In [ ]:
#need to predict points, rebounds, assists, steals, blocks, turnovers
#for PG, SG, SF, PF, C
In [2]:
import pandas as pd
from nba_py import player
//anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used
UserWarning)
In [28]:
#List of players for a given season
player_list = player.PlayerList()
len(player_list.info())
#player_list.info().ix[0,'PLAYERCODE']
Out[28]:
476
In [16]:
desc = player.PlayerSummary(203112).info()[['FIRST_NAME', 'LAST_NAME', 'BIRTHDATE', 'HEIGHT', 'WEIGHT', 'SEASON_EXP', 'POSITION', 'ROSTERSTATUS', 'TEAM_ID', 'TEAM_NAME']]
desc.ix[0,'WEIGHT']
Out[16]:
u'240'
In [11]:
#Player Summary: gives weight, height, age, season experience, position, roster status
lbj_id = player.get_player('quincy', 'acy')
lbj = player.PlayerSummary(lbj_id)
print lbj.info().T
0
PERSON_ID 203112
FIRST_NAME Quincy
LAST_NAME Acy
DISPLAY_FIRST_LAST Quincy Acy
DISPLAY_LAST_COMMA_FIRST Acy, Quincy
DISPLAY_FI_LAST Q. Acy
BIRTHDATE 1990-10-06T00:00:00
SCHOOL Baylor
COUNTRY USA
LAST_AFFILIATION Baylor/USA
HEIGHT 6-7
WEIGHT 240
SEASON_EXP 4
JERSEY 13
POSITION Forward
ROSTERSTATUS Active
TEAM_ID 1610612758
TEAM_NAME Kings
TEAM_ABBREVIATION SAC
TEAM_CODE kings
TEAM_CITY Sacramento
PLAYERCODE quincy_acy
FROM_YEAR 2012
TO_YEAR 2016
DLEAGUE_FLAG Y
GAMES_PLAYED_FLAG Y
In [17]:
#Player Summary: gets avg points, assists, rebounds and PIE(player impact estimate)
lbj.headline_stats()
Out[17]:
PLAYER_ID
PLAYER_NAME
TimeFrame
PTS
AST
REB
PIE
0
2544
LeBron James
2015-16
25.3
6.8
7.4
0.189
In [22]:
#Player Dashboard:
pdb = player._PlayerDashboard(player_id = lbj_id)
pdb.overall()
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
<ipython-input-22-99fdcb15ea12> in <module>()
1 #Player Dashboard:
----> 2 pdb = player._PlayerDashboard(player_id = lbj_id)
3 pdb.overall()
//anaconda/lib/python2.7/site-packages/nba_py/player.pyc in __init__(self, player_id, team_id, measure_type, per_mode, plus_minus, pace_adjust, rank, league_id, season, season_type, po_round, outcome, location, month, season_segment, date_from, date_to, opponent_team_id, vs_conference, vs_division, game_segment, period, shot_clock_range, last_n_games)
179 'Period': period,
180 'ShotClockRange': shot_clock_range,
--> 181 'LastNGames': last_n_games})
182
183 def overall(self):
//anaconda/lib/python2.7/site-packages/nba_py/__init__.pyc in _get_json(endpoint, params)
72 headers=HEADERS)
73 # print _get.url
---> 74 _get.raise_for_status()
75 return _get.json()
76
//anaconda/lib/python2.7/site-packages/requests/models.pyc in raise_for_status(self)
838
839 if http_error_msg:
--> 840 raise HTTPError(http_error_msg, response=self)
841
842 def close(self):
