With the help of Python I have explored data related to bike share systems for three major cities in the United States: Chicago, New York City, and Washington.
The following descriptive statistics were computed by using data of bike share systems in Chicago, New York City, and Washington:
- Popular times of travel
- most common month
- most common day of week
- most common hour of day
- Popular stations and trip
- most common start station
- most common end station
- most frequent combination of start station and end station trip
- Trip duration
- total travel time
- average travel time
- User info
- counts of each user type
- counts of each gender (it was only available for NYC and Chicago)
- earliest, most recent, most common year of birth (it was only available for NYC and Chicago)
My script prompts users if they want to see 5 lines of raw data. It continues iterating these prompts and displaying the next 5 lines of raw data at each iteration. The program stops when users say 'no' or there is no more raw data to display.
The following files of data were used during the project:
- chicago.csv
- new_york_city.csv
- washington.csv
The following software were applied:
- Python 3 (NumPy, and pandas were installed using Anaconda)
- PyCharm
- Project Workspace to complete and submit my project by Udacity
The following materials inspired me throughout the writing of my project:
The project was created on June 12, 2023.