Fun with bike data

Julia Evans
@b0rk
http://jvns.ca
julia@jvns.ca

Do we cycle in the winter?



In [3]:

    
import pandas as pd
import numpy as np



In [4]:

    
import matplotlib
# display graphs inline
%matplotlib inline 

# Make graphs prettier
pd.set_option('display.max_columns', 15)
pd.set_option('display.line_width', 400)
pd.set_option('display.mpl_style', 'default')

# Make the fonts bigger
matplotlib.rc('figure', figsize=(14, 7))
matplotlib.rc('font', family='normal', weight='bold', size=22)



In [56]:

    
bike_data_2012 = pd.read_csv("./2012.csv", encoding='latin1', sep=',', index_col='Date', parse_dates=True, dayfirst=True)
bike_data_2012 = bike_data_2012[['Berri 1', 'Maisonneuve 1', 'Rachel1']]
bike_data_2012.index = bike_data_2012['Rachel1'].index.map(lambda x: x + datetime.timedelta(366))
bike_data_2013 = pd.read_csv("./2013.csv", encoding='latin1', sep=',', index_col='Date', parse_dates=True, dayfirst=True)
bike_data_2013 = bike_data_2013[['Berri1', u'CSC', 'Mais1', 'Rachel1']]



In [63]:

    
rachel = pd.DataFrame(index=bike_data_2012.index)
rachel['2012'] = bike_data_2012['Rachel1']
rachel['2013'] = bike_data_2013['Rachel1']



In [68]:

    
rachel = pd.DataFrame({'2012': bike_data_2012['Rachel1'], '2013': bike_data_2013['Rachel1']})



In [69]:

    
rachel.plot()









    Out[69]:





<matplotlib.axes.AxesSubplot at 0x4168190>

Part 2: Do more people bike on weekdays or weekends?

Step 1: add a 'weekday' column to our dataframe



In [72]:

    
bike_data['weekday'] = bike_data.index.weekday
bike_data.head()









    Out[72]:






  
    
      
      Berri 1
      Côte-Sainte-Catherine
      Maisonneuve 1
      weekday
    
    
      Date
      
      
      
      
    
  
  
    
      2012-01-01
        35
       0
        38
       6
    
    
      2012-01-02
        83
       1
        68
       0
    
    
      2012-01-03
       135
       2
       104
       1
    
    
      2012-01-04
       144
       1
       116
       2
    
    
      2012-01-05
       197
       2
       124
       3
    
  

5 rows × 4 columns

Step 2: Use .groupby() and .aggregate() to get the counts



In [73]:

    
counts_by_day = bike_data.groupby('weekday').aggregate(np.sum)
counts_by_day









    Out[73]:






  
    
      
      Berri 1
      Côte-Sainte-Catherine
      Maisonneuve 1
    
    
      weekday
      
      
      
    
  
  
    
      0
       134298
       60329
        90051
    
    
      1
       135305
       58708
        92035
    
    
      2
       152972
       67344
       104891
    
    
      3
       160131
       69028
       111895
    
    
      4
       141771
       56446
        98568
    
    
      5
       101578
       34018
        62067
    
    
      6
        99310
       36466
        55324
    
  

7 rows × 3 columns

Do more people bike on weekdays or weekends?



In [74]:

    
counts_by_day.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
counts_by_day['Berri 1'].plot(kind='bar')









    Out[74]:





<matplotlib.axes.AxesSubplot at 0x4590f90>

There's more going on, though



In [19]:

    
bike_data['Berri 1'].plot()









    Out[19]:





<matplotlib.axes.AxesSubplot at 0x57c2650>

Part 3: Grab some weather data and look at the temperatures



In [81]:

    
weather_data = pd.read_csv('weather_2012.csv', parse_dates=True, index_col='Date/Time')

We need the temperatures every day, not every hour...



