interesting. It talks about how is possible to optimise Python code for operations that get called multiple times avoiding the usage of Object Orientation and using Closures instead.

While the “closures” gets the highlight, the main idea is a little more general. Avoid repeating code that is not necessary for the operation.

The difference between the first proposed code, in OOP way

and the last one

The main differences are that both the config dictionary and the methods (which are also implemented as a dictionary) are not accessed. We create a direct reference to the value (categories and mode) instead of making the Python interpreter search on the self methods over and over.

This generates a significant increase in performance, as described on the post (around 20%).

But why stop there? There is another clear win in terms of access, assuming that the…

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Ultimate guide for scraping JavaScript rendered web pages

I really wanted to write a guide for this myself but didn’t get the time. Here Naren arya wrote a great post and I think that you should definitely give it a look.


We all scraped web pages.HTML content returned as response has our data and we scrape it for fetching certain results.If web page has JavaScript implementation, original data is obtained after rendering process. When we use normal requests package in that situation then responses those are returned  contains no data in them.Browsers know how to render and display the final result,but how a program can know?. So I came with a power pack solution to scrape any JavaScript rendered website very easily.

Many of us use below libraries to perform scraping.



I don’t mention scrapy or dragline frameworks here since underlying basic scraper is lxml .My favorite one is lxml.why? ,It has the element traversal methods rather than relying on regular expressions methodology like BeautifulSoup.Here I am going to take a very interesting example.I am so amazed after finding that ,my article is appeared in recent PyCoders weekly issue…

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Writing C in Cython

Computational Linguistics

For the last two years, I’ve done almost all of my work in Cython. And I don’t mean, I write Python, and then “Cythonize” it, with various type-declarations etc. I just, write Cython. I use “raw” C structs and arrays, and occasionally C++ vectors, with a thin wrapper around malloc/free that I wrote myself. The code is almost always exactly as fast as C/C++, because it really is just C/C++ with some syntactic sugar — but with Python “right there”, should I need/want it.

This is basically the inverse of the old promise that languages like Python came with: that you would write your whole application in Python, optimise the “hot spots” with C, and voila! C speed, Python convenience, and money in the bank.

This was always much nicer in theory than practice. In practice, your data structures have a huge influence on both the efficiency of your…

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