Clean architectures in python - by leonardo giordani ( free book )
The clean architecture is the opposite of spaghetti code, where everything is interlaced and there are no single elements that can be easily detached from the rest and replaced without the whole system collapsing. The main point of the clean architecture is to make clear "what is where and why", and this should be your first concern while you design and implement a software system, whatever architecture or development methodology you want to follow.
The python graph gallery visualizing data
Welcome to the Python Graph Gallery. This website displays hundreds of charts, always providing the reproducible python code! It aims to showcase the awesome dataviz possibilities of python and to help you benefit it. Feel free to propose a chart or report a bug. Any feedback is highly welcome. Get in touch with the gallery by following it on Twitter, Facebook, or by subscribing to the blog. Note thatthis online course is another good resource to learn dataviz with python.
Episode #191 python's journey at microsoft - podcast
Join me along with Steve Dower (a core dev working at Microsoft), who just published an amazing retrospective of Python at Microsoft entitled: Python at Microsoft: flying under the radar.
Five languages - five stories kari marttila medium
See how Python compared with other languages.
Visual parameter tuning with facebook prophet and python
Facebook prophet is by far my favorite python package. It allows for quick and easy forecasting of many time series with a novel bayesian model, that estimates various parameters using a general additive model. More on Facebook prophet can be found right here. Forecasting is important many business situations, particularly supply chain management and demand planning.
Python and django logging in plain english django deconstructed
If you’ve ever written a program and printed out a value to see what’s going on during execution, then you understand at some level why logging is so valuable. Knowing what’s happening in your code at a point in time is enormously useful from both technical and business perspectives. This knowledge lets developers and product managers make smart choices about what systems to fix or modify and lets them see what actions users take when they use your software.
Programming ftdi devices in python
FTDI chips are frequently used as USB-to-serial adaptors, but the newer devices have the ability to drive more complex protocols such as SPI and I2C.I like to use Python when first experimenting with new PC hardware, and there are some Python libraries for interfacing to FTDI chips, but I couldn’t find any real projects or complete worked examples.
Advent of code 2018 solutions
Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as a speed contest, interview prep, company training, university coursework, practice problems, or to challenge each other.
8 reasons python sucks - the hacker factor blogAnd me, well... I just blurted it out: I hate Python. I hate it with a passion. If I have the choice between using some pre-existing Python code or rewriting it in C, I'd rather rewrite it in C.
Curator's Note - Someone give him a Django website to rewrite in C. ;) ... Nevertheless it's good to hear critique and ponder over it.
Python gets a new governance model
A new thread was started on the Python committers Discourse instance to discuss the pros and cons of various voting systems. Instant-runoff voting fell out of favor; there were concerns that it didn't truly represent the will of the electorate, as seen in a Burlington, Vermont mayoral election in 2009, for example. The fact that it was put in place by fiat under a self-imposed deadline based on in-person conversations at the core developer sprint, rather than being hashed out on the Discourse instance or the python-committers mailing list may have also been a factor.
First impressions of gpus and pydata
Like many PyData developers, I’m loosely aware that GPUs are sometimes fast, but don’t deal with them often enough to have strong feeling about them.To get a more visceral feel for the performance differences, I logged into a GPU machine, opened up CuPy (a Numpy-like GPU library developed mostly by Chainer in Japan) and cuDF (a Pandas-like library in development at NVIDIA) and did a couple of small speed comparisons:
Blazing fast python
Perhaps you’ve faced the fortunate challenge of scaling up a Python application to accommodate a steadily increasing user base. Though most cloud hosting providers make it easier than ever to throw more hardware at a problem, there comes a point when the cost outweighs convenience.Around the time scaling horizontally starts looking less attractive, developers turn to performance tuning to make applications more efficient. In the Python community there are a number of tools to help in this arena; from the built-in timeit module to profiling tools like cProfile, there are quick ways to test the difference between a particular line of code and any of its alternatives.Although profiling tools help you see important information about which calls in your application are time consuming, it’s difficult to exercise an application during local development the same way your users exercise it in real life. The solution to bridging this gap? Profile in production!PyflamePyflame is a profiling tool
Python io streams in examplesPython IO streams: BytesIO and StringIO in p
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