HTTPError: 404 Client Error: Not Found for url: http://stats.nba.com/stats/?PlusMinus=N&PlayerID=2544&TeamID=0&Location=&ShotClockRange=&SeasonType=Regular+Season&Season=2015-16&PaceAdjust=N&DateFrom=&VsConference=&OpponentTeamID=0&DateTo=&GameSegment=&LastNGames=0&VsDivision=&LeagueID=00&Outcome=&MeasureType=Base&PORound=0&PerMode=PerGame&SeasonSegment=&Period=0&Rank=N&Month=0
In [26]:
#Has last n game splits for a player: minutes, normal stats, +/-
ln_splits = player.PlayerLastNGamesSplits(player_id = lbj_id)
ln_splits.last5().columns
Out[26]:
Index([u'GROUP_SET', u'GROUP_VALUE', u'GP', u'W', u'L', u'W_PCT', u'MIN',
u'FGM', u'FGA', u'FG_PCT', u'FG3M', u'FG3A', u'FG3_PCT', u'FTM', u'FTA',
u'FT_PCT', u'OREB', u'DREB', u'REB', u'AST', u'TOV', u'STL', u'BLK',
u'BLKA', u'PF', u'PFD', u'PTS', u'PLUS_MINUS', u'DD2', u'TD3', u'CFID',
u'CFPARAMS'],
dtype='object')
In [28]:
#Player performance splits: see basic stats in different game outcomes
pp_splits = player.PlayerPerformanceSplits(player_id = lbj_id)
pp_splits.score_differential()
Out[28]:
GROUP_SET
GROUP_VALUE_ORDER
GROUP_VALUE
GROUP_VALUE_2
GP
W
L
W_PCT
MIN
FGM
...
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
DD2
TD3
CFID
CFPARAMS
0
Score Differential
0
W
All
56
56
0
1.0
35.0
9.6
...
0.6
0.8
1.8
5.4
25.2
13.0
23
3
73
NaN
1
Score Differential
1
W
5 Points and Under
11
11
0
1.0
39.0
10.3
...
0.5
1.5
2.0
6.5
27.6
3.5
6
0
74
NaN
2
Score Differential
2
W
6-10 Points
6
6
0
1.0
36.8
9.8
...
0.7
0.2
2.0
5.0
26.2
5.5
2
0
75
NaN
3
Score Differential
2
W
6-10 Points
10
10
0
1.0
36.7
11.2
...
0.5
1.0
2.2
5.3
28.2
14.2
4
0
75
NaN
4
Score Differential
3
W
11-15 Points
6
6
0
1.0
34.2
9.3
...
0.8
0.8
2.0
5.8
24.3
17.8
3
0
76
NaN
5
Score Differential
3
W
11-15 Points
8
8
0
1.0
34.2
9.1
...
1.1
0.5
2.1
5.3
23.3
11.6
1
1
76
NaN
6
Score Differential
4
W
16-20 Points
1
1
0
1.0
30.9
8.0
...
1.0
2.0
1.0
5.0
21.0
16.0
1
1
77
NaN
7
Score Differential
4
W
16-20 Points
2
2
0
1.0
33.3
9.5
...
1.0
0.5
1.0
6.5
26.0
16.0
2
0
77
NaN
8
Score Differential
5
W
Over 20 Points
8
8
0
1.0
31.0
8.0
...
0.4
0.6
1.3
4.0
20.9
21.3
3
0
78
NaN
9
Score Differential
5
W
Over 20 Points
4
4
0
1.0
30.1
9.0
...
0.3
0.5
1.3
4.8
24.3
24.5
1
1
78
NaN
10
Score Differential
0
L
All
20
0
20
0.0
37.4
9.9
...
0.7
1.2
2.0
5.7
25.4
-5.6
5
0
79
NaN
11
Score Differential
1
L
5 Points and Under
9
0
9
0.0
39.2
10.3
...
0.6
1.4
2.3
6.6
28.2
1.8
2
0
80
NaN
12
Score Differential
2
L
6-10 Points
6
0
6
0.0
38.6
10.2
...
0.8
1.0
1.5
5.2
25.0
-4.7
1
0
81
NaN
13
Score Differential
3
L
11-15 Points
1
0
1
0.0
37.4
8.0
...
1.0
1.0
3.0
7.0
24.0
-12.0
1
0
82
NaN
14
Score Differential
3
L
11-15 Points
1
0
1
0.0
39.7
11.0
...
