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Similar to others, I was able to match up the first 2 answers, but the 3rd is off. Perhaps the instructions need to be clarified a bit!
My goals for this challenge were to use some tools I don't typically use and to embrace AMP. My answers are also a little different, though I think I can explain why.
I also noticed that the tmdb_5000_movies.csv threw some import errors where 3 records had new lines that tripped up the import. I updated the .csv file so there wouldn't be any import issues (which I did outside of Alteryx and honestly feels like "cheating" but all is fair in love and war).
-- For Task 1, I used Dense Ranking so it ranks equal budget amounts the same. There's a few movies with a budget of 250,000,000 and I couldn't figure out why the posted solution chose the 2 it did for what would normally be place #9 and place #10. So my solution provides all of the $250,000,000 budgets which is 16 movies instead of 10.
-- My Task 2 matches the posted solution. Yay! I did notice that if I tried to sort within the Rank tools, it got tripped up in these examples. Not sure why? That's why I did a regular old sort tool first.
-- My Task 3 is way way different than the posted solution and other solutions I looked at. I included the code and language, even ones that aren't the typical A-Z characters. So my result is a whopping 48 languages vs the 27 in the posted solution.
Interesting exploration. Questions 1 & 2 are straight forward. Question 3 was more subjective, especially relative to the expected results. I, like others, did not come up with the result, but I think I understand why (see below).
First, here’s my workflow – it includes an exploration “leg” as I was attempting to account for why there were a variety of answers:
Exploring further into the languages, there are two fields that come out a JSON Parse (which in my opinion is easier than using REGEX). There appears to be a language_code and the language_name. The problem with the name is:
- There are blanks
- There are multiple name values per code
- There are special characters
- There are characters that are expressed in Unicode
The REGEX ("name":\s"\w+") path that some have taken does not recognize the Unicode strings. This is why, I believe, they (and the Author) are getting lower numbers. But there are, in fact, more languages when you allow the JSON Parse to do it’s thing.
This led me to conclude that using the language_code was more thorough and encompassing. This took care of blank values or when name values might be spelled in different ways. See numbered examples 1 and 2 for values that came from Unicode. Example numbers 2 and 3 for multiple values per language_code.
Unicode examples:
JSON Parsed Values Derivatives:
Anyway, fun challenge, and hope this helps others determine if they want to land on the expected result versus what I’m concluding is the “spirit of the ask.”
-Jay
