pdsnd_github's Introduction

Exploring-US-Bikeshare-Data Project Overview:

In this project, I 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 write 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.

Software I used:

Python 3
NumPy and pandas
A terminal application

The Datasets (Washington , NYC , Chicago)

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 ).

Statistics Computed:

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).

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