Bike sharing data

Contents

Bike sharing data#

This data set contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system, along with weather and seasonal information.

  • season: Season of the year, coded as Winter=1, Spring=2, Summer=3, Fall=4.

  • mnth: Month of the year, coded as a factor.

  • day: Day of the year, from 1 to 365

  • hr: Hour of the day, coded as a factor from 0 to 23.

  • holiday: Is it a holiday? Yes=1, No=0.

  • weekday: Day of the week, coded from 0 to 6, where Sunday=0, Monday=1, Tuesday=2, etc.

  • workingday: Is it a work day? Yes=1, No=0.

  • weathersit: Weather, coded as a factor.

  • temp: Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39.

  • atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50.

  • hum: Normalized humidity. The values are divided to 100 (max).

  • windspeed: Normalized wind speed. The values are divided by 67 (max).

  • casual: Number of casual bikers.

  • registered: Number of registered bikers.

  • bikers: Total number of bikers.

Source#

The UCI Machine Learning Repository.

from ISLP import load_data
Bikeshare = load_data('Bikeshare')
Bikeshare.columns

Index(['season', 'mnth', 'day', 'hr', 'holiday', 'weekday', 'workingday',
       'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',
       'registered', 'bikers'],
      dtype='object')

Bikeshare.shape

(8645, 15)

Bikeshare.columns

Index(['season', 'mnth', 'day', 'hr', 'holiday', 'weekday', 'workingday',
       'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',
       'registered', 'bikers'],
      dtype='object')

Bikeshare.describe().iloc[:,:4]
season day holiday weekday
count 8645.000000 8645.00000 8645.000000 8645.000000
mean 2.513592 184.39572 0.027646 3.012724
std 1.105477 104.82334 0.163966 2.006370
min 1.000000 1.00000 0.000000 0.000000
25% 2.000000 94.00000 0.000000 1.000000
50% 3.000000 185.00000 0.000000 3.000000
75% 3.000000 275.00000 0.000000 5.000000
max 4.000000 365.00000 1.000000 6.000000
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