In [1]:

    

%matplotlib inline


    

In [2]:

    

import requests
import pandas as pd
import matplotlib

matplotlib.style.use('ggplot')


    

In [3]:

    

r = requests.get('http://m.au.ufc.com/fm/api/event/detail/778.json')


    

In [4]:

    

r.json().keys()


    

    
Out[4]:





dict_keys(['GMT', 'Attendance', 'Organization', 'statid', 'Date', 'Time', 'Location', 'FightCard', 'Name', 'Status'])

In [5]:

    

r.json().get('Status')


    

    
Out[5]:





'Active'

In [6]:

    

r.json().get('Attendance')


    

In [7]:

    

r.json().get('Name')


    

    
Out[7]:





'UFC 200: Tate vs Nunes'

In [8]:

    

r.json().get('statid')


    

    
Out[8]:





'778'

In [9]:

    

r.json().get('Location')


    

    
Out[9]:





{'City': 'Las Vegas',
 'Country': 'USA',
 'State': 'Nevada',
 'Venue': 'T-Mobile Arena'}

In [10]:

    

r.json().get('Time')


    

    
Out[10]:





'18:30:00'

In [11]:

    

r.json().get('Date')


    

    
Out[11]:





'2016-07-09'

In [12]:

    

r.json().get('GMT')


    

    
Out[12]:





'2016-07-09T22:30Z'

In [13]:

    

r.json().get('Organization')


    

    
Out[13]:





{'Name': 'Ultimate Fighting Championship', 'OrganizationID': '1'}

In [14]:

    

len(r.json().get('FightCard'))


    

    
Out[14]:





12

In [15]:

    

r.json().get('FightCard')[1].keys()


    

    
Out[15]:





dict_keys(['Fighters', 'statid', 'Accolade', 'Result', 'Stats', 'RuleSet', 'FightOrder', 'WeightClass', 'Referee'])

In [16]:

    

r.json()['FightCard'][1]['statid']


    

    
Out[16]:





'6160'

In [17]:

    

pd.DataFrame.from_dict(r.json()['FightCard'][1]['Fighters'])


    

    
Out[17]:






  

    

      

      
Age
      
Born
      
DOB
      
FightingOutOf
      
Height
      
KOOfTheNight
      
MMAID
      
Name
      
Outcome
      
PerformanceOfTheNight
      
Reach
      
Record
      
Stance
      
SubmissionOfTheNight
      
UFCLink
      
UFCWeightClass
      
WeighIn
      
Weight
      
statid
    
  
  

    

      
0
      
38
      
{'State': 'South Dakota', 'City': 'Webster', '...
      
1977-07-12
      
{'State': 'Saskatchewan', 'City': 'Regina', 'C...
      
75
      
None
      
102665
      
{'FirstName': 'Brock', 'NickName': None, 'Last...
      
{'Outcome': 'Win', 'OutcomeID': '1'}
      
None
      
81.0
      
{'Losses': '3', 'Draws': '0', 'Wins': '6', 'No...
      
Orthodox
      
None
      
http://www.ufc.com/fighter/Brock-Lesnar
      
{'Abbreviation': 'HW', 'WeightClassID': '1', '...
      
None
      
265
      
305
    
    

      
1
      
42
      
{'State': None, 'City': 'Auckland', 'Country':...
      
1974-03-23
      
{'State': None, 'City': 'Auckland', 'Country':...
      
70
      
None
      
117463
      
{'FirstName': 'Mark', 'NickName': 'Super Samoa...
      
{'Outcome': 'Loss', 'OutcomeID': '2'}
      
None
      
72.0
      
{'Losses': '11', 'Draws': '1', 'Wins': '12', '...
      
Orthodox
      
None
      
http://www.ufc.com/fighter/mark-hunt
      
{'Abbreviation': 'HW', 'WeightClassID': '1', '...
      
None
      
265
      
735
    
  

In [52]:

    

df = pd.DataFrame(r.json()['FightCard'][1]['Stats'], 
                  index = ['{FirstName} {LastName}'.format(**f['Name'])
                           for f in r.json()['FightCard'][1]['Fighters']], 
                  dtype = 'float64')
df.drop(['statid'], axis=1, inplace=True)
df


    

    
Out[52]:






  

    

      

      
KnockDowns
      
Passes
      
Reversals
      
SigBodyStrikesAttempted
      
SigBodyStrikesLanded
      
SigHeadStrikesAttempted
      
SigHeadStrikesLanded
      
SigLegStrikesAttempted
      
SigLegStrikesLanded
      
SigStrikesAccuracy
      
SigStrikesAttempted
      
SigStrikesLanded
      
SubmissionsAttempted
      
TakedownsAccuracy
      
TakedownsAttempted
      
TakedownsLanded
      
TotalStrikesAttempted
      
TotalStrikesLanded
    
  
  

    

      
Brock Lesnar
      
0.0
      
4.0
      
0.0
      
2.0
      
2.0
      
71.0
      
47.0
      
2.0
      
2.0
      
68.00
      
75.0
      
51.0
      
0.0
      
50.0
      
8.0
      
4.0
      
167.0
      
137.0
    
    

      
Mark Hunt
      
0.0
      
0.0
      
0.0
      
1.0
      
1.0
      
31.0
      
12.0
      
0.0
      
0.0
      
40.63
      
32.0
      
13.0
      
0.0
      
0.0
      
0.0
      
0.0
      
68.0
      
46.0
    
  

In [63]:

    

df.T.plot.barh(figsize=(10, 10))


    

    
Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0x10c825630>






    

In [54]:

    

df['TotalStrikesMissed'] = df['TotalStrikesAttempted']- df['TotalStrikesLanded']


    

In [60]:

    

df[['TotalStrikesLanded', 'TotalStrikesMissed']].plot.bar(stacked=True)


    

    
Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x10c473358>






    

In [91]:

    

df[['TotalStrikesLanded', 'TotalStrikesMissed']].T.plot.pie(subplots=True, autopct='%.2f', figsize=(10, 5))


    

    
Out[91]:





array([<matplotlib.axes._subplots.AxesSubplot object at 0x1100b7b00>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x110103ac8>], dtype=object)






    

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