Python_Panda Project

timer Asked: May 22nd, 2018
account_balance_wallet $20

Question Description


In this project, you 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. 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.

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, you 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. You 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 you don't need to download them, but they can be accessed here if you'd 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 your analysis and the evaluation of your 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, you won't miss out on learning this important skill!

Statistics Computed

You will learn 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 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, 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 file, and you will do your scripting in there also. You 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. You may download and open up that zip file to do your project work on your local machine.

Some versions of this project also include a Project Workspace page in the classroom where the file and the city dataset files are all included, and you can do all your work with them there.

Unformatted Attachment Preview Explore US Bikeshare Data Code Quality CRITERIA MEETS SPECIFICATIONS Functionality of code All code cells can be run without error. Choice of data types and structures Appropriate data types (e.g. strings, floats) and data Use of loops and conditional statements Loops and conditional statements are used to proces Use of packages Packages are used to carry out advanced tasks. Use of functions Functions are used to reduce repetitive code. Use of good coding practices Docstrings, comments, and variable names enable r Script and Questions CRITERIA MEETS SPECIFICATIONS Solicit and handle raw user input Raw input is solicited and handled correctly to guid Use descriptive statistics to answer questions about the data Descriptive statistics are correctly computed and us Suggestions to Make Your Project Stand Out! • • • Change the structure of to make the code more efficient or in better style. Ask and answer additional questions about the data beyond the questions already provided. Make the interactive experience wow-worthy! Add images, make it into a web app, etc. Make it your own! ...
Purchase answer to see full attachment

Tutor Answer

School: Carnegie Mellon University

Let me kno...

flag Report DMCA

Top quality work from this guy! I'll be back!

Brown University

1271 Tutors

California Institute of Technology

2131 Tutors

Carnegie Mellon University

982 Tutors

Columbia University

1256 Tutors

Dartmouth University

2113 Tutors

Emory University

2279 Tutors

Harvard University

599 Tutors

Massachusetts Institute of Technology

2319 Tutors

New York University

1645 Tutors

Notre Dam University

1911 Tutors

Oklahoma University

2122 Tutors

Pennsylvania State University

932 Tutors

Princeton University

1211 Tutors

Stanford University

983 Tutors

University of California

1282 Tutors

Oxford University

123 Tutors

Yale University

2325 Tutors