Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/reddyprasade/bicycle-sharing-system-in-us

A bicycle-sharing system, public bicycle system, or bike-share scheme, is a service in which bicycles are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" and return it at another dock belonging to the same system. Docks are special bike racks that lock the bike, and only release it by computer control. The user enters payment information, and the computer unlocks a bike. The user returns the bike by placing it in the dock, which locks it in place. Other systems are dockless. For many systems, smartphone mapping apps show nearby available bikes and open docks.
https://github.com/reddyprasade/bicycle-sharing-system-in-us

dataanalysis jupyter maplotlib numpy pandas python r

Last synced: about 1 month ago
JSON representation

Host: GitHub
URL: https://github.com/reddyprasade/bicycle-sharing-system-in-us
Owner: reddyprasade
Created: 2019-06-19T10:25:26.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2020-08-13T16:50:19.000Z (over 4 years ago)
Last Synced: 2023-10-20T22:48:01.514Z (about 1 year ago)
Topics: dataanalysis, jupyter, maplotlib, numpy, pandas, python, r
Language: Jupyter Notebook
Homepage: https://en.wikipedia.org/wiki/Bicycle-sharing_system
Size: 21.1 MB
Stars: 1
Watchers: 1
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Bike-Sharing-System-In-US
## Introduction to Bicycle Sharing System
A bicycle-sharing system, public bicycle system, or bike-share scheme, is a service in which bicycles are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" and return it at another dock belonging to the same system. Docks are special bike racks that lock the bike, and only release it by computer control. The user enters payment information, and the computer unlocks a bike. The user returns the bike by placing it in the dock, which locks it in place. Other systems are dockless. For many systems, smartphone mapping apps show nearby available bikes and open docks.

Bike share in the U.S. has continued its brisk growth, with 35 million trips taken in 2017, 25% more than in 2016. This growth is attributable to increasing ridership in existing systems as well as the launch of several major new bike share systems across the country. Since 2010, 123 million trips have been taken on bike share bikes in the U.S. 2017 also saw the advent of a new bike share customer interface, commonly known as dockless bike share. Rollout has been uneven: after a series of unpermitted systems launched (and subsequently closed) in various cities across the U.S., numerous cities responded with pilot programs to permit dockless bike share operations. By the end of 2017, five major dockless bike share companies reported operating in approximately 25 cities and suburbs. More Systems, More Cities, More Bikes, More Companies
![Data](https://lh3.googleusercontent.com/-MI6075Ye2es/XQoV7RNYHiI/AAAAAAAAeVg/aELogad0bIc3k--RrBBR0pkjQ0gOGMDDACK8BGAs/s0/2019-06-19.png)

The number of bike share companies operating in the U.S. also grew dramatically in 2017. From 2010 to 2016, most U.S. bike share equipment and services were provided by three major companies, B-Cycle, Motivate, and Social Bicycles, with a few cities using equipment and services from smaller companies such as NextBike.

As of the end of 2017, five new major dockless companies – Jump (formerly Social Bicycles), Limebike, MoBike, Ofo, and Spin – and a number of smaller companies – e.g., Pace (formerly Zagster), Donkey Republic, VBike, LennyBike, and Riide – opened systems in the U.S. A sixth new major company, BlueGoGo, which was the first to roll out dockless bikeshare bikes in the U.S., declared bankruptcy over the summer.

In 2017, the number of bike share bikes in the U.S. more than doubled – from 42,500 bikes at the end of 2016 to about 100,000 bikes by the end of 2017. The majority of the increase in bikes came from new dockless systems. During the second half of 2017, dockless bike share companies introduced around 44,000 bikes in cities across the country. Station-based systems added approximately 14,000 bikes to their fleets, bringing the 2017 total to 54,000 station-based bikes. As of the close of 2017, dockless bike share bikes accounted for about 44% of all bike share bikes in the U.S.

The large influx of dockless bike share bikes across the U.S. has not yet translated into substantial mobility gains. NACTO estimates that up to 1.4 million trips were made on dockless bike share bikes in the U.S. in 2017, making up about 4% of trips. NACTO’s methodology for counting dockless bike share trips is provided in the Appendix (below).

![](https://lh3.googleusercontent.com/-nmmWzaLgfLA/XQoXq8ursbI/AAAAAAAAeVs/48cva6nvZBQtGQciSqXKWQwDk-9TrwC2ACK8BGAs/s0/2019-06-19.png)

# Intersting question and We have To Answers
1 Data Lading In Dict
2 Get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs
3 Get user input for month (all, january, february, ... , june)
4 Get user input for day of week (all, monday, tuesday, ... sunday)
5 Loads data for the specified city and filters by month and day if applicable.
6 load data file into a dataframe
7: convert the Start Time column to datetime
8: Extract month and day of week from Start Time to create new columns
9: filter by month if applicable
10: Displays statistics on the most frequent times of travel.
Q10.1: display the most common month
Q10.2: display the most common day of week
Q10.3 display the most common start hour
11:Display most commonly used start station
12: display most commonly used end station
13: display most frequent combination of start station and end station trip
14: Displays statistics on the total and average trip duration.
Q 14.1: display total travel time
Q14.2: display mean travel time
15: Displays statistics on bikeshare users
Q15.1: Display counts of user types
Q15.2: Display counts of gender
Q15.3 Display earliest, most recent, and most common year of birth
16: Descriptive Stastices

---
# References To Learn and Develop your Self:
* [Python](https://github.com/reddyprasade/Python-Basic-For-All-3.x)
* [Data Science With Python ](https://github.com/reddyprasade/Data-Science-With-Python)
* [Machine Learning with Python](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x)
* [Deep learning With python](https://github.com/reddyprasade/Deep-Learning)
* [Data Visulization](https://github.com/reddyprasade/Data-Science-With-Python/tree/master/Data%20Visualization)
* [Life Cycle of Data Science](https://github.com/reddyprasade/Data-Science-With-Python/tree/master/Life%20Cycle%20Process%20of%20Data%20Science%20In%20Real%20World%20project)
* [Statistics](https://github.com/reddyprasade/Data-Science-With-Python/tree/master/Statistics)