ozlerhakan / bikeshare

Analysing bike data from three different cities in the US

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BikeShare Data Analysis

$ pip3 install -r requirements.txt
$ python3 bikeshare.py
Hello! Let's explore some US bikeshare data!
Which city do you want to explore Chicago, New York or Washington?
> Chicago
All right! now it's time to provide us a month name or just say 'all' to apply no month filter.
(e.g. all, january, february, march, april, may, june)
> all
One last thing. Could you type one of the week day you want to analyze? You can type 'all' again to apply no day filter.
(e.g. all, monday, sunday)
> monday
----------------------------------------

Calculating The Most Frequent Times of Travel...

The most common month is : 6
The most common day of week is : Monday
The most common start hour is : 17

This took 0.011397838592529297 seconds.
----------------------------------------

Calculating The Most Popular Stations and Trip...

The most commonly used start station : Streeter Dr & Grand Ave
The most commonly used end station : Streeter Dr & Grand Ave
The most commonly used start station and end station : Streeter Dr & Grand Ave, Streeter Dr & Grand Ave

This took 0.05916190147399902 seconds.
----------------------------------------

Calculating Trip Duration...

Total travel time : 39945856
Mean travel time : 890.039348499365
Max travel time : 83766
Travel time for each user type:

  Customer: 13933836
  Subscriber: 26012020

This took 0.02193307876586914 seconds.
----------------------------------------

Calculating User Stats...

Counts of user types:

  Subscriber: 37316
  Customer: 7565

Counts of gender:

  Male: 28646
  Female: 8689

The most common birth year: 1989.0
The most recent birth year: 2016.0
The most earliest birth year: 1899.0

This took 0.03065013885498047 seconds.
----------------------------------------

Calculating Dataset Stats...

The number of missing values in the chicago dataset : 15088
The number of missing values in the 'User Type' column: 15088

Would you like to examine the particular user trip data? Type 'yes' or 'no'
> yes
{
  "Unnamed: 0": 304487,
  "Start Time": 1488808178000,
  "End Time": "2017-03-06 13:55:28",
  "Trip Duration": 350,
  "Start Station": "Christiana Ave & Lawrence Ave",
  "End Station": "St. Louis Ave & Balmoral Ave",
  "User Type": "Subscriber",
  "Gender": "Male",
  "Birth Year": 1986.0,
  "month": 3,
  "day_of_week": "Monday",
  "hour": 13
}
{
  "Unnamed: 0": 1473887,
  "Start Time": 1498467680000,
  "End Time": "2017-06-26 09:11:06",
  "Trip Duration": 586,
  "Start Station": "Clinton St & Washington Blvd",
  "End Station": "Canal St & Taylor St",
  "User Type": "Subscriber",
  "Gender": "Male",
  "Birth Year": 1990.0,
  "month": 6,
  "day_of_week": "Monday",
  "hour": 9
}
{
  "Unnamed: 0": 135470,
  "Start Time": 1486404047000,
  "End Time": "2017-02-06 18:09:00",
  "Trip Duration": 493,
  "Start Station": "Stetson Ave & South Water St",
  "End Station": "Clinton St & Washington Blvd",
  "User Type": "Subscriber",
  "Gender": "Male",
  "Birth Year": 1979.0,
  "month": 2,
  "day_of_week": "Monday",
  "hour": 18
}
{
  "Unnamed: 0": 1359055,
  "Start Time": 1497894197000,
  "End Time": "2017-06-19 17:59:35",
  "Trip Duration": 978,
  "Start Station": "Larrabee St & Armitage Ave",
  "End Station": "Dearborn Pkwy & Delaware Pl",
  "User Type": "Subscriber",
  "Gender": "Female",
  "Birth Year": 1993.0,
  "month": 6,
  "day_of_week": "Monday",
  "hour": 17
}
{
  "Unnamed: 0": 165328,
  "Start Time": 1486995445000,
  "End Time": "2017-02-13 14:22:52",
  "Trip Duration": 327,
  "Start Station": "University Ave & 57th St",
  "End Station": "Kimbark Ave & 53rd St",
  "User Type": "Subscriber",
  "Gender": "Female",
  "Birth Year": 1984.0,
  "month": 2,
  "day_of_week": "Monday",
  "hour": 14
}

Would you like to examine the particular user trip data? Type 'yes' or 'no'
> no

Would you like to restart? Enter yes or no.
no

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Analysing bike data from three different cities in the US


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