1.0
1.0
3.0
5.0
26.0
-2.0
1
0
82
NaN
15
Score Differential
5
L
Over 20 Points
2
0
2
0.0
26.5
8.5
...
0.0
0.5
1.5
3.0
19.0
-26.0
0
0
84
NaN
16
Score Differential
5
L
Over 20 Points
1
0
1
0.0
32.9
7.0
...
1.0
1.0
1.0
5.0
16.0
-34.0
0
0
84
NaN
17 rows × 34 columns
In [29]:
#Player Game Logs. Has all basic stats for every game in a season--> useful
g_logs = player.PlayerGameLogs(player_id = lbj_id)
g_logs.info()
Out[29]:
SEASON_ID
Player_ID
Game_ID
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
...
DREB
REB
AST
STL
BLK
TOV
PF
PTS
PLUS_MINUS
VIDEO_AVAILABLE
0
22015
2544
0021501203
APR 11, 2016
CLE vs. ATL
W
32
13
16
0.813
...
5
6
6
2
1
4
2
34
13
1
1
22015
2544
0021501191
APR 09, 2016
CLE @ CHI
L
39
13
17
0.765
...
4
7
3
0
1
4
1
33
7
1
2
22015
2544
0021501159
APR 05, 2016
CLE @ MIL
W
28
7
9
0.778
...
5
5
9
0
1
4
0
17
22
1
3
22015
2544
0021501144
APR 03, 2016
CLE vs. CHA
W
41
14
22
0.636
...
5
8
12
2
0
5
4
31
10
1
4
22015
2544
0021501131
APR 01, 2016
CLE @ ATL
W
44
12
26
0.462
...
13
16
9
3
1
3
4
29
6
1
5
22015
2544
0021501122
MAR 31, 2016
CLE vs. BKN
W
31
8
11
0.727
...
4
4
11
2
1
6
0
24
28
1
6
22015
2544
0021501086
MAR 26, 2016
CLE @ NYK
W
36
10
21
0.476
...
8
11
11
1
2
2
4
27
20
1
7
22015
2544
0021501069
MAR 24, 2016
CLE @ BKN
L
35
13
16
0.813
...
5
6
5
1
0
4
2
30
-8
1
8
22015
2544
0021501059
MAR 23, 2016
CLE vs. MIL
W
37
9
22
0.409
...
1
6
8
2
1
2
0
26
12
1
9
22015
2544
0021501044
MAR 21, 2016
CLE vs. DEN
W
33
12
19
0.632
...
8
11
11
0
0
3
2
33
38
1
10
22015
2544
0021501033
MAR 19, 2016
CLE @ MIA
L
27
13
20
0.650
...
3
3
3
1
0
3
1
26
-23
1
11
22015
2544
0021501020
MAR 18, 2016
CLE @ ORL
W
36
6
15
0.400
...
6
7
8
1
1
4
3
18
-5
1
12
22015
2544
0021500994
MAR 14, 2016
CLE @ UTA
L
37
10
20
0.500
...
6
12
3
1
0
3
0
23
6
1
13
22015
2544
0021500983
MAR 13, 2016
CLE @ LAC
W
31
9
15
0.600
...
5
6
5
1
0
2
2
27
16
1
14
22015
2544
0021500962
MAR 10, 2016
CLE @ LAL
W
35
9
18
0.500
...
3
5
7
0
2
3
2
24
6
1
15
22015
2544
0021500957
MAR 09, 2016
CLE @ SAC
W
37
8
19
0.421
...
7
11
6
0
0
5
1
25
16
1
16
22015
2544
0021500938
MAR 07, 2016
CLE vs. MEM
L
38
11
19
0.579
...
7
9
5
1
0
4
2
28
-9
1
17
22015
2544
0021500922
MAR 05, 2016
CLE vs. BOS
W
36
11
20
0.550
...
7
11
8
2
1
4
2
28
4
1
18
22015
2544
0021500917
MAR 04, 2016
CLE vs. WAS
W
30
7
18
0.389
...
