Summary - Githubissues

Overall, this project was incredible. The queries were super clear; the graphs were brilliantly labeled and correctly displayed; the code worked flawlessly and conformed to all of Python's best practices; and the PowerPoint was a pleasure to read. I did want to give a couple things that may help overtime. Fair warning, most of these are preferential recommendations. The first is with lambda operator, and I'll just use the following function as an example:

df['is_application'] = df.application_date.apply(lambda b: 'Application' if b != None else 'No Application')

Note, this function worked perfectly and completed the task at hand with ease. The only thing that could be changes is the if statement. Instead of if b != None, we could have used pandas native function notnull() to determine if the variable b was null or not. Here is an example:

df['is_application'] = df.application_date.apply(lambda b: 'Application' if pd.notnull(b) else 'No Application')

To be honest, both ways are 100% correct and there isn't much of a difference. Truly, the only difference is that notnull() is native to pandas so we can be certain it will always evaluate correctly for a DataFrame. But, honestly, either way is perfectly fine.

The next has to do with listing the columns we want to grab in our SQL statements. Again, this is more of a preferential thing, but it might be better to list them one at a time, and have each be on a separate line. Here is an example:

SELECT visits.first_name,
       visits.last_name,
       visits.visit_date,
       fitness_tests.fitness_test_date,
       applications.application_date,
       purchases.purchase_date
       ...

This way, the reader can easily let know which columns they are grabbing without having to read through various variables on a single line. But, again, this is more preference. The way it was initially implemented was perfectly acceptable and just as readable.

In any case, I just want to reiterate that this project was absolutely fantastic. From code to presentation, everything was done magnificently. Excellent job!

DeepPupper / intro-data-capstone-musclehub

Summary #6