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[Python/data] Bike Sharing

In this project, you will make use of Python to explore data related to bike share systems for the three major cities in the United States - Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. You will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

Instructions

  • Complete the "to do" lines in the template python file "bike_investigation.py"
  • Push your code to a private Github repository
  • Document what you've done in the code and with a README

The subject

You will investigate about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, you'll 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 the 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)

Guidelines

To answer these questions using Python, you will need to write a Python script. To help guide your work in this project, a template with helper code and comments is provided in a bike_investigation.py file, and you will do your scripting in there also. You will need the three city dataset files that are in the ZIP file éBike_raw_data" : chicago.csv new_york_city.csv washington.csv

Data sets

Randomly selected data from https://www.capitalbikeshare.com/system-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 columns:

  • Start Time (e.g., 23/06/2017 15:09:32)
  • End Time (e.g., 23/06/2017 15:14:53)
  • Trip Duration (in seconds - e.g., 321)
  • Start Station (e.g., Wood St & Hubbard St)
  • End Station (e.g., Damen Ave & Chicago Ave)
  • User Type (Subscriber or Customer)

Evaluation

  • Quality of the code
  • Scalability of the algorithm
  • Usage of good practices and modern Python