Python argparse multiline help

  • Inline Multiline Strings
  • Pretty-printing JSON
  • Argparse template
  • Zipping Files
  • Creating Context Managers
  • Search and Replace with Named Groups in Python
  • Interactive Console for dev
  • HTTP GET with urllib
  • Converting a list of namedtuples to .csv
  • Executing a subprocess and capturing the output as text
  • Useful f-strings

This is just a collection of Python snippits that are too small for their own posts. All code is for Python 3.

Inline Multiline Strings

This is a quick post on inline multiline strings. I like to use the following style:

from textwrap import dedent def main(): query = dedent(“”” first line second line “””).strip() print(repr(query)) if __name__ == ‘__main__’: main() # ‘first linensecond line’

So it prints it without the preceding and trailing newlines and without the indentation to make it line up with the rest of the function

Pretty-printing JSON

json.dump(obj, sys.stdout, indent=2, sort_keys=True)


print(json.dumps(obj, indent=2, sort_keys=True))

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I kind of prefer the first version, even if it involves an extra sys import because it’s easy to change the dump to a file (though it’s not much harder to add the file argument to print either…).

with open(‘file.json’, ‘w’) as fp: json.dump(obj, fp, indent=2, sort_keys=True)

Argparse template

This code is for shorter one file scripts that need command line arguments with the argparse library.

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import argparse import sys __author__ = “Benjamin Kane” __version__ = “0.1.0” __doc__ = f””” <description> Examples: {sys.argv[0]} Help: Please see Benjamin Kane for help. Code at <repo> “”” def parse_args(*args, **kwargs): parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter ) # Use a file or stdin for an argument # parser.add_argument( ‘infile’, nargs=’?’, type=argparse.FileType(‘r’), default=sys.stdin, help=’Use a file or stdin’ ) return parser.parse_args(*args, **kwargs) def main(): args = parse_args() with args.infile: pass if __name__ == “__main__”: main()

For subcommands, I prefer to put something like subcommands = parser.add_subparsers(dest=”subcommand_name”, required=True) in parse_args, then add the commands to the new parser: subcommand1 = subcommands.add_parser(‘name’, help=’I need help’). This command can take arguments: subcommand1.add_argument(‘name’, …), and you can easily parse it with code like the following in main:

if args.subcommand_name == ‘name’: … # tada!

Zipping Files

The shutil.make_archive function is a bit hard to use. Here’s my notes on it and some code to erase partially zipped files on exceptions. This function works well with pathlib.Path.

try: # how params work: # change into root_dir # creating and adding base_dir to it # NOTE: not threadsafe! shutil.make_archive( base_name=base_name, format=’zip’, root_dir=root_dir,, dry_run=False, logger=logger ) # KeyboardInterrupt doesn’t inherit from Exception except (Exception, KeyboardInterrupt): logger.exception(f’Exception! Deleting {dest_path_zip}’) dest_path_zip.unlink() raise

Creating Context Managers

Add the following two methods to create a context manager for a class:

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This is useful when working with resources.

def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.clean()

Search and Replace with Named Groups in Python

Every once in a while (usually when making changes to config files or source code), a smart search and replace can save a lot of work. Python’s re.sub method is useful, but can be confusing to understand. Here’s a common use case for it:

res = re.sub(r’bob was (?P<loc>w+)’, # search pattern r’bob was seen at g<loc>’, # replacement pattern r’bob was in bed’) # “target” string

This very contrived example has several useful concepts:

  • The search pattern can have named patterns to find with the following schema: (?P<name_of_group>pattern). In this example, the name_of_group is loc and the pattern is w+. w refers to a non-whitespace Unicode character.
  • The replacement pattern can reference captured patterns (referred to as groups), with g<name>. In this case the name is loc (which matches the search pattern we named loc).
  • The “target” string is just the string to run through this regex machinery and res is what comes out of it.

re.sub is even more powerful (see the docs linked above), but I think this example covers the most common case for me.

Interactive Console for dev

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Sometimes when writing code, it’s super helpful to just open an interpreter with the current variables so you can play with them. For example, say I have the following function:

def get_my_age(): birth_date = datetime.datetime(1990, 1, 1) today = difference = today – birth_date # now what? How do I want to format this? get_my_age()

Date formatting can get complicated, and the format specifiers can be hard to remember (tools can help)). However, you can open a console to figure it out by adding the following lines inside the function (underneath # now what? in the above).

import code code.interact(local=locals())

Then when you run the function, it opens an interactive console at that line and you can play with defined variables directly. Once you have something you like, you can remove that code and use what you made in it’s place. This can also be handy when debugging and you want to inspect variables (though also check out pdb).

HTTP GET with urllib

Sometimes you just want to GET a URL and you don’t want to install requests. urllib is confusing, but here’s how I do that for simple cases:

import json import urllib.request headers = {“Content-Type”: “application/json”} req = urllib.request.Request(“”, headers=headers) with urllib.request.urlopen(req) as resp: # guess UTF-8 if no encoding found encoding =“utf-8″) content = return_code = resp.getcode() headers = if return_code != 200: raise ValueError(f”Error for fqdn: {fqdn}”) json_data = json.loads(content.decode(encoding))

Converting a list of namedtuples to .csv

Python’s collections.namedtuple / typing.NamedTuple library interacts really nicely with the csv module. Check this out:

import csv import sys import typing class Student(typing.NamedTuple): first_name: str last_name: str age: int students = [ Student(“Bob”, “Smith”, 10), Student(“Rachel”, “Kilkenny”, 14), Student(“Martin”, “Gonzalez”, 16), ] writer = csv.DictWriter(sys.stdout, fieldnames=Student._fields) writer.writeheader() writer.writerows([s._asdict() for s in students])

Executing a subprocess and capturing the output as text

from subprocess import run res = run([“echo”, “hi”], capture_output=True, encoding=”utf-8″, text=True)

Useful f-strings

a = “bob” print(f”{a}”) # bob print(f”{a!r}”) # ‘bob’ print(f”{a=}”) # a=’bob’ print(f”{a = }”) # a = ‘bob’

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