

Pandas 0.15 has been released - jvm
http://pandas.pydata.org/pandas-docs/stable/whatsnew.htmlhttp://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-15-0-october-18-2014

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elliott34
I work in pandas 95% of my day doing data automation tasks, manipulating sql
queries, and data-munging for machine learning. It is literally life changing
for someone like me.

I used to program solely in R, but after discovering pandas I really have no
need to go back to R. My project workflow consists of several IPython
notebooks+pandas+sklearn.

Works extremely well in production, as in, on a flask web server, as well.

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datahipster
You ought to take a look at [https://pypi.python.org/pypi/ipython-
sql](https://pypi.python.org/pypi/ipython-sql). This, in conjunction with
IPython magic helpers specified in your IPython profile, can turn IPython into
a SQL client. Here's a good example:
[https://gist.github.com/slnovak/583e35083ebd42892fab](https://gist.github.com/slnovak/583e35083ebd42892fab)

~~~
b_friedland
Thanks for this, looks like it's going to be pretty handy.

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makmanalp
No discussion of the new features?

Categorical is awesome, and equivalent to R's c() iirc. This should make plots
easier in terms of automatically deciding whether to facet something, or
showing legends nicely etc.

The memory usage feature is super neat.

Also for those of us stuck with STATA, the to_stata() and read_stata() just
got much better.

I'm eagerly awaiting a numpy native NA value instead of np.NaN.

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Bootvis
I have some experience using Pandas but I'd love to read about peoples
experience using it in production or in large teams. Please share!

~~~
PudgePacket
It seems quite quick to do work on large datasets, I've found the
documentation lacking however.

There are no links in docs to the types that are being referenced, some types
do not have documentation, some documentation is the function header with no
other info ie no documentation, functions that take string formatting info eg
'5min' do not have their argument possibilities documented anywhere I can
find.

~~~
raymondh
Really? You found the documentation to be lacking.

FWIW, there are over 1500 pages in the docs including a short-over view,
tutorials, extensive feature coverage, and interaction with other tools:
[http://pandas.pydata.org/pandas-
docs/version/0.15.0/pandas.p...](http://pandas.pydata.org/pandas-
docs/version/0.15.0/pandas.pdf)

The docs may have some issues, but they certainly can't be characterized as
lacking.

~~~
Bootvis
In my eyes, you both make a valid point: yes there's plenty of documentation
but sometimes I just can't find what I'm looking for. I like how the book[1]
is structured, it really helped me but it isn't complete.

Don't get me wrong, I'm grateful for all the work and I know I haven't
contributed much but I think the online could be improved with more examples
and recipes.

Edit: There really is no excuse, getting started is easy[2].

[1]:
[http://shop.oreilly.com/product/0636920023784.do](http://shop.oreilly.com/product/0636920023784.do)

[2]:
[http://pandas.pydata.org/developers.html](http://pandas.pydata.org/developers.html)

~~~
EmlynC
Correct me if I'm wrong but perhaps you had the same issue as me. The
documentation is plentiful, lots of good examples and the book, similarly,
increases with a nice linear complexity from basic "how do I select a (cell |
row | column) ..." to full blown how do I do timeseries analysis on a
dataseries pulled in from a remote source.

The issue I had was not the documentation but the language of pandas mirrors
the language used in R (I think this is something Wes McKinney intentional
did) and it's the burden of all that new verbage that makes the documentation
harder to sift through. Some choice exampels; "melt", "stack/unstack" and
"reindex" — necessary, I grant you, so that functions can be aptly named and
in turn encapsulate vectorised procedures that are composable.

I found that the documentation was harder to search because I lacked the
domain language and the documentation, for better for worse, doesn't dawdle
with educating the reader about the verbage — worked examples often provide a
easier route. It reads like a mathematical proof rather than prose and I used
to think that the documentation was too terse but now I appreciate that
probably just succinct.

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japaget
Change log: [http://pandas.pydata.org/pandas-
docs/version/0.15.0/whatsnew...](http://pandas.pydata.org/pandas-
docs/version/0.15.0/whatsnew.html)

In particular, note that NumPy 1.7.0 or newer is required.

