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Contributing os.scandir() to Python
August 2016 This article describes my experience contributing a medium-sized feature to Python. In short: I wrote PEP 471 and contributed os.scandir() to the Python standard library and the CPython codebase. It was a lot more work than I expected, but it was a fun journey, and as a result the end product has a better API than my initial one. What scandir does is provide a lowish-level API that giv
Generating fantasy maps
mewo2.com is the website of Martin O'Leary artist, designer, teacher, researcher These are some notes on how I generate the maps for my Twitter bot @unchartedatlas, which is based on a generator I originally produced during NaNoGenMo 2015. There's JavaScript code for the generator on Github here, and the original messy Python generator code can be seen here. You may also be interested in this comp
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Reverse debugging for Python
RevPDB A "reverse debugger" is a debugger where you can go forward and backward in time. It is an uncommon feature, at least in the open source world, but I have no idea why. I have used undodb-gdb and rr, which are reverse debuggers for C code, and I can only say that they saved me many, many days of poking around blindly in gdb. The PyPy team is pleased to give you "RevPDB", a reverse-debugger s
Zero Knowledge Proofs — A Primer
This article was ported from my old Wordpress blog here, If you see any issues with the rendering or layout, please send me an email. In this post we’ll get a strong taste for zero knowledge proofs by exploring the graph isomorphism problem in detail. In the next post, we’ll see how this relates to cryptography and the bigger picture. The goal of this post is to get a strong understanding of the t
Python に現在実装中の Compact dict の紹介 : DSAS開発者の部屋
背景 2015年1月に、 PyPy の開発者Blogにこんな記事がポストされました。 Faster, more memory efficient and more ordered dictionaries on PyPy その後リリースされた PyPy 2.5.1 から dict は挿入順を維持するようになり、メモリ使用量も削減されました。 一方 CPython では、 PEP 468 で、キーワード引数を **kwargs という形式の仮引数で受け取るときに、引数の順序を保存しようという提案がされました。 例えば、 SQLAlchemy のクエリーで .filter_by(name="methane", age=32) と書いたときに生成されるクエリーが WHERE name = "methane" AND age = 32 になるか WHERE age = 32 AND name="m
Python の dict の実装詳解 : DSAS開発者の部屋
@methane です。 最近 Python の dict をハックしているので、その紹介をしたいと思います。 ですが、まずこの記事では現在 (Python 3.6a2) の dict の実装を詳解します。 データ構造 基本となる構造体は3つです。(Python 3.6a2 のソースより引用) typedef struct _dictkeysobject PyDictKeysObject; typedef struct { PyObject_HEAD Py_ssize_t ma_used; PyDictKeysObject *ma_keys; PyObject **ma_values; } PyDictObject; typedef struct { /* Cached hash code of me_key. */ Py_hash_t me_hash; PyObject *me_key;
Pythonでネイティブコードを実行する - ももいろテクノロジー
Pythonでネイティブコード（x86バイトコードなど）を実行する方法のメモ。 ctypesモジュールをインポートし、次のような関数を定義すればよい。 import ctypes def native_func(bytecode): libc = ctypes.CDLL('libc.so.6') libc.mmap.restype = ctypes.c_void_p buf = libc.mmap(None, len(bytecode), 7, 0x22, -1, 0) libc.memcpy(ctypes.c_void_p(buf), ctypes.c_char_p(bytecode), len(bytecode)) return ctypes.CFUNCTYPE(ctypes.c_void_p)(buf) rdrand = native_func('\x48\x0f\xc7\xf0\x
Decorated Concurrency - Python multiprocessing made really really easy - Peterbe.com
Decorated Concurrency - Python multiprocessing made really really easy tl;dr There's a new interesting wrapper on Python multiprocessing called deco, written by Alex Sherman and Peter Den Hartog, both at University of Wisconsin - Madison. It makes Python multiprocessing really really easy. The paper is here (PDF) and the code is here: https://github.com/alex-sherman/deco. This library is based on
The Sigmoid Function in Logistic Regression | iPython Notebooks
The Sigmoid Function in Logistic Regression¶In learning about logistic regression, I was at first confused as to why a sigmoid function was used to map from the inputs to the predicted output. I mean, sure, it's a nice function that cleanly maps from any real number to a range of $-1$ to $1$, but where did it come from? This notebook hopes to explain. Logistic Regression¶With classification, we ha
Car Speed Detector
(2016-12-18 Version 2 is now on github. It fixes one bug, makes the code style more consistent, and adds a few additional options for tracking the results.) It started with a Facebook post from my brother-in-law: “Okay Facebook, I need your help. How do you deal with A$$hole’s that live on your street and constantly speed (75 in a 25) up and down the road? I’ve called the sheriff and they are unde
Kaggle Competition: How I Ranked in the Top 15 with My First Attempt – Dataquest
Kaggle Competition: How I Ranked in the Top 15 with My First Attempt Kaggle competitions are a fantastic way to learn data science and build your portfolio. I personally used Kaggle to learn many data science concepts. I started out with Kaggle a few months after learning basic Python programming, and later won several competitions. Doing well in a Kaggle competition requires more than just knowin
uvloop: Blazing fast Python networking — magicstack
asyncio is an asynchronous I/O framework shipping with the Python Standard Library. In this blog post, we introduce uvloop: a full, drop-in replacement for the asyncio event loop. uvloop is written in Cython and built on top of libuv. uvloop makes asyncio fast. In fact, it is at least 2x faster than nodejs, gevent, as well as any other Python asynchronous framework. The performance of uvloop-based
Comparing Python Clustering Algorithms
Comparing Python Clustering Algorithms¶(Why you should use HDBSCAN)¶There are a lot of clustering algorithms to choose from. The standard sklearn clustering suite has thirteen different clustering classes alone. So what clustering algorithms should you be using? As with every question in data science and machine learning it depends on your data. A number of those thirteen classes in sklearn are sp
Stator
Rust + Python The Perfect Combination Python Flexibility Dynamic Typing Time To Market Rust Full Control Error Handling Threading Agenda Performance Asynchronous I/O Applicability Subjects stator stator+asyncio uwsgi uwsgi-gevent tornado Hello World Hello World Http + Redis Redis + 10ms Sleep Redis + 10ms Busy What's the Point? Microservices go erlang Microservices Rate-Limits Health-Check Metrics
6 Lesser Known Python Data Analysis Libraries
Posted on: 19-04-2016 Python offers a great environment and rich set of libraries to developers while working with data. There are tons of useful libraries out there for novice or experienced developers or analysts for helping out with processing or visualizing datasets. Some of the libraries are really popular and used by millions of developers, for example - Pandas, Numpy, Scikit-learn, NTLK etc
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GitHub - thalesians/pythalesians: The Thalesians' Python library
GitHub - alex-sherman/deco
A polyglot's guide to multiple dispatch - part 2 - Eli Bendersky's website