In [85]:

    
bike_data['mean temp'] = weather_data['Temp (C)'].resample('D', how='mean')



In [86]:

    
bike_data.head()









    Out[86]:






  
    
      
      Berri 1
      Côte-Sainte-Catherine
      Maisonneuve 1
      weekday
      mean temp
    
    
      Date
      
      
      
      
      
    
  
  
    
      2012-01-01
        35
       0
        38
       6
        0.629167
    
    
      2012-01-02
        83
       1
        68
       0
        0.041667
    
    
      2012-01-03
       135
       2
       104
       1
      -14.416667
    
    
      2012-01-04
       144
       1
       116
       2
      -13.645833
    
    
      2012-01-05
       197
       2
       124
       3
       -6.750000
    
  

5 rows × 5 columns

Weather data



In [91]:

    
weather_data[:5]









    Out[91]:






  
    
      
      Temp (C)
      Dew Point Temp (C)
      Rel Hum (%)
      Wind Spd (km/h)
      Visibility (km)
      Stn Press (kPa)
      Weather
    
    
      Date/Time
      
      
      
      
      
      
      
    
  
  
    
      2012-01-01 00:00:00
      -1.8
      -3.9
       86
       4
       8.0
       101.24
                        Fog
    
    
      2012-01-01 01:00:00
      -1.8
      -3.7
       87
       4
       8.0
       101.24
                        Fog
    
    
      2012-01-01 02:00:00
      -1.8
      -3.4
       89
       7
       4.0
       101.26
       Freezing Drizzle,Fog
    
    
      2012-01-01 03:00:00
      -1.5
      -3.2
       88
       6
       4.0
       101.27
       Freezing Drizzle,Fog
    
    
      2012-01-01 04:00:00
      -1.5
      -3.3
       88
       7
       4.8
       101.23
                        Fog
    
  

5 rows × 7 columns

Do we bike when it's cold?



In [88]:

    
bike_data[['Berri 1', 'mean temp']].plot(subplots=True)









    Out[88]:





array([<matplotlib.axes.AxesSubplot object at 0x5316c50>,
       <matplotlib.axes.AxesSubplot object at 0x5d43c10>], dtype=object)

Do people bike when it's raining?



In [89]:

    
bike_data['Rain'] = weather_data['Weather'].str.contains('Rain').resample('D', how='mean')



In [90]:

    
bike_data[['Berri 1', 'Rain']].plot(subplots=True)









    Out[90]:





array([<matplotlib.axes.AxesSubplot object at 0x6353ed0>,
       <matplotlib.axes.AxesSubplot object at 0x682c590>], dtype=object)

	Berri 1	Côte-Sainte-Catherine	Maisonneuve 1	weekday
Date
2012-01-01	35	0	38	6
2012-01-02	83	1	68	0
2012-01-03	135	2	104	1
2012-01-04	144	1	116	2
2012-01-05	197	2	124	3

	Berri 1	Côte-Sainte-Catherine	Maisonneuve 1
weekday
0	134298	60329	90051
1	135305	58708	92035
2	152972	67344	104891
3	160131	69028	111895
4	141771	56446	98568
5	101578	34018	62067
6	99310	36466	55324

	Berri 1	Côte-Sainte-Catherine	Maisonneuve 1	weekday	mean temp
Date
2012-01-01	35	0	38	6	0.629167
2012-01-02	83	1	68	0	0.041667
2012-01-03	135	2	104	1	-14.416667
2012-01-04	144	1	116	2	-13.645833
2012-01-05	197	2	124	3	-6.750000

	Temp (C)	Dew Point Temp (C)	Rel Hum (%)	Wind Spd (km/h)	Visibility (km)	Stn Press (kPa)	Weather
Date/Time
2012-01-01 00:00:00	-1.8	-3.9	86	4	8.0	101.24	Fog
2012-01-01 01:00:00	-1.8	-3.7	87	4	8.0	101.24	Fog
2012-01-01 02:00:00	-1.8	-3.4	89	7	4.0	101.26	Freezing Drizzle,Fog
2012-01-01 03:00:00	-1.5	-3.2	88	6	4.0	101.27	Freezing Drizzle,Fog
2012-01-01 04:00:00	-1.5	-3.3	88	7	4.8	101.23	Fog

Fun with bike data

Do we cycle in the winter?

Part 2: Do more people bike on weekdays or weekends?

Step 1: add a 'weekday' column to our dataframe

Step 2: Use .groupby() and .aggregate() to get the counts

Do more people bike on weekdays or weekends?

There's more going on, though

Part 3: Grab some weather data and look at the temperatures

We need the temperatures every day, not every hour...

Weather data

Do we bike when it's cold?

Do people bike when it's raining?

Other questions to explore