12
13
8
3
0
1
1
19
15
1
19
22015
2544
0021500884
FEB 29, 2016
CLE vs. IND
W
37
14
22
0.636
...
5
5
4
2
0
5
2
33
12
1
20
22015
2544
0021500864
FEB 26, 2016
CLE @ TOR
L
40
9
18
0.500
...
7
8
7
1
0
6
2
25
10
1
21
22015
2544
0021500845
FEB 24, 2016
CLE vs. CHA
W
30
8
13
0.615
...
6
7
7
2
0
2
1
23
4
1
22
22015
2544
0021500833
FEB 22, 2016
CLE vs. DET
L
37
5
18
0.278
...
7
8
5
3
0
6
2
12
-4
1
23
22015
2544
0021500824
FEB 21, 2016
CLE @ OKC
W
37
11
22
0.500
...
4
7
11
3
0
5
3
25
22
1
24
22015
2544
0021500803
FEB 18, 2016
CLE vs. CHI
W
35
11
19
0.579
...
7
9
9
0
1
2
3
25
13
1
25
22015
2544
0021500795
FEB 10, 2016
CLE vs. LAL
W
37
12
22
0.545
...
6
7
11
1
0
1
1
29
16
1
26
22015
2544
0021500775
FEB 08, 2016
CLE vs. SAC
W
31
8
16
0.500
...
9
10
10
2
1
4
1
21
16
1
27
22015
2544
0021500763
FEB 06, 2016
CLE vs. NOP
W
37
11
20
0.550
...
3
3
8
1
0
2
2
27
8
1
28
22015
2544
0021500755
FEB 05, 2016
CLE vs. BOS
L
38
9
23
0.391
...
4
7
4
2
1
6
2
30
-7
1
29
22015
2544
0021500735
FEB 03, 2016
CLE @ CHA
L
39
10
21
0.476
...
6
6
6
1
2
1
1
23
-9
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
46
22015
2544
0021500499
JAN 02, 2016
CLE vs. ORL
W
29
11
18
0.611
...
5
5
3
2
0
0
2
29
28
1
47
22015
2544
0021500473
DEC 29, 2015
CLE @ DEN
W
34
13
24
0.542
...
3
6
2
2
1
5
2
34
3
1
48
22015
2544
0021500466
DEC 28, 2015
CLE @ PHX
W
35
4
10
0.400
...
4
4
7
2
1
2
3
14
3
1
49
22015
2544
0021500453
DEC 26, 2015
CLE @ POR
L
26
4
13
0.308
...
4
4
5
0
0
3
2
12
-29
1
50
22015
2544
0021500438
DEC 25, 2015
CLE @ GSW
L
39
10
26
0.385
...
7
9
2
1
2
4
0
25
-9
1
51
22015
2544
0021500424
DEC 23, 2015
CLE vs. NYK
W
38
9
22
0.409
...
8
9
5
0
0
1
2
24
7
1
52
22015
2544
0021500405
DEC 20, 2015
CLE vs. PHI
W
25
10
17
0.588
...
5
5
4
3
1
2
0
23
33
1
53
22015
2544
0021500384
DEC 17, 2015
CLE vs. OKC
W
40
12
27
0.444
...
7
9
11
2
0
7
2
33
5
1
54
22015
2544
0021500367
DEC 15, 2015
CLE @ BOS
W
36
10
20
0.500
...
6
7
3
2
1
6
3
24
17
1
55
22015
2544
0021500334
DEC 11, 2015
CLE @ ORL
W
29
10
15
0.667
...
3
3
8
4
0
4
0
25
36
1
56
22015
2544
0021500313
DEC 08, 2015
CLE vs. POR
W
40
14
24
0.583
...
9
10
3
2
3
2
3
33
7
1
57
22015
2544
0021500288
DEC 04, 2015
CLE @ NOP
L
45
13
29
0.448
...
5
7
8
1
1
5
4
37
-4
1
58
22015
2544
0021500262
DEC 01, 2015
CLE vs. WAS
L
37
8
20
0.400
...
