The documentation does a great job explaining how things normally get looked up, and how you can hook them.
But to understand how the hooking works, you need to go under the covers to see how that normal lookup actually happens.
When I say "Python" below, I'm mostly talking about CPython 3.5.
But to understand how the hooking works, you need to go under the covers to see how that normal lookup actually happens.
When I say "Python" below, I'm mostly talking about CPython 3.5.
In Python (I'm mostly talking about CPython here, but other implementations do similar things), when you write the following:
def spam(x): return x+1 spam(3) What happens?
Really, it's not that complicated, but there's no documentation anywhere that puts it all together.
def spam(x): return x+1 spam(3) What happens?
Really, it's not that complicated, but there's no documentation anywhere that puts it all together.
I've seen a number of people ask why, if you can have arbitrary-sized integers that do everything exactly, you can't do the same thing with floats, avoiding all the rounding problems that they keep running into.
In a recent thread on python-ideas, Stephan Sahm suggested, in effect, changing the method resolution order (MRO) from C3-linearization to a simple depth-first search a la old-school Python or C++.
Note: This post doesn't talk about Python that much, except as a point of comparison for JavaScript.
Most object-oriented languages out there, including Python, are class-based. But JavaScript is instead prototype-based.
Most object-oriented languages out there, including Python, are class-based. But JavaScript is instead prototype-based.
About a year and a half ago, I wrote a blog post on the idea of adding pattern matching to Python.
I finally got around to playing with Scala semi-seriously, and I realized that they pretty much solved the same problem, in a pretty similar way to my straw man proposal, and it works great.
I finally got around to playing with Scala semi-seriously, and I realized that they pretty much solved the same problem, in a pretty similar way to my straw man proposal, and it works great.
About a year ago, Jules Jacobs wrote a series (part 1 and part 2, with part 3 still forthcoming) on the best collections library design.
In three separate discussions on the Python mailing lists this month, people have objected to some design because it leaks something into the enclosing scope. But "leaks into the enclosing scope" isn't a real problem.
There's a lot of confusion about what the various kinds of things you can iterate over in Python. I'll attempt to collect definitions for all of the relevant terms, and provide examples, here, so I don't have to go over the same discussions in the same circles every time.
Python has a whole hierarchy of collection-related abstract types, described in the collections.abc module in the standard library. But there are two key, prototypical kinds. Iterators are one-shot, used for a single forward traversal, and usually lazy, generating each value on the fly as requested.
There are a lot of novice questions on optimizing NumPy code on StackOverflow, that make a lot of the same mistakes. I'll try to cover them all here.
What does NumPy speed up?
Let's look at some Python code that does some computation element-wise on two lists of lists.
What does NumPy speed up?
Let's look at some Python code that does some computation element-wise on two lists of lists.
When asyncio was first proposed, many people (not so much on python-ideas, where Guido first suggested it, but on external blogs) had the same reaction: Doing the core reactor loop in Python is going to be way too slow. Something based on libev, like gevent, is inherently going to be much faster.
Let's say you have a good idea for a change to Python.
There are hundreds of questions on StackOverflow that all ask variations of the same thing. Paraphrasing:
lst is a list of strings and numbers. I want to convert the numbers to int but leave the strings alone.
lst is a list of strings and numbers. I want to convert the numbers to int but leave the strings alone.
In Haskell, you can section infix operators. This is a simple form of partial evaluation. Using Python syntax, the following are equivalent:
(2*) lambda x: 2*x (*2) lambda x: x*2 (*) lambda x, y: x*y So, can we do the same in Python?
Grammar
The first form, (2*), is unambiguous.
(2*) lambda x: 2*x (*2) lambda x: x*2 (*) lambda x, y: x*y So, can we do the same in Python?
Grammar
The first form, (2*), is unambiguous.
Many people—especially people coming from Java—think that using try/except is "inelegant", or "inefficient". Or, slightly less meaninglessly, they think that "exceptions should only be for errors, not for normal flow control".
These people are not going to be happy with Python.
These people are not going to be happy with Python.
If you look at Python tutorials and sample code, proposals for new language features, blogs like this one, talks at PyCon, etc., you'll see spam, eggs, gouda, etc. all over the place.
Most control structures in most most programming languages, including Python, are subordinating conjunctions, like "if", "while", and "except", although "with" is a preposition, and "for" is a preposition used strangely (although not as strangely as in C…).
There are two ways that some Python programmers overuse lambda. Doing this almost always mkes your code less readable, and for no corresponding benefit.
Some languages have a very strong idiomatic style—in Python, Haskell, or Swift, the same code by two different programmers is likely to look a lot more similar than in Perl, Lisp, or C++.
There's an advantage to this—and, in particular, an advantage to you sticking to those idioms.
There's an advantage to this—and, in particular, an advantage to you sticking to those idioms.
Python doesn't have a way to clone generators.
At least for a lot of simple cases, however, it's pretty obvious what cloning them should do, and being able to do so would be handy. But for a lot of other cases, it's not at all obvious.
At least for a lot of simple cases, however, it's pretty obvious what cloning them should do, and being able to do so would be handy. But for a lot of other cases, it's not at all obvious.
Every time someone has a good idea, they believe it should be in the stdlib. After all, it's useful to many people, and what's the harm? But of course there is a harm.
This confuses every Python developer the first time they see it—even if they're pretty experienced by the time they see it:
>>> t = ([], []) >>> t[0] += [1] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <stdin> in <module>()
>>> t = ([], []) >>> t[0] += [1] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <stdin> in <module>()
On the Python-ideas list, in yet another thread on a way to embed statements in expressions, I raised the issue that the statement-expression distinction, and not having a way to escape it, is important to why Python is so readable. But I couldn't explain exactly why.
In IEEE Floats and Python, I chose to use a tiny "binary6" type because it's easy to show a table of values that helps clarify things.
Someone suggested that you could just as easily write a table of binary64 values, ellipsizing large chunks of it, and it would also be useful for clarifying things.
Someone suggested that you could just as easily write a table of binary64 values, ellipsizing large chunks of it, and it would also be useful for clarifying things.
Everybody knows that "floating point numbers cause problems." Many people have misconceptions about what those problems are, and how to deal with them.
Many tutorials on object-oriented programming conflate inheritance and subtyping. In fact, it's often considered part of the OO dogma that they should be conflated.
This is wrong for Python in a wide variety of ways.
This is wrong for Python in a wide variety of ways.
It's very common in a program to want to do two things at once: repaginate a document while still responding to user input, or handle requests from two (or 10000) web browsers at the same time. In fact, pretty much any GUI application, network server, game, or simulator needs to do this.
If you've migrated to Python from C++ or one of its descendants (Java, C#, D, etc.), or to a lesser extent from other OO languages (Objective C, Ruby, etc.), the first time you asked for help on StackOverflow or CodeReview or python-list or anywhere else, the first response you got was probably: "Ge
