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data['events'][0].keys()
data [ 'events' ][ 0 ] . keys ()
 

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def acquire_gameData(data):
    import requests
    header_data = { #I pulled this header from the py goldsberry library
        'Accept-Encoding': 'gzip, deflate, sdch',
        'Accept-Language': 'en-US,en;q=0.8',
        'Upgrade-Insecure-Requests': '1',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64)'
        ' AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.82 '
        'Safari/537.36',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9'
        ',image/webp,*/*;q=0.8',
        'Cache-Control': 'max-age=0',
        'Connection': 'keep-alive'
    }
    game_url = 'http://stats.nba.com/stats/playbyplayv2?EndPeriod=0&EndRange=0&GameID='+data['gameid']+
                '&RangeType=0&StartPeriod=0&StartRange=0' #address for querying the data
    response = requests.get(game_url,headers = header_data) #go get the data
    headers = response.json()['resultSets'][0]['headers'] #get headers of data
    gameData = response.json()['resultSets'][0]['rowSet'] #get actual data from json object
    df = pd.DataFrame(gameData, columns=headers) #turn the data into a pandas dataframe
    df = df[[df.columns[1], df.columns[2],df.columns[7],df.columns[9],df.columns[18]]] #there's a ton of data here, so I trim  it doown
    df['TEAM'] = df['PLAYER1_TEAM_ABBREVIATION']
    df = df.drop('PLAYER1_TEAM_ABBREVIATION', 1)
    return df
def acquire_gameData ( data ):import requestsheader_data = { #I pulled this header from the py goldsberry library'Accept-Encoding' : 'gzip, deflate, sdch' ,'Accept-Language' : 'en-US,en;q=0.8' ,'Upgrade-Insecure-Requests' : '1' ,'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; WOW64)'' AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.82 ''Safari/537.36' ,'Accept' : 'text/html,application/xhtml+xml,application/xml;q=0.9'',image/webp,*/*;q=0.8' ,'Cache-Control' : 'max-age=0' ,'Connection' : 'keep-alive'}game_url = 'http://stats.nba.com/stats/playbyplayv2?EndPeriod=0&EndRange=0&GameID=' + data [ 'gameid' ] +'&RangeType=0&StartPeriod=0&StartRange=0' #address for querying the dataresponse = requests . get ( game_url , headers = header_data ) #go get the dataheaders = response . json ()[ 'resultSets' ][ 0 ][ 'headers' ] #get headers of datagameData = response . json ()[ 'resultSets' ][ 0 ][ 'rowSet' ] #get actual data from json objectdf = pd . DataFrame ( gameData , columns = headers ) #turn the data into a pandas dataframedf = df [[ df . columns [ 1 ], df . columns [ 2 ], df . columns [ 7 ], df . columns [ 9 ], df . columns [ 18 ]]] #there's a ton of data here, so I trim  it doowndf [ 'TEAM' ] = df [ 'PLAYER1_TEAM_ABBREVIATION' ]df = df . drop ( 'PLAYER1_TEAM_ABBREVIATION' , 1 )return df
 

df = acquire_gameData(data)
df.head()
#EVENTMSGTYPE
#1 - Make
#2 - Miss
#3 - Free Throw
#4 - Rebound
#5 - out of bounds / Turnover / Steal
#6 - Personal Foul
#7 - Violation
#8 - Substitution
#9 - Timeout
#10 - Jumpball
#12 - Start Q1?
#13 - Start Q2?
df = acquire_gameData ( data )df . head ()#EVENTMSGTYPE#1 - Make #2 - Miss #3 - Free Throw #4 - Rebound #5 - out of bounds / Turnover / Steal #6 - Personal Foul #7 - Violation #8 - Substitution #9 - Timeout #10 - Jumpball #12 - Start Q1? #13 - Start Q2?
 

