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In [1]:



    


import requests as rq
import pandas as pd
import json
from StringIO import StringIO
import matplotlib.pyplot as plt



    











In [2]:



    


token = 'e2ce375f9642d036ff9362e829d26c273a2522a4'

payload = {'access_token': token, 'per_page': '50'}
headers = {'content-type': 'application/json'}#, 'login': token}

# Segments
# Farnley Line Full - 2314277

r = rq.get('https://www.strava.com/api/v3/segments/2314277/leaderboard', headers=headers, params=payload)



    











In [60]:



    


data = json.loads(r.text)
#print json.dumps(data, sort_keys=True, indent=4)
data = data['entries']

df = pd.DataFrame(data)
df['speed'] = 3.6*df['distance']/df['elapsed_time']
print df.info()
print df.columns



    


















    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 50 entries, 0 to 49
Data columns (total 15 columns):
activity_id         50  non-null values
athlete_gender      49  non-null values
athlete_id          50  non-null values
athlete_name        50  non-null values
athlete_profile     50  non-null values
average_hr          21  non-null values
average_watts       47  non-null values
distance            50  non-null values
effort_id           50  non-null values
elapsed_time        50  non-null values
moving_time         50  non-null values
rank                50  non-null values
start_date          50  non-null values
start_date_local    50  non-null values
speed               50  non-null values
dtypes: float64(4), int64(6), object(5)None
Index([u'activity_id', u'athlete_gender', u'athlete_id', u'athlete_name', u'athlete_profile', u'average_hr', u'average_watts', u'distance', u'effort_id', u'elapsed_time', u'moving_time', u'rank', u'start_date', u'start_date_local', u'speed'], dtype=object)

















In [76]:



    


fig = plt.figure(figsize=(16,8))

ax1 = fig.add_subplot(211)
plt.scatter(df['elapsed_time'], df['distance'], c=df['average_watts'],s=100)
plt.colorbar()
#ax1.set_xlim([0,51])
plt.title('Distance traveled v Ranking (Power output in colour)')

ax2 = fig.add_subplot(212)
plt.title('Distribution of segment times')
df['elapsed_time'].plot(kind='kde')



    


















    
Out[76]:








<matplotlib.axes.AxesSubplot at 0x127163d0>










    
























In [69]:



    


df['distance_delta'] = df['distance']-df['distance'].mean()
df['true_time'] = df['elapsed_time'] - 3.6*df['distance_delta']/df['speed']



    











In [68]:



    


print df.head()



    


















    






   activity_id athlete_gender  athlete_id      athlete_name  \
0     61712139              M     1183222  chris fothergill   
1     48559099              M      304569    Michael Harris   
2    167671050              M     1326260        steve bray   
3     76197400           None     2645330   Huw Littlestone   
4    164519027              M     1503199     adam birdsall   

                                     athlete_profile  average_hr  \
0  http://dgalywyr863hv.cloudfront.net/pictures/a...       143.6   
1                           avatar/athlete/large.png       157.1   
2  http://dgalywyr863hv.cloudfront.net/pictures/a...         NaN   
3  http://dgalywyr863hv.cloudfront.net/pictures/a...         NaN   
4  http://dgalywyr863hv.cloudfront.net/pictures/a...         NaN   

   average_watts  distance   effort_id  elapsed_time  moving_time  rank  \
0          325.6    3411.8  1158267916           521          521     1   
1          339.0    3412.8   851546989           524          524     2   
2          326.6    3427.9  3899845098           526          526     3   
3          289.5    3412.5  1522395024           540          540     4   
4            NaN    3420.1  3821152663           565          565     5   

             start_date      start_date_local      speed   true_time  \
0  2013-06-20T10:44:57Z  2013-06-20T11:44:57Z  23.574818  521.000238   
1  2013-04-13T11:34:14Z  2013-04-13T12:34:14Z  23.446718  524.000197   
2  2014-07-18T18:54:33Z  2014-07-18T19:54:33Z  23.460913  525.999553   
3  2013-08-20T17:24:48Z  2013-08-20T18:24:48Z  22.750000  540.000216   
4  2014-07-11T14:28:57Z  2014-07-11T15:28:57Z  21.791788  564.999877   

   distance_delta  
0          -5.618  
1          -4.618  
2          10.482  
3          -4.918  
4           2.682  

















In [70]:



    


print df[df['rank']==18][['athlete_name','distance','elapsed_time','speed', 'true_time']]



    


















    






   athlete_name  distance  elapsed_time      speed   true_time
17  Phil Wilson    3397.9           606  20.185545  609.480946
18  Andy Mellor    3418.5           606  20.307921  605.808193

















