dlab-berkeley/python-for-everything

Materials for teaching the Python for Everything workshop at UC Berkeley's D-lab

Python for Everything

=========================

This repository holds all of the necessary materials, code, and data for D-Lab's complete introduction to the Python Language.

If you are a student

You can download the contents of this repository with:

git clone https://github.com/dlab-berkeley/python-for-everything.git

or, by clicking the "Download Zip" button and then extracting the .zip file.

The instructor of this workshop series will lead you through the activities for each day.

For more Python resources, visit python.berkeley.edu.

If you are a D-Lab instructor

You'll see accumulated teaching notes and examples for each day's topics in the instructor folder. For your convenience, these are available as Jupyter notebooks, commented python files, and pdfs.

Note : like Python, class days are zero-indexed

For information on contributing to this repository, see CONTRIBUTING.md

If you are a D-Lab facilitator

The standard Drupal workshop descriptions and facetweet postings for this workshop series are in PUBLICITY.md

Description

• challenges/ : pytest challenges for each day of the series
• data/ : data necessary for interactive coding examples
• etc/ : examples of cron, cron.d, and credentials files
• instructor/ : teaching notes
• scripts/
  • generate_tables.py : for generating random tabular data
  • regenerate_documents.sh : for regenerating .py and .pdf files from .ipynb
  • simple.py : sample python script used in Day 0
  • twitter_bot.py : sample python script used in Day 2

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D-Lab == Data Intensive Social Science, For All!

python-for-everything's Issues

Day 02 Challenge A (HTML): Unit test assertion error

For the HTML parsing challenge, the unit test test_header() has a problematic assertion in this line.

• The length of a string joining all those HTML tags is 90, not 17 (see this gist). Instead, 17 is the length of the list of tags before joining as a string.
• The variable that the unit test looks for is named slightly differently from the challenge ('head' versus 'header').

Solution:

• Change the assertion to == 90 (or change the challenge to ask for a list instead of a string).
• Name both variables the same thing.

Add explicit half hour at end of each day to troubleshoot

Set expected length of token list in challenge to be 400

Test currently fails for intrepid students who remove punctuation from token list:

assert len(B.token_list) >= 670

Reconsider IDE

Please vote with a comment below -- here are your choices:

• Spyder
• Jupyter

Challenge modifications

• Simplify challenges into more, smaller tasks
• Add more exposition at top
• Add more inline comments explaining keywords and control structures

Rearrange materials into five days of instruction

Day 0

Fundamentally unchanged

Day 1

Data structures
Loops
File i/o

Day 2

Http requests
Html
Conditionals
Exceptions

Day 3

APIs
Tabular data
Statistics
Plotting

Day 4

Regular expressions
Text data
Topics / sentiment

Create separate Python for data science workshop

Add in knowledge integration tests as homework

Pandas reshaping example gives wonky result

regenerate_documents.sh not generating new .py files

Add plotting to day 3 materials

Day 2 challenges: wrong modules referenced in unit tests

When the order of the challenges was switched from (A_html, B_try) to (A_try, B_html), the corresponding import statements in the unit tests were not changed. For example, it still says import B_try as B. The unit tests won't run without updating this.

Remove old challenges from new master branch

TTC suggestions and mandatory markers in instructor notes

The time spent on any particular major topic in a day's worth of lessons can vary by a factor of 4 from instructor to instructor, and some instructors choose not to cover certain topics at all. This causes problems when knowledge necessary for future days in the course are not covered, either due to purposive omission or time constraints. It may help to have a list of required topics to be covered at the top of each day, with suggested times to completion at the top of each topic.

Suggested improvement for 04/test_B

Day 4 test B, here, could also check if students actually took the top 10 terms -- assert len(B.stop_list) == 10.

Add anaconda install instructions to website description

Specifically, many users did not prepend the path to Anaconda python, and were accessing their system python accidentally.

Explain logic behind using re.compile

Instruction Format

Hi all,

Apologies if this has already been discussed and I didn't take notice. I'm trying to prepare for teaching and need some guidelines to make it seamless for the students. Dillon, what are you planning for Day 0? Live coding with PyCharm? Are students cloning the github repo? Any guidance you have would be most appreciated.

Thanks, R

Reconsider IDE

• PyCharm
• Spyder
• Jupyter
• Light Table