There are tons of Python packages out there. So many that no one man or woman could possibly catch them all. PyPialone 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 pandas, scikit-learn, and numpy, 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!
Delorean 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 moment 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 Futurereferences.
from delorean importDelorean
EST ="US/Eastern"
d =Delorean(timezone=EST)
There’s a chance you haven’t heard of prettytable 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__.
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 importEnglishStemmer,SpanishStemmerEnglishStemmer().stemWord("Gregory")# GregoriSpanishStemmer().stemWord("amarillo")# amarill
Remember every time you wrote that web crawler for some specific purpose? Turns out somebody built it…and it’s called wget. Recursively download a website? Grab every image from a page? Sidestep cookie traces? Done, done, and done.
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 Python version comes with just about every feature you could ask for and is easy to use.
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.
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 in1.4 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 Bayesian Methods for Hackers and has made cameos on a lot of popular data science/python blogs, but has never received the cult following akin to scikit-learn.
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 find
find("/tmp")/tmp/foo
/tmp/foo/file1.json
/tmp/foo/file2.json
/tmp/foo/file3.json
/tmp/foo/bar/file3.json
Ranking in the top 10 of simplest libraries I’ve ever used (if you have 2-3 minutes, you can read through the source), fuzzywuzzy is a fuzzy string matching library built by the fine people at SeatGeek.
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 fuzz
fuzz.ratio("Hit me with your best shot","Hit me with your pet shark")# 85
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 importProgressBarimport time
pbar =ProgressBar(maxval=10)for i in range(1,11):
pbar.update(i)
time.sleep(1)
pbar.finish()# 60% |######################################################## |
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:
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 UUID standards 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 to the number of atoms in the universe.
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
BEGIN TRY
BEGIN TRAN
INSERT INTO
dbo.[User]
SELECT
us.UserName,
us.UserAddress,
us.UserPhone,
@fkScreenID
FROM
dbo.[User] as us
WHERE
UserID= @userID
COMMIT TRAN
END TRY
BEGIN CATCH
ROLLBACK TRAN
END
END CATCH //其他table的代码与此类似。添加Try Catch到整个SP执行块（Executing Block）会更好
SELECT items.subject,
items.last_mod_date
,l.description FROM msdb.dbo.sysmail_faileditems as items
INNER JOIN msdb.dbo.sysmail_event_log AS l
ON items.mailitem_id = l.mailitem_id
GO
结果类似于：
上面的错误描述为“No Such Host”错误。该错误通常发生在有一些SMTP服务器连接设置错了的时候。我们需要靠自己排除故障——重新检查设置认证信息，然后再试试。如果依然不能工作，那么就需要检查DNS服务器设置，再次重试配置。
