Bikeshare Statistics CLI App(August 2022)

I did this in 2022 when I started to learn about python.

Welcome to the Bikeshare Statistics CLI App! This application allows users to explore and analyze bikeshare data from three major US cities: Chicago, New York City, and Washington.

Table of Contents

• Bikeshare Statistics CLI App(August 2022)
  • Table of Contents
  • Features
  • Getting Started
    • Clone the repository
    • Install required libraries
  • Usage
  • Statistics Computed
  • Data Files
  • Contributing
  • License

Features

• Interactive command-line interface for easy data exploration
• Provides various descriptive statistics related to bikeshare usage
• Filters data by city, month, and day of the week
• Displays raw data upon user request

Getting Started

To get started with this project, you will need Python installed on your machine. You can download Python from python.org.

Clone the repository

You can clone the repository to your local machine using the following command:

git clone https://github.com/OmarAI2003/bikeshare-statistics-cli.git

Install required libraries

This project uses built-in Python libraries, so no additional installations are necessary. However, ensure you have Pandas and Numpy installed for data manipulation. You can install it via pip:

pip install pandas
pip install numpy

Usage

To run the application, navigate to the project directory and execute the following command in your terminal:

python bikeshare.py

Follow the prompts to select the city, month, and day of the week you want to analyze. Here's an example of running the application:

Hello! Let's explore some US bikeshare data!
Enter the city name you would like to see data for (ch, ny, or w):

w

To filter data by a chosen month type a month or 'all' for no filtering by a month:

january
february
march
april
may
june
all

April
Type a weekday you want to filter the data by or the string 'All' for no filtering by days:

Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
All

Monday
----------------------------------------

Calculating The Most Frequent Times of Travel...

the most common month in the dataset is:  4
The most common day that people travel in this dataset is: Monday
The most common hour that people start to travel in this dataset is: 7

This took 0.019 seconds.
----------------------------------------

Calculating The Most Popular Stations and Trip...

the most start station people usually use is: Columbus Circle / Union Station
the most end station people usually use is: Jefferson Dr & 14th St SW
most travel that usually occurs is between: Jefferson Dr & 14th St SW----Jefferson Dr & 14th St SW

This took 0.012 seconds.
----------------------------------------

Calculating Trip Duration...

The total travel time for this dataset is about: 8613095.0
The average travel time for this dataset is about: 8613095.0

This took 0.0005 seconds.
----------------------------------------

Raw data is available to check it.

If you want to see the available raw data in scraps of 5 rows type: Yes
No

Fleeing...

Would you like to restart? Enter yes or no.

Statistics Computed

The application computes the following statistics:

1. Popular times of travel
   • 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
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)

Data Files

To answer the above questions using Python for the three cities, the following CSV files are required:

• chicago.csv
• new_york_city.csv
• washington.csv

These files should be placed in the same directory as the bikeshare.py file.

Contributing

Contributions are welcome! If you would like to contribute to this project, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to customize any sections as needed or add any additional information specific to your project!