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Ned Batchelder's blog

Ned Batchelder's personal blog.


Makefile help target


In a pull request today, I was struck again by the difficulty of providing a “help” target for Makefiles. The make command doesn’t natively have a way to see what targets are available, because the set is dynamic and large, so we are left to cobble things together ourselves.We’d been cargo-culting this target across Makefiles for a while:help: ## display this help message         @echo "Please use \`make ' where  is one of"         @perl -nle'print $& if m{^[a-zA-Z_-]+:.*?## .*$$}' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m  %-25s\033[0m %s\n", $$1, $$2}' Here’s the meaty line, split across lines for readability, as all the rest of the code in this post will be:perl -nle'print $& if m{^[a-zA-Z_-]+:.*?## .*$$}' $(MAKEFILE_LIST) |\     sort |\     awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m  %-25s\033[0m %s\n", $$1, $$2}' It finds labelled lines with double-hash comments, and prints them, sorted, in a nice two-column layout, with the target names in cyan.We’re a Python shop, so that Perl command really seemed out of place. What would it look like in Python? Longer, that’s what:python -c 'import fileinput,re; \     ms=filter(None, ("([a-zA-Z_-]+):.*?## (.*)$$",l) for l in fileinput.input())); \     print("\n".join(sorted("\033[36m  {:25}\033[0m {}".format(*m.groups()) for m in ms)))' $(MAKEFILE_LIST) But looking at that original line more, what is the Perl even doing? It’s just selecting lines. That’s what grep is for:grep -E '^[a-zA-Z_-]+:.*?##' $(MAKEFILE_LIST) | \     sort | \     awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m  %-25s\033[0m %s\n", $$1, $$2}' That’s shorter than the original, but we can do even better by using awk more effectively:grep '^[a-zA-Z]' $(MAKEFILE_LIST) | \     sort | \     awk -F ':.*?## ' 'NF==2 {printf "\033[36m  %-25s\033[0m %s\n", $$1, $$2}' The terminal coloring is cute, but unnecessary and can actually be counterproductive depending on your terminal’s natural colors, so:grep '^[a-zA-Z]' $(MAKEFILE_LIST) | \     sort | \     awk -F ':.*?## ' 'NF==2 {printf "  %-26s%s\n", $$1, $$2}' Looking around for other people’s techniques, marmelab had a very similar line, while Rodrigo Machado and O. Libre went down the all-awk path with fancier behavior.In many ways, this doesn’t matter at all. But it’s a fun rabbit-hole... [...]

Is Python interpreted or compiled? Yes.


A common question: “Is Python interpreted or compiled?” Usually, the asker has a simple model of the world in mind, and as is typical, the world is more complicated.In the simple model of the world, “compile” means to convert a program in a high-level language into a binary executable full of machine code (CPU instructions). When you compile a C program, this is what happens. The result is a file that your operating system can run for you.In the simple definition of “interpreted”, executing a program means reading the source file a line at a time, and doing what it says. This is the way some shells operate.But the real world is not so limited. Making real programming languages useful and powerful involves a wider range of possibilities about how they work. Compiling is a more general idea: take a program in one language (or form), and convert it into another language or form. Usually the source form is a higher-level language than the destination form, such as when converting from C to machine code. But converting from JavaScript 8 to JavaScript 5 is also a kind of compiling.In Python, the source is compiled into a much simpler form called bytecode. These are instructions similar in spirit to CPU instructions, but instead of being executed by the CPU, they are executed by software called a virtual machine. (These are not VM’s that emulate entire operating systems, just a simplified CPU execution environment.)Here’s an example of a short Python function, and its bytecode:>>> import dis >>> def example(x): ...     for i in range(x): ...         print(2 * i) ... >>> dis.dis(example)   2           0 SETUP_LOOP              28 (to 30)               2 LOAD_GLOBAL              0 (range)               4 LOAD_FAST                0 (x)               6 CALL_FUNCTION            1               8 GET_ITER         >>   10 FOR_ITER                16 (to 28)              12 STORE_FAST               1 (i)   3          14 LOAD_GLOBAL              1 (print)              16 LOAD_CONST               1 (2)              18 LOAD_FAST                1 (i)              20 BINARY_MULTIPLY              22 CALL_FUNCTION            1            [...]

