11 Python Libraries You Might Not Know
by Greg | January 20, 2015
There are tons of Python packages out there. So many that no one man or woman could possibly catch them all. alone has over 47,000 packages listed!
Recently, with so many data scientists making the switch to Python, I couldn't help but think that while they're getting some of the great benefits of , , and , they're missing out on some older yet equally helpful Python libraries.
In this post, I'm going to highlight some lesser-known libraries. Even you experienced Pythonistas should take a look, there might be one or two in there you've never seen!
1)
is a really cool date/time library. Apart from having a sweet name, it's one of the more natural feeling date/time munging libraries I've used in Python. It's sort of like in javascript, except I laugh every time I import it. The docs are also good and in addition to being technically helpful, they also make countless Back to the Future references.
from delorean import Delorean EST = "US/Eastern" d = Delorean(timezone=EST)
2)
There's a chance you haven't heard of because it's listed on GoogleCode, which is basically the coding equivalent of Siberia.
Despite being exiled to a cold, snowy and desolate place, prettytable is great for constructing output that looks good in the terminal or in the browser. So if you're working on a new plug-in for the IPython Notebook, check out prettytable for your HTML __repr__.
from prettytable import PrettyTable table = PrettyTable(["animal", "ferocity"]) table.add_row(["wolverine", 100]) table.add_row(["grizzly", 87]) table.add_row(["Rabbit of Caerbannog", 110]) table.add_row(["cat", -1]) table.add_row(["platypus", 23]) table.add_row(["dolphin", 63]) table.add_row(["albatross", 44]) table.sort_key("ferocity") table.reversesort = True +----------------------+----------+ | animal | ferocity | +----------------------+----------+ | Rabbit of Caerbannog | 110 | | wolverine | 100 | | grizzly | 87 | | dolphin | 63 | | albatross | 44 | | platypus | 23 | | cat | -1 | +----------------------+----------+ 3)
Ok so the first time I installed snowballstemmer, it was because I thought the name was cool. But it's actually a pretty slick little library. snowballstemmer will stem words in 15 different languages and also comes with a porter stemmer to boot.
from snowballstemmer import EnglishStemmer, SpanishStemmer EnglishStemmer().stemWord("Gregory") # Gregori SpanishStemmer().stemWord("amarillo") # amarill 4)
Remember every time you wrote that web crawler for some specific purpose? Turns out somebody built it...and it's called . Recursively download a website? Grab every image from a page? Sidestep cookie traces? Done, done, and done.
even says it himself
First up is Kirkland, they keep everything open and allow indexes on their apache configuration, so a little wget magic is enough to download the entire Kirkland facebook. Kid stuff!
The comes with just about every feature you could ask for and is easy to use.
import wgetwget.download("http://www.cnn.com/") # 100% [............................................................................] 280385 / 280385 Note that another option for linux and osx users would be to use do: from sh import wget. However the Python wget module does have a better argument handline.
5)
I'm not sure how PyMC gets left out of the mix so often. scikit-learn seems to be everyone's darling (as it should, it's fantastic), but in my opinion, not enough love is given to PyMC.
from pymc.examples import disaster_model from pymc import MCMC M = MCMC(disaster_model) M.sample(iter=10000, burn=1000, thin=10) [-----------------100%-----------------] 10000 of 10000 complete in 1.4 sec
If you don't already know it, PyMC is a library for doing Bayesian analysis. It's featured heavily in Cam Davidson-Pilon's and has made cameos on a lot of popular data science/python blogs, but has never received the cult following akin to scikit-learn.
6)
I can't risk you leaving this page and not knowing about sh. sh lets you import shell commands into Python as functions. It's super useful for doing things that are easy in bash but you can't remember how to do in Python (i.e. recursively searching for files).
from sh import findfind("/tmp") /tmp/foo /tmp/foo/file1.json /tmp/foo/file2.json /tmp/foo/file3.json /tmp/foo/bar/file3.json 7)
Ranking in the top 10 of simplest libraries I've ever used (if you have 2-3 minutes, you can ), fuzzywuzzy is a fuzzy string matching library built by the fine people at .
fuzzywuzzy implements things like string comparison ratios, token ratios, and plenty of other matching metrics. It's great for creating feature vectors or matching up records in different databases.
from fuzzywuzzy import fuzzfuzz.ratio("Hit me with your best shot", "Hit me with your pet shark") # 85 8)
You know those scripts you have where you do a print "still going..." in that giant mess of a for loop you call your __main__? Yeah well instead of doing that, why don't you step up your game and start using progressbar?
progressbar does pretty much exactly what you think it does...makes progress bars. And while this isn't exactly a data science specific activity, it does put a nice touch on those extra long running scripts.
Alas, as another GoogleCode outcast, it's not getting much love (the docs have 2 spaces for indents...2!!!). Do what's right and give it a good ole pip install.
from progressbar import ProgressBar import time pbar = ProgressBar(maxval=10) for i in range(1, 11): pbar.update(i) time.sleep(1) pbar.finish() # 60% |######################################################## |
9)
So while you're making your logs have nice progress bars, why not also make them colorful! It can actually be helpful for reminding yourself when things are going horribly wrong.
colorama is super easy to use. Just pop it into your scripts and add any text you want to print to a color:
10)
I'm of the mind that there are really only a few tools one needs in programming: hashing, key/value stores, and universally unique ids. uuid is the built in Python UUID library. It implements versions 1, 3, 4, and 5 of the and is really handy for doing things like...err...ensuring uniqueness.
That might sound silly, but how many times have you had records for a marketing campaign, or an e-mail drop and you want to make sure everyone gets their own promo code or id number?
And if you're worried about running out of ids, then fear not! The number of UUIDs you can generate is comparable .
import uuidprint uuid.uuid4() # e7bafa3d-274e-4b0a-b9cc-d898957b4b61
11)
Shameless self-promotion here, bashplotlib is one of my creations. It lets you plot histograms and scatterplots using stdin. So while you might not find it replacing ggplot or matplotlib as your everyday plotting library, the novelty value is quite high. At the very least, use it as a way to spruce up your logs a bit.
$ pip install bashplotlib$ scatter --file data/texas.txt --pch x