Community Data Science Workshops (Spring 2014)/Saturday May 31st lecture: Difference between revisions

no edit summary
imported>Mako
No edit summary
imported>Mako
No edit summary
 
(11 intermediate revisions by the same user not shown)
Line 1:
{{CDSW Moved}}
 
== Material for the lecture ==
 
For the lecture, you really will need two files. Download both of these to your computer by using right or control click on the link and then using ''Save as'' or ''Save link as''. Keep track of where you put the files:
 
* http://mako.cc/ teaching/2014/cdsw/build_hpwp_dataset.py
* http://makonada.cccom.washington.edu/~mako/hp_wiki.csv
 
 
== Overview of the day ==
 
* Lecture
** New tools!
** Our philosophy around data visualization: use the tools you have
** We're going to walk through some analysis of edits to Harry Potter in Wikipedia, start to finish
** We'll focus on manipulating data in Python
Line 28 ⟶ 30:
** break
** string.join()
* My philosophy about data analysis: ''use the tools you have''
* Walk-through of <code>get_hpwp_dataset.py</code>
* Look at dataset with <code>more</code> and/or in spreadsheet
* Load data into Python
** review of opening files
** csv module and and csv.reader() function
** csv.DictReader()
* Basic counting
** Answer question: ''What proportion of edits to Wikipedia Harry Potter articles are minor?''
*** Count the number of minor edits and calculate proportion
** Answer question: ''What proportion of edits to Wikipedia Harry Potter articles are made by "anonymous" contributors?''
*** Count the number of anonymous edits and calculate proportion
 
We mostly worked on these questions in the afternoon:
 
* More advanced counting
** Answer question: ''What are the most edited articles on Harry Potter?''
*** Count the number of edits per articles
** Answer question: ''Who are the most active editors on articles in Harry Potter?''
*** Count the number of edits per user
* Looking at time series data
** "Bin" data by day to generate the trend line
* Exporting and visualizing data
** Export dataset on edits over time
** Export dataset on articles over users
** Load data into Google Docs
Anonymous user