What’s in which Python 3.4–3.6?


This is the third in a series of summarizations of what’s in each release of Python. The first two were What’s in which Python 2.x? and What’s in which Python 3.0–3.3?.

3.4: March 16, 2014

  • pip is always available, via ensurepip
  • asyncio (provisional API)
  • enum
  • Other stdlib modules: statistics, pathlib, and tracemalloc

Full list of 3.4 changes.

3.5: September 13, 2015

  • async and await syntax
  • matrix multiplication operator @
  • more unpacking generalizations
  • The typing module for type hints
  • os.scandir()

Full list of 3.5 changes.

3.6: December 23, 2016

  • f-strings
  • kwargs and class attributes order is preserved
  • dicts happen to be (but are not guaranteed to be) ordered
  • underscores in numeric literals
  • variable annotations
  • secrets module in stdlib

Full list of 3.6 changes.

A Python gargoyle


In the #python IRC channel today, someone asked:Does anyone know of any libraries that could convert ‘1-5,7,9,10-13’ to [1,2,3,4,5,7,9,10,11,12,13] ?This seemed like an interesting challenge, and people started offering code. This was mine. It’s ungainly and surprising, and I wouldn’t want to keep it, so I call it a gargoyle:[     i for p in s.split(',')     for a, _, b in [p.partition('-')]     for i in range(int(a), int(b or a)+1) ] There are a few things going on here. First, this is a list comprehension, but with three nested loops. A simple list comprehension has this form:result = [ EXPR for NAMES in ITERABLE ] which is the same as this code:result = [] for NAMES in ITERABLE:     result.append(EXPR) For many, the list comprehension seems kind of backwards, where the expression comes first, before the loop produces it. Then the multi-loop form can seem like another surprise:result = [ EXPR for NAMES1 in ITERABLE1 for NAMES2 in ITERABLE2 ] which is equivalent to:result = [] for NAMES1 in ITERABLE1:     for NAMES2 in ITERABLE2:         result.append(EXPR) The first time I ever tried to write a double list comprehension, I thought the loops should go in the other order, in keeping with the Yoda-style of EXPR coming first. They don’t.Back to the gargoyle. It’s a triply-nested loop (this time formatted a little differently):[     i      for p in s.split(',')         for a, _, b in [p.partition('-')]             for i in range(int(a), int(b or a)+1) ] The first loop splits the number ranges on comma, to produce the individual chunks: ‘1-5’, ‘7’, ‘9’, ‘10-13’. This seems like an obvious first step.The next loop is the most surprising. Strings in Python have a .partition method, which is super-handy and under-used. It takes a separator, and produces three values: the part of the string before the separator, the separator itself, and the part of the string after the separator. The best thing about .partition is that is always produces three values, even if the separator doesn’t appear in the string. In that case, the first value is the whole string, and the other two values are empty strings:>>> '1-5'.partition('-') ('1', '-', '5') >>> '12'.partition('-') ('12', '', '') >>> This means we can always assign the result to three names. Super-handy.But list comprehensions can’t have assignments in them, so what to do? Recently, the Python-Ideas mailing list had a thread about adding assignments to list comprehensions, which can simplify complicated comprehensions. In that thread, Stephan Houben pointed out that you can already get the same effect with a cute trick:for x in [17]:     do_something() # has the same effect as: x = 17 do_something() We can explicitly make a one-element list, and “iterate” over it to assign its value to a name. In my gargoyle, we get a and b as the two numbers in the chunk.The third loop is where we actually generate numbers. We’ll use range(), and we always want to start from the first number in the chunk. If there was a [...]

Coverage 4.5


Just out: v4.5.

