Description: Explore data related to bike share systems for three major cities in the United States-Chicago, New York City, and Washington. All three Cities of the data files contain the same core six (6) columns:
- Start Time (e.g., 2017-01-01 00:07:57)
- End Time (e.g., 2017-01-01 00:20:53)
- Trip Duration (in seconds - e.g., 776)
- Start Station (e.g., Broadway & Barry Ave)
- End Station (e.g., Sedgwick St & North Ave)
- User Type (Subscriber or Customer)
The Chicago and New York City files also have the following two columns:
- Gender
- Birth Year
- Writing code to import the data.
- Answer interesting questions about the data by computing descriptive statistics.
- writing a script that takes in raw input to create an interactive experience in the terminal to present these statistics.
The data set of each cities {United States-Chicago, New York City, and Washington} are available in Kaggle through this link: https://www.kaggle.com/datasets/mohebmhanna/udacity-us-bikeshare-data
The following software requirements needed to complete the project:
- You should have Python 3, NumPy, and pandas installed.
- A text editor.
- A terminal application.
By using the softwares mentioned above, write code to provide the following information:
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)
- Python and it's libraries.
- Descriptive statistics.
Email: [email protected]