9
13
4
1
1
9
3
24
-12
1
59
22015
2544
0021500240
NOV 28, 2015
CLE vs. BKN
W
36
10
22
0.455
...
7
9
5
1
0
2
0
26
-1
2
60
22015
2544
0021500227
NOV 27, 2015
CLE @ CHA
W
38
8
20
0.400
...
11
13
5
0
0
3
2
25
1
1
61
22015
2544
0021500219
NOV 25, 2015
CLE @ TOR
L
40
6
16
0.375
...
4
6
8
0
0
2
1
24
13
1
62
22015
2544
0021500203
NOV 23, 2015
CLE vs. ORL
W
35
7
14
0.500
...
6
6
13
1
0
2
0
15
29
1
63
22015
2544
0021500191
NOV 21, 2015
CLE vs. ATL
W
33
8
15
0.533
...
9
11
8
0
2
3
1
19
17
1
64
22015
2544
0021500176
NOV 19, 2015
CLE vs. MIL
W
35
9
13
0.692
...
6
9
6
0
0
4
3
27
13
1
65
22015
2544
0021500160
NOV 17, 2015
CLE @ DET
L
40
11
21
0.524
...
6
6
3
0
0
4
3
30
-6
1
66
22015
2544
0021500141
NOV 14, 2015
CLE @ MIL
L
45
13
27
0.481
...
8
12
5
1
3
7
3
37
-2
1
67
22015
2544
0021500130
NOV 13, 2015
CLE @ NYK
W
39
12
21
0.571
...
3
3
6
2
1
3
4
31
17
1
68
22015
2544
0021500106
NOV 10, 2015
CLE vs. UTA
W
38
11
19
0.579
...
4
7
8
2
0
5
2
31
4
1
69
22015
2544
0021500094
NOV 08, 2015
CLE vs. IND
W
35
10
23
0.435
...
6
6
4
0
0
2
0
29
1
1
70
22015
2544
0021500078
NOV 06, 2015
CLE vs. PHI
W
36
12
22
0.545
...
4
4
13
2
0
2
1
31
24
1
71
22015
2544
0021500063
NOV 04, 2015
CLE vs. NYK
W
35
9
23
0.391
...
3
5
3
4
1
3
1
23
14
1
72
22015
2544
0021500046
NOV 02, 2015
CLE @ PHI
W
33
9
19
0.474
...
9
9
11
4
2
3
3
22
21
1
73
22015
2544
0021500021
OCT 30, 2015
CLE vs. MIA
W
34
13
19
0.684
...
3
5
4
1
0
4
3
29
7
1
74
22015
2544
0021500011
OCT 28, 2015
CLE @ MEM
W
31
4
13
0.308
...
6
7
5
3
0
3
1
12
10
1
75
22015
2544
0021500002
OCT 27, 2015
CLE @ CHI
L
36
12
22
0.545
...
10
10
5
1
0
1
3
25
1
1
76 rows × 27 columns
In [33]:
#Player vs Player. not too sure of what it outputs
pg_id = player.get_player('paul', 'george')
pvp = player.PlayerVsPlayer(player_id = lbj_id, vs_player_id = pg_id)
pvp.on_off_court()
Out[33]:
GROUP_SET
PLAYER_ID
PLAYER_NAME
VS_PLAYER_ID
VS_PLAYER_NAME
COURT_STATUS
GP
W
L
W_PCT
...
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
CFID
CFPARAMS
0
Vs. Player
2544
LeBron James
202331
George, Paul
On
3
0
0
0.0
...
2.3
1.3
0.3
2.0
1.0
5.7
27.3
4.0
86
202331
1
Vs. Player
2544
LeBron James
202331
George, Paul
Off
3
0
0
0.0
...
0.3
0.0
0.0
0.0
0.3
0.0
1.3
0.0
87
202331
2 rows × 34 columns
In [3]:
glogs = pd.read_csv('player_glogs.csv')
In [4]:
glogs
Out[4]:
SEASON_ID
Player_ID
Game_ID
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
...