There’s one new feature: configurator plug-ins, that let you run Python code at startup to set the configuration for coverage. This side-steps a requested feature to have different exclusion pragmas for different versions of Python.

People wanted to be able to say, this line of code is excluded from coverage when run under Python 3, but not under Python 2. That sounds simple enough, but then some wanted to be able to exclude for Python 3.5, but not 3.6. Or, excluded when running under PyPy, but not under CPython.

I could see this turning into a never-ending road of finer and finer differentiation. Next would be operating systems, or versions of Django, or, etc, etc, etc.

Rather than me building all that into coverage itself, now you can write your own plug-in that makes all those determinations, and sets the exclude pragmas as you like.

Python’s misleading readability


One of the things that has made Python successful is its readability. Code is clear and easy to understand. One of the reasons is that Python uses words for a few things that other languages use symbols for. But sometimes the readability is misleading. Beginners construct valid Python expressions that don’t do what they seem like they should do.Let’s say you want to know if your variable x is equal to 17. You could do:if x is 17: This might work. But then if you try:if name is "Ned": it doesn’t work? What!? Why not? It’s so clear.The problem is that “is” doesn’t check two values for equality, it checks if the left and right side are precisely the same object. But you can have two different string objects, each of which has the value “Ned”. You don’t use “is” to check equality, you use “==”:if name == "Ned": It’s not just strings: numbers can also do surprising things:>>> 1000 + 1 is 1001 False “x is 17” is more English-like than “x == 17”, but it isn’t right. This is one time that Python’s famed readability leads you to the wrong construct.Another example: you need to know if the answer was either “y” or “yes”, so you try this:if answer == "y" or "yes":     print("Thanks") and now your program doesn’t work. No matter what answer is, it prints “Thanks.” Why?The “or” operator is for combining boolean (true/false) expressions. The result is true if either of its operands is true. So your code is equivalent to:if (answer == "y") or ("yes"):     print("Thanks") If answer is “y”, then the if will be true. If answer isn’t “y”, then the or will consider the right-hand side, “yes”. Strings are true if they are not empty, so “yes” is always true. So the if condition will always be true, no matter what value answer has.The right ways to do this are:if answer == "y" or answer == "yes":     print("Thanks") or if you want to be fancier,if answer in {"y", "yes"}:     print("Thanks") (a list or a tuple would also work here instead of a set, though then you get into philosophical debates about how many data structures fit on the head of a pin.)Don’t get me wrong, I agree that Python is very readable. And every language has constructs that seem like they should work, but don’t. You have to study well, and be careful to use your chosen language properly. [...]



When I was in high school, I was not a diligent student of English. I would try to do the reading on the subway ride, and not very well. One morning, I was on page 38 when I got to my stop. I didn’t have a bookmark, and I didn’t like to turn down the corners of pages, so to remember my place, I thought about what was interesting about the number 38.

Hmm, 38 is twice a prime. It’s one more than 37, which is a prime. Oh, and 39 is three times a prime. Interesting: a sequence of three consecutive numbers that are respectively 1-times, 2-times, and 3-times a prime. BTW, it worked: to this day, I remember what page I was on!

Then a recent tweet brought back this interesting confluence: 2018 has the same property. 2017 is a prime, 2018 is twice a prime, and 2019 is three times a prime.

It turns out this property is rare. There are only eleven numbers less than 3000 like this: 14, 38, 158, 542, 878, 1202, 1382, 1622, 2018, 2558, 2858; and only 541 such numbers less than 1,000,000.

So 2018 is a truly unusual year. 2017 was difficult politically, but there are hopeful signs of a turning tide. Here’s to the year ahead, and fighting the good fights: personally, locally, and nationally.

