def getteamprofile(filter_season):
gameids = goldsberry.GameIDs()
gameids = pd.DataFrame(gameids)
teamids = gameids['HOME_TEAM_ID'].ix[gameids['SEASON']
==filter_season].drop_duplicates()
teamids_full = pd.DataFrame() # Create empty Data Frame
for i in teamids.values:
team = goldsberry.team.team_info(i)
teamids_full = pd.concat([teamids_full,
pd.DataFrame(team.info())])
return teamids_full
import goldsberry
import pandas as pd
import urllib
gameids = goldsberry.GameIDs()
print("game ids: {}".format(gameids))
print(gameids.game_list())
def get_player_img(player_id):
"""Returns the image of the player from stats.nba.com
Parameters
----------
player_id: int
The player ID used to find the image.
"""
url = "http://stats.nba.com/media/players/230x185/"+str(player_id)+".png"
img_file = str(player_id) + ".png"
# img = plt.imread(urllib.urlretrieve(url, img_file)[0])
# return plt.imshow(img)
print ("img file: {}".format(url))
import pandas as pd
import numpy as np
westTeams = [['Golden State Warriors', 1610612744],
['San Antonio Spurs', 1610612759],
['Oklahoma City Thunder', 1610612760],
['Los Angeles Clippers', 1610612746],
['Memphis Grizzlies', 1610612763],
['Portland Trailblazers', 1610612757],
['Dallas Mavericks', 1610612742], ['Houston Rockets', 1610612745]]
eastTeams = [['Cleveland Cavaliers', 1610612739],
['Toronto Raptors', 1610612761], ['Atlanta Hawks', 1610612737],
['Boston Celtics', 1610612738], ['Miami Heat', 1610612748],
['Charlotte Hornets', 1610612766],
['Detroit Pistons', 1610612765], ['Indiana Pacers', 1610612754]]
gameids = gb.GameIDs()
players = gb.PlayerList()
gameids2015 = pd.DataFrame(gameids.game_list())
gameids2015.to_csv('AllTeams.csv')
players2015 = pd.DataFrame(players.players())
for i in range(0, 4):
team1WestName = westTeams[i][0]
team1WestID = westTeams[i][1]
team1WestPlayers = players2015[players2015['TEAM_ID'] == team1WestID]
team1WestLog = gameids2015[gameids2015['TEAM_ID'] == team1WestID]
team1WestPlayerID = team1WestPlayers['PERSON_ID']
team1WestPlayerGameLog = []
for personID in team1WestPlayerID:
team1WestPlayerGameLog.append(
def get_games():
games_json = goldsberry.GameIDs()
df_games = pd.DataFrame(games_json.game_list())
df_games.sort_values('GAME_ID', inplace=True)
df_games.reset_index(drop=True, inplace=True)
return df_games
def pre_post_ASB_2017_18(playercode):
first, last = playercode.split('_')
first = first.title()
last = last.title()
players = goldsberry.PlayerList(Season='2017-18')
players2017 = pd.DataFrame(players.players())
games = goldsberry.GameIDs()
games.get_new_data(Season='2017-18')
games2017 = pd.DataFrame(games.game_list())
player_id = players2017.loc[players2017['PLAYERCODE'] ==
playercode]['PERSON_ID']
team_id = players2017.loc[players2017['PLAYERCODE'] ==
playercode]['TEAM_ID']
player_game_logs = goldsberry.player.game_logs(player_id)
player_game_logs_2017 = pd.DataFrame(player_game_logs.logs())
player_game_logs_2017['GmSc'] = player_game_logs_2017.apply(
lambda row: gamescore(row), axis=1)
player_game_logs_2017['TSP'] = player_game_logs_2017.apply(
lambda row: TSPercent(row), axis=1)
post_all_star = player_game_logs_2017.loc[58:82]
pre_all_star = player_game_logs_2017.loc[0:57]
# lgl17 = post_all_star.loc[
# (post_all_star['PTS'] > 25)
# & (post_all_star['PTS'] < 30)
# ]
print getGameLog(player_game_logs_2017.loc[5], playercode, players2017,
games2017)
sortedAfter = post_all_star.sort_values(by=['TSP'])
sortedBefore = pre_all_star.sort_values(by=['TSP'])
afterGS = np.array(pd.DataFrame(sortedAfter, columns=['GmSc']))
afterTSP = np.array(pd.DataFrame(sortedAfter, columns=['TSP']))
beforeGS = np.array(pd.DataFrame(sortedBefore, columns=['GmSc']))
beforeTSP = np.array(pd.DataFrame(sortedBefore, columns=['TSP']))
print afterGS.mean()
print "\n"
print beforeGS.mean()
# MAKE A LINE PLOT
# #######################
# USE FOR A BASIC LINE PLOT
# scaled_post = (pd.Series(range(1,len(after) + 1)) * len(before)/len(after)).tolist()
# print afterTSP
# print afterGS
# plt.plot(afterTSP,afterGS,beforeTSP,beforeGS)
# plt.xlabel('True Shooting %')
# plt.ylabel('GameScore')
# # MAKE A BOX/WHISKER
# #######################
# fig, axs = plt.subplots(1,2)
# axs[0].boxplot(before)
# axs[0].set_title("Before All-Star Break (PPG)")
# axs[1].boxplot(after)
# axs[1].set_title("After All-Star Break (PPG)")
# axs[1].set_ylim(axs[0].get_ylim())
# fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9, hspace =0.4, wspace=0.3)
# MAKE A HISTOGRAM
# #####################
fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
axs[0].hist(beforeGS, bins=15)
axs[0].set_title('GameScore Before All-Star Break')
axs[1].hist(afterGS, bins=15)
axs[1].set_title('GameScore After All-Star Break')
plt.savefig('figure.png')
def getgameid():
gameids = goldsberry.GameIDs()
gameids = pd.DataFrame(gameids)
return gameids