DREB
REB
AST
STL
BLK
TOV
PF
PTS
PLUS_MINUS
VIDEO_AVAILABLE
0
22015
2544
21501203
APR 11, 2016
CLE vs. ATL
W
32
13
16
0.813
...
5
6
6
2
1
4
2
34
13
1
1
22015
2544
21501191
APR 09, 2016
CLE @ CHI
L
39
13
17
0.765
...
4
7
3
0
1
4
1
33
7
1
2
22015
2544
21501159
APR 05, 2016
CLE @ MIL
W
28
7
9
0.778
...
5
5
9
0
1
4
0
17
22
1
3
22015
2544
21501144
APR 03, 2016
CLE vs. CHA
W
41
14
22
0.636
...
5
8
12
2
0
5
4
31
10
1
4
22015
2544
21501131
APR 01, 2016
CLE @ ATL
W
44
12
26
0.462
...
13
16
9
3
1
3
4
29
6
1
5
22015
2544
21501122
MAR 31, 2016
CLE vs. BKN
W
31
8
11
0.727
...
4
4
11
2
1
6
0
24
28
1
6
22015
2544
21501086
MAR 26, 2016
CLE @ NYK
W
36
10
21
0.476
...
8
11
11
1
2
2
4
27
20
1
7
22015
2544
21501069
MAR 24, 2016
CLE @ BKN
L
35
13
16
0.813
...
5
6
5
1
0
4
2
30
-8
1
8
22015
2544
21501059
MAR 23, 2016
CLE vs. MIL
W
37
9
22
0.409
...
1
6
8
2
1
2
0
26
12
1
9
22015
2544
21501044
MAR 21, 2016
CLE vs. DEN
W
33
12
19
0.632
...
8
11
11
0
0
3
2
33
38
1
10
22015
2544
21501033
MAR 19, 2016
CLE @ MIA
L
27
13
20
0.650
...
3
3
3
1
0
3
1
26
-23
1
11
22015
2544
21501020
MAR 18, 2016
CLE @ ORL
W
36
6
15
0.400
...
6
7
8
1
1
4
3
18
-5
1
12
22015
2544
21500994
MAR 14, 2016
CLE @ UTA
L
37
10
20
0.500
...
6
12
3
1
0
3
0
23
6
1
13
22015
2544
21500983
MAR 13, 2016
CLE @ LAC
W
31
9
15
0.600
...
5
6
5
1
0
2
2
27
16
1
14
22015
2544
21500962
MAR 10, 2016
CLE @ LAL
W
35
9
18
0.500
...
3
5
7
0
2
3
2
24
6
1
15
22015
2544
21500957
MAR 09, 2016
CLE @ SAC
W
37
8
19
0.421
...
7
11
6
0
0
5
1
25
16
1
16
22015
2544
21500938
MAR 07, 2016
CLE vs. MEM
L
38
11
19
0.579
...
7
9
5
1
0
4
2
28
-9
1
17
22015
2544
21500922
MAR 05, 2016
CLE vs. BOS
W
36
11
20
0.550
...
7
11
8
2
1
4
2
28
4
1
18
22015
2544
21500917
MAR 04, 2016
CLE vs. WAS
W
30
7
18
0.389
...
12
13
8
3
0
1
1
19
15
1
19
22015
2544
21500884
FEB 29, 2016
CLE vs. IND
W
37
14
22
0.636
...
5
5
4
2
0
5
2
33
12
1
20
22015
2544
21500864
FEB 26, 2016
CLE @ TOR
L
40
9
18
0.500
...
7
8
7
1
0
6
2
25
10
1
21
22015
2544
21500845
FEB 24, 2016
CLE vs. CHA
W
30
8
13
0.615
...
6
7
7
2
0
2
1
23
4
1
22
22015
2544
21500833
FEB 22, 2016
CLE vs. DET
L
37
5
18
0.278
...
