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General description of the project :

This Python script is written for Udacity's Data Analyst Nanodegree (DAND) course and is used to explore data related to bike share systems for Chicago, New York City, and Washington. It also takes in users' raw input to create an interactive experience in the terminal to present these statistics.

Software Installation URL:

For this project to work you need to install python from https://www.python.org/downloads/

List of Files included:

The file included in the project is only bikeshare.py which gives the code which is user for the data manipulation

Purpose of this project:

The script answers the following questions about the bike share data:

What is the most popular month for start time? What is the most popular day of week (Monday, Tuesday, etc.) for start time? What is the most popular hour of day for start time? What is the total trip duration and average trip duration? What is the most popular start station and most popular end station? What is the most popular trip? What are the counts of each user type? What are the counts of gender? What are the earliest (i.e. oldest person), most recent (i.e. youngest person), and most popular birth years?

Input: The user input to be given in the numerical format where the user will enter which data set he wants to choose or which day he wants to get the data for or if he wants to get for all the 7 days. Based on the input given by the user the data manipulation is done and the above questions are answered to the user.

Credits: I have done this project based on the information learnt from the Udacity Site

Bike Share Python project

Resources referred to complete this project:

Filter date:

https://stackoverflow.com/questions/29370057/select-dataframe-rows-between-two-dates

Concatenate strings of two columns:

https://stackoverflow.com/questions/19377969/combine-two-columns-of-text-in-dataframe-in-pandas-python

Add a day to a date:

http://www.pressthered.com/adding_dates_and_times_in_python/

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GitHub project (Project 3) repository for PDSND

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