EuroScipy 2015 pandas sprint

[jorisvandenbossche]

There will be a pandas sprint at EuroScipy (https://www.euroscipy.org/2015/sprints/) on Sunday the 30th of August.
The sprints will take place at the Seminar Centre of the Hauser Forum (very near to the conference venue) from 10am to 5pm.

This issue will be the landing page for contributors.

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

See our shiny new Contributing Guidelines & Instructions

Here are various issues that are open and would like your attention:

• Difficulty Novice
• Difficulty Intermediate
• Difficulty Advanced

Please comment on an issue if you are working on it.

Here is a link to the gitter dev chatroom.

How to prepare?

If you want to prepare the sprint, the most important is to set up a development environment: forking the pandas repo, cloning your fork locally, installing all development dependencies in an environment and testing that you can build pandas locally, ...
You can find some information about this in our contributing docs: http://pandas-docs.github.io/pandas-docs-travis/contributing.html

What to work on?

There is no plan to work on something specific, but the idea is that everybody can choose on something they would like to work, and that there is some guidance to do that (but of course, if some of the attendants want to work together on a specific topic, this is certainly possible!).

You can work on whatever you like, but to facilitate finding an appropriate issue, we have the 'Difficulty Novice / Intermediate / Advanced' labels.
I will also put some ideas of issues I would like to see tackled during the sprints here in the coming days.

Please comment on an issue if you are working on it.

[jreback]

@jorisvandenbossche awesome!

[jorisvandenbossche]

Some possible ideas that I would like to see tackled on the sprint (just some ideas, not restrictive, you are free to choose whatever you like!):

Ensuring correct examples in the docstrings with doctest

• First try to run the doctest, and start fixing those
• Enable the tests on travis

Writing a really basic introduction to pandas (which would also fit in the scipy-lecture-notes: scipy-lectures/scientific-python-lectures#147)

Contributing to the documentation

• Important for upcoming release (and easy!): updating docs with new sort methods: DOC: update docs to use new sort_values #10886
• Provide more examples in the docstring
• Consistent imports DOC: consistent imports ('import pandas as pd' et al) #9886
• Fix error that prevents documenting our Cython classes: DOC: documenting cython class (eg Timestamp): "cyfunction is not a python function" #5218
• many more ... See Docs label

Getting our ASV benchmark suite up to speed

• Recently the benchmark suite has been converted to ASV (which should support python 3 and windows), but there is still some clean-up / testing needed: PERF: follow up to ASV #10849

Do you use latex / DataFrame.to_latex? There are quite some, but rather easy bugs to fix!

• The list can be seen with the label IO LaTeX (perfect to work with a small group together on this, as these several issues can possibly interfere)

Familiar with rpy2?

• There are still some things that should be moved over there: DEPR/DOC: rpy deprecation #9602

Using the SQL import/export functions?

• Enhancement to add dtype keyword to read_sql_query: Add dtype keyword to read_sql_query to control per column dtypes. #10285
• Enhancement: support SQLAlchemy Select object in read_sql_query: Support sqlalchemy.Executable in pandas.read_sql(sql) #10846
• Enhancement (but needs some design thought): add get_schema as public function: Status of io.sql.get_schema as public function? #9960 (related: add index keyword to get_schema: ENH: support index=True for SQL io.sql.get_schema #9084)

Some other things:

• Lots of bugs, see the Difficulty Novice label

[qwhelan]

Anyone interested in digging into regressions identified by asv can reference my run data here (equivalent to asv preview): http://qwhelan.github.io/pandas_asv/

An explicit regression list is here: http://qwhelan.github.io/pandas_asv/#regressions

[jorisvandenbossche]

There are some other issues listed here as well: #8323 (from a previous sprint, so some of them can already be solved)