Iter-tools for puzzles: oddity


It’s December, which means Advent of Code is running again. It provides a new two-part puzzle every day until Christmas. They are a lot of fun, and usually are algorithmic in nature.One of the things I like about the puzzles is they often lend themselves to writing unusual but general-purpose helpers. As I have said before, abstraction of iteration is a powerful and under-used feature of Python, so I enjoy exploring it when the opportunity arises.For yesterday’s puzzle I needed to find the one unusual value in an otherwise uniform list. This is the kind of thing that might be in itertools if itertools had about ten times more functions than it does now. Here was my definition of the needed function:def oddity(iterable, key=None):     """     Find the element that is different.     The iterable has at most one element different than the others. If a     `key` function is provided, it is a function used to extract a comparison     key from each element, otherwise the elements themselves are compared.     Two values are returned: the common comparison key, and the different     element.     If all of the elements are equal, then the returned different element is     None.  If there is more than one different element, an error is raised.     """ The challenge I set for myself was to implement this function in as general and useful a way as possible. The iterable might not be a list, it could be a generator, or some other iterable. There are edge cases to consider, like if there are more than two different values.If you want to take a look, My code is on GitHub (with tests, natch.) Fair warning: that repo has my solutions to all of the Advent of Code problems so far this year.One problem with my implementation: it stores all the values from the iterable. For the actual Advent of Code puzzle, that was fine, it only had to deal with less than 10 values. But how would you change the code so that it didn’t store them all?My code also assumes that the comparison values are hashable. What if you didn’t want to require that?Suppose the iterable could be infinite? This changes the definition somewhat. You can’t detect the case of all the values being the same, since there’s no such thing as “all” the values. And you can’t detect having more than two distinct values, since you’d have to read values forever on the possibility that it might happen. How would you change the code to handle infinite iterables?These are the kind of considerations you have to take into account to write truly general-purpose itertools functions. It’s an interesting programming exercise to work through each version would differ.BTW: it might be that there is a way to implement my oddity function with a clever combination of things already in itertools. If so, let me know! [...]

Bite-sized command line tools: pylintdb


One of the things I love about Python is the abundance of handy libraries to cobble together small but useful tools. At work we had a large pylint report, and I wanted to understand it better. In particular, I wanted to trace back to which commit had introduced the violations. I wrote to do the work.

Since we had a lot of violations (>5000!) I figured it would take some time to use git blame to find the commit for each line. I wanted a way to persist the progress through the lines. SQLite seemed like a good choice. It also would give me ad-hoc queryability, though to be honest, I didn’t even consider that at the time.

SQLite is part of the Python standard library, but there’s a third-party library that makes it super-convenient to use. Dataset lets you use a database without creating a schema or even model first. You just open a database, choose a table name, and then start writing dictionaries to it. It handles all the schema creation (or modification!) behind the scenes. Awesome.

These days, click is the tool of choice for command-line parsing, and other chores needed in the terminal. I used the progress bar functions. They aren’t perfect, but in only a few lines I had a workable indicator.

Other useful things from the Python standard library:

  • concurrent.futures for parallelizing the git blame work. It’s got a high-level “map” interface that did exactly what I needed without having to think about queues, threads, and so on.
  • subprocess.check_output does the subprocess thing people usually want: just run the command and give me the output.

pylintdb isn’t earth-shattering, it just does exactly what I needed in 120 lines with a minimum of fuss, thanks to dataset, click, and Python.

New design


I’ve just updated the design of this site. The goal was to make it responsive, so that it would work well on small screens, but I made other changes along the way. The body type is now serif rather than sans, and much larger. I made lots of other tweaks as I worked on pages.

Making a responsive design was fun: it meant working out mechanisms for the layout rather than just a static design.

Of course, it’s easy to get carried away. Take a look at what happens to my name in the header when the screen gets below 300 pixels: Ned Batchelder becomes nedbat to save space. This was accomplished with the help of a span with class “chelder”...

It took me a long time to make this design. I started it 15 months ago, but stopped work on it for more than a year. I picked it up again two weeks ago, and powered through the remaining work.

Behind the scenes, I changed only one thing: using Sass to generate the CSS. The rest is still as janky and difficult as always.

For comparison (and posterity), here is the design I just replaced. If anything seems amiss with the new design, just let me know.