player_fields = data['events'][0]['home']['players'][0].keys()
home_players = pd.DataFrame(data=[i for i in data['events'][0]['home']['players']], columns=player_fields)
away_players = pd.DataFrame(data=[i for i in data['events'][0]['visitor']['players']], columns=player_fields)
players = pd.merge(home_players, away_players, how='outer')
jerseydict = dict(zip(players.playerid.values, players.jersey.values))
player_fields = data [ 'events' ][ 0 ][ 'home' ][ 'players' ][ 0 ] . keys ()home_players = pd . DataFrame ( data = [ i for i in data [ 'events' ][ 0 ][ 'home' ][ 'players' ]], columns = player_fields )away_players = pd . DataFrame ( data = [ i for i in data [ 'events' ][ 0 ][ 'visitor' ][ 'players' ]], columns = player_fields )players = pd . merge ( home_players , away_players , how = 'outer' )jerseydict = dict ( zip ( players . playerid . values , players . jersey . values ))
 

# Animation function / loop
def draw_court(axis):
    import matplotlib.image as mpimg
    img = mpimg.imread('./nba_court_T.png') #read image. I got this image from gmf05's github.
    plt.imshow(img,extent=axis, zorder=0) #show the image. 

def animate(n): #matplotlib's animation function loops through a function n times that draws a different frame on each iteration
    for i,ii in enumerate(player_xy[n]): #loop through all the players
        player_circ[i].center = (ii[1], ii[2]) #change each players xy position
        player_text[i].set_text(str(jerseydict[ii[0]])) #draw the text for each player.
        player_text[i].set_x(ii[1]) #set the text x position
        player_text[i].set_y(ii[2]) #set text y position
    ball_circ.center = (ball_xy[n,0],ball_xy[n,1]) #change ball xy position
    ball_circ.radius = 1.1 #i could change the size of the ball according to its height, but chose to keep this constant
    return tuple(player_text) + tuple(player_circ) + (ball_circ,)

def init(): #this is what matplotlib's animation will create before drawing the first frame.
    for i in range(10): #set up players
        player_text[i].set_text('')
        ax.add_patch(player_circ[i])
    ax.add_patch(ball_circ) #create ball
    ax.axis('off') #turn off axis
    dx = 5
    plt.xlim([0-dx,100+dx]) #set axis
    plt.ylim([0-dx,50+dx])
    return tuple(player_text) + tuple(player_circ) + (ball_circ,)
# Animation function / loopdef draw_court ( axis ):import matplotlib.image as mpimgimg = mpimg . imread ( './nba_court_T.png' ) #read image. I got this image from gmf05's github.plt . imshow ( img , extent = axis , zorder = 0 ) #show the image. def animate ( n ): #matplotlib's animation function loops through a function n times that draws a different frame on each iterationfor i , ii in enumerate ( player_xy [ n ]): #loop through all the playersplayer_circ [ i ] . center = ( ii [ 1 ], ii [ 2 ]) #change each players xy positionplayer_text [ i ] . set_text ( str ( jerseydict [ ii [ 0 ]])) #draw the text for each player. player_text [ i ] . set_x ( ii [ 1 ]) #set the text x positionplayer_text [ i ] . set_y ( ii [ 2 ]) #set text y positionball_circ . center = ( ball_xy [ n , 0 ], ball_xy [ n , 1 ]) #change ball xy positionball_circ . radius = 1.1 #i could change the size of the ball according to its height, but chose to keep this constantreturn tuple ( player_text ) + tuple ( player_circ ) + ( ball_circ ,)def init (): #this is what matplotlib's animation will create before drawing the first frame. for i in range ( 10 ): #set up playersplayer_text [ i ] . set_text ( '' )ax . add_patch ( player_circ [ i ])ax . add_patch ( ball_circ ) #create ballax . axis ( 'off' ) #turn off axisdx = 5plt . xlim ([ 0 - dx , 100 + dx ]) #set axisplt . ylim ([ 0 - dx , 50 + dx ])return tuple ( player_text ) + tuple ( player_circ ) + ( ball_circ ,)
 

df[37:38]
df [ 37 : 38 ]
 

