Overview In this project, we will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. We will write code to import the data and answer interesting questions about it by computing descriptive statistics. We will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. What Software Do I Need? To complete this project, the following software requirements apply: We should have Python 3, NumPy, and pandas installed Divvy is a bicycle sharing system in the City of Chicago and two adjacent suburbs (image: Wikipedia) Bike Share Data Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, We will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. We will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. The Datasets Randomly selected data for the first six months of 2017 are provided for all three cities. All three 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 Data for the first 10 rides in the new_york_city.csv file The original files are much larger and messier, and we don't need to download them, but they can be accessed here if We would like to see them (Chicago, New York City, Washington). These files had more columns and they differed in format in many cases. Some data wrangling has been performed to condense these files to the above core six columns to make our analysis and the evaluation of our Python skills more straightforward. In the Data Wrangling course that comes later in the Data Analyst Nanodegree program, students learn how to wrangle the dirtiest, messiest datasets, so don't worry, we won't miss out on learning this important skill! Statistics Computed We will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project,we will 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) The Files To answer these questions using Python, we will need to write a Python script. To help guide our work in this project, a template with helper code and comments is provided in a bikeshare.py file, and we will do our scripting in there also. We will need the three city dataset files too: chicago.csv new_york_city.csv washington.csv All four of these files are zipped up in the Bikeshare file in the resource tab in the sidebar on the left side of this page. We may download and open up that zip file to do our project work on our local machine. Some versions of this project also include a Project Workspace page in the classroom where the bikeshare.py file and the city dataset files are all included, and we can do all our work with them there. We may use the template provided in bikeshare.py to complete this project. We should feel free to change the template however we would like, as long as our code provides the statistics shown in the template, and allows a user to give input on which data they would like to see. An Interactive Experience The bikeshare.py file is set up as a script that takes in raw input to create an interactive experience in the terminal that answers questions about the dataset. The experience is interactive because depending on a user's input, the answers to the questions on the previous page will change! There are four questions that will change the answers: Would we like to see data for Chicago, New York, or Washington? Would we like to filter the data by month, day, or not at all? (If they chose month) Which month - January, February, March, April, May, or June? (If they chose day) Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? he answers to the questions above will determine the city and timeframe on which we would do data analysis. After filtering the dataset, users will see the statistical result of the data, and