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Description
Since pythonwhat v2.16.0, different SCT functions do some assertions and verifications to figure out whether the function is appropriately used. You can see it in the CHANGELOG here.
Some of these assertions are only done for courses whose course image contains the PYTHONWHAT_V2_ONLY env variable (more recent courses), because otherwise these changes would break 55 exercises that contain SCTs that violate these rules:
course_id chapter_id id number title
1 998 2244 18929 3 Loading a pickled file
2 998 2284 18950 8 Filtering your database records using SQL's WHERE
3 998 2284 18951 14 The power of SQL lies in relationships between tables: INNER JOIN
4 998 2284 19468 7 Customizing the Hello World of SQL Queries
5 998 2284 19469 9 Ordering your SQL records with ORDER BY
6 998 2284 19793 11 Pandas and The Hello World of SQL Queries!
7 998 2372 20216 12 Turning a webpage into data using BeautifulSoup: getting the hyperlinks
8 998 2388 20437 13 Load and explore your Twitter data
9 998 2388 20441 15 A little bit of Twitter text analysis
10 998 2388 20834 3 Loading and exploring a JSON
11 1115 2469 21424 3 Filter data selected from a Table - Simple
12 1115 2470 21436 3 Calculating a Difference between Two Columns
13 1115 2470 21442 10 Using alias to handle same table joined queries
14 1115 2471 21450 7 Loading a CSV into a Table
15 1115 2471 21452 9 Updating individual records
16 1115 2472 21463 5 Reading the Data from the CSV
17 1115 2472 21466 8 Build a Query to Determine the Average Age by Population
18 1115 2472 21467 9 Build a Query to Determine the Percentage of Population by Gender and State
19 1115 2472 21468 10 Build a Query to Determine the Difference by State from the 2000 and 2008 Censuses
20 1115 2470 33053 11 Leveraging Functions and Group_bys with Hierarchical Data
21 1531 3844 38857 8 Using zip
22 1531 3845 38874 9 Dict comprehensions
23 1550 3936 40011 8 How is it optimal?
24 1550 3939 40032 8 Hypothesis test on Pearson correlation
25 1550 3940 40049 17 Is beak depth heritable at all in G. scandens?
26 1550 3937 40360 4 Visualizing bootstrap samples
27 1550 3938 47093 3 Visualizing permutation sampling
28 1550 3936 63252 12 Linear regression on all Anscombe data
29 1606 4135 42717 12 Turning a webpage into data using BeautifulSoup: getting the hyperlinks
30 1606 4136 42720 3 Loading and exploring a JSON
31 1606 4140 42795 6 A little bit of Twitter text analysis
32 1607 4138 42757 3 Loading a pickled file
33 1607 4139 42780 8 Customizing the Hello World of SQL Queries
34 1607 4139 42781 9 Filtering your database records using SQL's WHERE
35 1607 4139 42782 10 Ordering your SQL records with ORDER BY
36 1639 4284 59225 10 Sunny or cloudy
37 1681 4403 62438 10 Concatenating vertically to get MultiIndexed rows
38 1681 4403 62441 13 Concatenating DataFrames from a dict
39 1681 4403 92426 8 Reading multiple files to build a DataFrame
40 1822 4794 51256 4 Finding open triangles
41 2072 5698 63534 6 How many clusters of grain?
42 2533 7496 86163 9 Encode the labels as categorical variables
43 2533 7496 95226 10 Counting unique labels
44 3629 10390 119216 9 Regex with NLTK tokenization
45 3629 10390 119217 10 Non-ascii tokenization
46 3629 10390 119219 12 Charting practice
47 3679 10556 121072 8 Using regularization in XGBoost
48 3679 10557 121076 3 Tuning the number of boosting rounds
49 3679 10557 227050 6 Tuning eta
50 3679 10557 227051 7 Tuning max_depth
51 3679 10557 227052 8 Tuning colsample_bytree
52 4299 13274 151880 8 Filtering on a phrase
53 6221 19972 258084 11 Balancing classes
54 6221 19972 258085 12 Comparison of Employee attrition models
55 7032 27285 321296 14 Detect edges (2)
This issue is a reminder that we have to rewrite the SCTs for these exercises because they are smelly. As soon as that is done, we can remove this v2_only() check and do the assertions for all SCTs, independent of the PYTHONWHAT_V2_ONLY env variable.
How I came up with this list
- Make a branch on
pythonwhatthat does not do theif v2_only()check - Install this branch of pythonwhat in
docker-python-shared(usingpip install git+...) and tag it (with a release candidate format, as it is never the intention to deploy) - This makes the validator go through all exercises with this hypothetical shared image, and spit out the exercises where it goes wrong.
- Finally, you can use this script to programmatically a list of exercise ids from the validator endpoint and their corresponding chapters and courses.