In this project, Python code is used to import US bike share data of given cities(Chicago,New York,Washington) and answer questions (usage by city,gender,age,station etc) about given dataset by using computing descriptive statistics.
- Python
$ python bikeshare.py
Hello! Let's explore some US bikeshare data!
Select City :
1.chicago
2.New York
3.washington
Please enter city from above options :chicago
Selected city : Chicago
Select month :
All
January,
February,
March,
April,
May,
June
Please Enter month from above options:All
Select day :
All
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Please enter day from above options:Monday
----------------------------------------
Calculating The Most Frequent Times of Travel...
Most common Month : june
Most common Day of week : Monday
Most Common Start Hour : 17
This took 0.007082939147949219 seconds.
----------------------------------------
Calculating The Most Popular Stations and Trip...
Common STart Station Streeter Dr & Grand Ave
Common End Station Streeter Dr & Grand Ave
Common Trip ('Lake Shore Dr & Monroe St', 'Streeter Dr & Grand Ave')
This took 0.02377796173095703 seconds.
----------------------------------------
Calculating Trip Duration...
Total Travel Time 39945856
Mean Travel Time 890.039348499365
This took 0.0007648468017578125 seconds.
----------------------------------------
Calculating User Stats...
User Types : User Type
Customer 7565
Subscriber 37316
Name: User Type, dtype: int64
Gender : Gender
Female 8689
Male 28646
Name: Gender, dtype: int64
Earliest birth Year 1899.0
Recent birth Year 2016.0
Common Birth Year 1989.0
This took 0.014342069625854492 seconds.
----------------------------------------
Number of Rows in Filtered Data 44881
Would you like to display 5 lines of Filtered data? Enter Yes or No :No
Would you like to restart? Enter yes or no.
no
$
- Python 3, NumPy, and pandas installed using Anaconda
- Atom
- Terminal(Mac)