#pandasSprint at PyData London

The 10th of March, around 500 people in 30 different locations worldwide, contributed together to improve pandas documentation.

While many great contributions were made, pandas has around 1,200 API pages. So, there is still some work to do.

In this sprint, each pair of participants (or each participant if you prefer not to pair-program) will work on making pandas documentation better, or other fixes.

By the end of the sprint, you should be able to convert a page like this:

• https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.kde.html

In a page like this:

• http://pandas-docs.github.io/pandas-docs-travis/generated/pandas.DataFrame.plot.kde.html

Before the sprint, it’s recommended to:

• Bring your own laptop
• Set up your environment
• Join the pandas-sprint channel in PyData London slack

Docstrings to improve:

• pandas.Series.str.slice (see pandas.Sereies.str.slice_replace for reference)
• pandas.Series.str.rsplit (see pandas.Series.str.split for reference)
• pandas.Series.str.contains (see pandas.Series.str.startswith/endwith for reference)
• pandas.Series.str.zfill
• pandas.Series.str.repeat
• pandas.Series.str.match
• pandas.Series.str.decode / pandas.Series.str.encode
• pandas.Series.str.ljust / pandas.Series.str.rjust
• pandas.Series.str.strip (shared docstring lstrip, rstrip), difficulty medium
• pandas.Seeries.str.ismethods (shared docstring between isalnum, isalpha, isdigit, isspace, islower, isupper, istitle, isnumeric, isdecimal), difficulty medium

Other tickets to work on:

• Confusing docstring https://github.com/pandas-dev/pandas/issues/19411
• Test splitting https://github.com/pandas-dev/pandas/issues/18644
• Test splitting https://github.com/pandas-dev/pandas/issues/18498
• Groupby documentation https://github.com/pandas-dev/pandas/issues/17775
• Reusing docstrings isna… https://github.com/pandas-dev/pandas/issues/17096
• Error message https://github.com/pandas-dev/pandas/issues/13871

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.

More information about Pandas: https://pandas.pydata.org/

Set up instructions

Please follow the instruction in this link: https://python-sprints.github.io/pandas/guide/index.html