In [78]:



    


fig = plt.figure(figsize=(16,8))

ax1 = fig.add_subplot(211)
plt.scatter(df['true_time'], df['distance'], c=df['rank'],s=100)
plt.colorbar()
ax1.set_xlabel('True Time')
plt.title('Distance traveled v Ranking (Power output in colour)')



    


















    
Out[78]:








<matplotlib.text.Text at 0x136a9410>










    
























In [10]:



    


token = 'e2ce375f9642d036ff9362e829d26c273a2522a4'

payload = {'access_token': token, 'per_page': '100'}
headers = {'content-type': 'application/json'}#, 'login': token}

# Segments
# Farnley Line Full - 2314277

r = rq.get('https://www.strava.com/api/v3/segments/2314277/leaderboard', headers=headers, params=payload)



    











In [11]:



    


data = json.loads(r.text)
#print json.dumps(data, sort_keys=True, indent=4)
data = data['entries']

df = pd.DataFrame(data)
df['speed'] = 3.6*df['distance']/df['elapsed_time']
print df.info()
print df.columns



    


















    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 15 columns):
activity_id         100  non-null values
athlete_gender      98  non-null values
athlete_id          100  non-null values
athlete_name        100  non-null values
athlete_profile     100  non-null values
average_hr          38  non-null values
average_watts       91  non-null values
distance            100  non-null values
effort_id           100  non-null values
elapsed_time        100  non-null values
moving_time         100  non-null values
rank                100  non-null values
start_date          100  non-null values
start_date_local    100  non-null values
speed               100  non-null values
dtypes: float64(4), int64(6), object(5)None
Index([u'activity_id', u'athlete_gender', u'athlete_id', u'athlete_name', u'athlete_profile', u'average_hr', u'average_watts', u'distance', u'effort_id', u'elapsed_time', u'moving_time', u'rank', u'start_date', u'start_date_local', u'speed'], dtype=object)

















In [12]:



    


df['elapsed_time'].plot(kind='kde')



    


















    
Out[12]:








<matplotlib.axes.AxesSubplot at 0x722e710>










    
























In [22]:



    


full_df = []

token = 'e2ce375f9642d036ff9362e829d26c273a2522a4'

for x in xrange(1,16):
    payload = {'access_token': token, 'per_page': '200', 'page':'{}'.format(x)}
    headers = {'content-type': 'application/json'}

    r = rq.get('https://www.strava.com/api/v3/segments/2521267/leaderboard', headers=headers, params=payload)

    data = json.loads(r.text)
    data = data['entries']
    df = pd.DataFrame(data)
    
    full_df.append(df)



    











In [23]:



    


full_df = pd.concat(full_df)



    











In [24]:



    


full_df.info



    


















    
Out[24]:








<bound method DataFrame.info of <class 'pandas.core.frame.DataFrame'>
Int64Index: 3070 entries, 0 to 204
Data columns (total 14 columns):
activity_id         3070  non-null values
athlete_gender      2992  non-null values
athlete_id          3070  non-null values
athlete_name        3070  non-null values
athlete_profile     3070  non-null values
average_hr          907  non-null values
average_watts       2693  non-null values
distance            3070  non-null values
effort_id           3070  non-null values
elapsed_time        3070  non-null values
moving_time         3070  non-null values
rank                3070  non-null values
start_date          3070  non-null values
start_date_local    3070  non-null values
dtypes: float64(3), int64(6), object(5)>

















In [25]:



    


full_df['elapsed_time'].plot(kind='kde')



    


















    
Out[25]:








<matplotlib.axes.AxesSubplot at 0x740f410>










    
























In [34]:



    


plt.hist(full_df['elapsed_time'].values, bins=30);



    


















    
























In [33]:



    


plt.hist(full_df['distance'].values, bins=20);



    


















    
























In [37]:



    


plt.scatter(full_df['elapsed_time'], full_df['average_watts'])



    


















    
Out[37]:








<matplotlib.collections.PathCollection at 0xf3c5ab0>










    
























In [39]:



    


full_df['start_date'].plot(kind='hist')



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-39-53ba63b4b6f7> in <module>()
----> 1 full_df['start_date'].plot(kind='hist')

C:\WinPython\python-2.7.5\lib\site-packages\pandas\tools\plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
   1694         klass = KdePlot
   1695     else:
-> 1696         raise ValueError('Invalid chart type given %s' % kind)
   1697 
   1698     """

ValueError: Invalid chart type given hist
















In [ ]:

Content source: empiricalstateofmind/blog

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