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CONTENETS OF THIS FILE
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* Introduction
* Softwares Used
* Statistics Computed
* Useful Resources I used in Completing the Project

INTRODUCTION
In this project, I made 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. 
I wrote several lines of code to import the data and answer interesting questions about it by computing descriptive statistics. 
I also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

SOFTWARES USED
Most of my scripting was done on jupyer notebook.
I installed python, numpy, and pandas using Anaconda
I downloaded the text editor Atom, which i used to create the final .py file
I used git bash for windows as my terminal application to view the .py project file to ensure that it runs without errors.

STATISTICS COMPUTED
I learned about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. 
In this project, I wrote several lines of code to provide the following statistics:

#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)


USEFUL RESOURCES I USED IN COMPLETING PROJECT
1. Project Walkthrough - Explore US Bikeshare - 12.7.2022 - US [Video]. Available at https://vimeo.com/729550011/e9d42ba53c
2. Pandas API reference. Available at https://pandas.pydata.org/docs/reference/index.html
3. Combine two columns of text in pandas dataframe. Available at https://stackoverflow.com/questions/19377969/combine-two-columns-of-text-in-pandas-dataframe
4. Lessons and practices in the Udcatiy - Masterschool Data Analysis Pre-Course Nanodegree Program
5. Recordings from the Pre-course student guide
6. Slack - Data Analysis Pre-Course Channel Resourcs.  Including pre_course_project_intro.ipynb file available at https://masterschool-campus.slack.com/archives/C03HKR2D6MU/p1657654420347839
7. __main__ — Top-level code environment. Available at https://docs.python.org/3/library/__main__.html