#the order of events does not match up, so we have to use the eventIds. This loop finds the correct event for a given id#.
search_id = 41
def find_moment(search_id):
    for i,events in enumerate(data['events']):
        if events['eventId'] == str(search_id):
            finder = i
            break
    return finder

event_num = find_moment(search_id)
ball_xy = np.array([x[5][0][2:5] for x in data['events'][event_num]['moments']]) #create matrix of ball data
player_xy = np.array([np.array(x[5][1:])[:,1:4] for x in data['events'][event_num]['moments']]) #create matrix of player data
#the order of events does not match up, so we have to use the eventIds. This loop finds the correct event for a given id#.search_id = 41def find_moment ( search_id ):for i , events in enumerate ( data [ 'events' ]):if events [ 'eventId' ] == str ( search_id ):finder = ibreakreturn finderevent_num = find_moment ( search_id )ball_xy = np . array ([ x [ 5 ][ 0 ][ 2 : 5 ] for x in data [ 'events' ][ event_num ][ 'moments' ]]) #create matrix of ball dataplayer_xy = np . array ([ np . array ( x [ 5 ][ 1 :])[:, 1 : 4 ] for x in data [ 'events' ][ event_num ][ 'moments' ]]) #create matrix of player data
 

import matplotlib.animation as animation

fig = plt.figure(figsize=(15,7.5)) #create figure object
ax = plt.gca() #create axis object

draw_court([0,100,0,50]) #draw the court
player_text = range(10) #create player text vector
player_circ = range(10) #create player circle vector
ball_circ = plt.Circle((0,0), 1.1, color=[1, 0.4, 0]) #create circle object for bal
for i in range(10): #create circle object and text object for each player
    col=['w','k'] if i<5 else ['k','w'] #color scheme
    player_circ[i] = plt.Circle((0,0), 2.2, facecolor=col[0],edgecolor='k') #player circle
    player_text[i] = ax.text(0,0,'',color=col[1],ha='center',va='center') #player jersey # (text)

ani = animation.FuncAnimation(fig, animate, frames=np.arange(0,np.size(ball_xy,0)), init_func=init, blit=True, interval=5, repeat=False,
                             save_count=0) #function for making video
ani.save('Event_%d.mp4' % (search_id),dpi=100,fps=25) #function for saving video
plt.close('all') #close the plot
import matplotlib.animation as animationfig = plt . figure ( figsize = ( 15 , 7.5 )) #create figure objectax = plt . gca () #create axis objectdraw_court ([ 0 , 100 , 0 , 50 ]) #draw the courtplayer_text = range ( 10 ) #create player text vectorplayer_circ = range ( 10 ) #create player circle vectorball_circ = plt . Circle (( 0 , 0 ), 1.1 , color = [ 1 , 0.4 , 0 ]) #create circle object for balfor i in range ( 10 ): #create circle object and text object for each playercol = [ 'w' , 'k' ] if i < 5 else [ 'k' , 'w' ] #color schemeplayer_circ [ i ] = plt . Circle (( 0 , 0 ), 2.2 , facecolor = col [ 0 ], edgecolor = 'k' ) #player circleplayer_text [ i ] = ax . text ( 0 , 0 , '' , color = col [ 1 ], ha = 'center' , va = 'center' ) #player jersey # (text)ani = animation . FuncAnimation ( fig , animate , frames = np . arange ( 0 , np . size ( ball_xy , 0 )), init_func = init , blit = True , interval = 5 , repeat = False ,save_count = 0 ) #function for making videoani . save ( 'Event_ %d .mp4' % ( search_id ), dpi = 100 , fps = 25 ) #function for saving videoplt . close ( 'all' ) #close the plot