kimmyd70 / bike-stats

Jupyter notebook analyzing Seattle bike trip data from Kaggle

Data Analysis with Pandas; seattle-401pyn2

Seattle Bike Trip Stats

• Kim Damalas, 20 Jan 2021
• using stats from Kaggle: trips.csv

Submission pull request: #1

Description

---

Taking a tour into Data Science to learn a bit more about the field and tools used in this space

---

Feature Tasks and Requirements

---

Answer the following questions/do the following tasks.

1. What is the average trip duration for a borrowed bicycle?
2. What’s the most common age of a bicycle-sharer?
3. Given all the weather data here, find the average precipitation per month
   • and the median precipitation.
4. What’s the average number of bikes at a given bike station?
5. When a bike station is modified, is it more likely that it’ll lose bikes or gain bikes? How do you know?
6. Come up with 3 more questions that can be answered with this data set.
   • this question
   • this question
   • this question

---

Configuration and Technologies

---

The user must have Python and all associated dependencies installed. The project is run inline using a Jupyter notebook

---

Changes

---

20 Jan: files set up; grid coded; first PR

---

Testing

---

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()

---