- Kim Damalas, 20 Jan 2021
- using stats from Kaggle: trips.csv
Submission pull request: #1
Taking a tour into Data Science to learn a bit more about the field and tools used in this space
Answer the following questions/do the following tasks.
- What is the average trip duration for a borrowed bicycle?
- What’s the most common age of a bicycle-sharer?
- Given all the weather data here, find the average precipitation per month
- and the median precipitation.
- What’s the average number of bikes at a given bike station?
- When a bike station is modified, is it more likely that it’ll lose bikes or gain bikes? How do you know?
- Come up with 3 more questions that can be answered with this data set.
- this question
- this question
- this question
The user must have Python and all associated dependencies installed. The project is run inline using a Jupyter notebook
20 Jan: files set up; grid coded; first PR
Testing accomplished using inline testing. The program must pass the provided tests given in the file as well as any custom tests coded by the developer
def test():
def assert_equal(actual,expected):
assert actual == expected, f"Expected {expected} but got {actual}"
assert_equal(avg_trip, None)
assert_equal(most_common_age, None)
assert_equal(avg_precip_month, None)
assert_equal(median_precip, None)
assert_equal(avg_bikes_per_station, None)
assert_equal(lose_or_gain_bikes, None)
-- test Q8.1
-- test Q8.2
-- test Q8.3
print("Success!!!")
test()