Date created
18th of June, 2020.
Project Title
Exploring US Bikeshare Data
Description
In this project, I wrote Python code to import US bike share data and answer interesting questions about it by computing descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics some of which are:
-
1 Popular times of travel (i.e., occurs most often in the start time)
- most common month
- most common day of week
- most common hour of day
-
2 Popular stations and trip
- most common start station
- most common end station
- most common trip from start to end (i.e., most frequent combination of start station and end station)
-
3 Trip duration
- total travel time
- average travel time
-
4 User info
- counts of each user type
- counts of each gender (only available for NYC and Chicago)
- earliest, most recent, most common year of birth (only available for NYC and Chicago)
Files used
In this project, dataset from https://www.motivateco.com/ was used. Randomly selected data for the first six months of 2017 were provided for the cities of Chicago, New York City and Washington:
- chicago.csv
- new_york_city.csv
- washington.csv
Credits
Udacitys' Programming for Data Science Nanodegree