Description
Question 0 – Code review warmup \[10 points\]
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In this question, you will interpret a code snippet you did not write and then write a “code review” for that snippet.
### Code Reviews
Many organizations that produce software utilize [code reviews](https://en.wikipedia.org/wiki/Code_review) to help mantain a high quality code base. A good code review should address the following questions:
1. Does the code work? Does it do what it is supposed to?
2. How is the style of the code? Does it follow the style guidelines?
3. Is the code clearly structured and easy to understand?
4. Is the code efficient? If clarity is sacrificed for efficiency, are there comments that help to alleviate this?
In addition, a good code review is kind to the author. Comments are framed as suggestions to the author and written in a positive, respectful, tone. Comments address the good qualities of the code as well as any areas for improvement.
Finally, a good code review begins with the most essential aspects related to the four questions above. More minor items – called ‘nitpicks’ – are clearly labeled as such and kept to a minimum.
Here are two optional readings about code reviews:
1. [Code Review for Data Science](https://medium.com/apteo/code-reviewing-data-science-work-774747248e33),
2. [How to Make Good Code Reviews Better](https://stackoverflow.blog/2019/09/30/how-to-make-good-code-reviews-better/).
### Code snippet
sample_list = [(1, 3, 5), (0, 1, 2), (1, 9, 8)]
op = []
for m in range(len(sample_list)):
li = [sample_list[m]]
for n in range(len(sample_list)):
if (sample_list[m][0] == sample_list[n][0] and
sample_list[m][3] != sample_list[n][3]):
li.append(sample_list[n])
op.append(sorted(li, key=lambda dd: dd[3], reverse=True)[0])
res = list(set(op))
### Instructions
1. Concisely describe what task the code above accomplishes. Say _what_ it does (in total) and not _how_ it accomplishes it. You may wish to understand the snippet step-by-step, but your description should _not_ state each step individually.
2. Write a short code review that offers 3-5 (no more) concrete suggestions to make the snippent more efficient, literate (easier to read), or “pythonic”. Focus your suggestions on concepts or principles that would help the author of this code snippet write better code in the future.
Question 1 – List of Tuples \[20 points\]
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Write a function that uses NumPy and a list comprehension to generate a random list of `n` k-tuples containing integers ranging from `low` to `high`. Choose an appropriate name for your function, and reasonable default values for k, `low`, and `high`.
Use `assert` to test that your function returns a list of tuples.
Question 2 – Refactor the Snippet \[40 points\]
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In this question, you will write functions to accomplish the goal you concisely described in part “a” of the warm up.
1. Encapsulate the code snippet from the warmup into a function that parameterizes the role of `0` and `3` and is otherwise unchanged. Choose appropriate names for these paramters.
2. Write an improved version of the function form part a that implements the suggestions from the code review you wrote in part b of the warmup.
3. Write a function from scratch to accomplish the same task as the previous two parts. Your solution should traverse the input list of tuples no more than twice. Hint: consider using a dictionary or a default dictionary in your solution.
4. Use the function you wrote in question 1 to generate a list of tuples as input(s), run and summarize a small Monte Carlo study comparing the execution times of the three functions above (a-c).
Question 3 – \[30 points\]
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In this question you will use Pandas to read, clean, and append several data files from the National Health and Nutrition Examination Survey [NHANES](https://www.cdc.gov/nchs/nhanes/index.htm). We will use the data you prepare in this question as the starting point for analyses in one or more future problem sets. For this problem, you should use the four cohorts spanning the years 2011-2018. You can find links to different NHANES cohorts [here](https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).
1. Use Python and Pandas to read and append the demographic datasets keeping only columns containing the unique ids (SEQN), age (RIDAGEYR), race and ethnicity (RIDRETH3), education (DMDEDUC2), and marital status (DMDMARTL), along with the following variables related to the survey weighting: (RIDSTATR, SDMVPSU, SDMVSTRA, WTMEC2YR, WTINT2YR). Add an additional column identifying to which cohort each case belongs. Rename the columns with literate variable names using all lower case and convert each column to an appropriate type. Finally, save the resulting data frame to a serialized “round-trip” format of your choosing (e.g. pickle, feather, or parquet).
2. Repeat part a for the oral health and dentition data (OHXDEN\_\*.XPT) retaining the following variables: SEQN, OHDDESTS, tooth counts (OHXxxTC), and coronal cavities (OHXxxCTC).
3. In your notebook, report the number of cases there are in the two datasets above.
Hint: Use `pd.read_sas()` to import files with the `.XPT` (“SAS transport”) extension.