7
8
5
3
0
6
2
12
-4
1
23
22015
2544
21500824
FEB 21, 2016
CLE @ OKC
W
37
11
22
0.500
...
4
7
11
3
0
5
3
25
22
1
24
22015
2544
21500803
FEB 18, 2016
CLE vs. CHI
W
35
11
19
0.579
...
7
9
9
0
1
2
3
25
13
1
25
22015
2544
21500795
FEB 10, 2016
CLE vs. LAL
W
37
12
22
0.545
...
6
7
11
1
0
1
1
29
16
1
26
22015
2544
21500775
FEB 08, 2016
CLE vs. SAC
W
31
8
16
0.500
...
9
10
10
2
1
4
1
21
16
1
27
22015
2544
21500763
FEB 06, 2016
CLE vs. NOP
W
37
11
20
0.550
...
3
3
8
1
0
2
2
27
8
1
28
22015
2544
21500755
FEB 05, 2016
CLE vs. BOS
L
38
9
23
0.391
...
4
7
4
2
1
6
2
30
-7
1
29
22015
2544
21500735
FEB 03, 2016
CLE @ CHA
L
39
10
21
0.476
...
6
6
6
1
2
1
1
23
-9
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
46
22015
2544
21500499
JAN 02, 2016
CLE vs. ORL
W
29
11
18
0.611
...
5
5
3
2
0
0
2
29
28
1
47
22015
2544
21500473
DEC 29, 2015
CLE @ DEN
W
34
13
24
0.542
...
3
6
2
2
1
5
2
34
3
1
48
22015
2544
21500466
DEC 28, 2015
CLE @ PHX
W
35
4
10
0.400
...
4
4
7
2
1
2
3
14
3
1
49
22015
2544
21500453
DEC 26, 2015
CLE @ POR
L
26
4
13
0.308
...
4
4
5
0
0
3
2
12
-29
1
50
22015
2544
21500438
DEC 25, 2015
CLE @ GSW
L
39
10
26
0.385
...
7
9
2
1
2
4
0
25
-9
1
51
22015
2544
21500424
DEC 23, 2015
CLE vs. NYK
W
38
9
22
0.409
...
8
9
5
0
0
1
2
24
7
1
52
22015
2544
21500405
DEC 20, 2015
CLE vs. PHI
W
25
10
17
0.588
...
5
5
4
3
1
2
0
23
33
1
53
22015
2544
21500384
DEC 17, 2015
CLE vs. OKC
W
40
12
27
0.444
...
7
9
11
2
0
7
2
33
5
1
54
22015
2544
21500367
DEC 15, 2015
CLE @ BOS
W
36
10
20
0.500
...
6
7
3
2
1
6
3
24
17
1
55
22015
2544
21500334
DEC 11, 2015
CLE @ ORL
W
29
10
15
0.667
...
3
3
8
4
0
4
0
25
36
1
56
22015
2544
21500313
DEC 08, 2015
CLE vs. POR
W
40
14
24
0.583
...
9
10
3
2
3
2
3
33
7
1
57
22015
2544
21500288
DEC 04, 2015
CLE @ NOP
L
45
13
29
0.448
...
5
7
8
1
1
5
4
37
-4
1
58
22015
2544
21500262
DEC 01, 2015
CLE vs. WAS
L
37
8
20
0.400
...
9
13
4
1
1
9
3
24
-12
1
59
22015
2544
21500240
NOV 28, 2015
CLE vs. BKN
W
36
10
22
0.455
...
7
9
5
1
0
2
0
26
-1
2
60
22015
2544
21500227
NOV 27, 2015
CLE @ CHA
W
38
8
20
0.400
...
11
13
5
0
0
3
2
25
1
1
61
22015
2544
21500219
NOV 25, 2015
CLE @ TOR
L
40
6
16
0.375
...
4
6
8
0
0
2
1
24
13
1
62
22015
2544
21500203
NOV 23, 2015
CLE vs. ORL
W
35
7
14
0.500
...
