Editing Community Data Science Course (Spring 2015)/Day 5 Lecture
From CommunityData
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* My philosophy about data analysis: ''use the tools you have'' | * My philosophy about data analysis: ''use the tools you have'' | ||
* | * Four things in Python I have to teach you: | ||
** while loops | |||
*** infinite loops | |||
*** loops with a greater than or less than | |||
** break / continue | |||
** string.join() | ** string.join() | ||
** defining your own functions with <code>def foo(argument):</code> | ** defining your own functions with <code>def foo(argument):</code> | ||
* Walk-through of <code>get_hpwp_dataset.py</code> | * Walk-through of <code>get_hpwp_dataset.py</code> | ||
* Look at dataset with <code>more</code> and/or in spreadsheet | * Look at dataset with <code>more</code> and/or in spreadsheet | ||
* Load data into Python | |||
** review of opening files | ** review of opening files | ||
*** we can also open them for reading | *** we can also open them for reading | ||
** csv module and and csv.reader() function | ** csv module and and csv.reader() function | ||
** csv.DictReader() | ** 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 | |||
* 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 | |||
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 | |||