6
6
13
1
0
2
0
15
29
1
63
22015
2544
21500191
NOV 21, 2015
CLE vs. ATL
W
33
8
15
0.533
...
9
11
8
0
2
3
1
19
17
1
64
22015
2544
21500176
NOV 19, 2015
CLE vs. MIL
W
35
9
13
0.692
...
6
9
6
0
0
4
3
27
13
1
65
22015
2544
21500160
NOV 17, 2015
CLE @ DET
L
40
11
21
0.524
...
6
6
3
0
0
4
3
30
-6
1
66
22015
2544
21500141
NOV 14, 2015
CLE @ MIL
L
45
13
27
0.481
...
8
12
5
1
3
7
3
37
-2
1
67
22015
2544
21500130
NOV 13, 2015
CLE @ NYK
W
39
12
21
0.571
...
3
3
6
2
1
3
4
31
17
1
68
22015
2544
21500106
NOV 10, 2015
CLE vs. UTA
W
38
11
19
0.579
...
4
7
8
2
0
5
2
31
4
1
69
22015
2544
21500094
NOV 08, 2015
CLE vs. IND
W
35
10
23
0.435
...
6
6
4
0
0
2
0
29
1
1
70
22015
2544
21500078
NOV 06, 2015
CLE vs. PHI
W
36
12
22
0.545
...
4
4
13
2
0
2
1
31
24
1
71
22015
2544
21500063
NOV 04, 2015
CLE vs. NYK
W
35
9
23
0.391
...
3
5
3
4
1
3
1
23
14
1
72
22015
2544
21500046
NOV 02, 2015
CLE @ PHI
W
33
9
19
0.474
...
9
9
11
4
2
3
3
22
21
1
73
22015
2544
21500021
OCT 30, 2015
CLE vs. MIA
W
34
13
19
0.684
...
3
5
4
1
0
4
3
29
7
1
74
22015
2544
21500011
OCT 28, 2015
CLE @ MEM
W
31
4
13
0.308
...
6
7
5
3
0
3
1
12
10
1
75
22015
2544
21500002
OCT 27, 2015
CLE @ CHI
L
36
12
22
0.545
...
10
10
5
1
0
1
3
25
1
1
76 rows × 27 columns
In [9]:
len(glogs[glogs['Player_ID']==2544])
Out[9]:
76
In [10]:
from Player import Player
lbj=Player(f_name='Lebron', l_name = 'James')
1775 2544
Name: PERSON_ID, dtype: int64
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-d2f2838019ab> in <module>()
1 from Player import Player
----> 2 lbj=Player(f_name='Lebron', l_name = 'James')
/Users/Matt/Documents/!Research/Github/NBA/Player/Player.py in __init__(self, pid, f_name, l_name, season)
23 self.f_name = self.desc.ix[0, 'FIRST_NAME']
24 self.l_name = self.desc.ix[0, 'LAST_NAME']
---> 25 self.game_logs = self.get_game_logs()
26
27 def get_desc(self):
/Users/Matt/Documents/!Research/Github/NBA/Player/Player.py in get_game_logs(self)
48 if os.path.isfile('player_glogs.csv'):
49 saved_glogs = pd.read_csv('player_glogs.csv')
---> 50 if saved_glogs[saved_glogs['Player_ID']==self.p_id] is not None:
51 game_logs = saved_glogs[saved_glogs['Player_ID']==self.p_id]
52 else:
//anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(self, other, axis)
731 name = _maybe_match_name(self, other)
732 if len(self) != len(other):
--> 733 raise ValueError('Series lengths must match to compare')
734 return self._constructor(na_op(self.values, other.values),
735 index=self.index, name=name)
ValueError: Series lengths must match to compare
In [13]:
pg_id = player.get_player('paul', 'george')
pg_id
Out[13]:
1265 202331
Name: PERSON_ID, dtype: int64
In [27]:
pg_values = pg_id.values
pg_values[0]
Out[27]:
202331
In [ ]:
Content source: mprego/NBA
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