def stats():
playerId = players.find_players_by_full_name(
request.args.get('player'))[0].get('id', None)
player_info = json.loads(
commonplayerinfo.CommonPlayerInfo(
player_id=playerId).get_normalized_json()).get(
'CommonPlayerInfo', None)[0]
player_headline_stats = json.loads(
commonplayerinfo.CommonPlayerInfo(
player_id=playerId).get_normalized_json()).get(
'PlayerHeadlineStats', None)[0]
player_stats = json.loads(
playerprofilev2.PlayerProfileV2(
per_mode36="PerGame", player_id=playerId).get_normalized_json())
teamId = player_info.get('TEAM_ID', None)
profile_picture = "https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/%s/2019/260x190/%s.png" % (
teamId, playerId)
return {
**player_info,
**player_headline_stats,
**player_stats, "profile_picture": profile_picture
}
def nba_player_name_from_jersey_search(team_player_ids, yahoo_jersey_number):
for id in team_player_ids:
nba_jersey_number = commonplayerinfo.CommonPlayerInfo(id).get_normalized_dict() \
[COMMON_PLAYER_INFO][0][JERSEY_KEY]
if nba_jersey_number == yahoo_jersey_number:
nba_name = commonplayerinfo.CommonPlayerInfo(id).get_normalized_dict() \
[COMMON_PLAYER_INFO][0][DISPLAY_FIRST_LAST]
return nba_name
def create_currentplayers_lists(season): season = '2021' players_df, players_list, playersid_list = create_players_list(season) # append to a height list #for player_id in player_id_list: response = commonplayerinfo.CommonPlayerInfo(player_id = playersid_list[69]) time.sleep(0.1) content = json.loads(response.get_json()) # transform contents into dataframe playersinfo_raw = content['resultSets'][0] headers = playersinfo_raw['headers'] rows = playersinfo_raw['rowSet'] playersinfo_df = pd.DataFrame(rows) playersinfo_df.columns = headers return playersinfo_df
def generate_players_list():
player_dict = players.get_players()
playerz = len(player_dict)
ballers = []
for i in range(playerz):
try:
doc = {}
baller = commonplayerinfo.CommonPlayerInfo(
player_id=i).get_dict()['resultSets'][0]['rowSet'][0]
doc['name'] = '{} {}'.format(baller[1], baller[2])
doc['birth_date'] = datetime.strptime(baller[7].split('T')[0],
"%Y-%m-%d")
doc['nationality'] = baller[9]
doc['height'] = baller[11]
doc['weight'] = int(baller[12])
doc['jersey'] = int(baller[14])
doc['position'] = baller[15]
doc['team'] = '{} {}'.format(baller[22], baller[19])
doc['school'] = baller[8]
doc['draft'] = {}
doc['draft']['draft_year'] = int(baller[29])
doc['draft']['draft_round'] = int(baller[30])
doc['draft']['draft_number'] = int(baller[31])
ballers.append(doc)
except (json.decoder.JSONDecodeError, ValueError, TypeError):
pass
return ballers
def get_player_shots_data(player_full_name):
pd.set_option("display.max_rows", None)
nba_players = players.get_players()
try:
current_player = [
player for player in nba_players
if player["full_name"] == player_full_name
][0]
except:
return "Player not found."
if not os.path.isdir(
str(os.path.dirname(os.path.abspath(__file__))) + "/../data/" +
str(current_player["id"]) + "/"):
try:
os.makedirs(str(os.path.dirname(os.path.abspath(__file__))) +
"/../data/" + str(current_player["id"]) + "/",
mode=0o777,
exist_ok=True)
except OSError as error:
print(error)
player = commonplayerinfo.CommonPlayerInfo(
player_id=str(current_player["id"]))
player_df = player.get_data_frames()[0]
player_df = player_df.loc[:, [
"FIRST_NAME", "LAST_NAME", "COUNTRY", "HEIGHT", "WEIGHT", "JERSEY",
"POSITION", "TEAM_ABBREVIATION", "FROM_YEAR", "TO_YEAR"
]]
player_df.to_json(str(os.path.dirname(os.path.abspath(__file__))) +
'/../data/' + str(current_player["id"]) +
'/player_data.json',
orient='records')
shots = shotchartdetail.ShotChartDetail(team_id=0,
player_id=str(
current_player["id"]),
context_measure_simple='FGA')
shots_df = shots.get_data_frames()[0]
shots_df = shots_df.loc[:, [
"TEAM_NAME", "PERIOD", "MINUTES_REMAINING", "SECONDS_REMAINING",
"ACTION_TYPE", "SHOT_TYPE", "SHOT_DISTANCE", "LOC_X", "LOC_Y",
"SHOT_MADE_FLAG", "GAME_DATE", "HTM", "VTM"
]]
shots_distances = []
for i in range(shots_df.shape[0]):
shots_distances += [
(shots_df.at[i, "LOC_X"]**2 + shots_df.at[i, "LOC_Y"]**2)**0.5
/ 10
]
shots_df.drop(["SHOT_DISTANCE"], axis=1, inplace=True)
shots_df.insert(5, "SHOT_DISTANCE", shots_distances, True)
shots_angles = []
for i in range(shots_df.shape[0]):
shots_angles += [shots_df.at[i, "LOC_Y"] / shots_df.at[i, "LOC_X"]]
shots_df.insert(5, "SHOT_ANGLE", shots_angles, True)
shots_df.drop(["LOC_X", "LOC_Y"], axis=1, inplace=True)
shots_df.to_json(str(os.path.dirname(os.path.abspath(__file__))) +
'/../data/' + str(current_player["id"]) +
'/shots_data.json',
orient='records')
return "Player found and data added to database."
def player_info(request):
if request.method == 'GET':
id = request.GET['id']
info = commonplayerinfo.CommonPlayerInfo(player_id=id)
info = info.get_normalized_json()
return HttpResponse(info) # Sending a success response
else:
return HttpResponse("Request method is not a GET")
def get_player_info(name):
player_in = players.find_players_by_full_name(name)
player_stats = commonplayerinfo.CommonPlayerInfo(player_in[0]['id']).player_headline_stats
players_list.append('Name: ' + player_in[0]['full_name'])
players_list.append('Year: ' + player_stats.get_dict()['data'][0][2]
+ ', PPG: ' + str(player_stats.get_dict()['data'][0][3])
+ ', APG: ' + str(player_stats.get_dict()['data'][0][4]) +
', RPG: ' + str(player_stats.get_dict()['data'][0][5])
+ ', PIE: ' + str(player_stats.get_dict()['data'][0][6])
)
def download_common_player_info(database=None):
for player_id in database:
# call nba_api and get common player info
cpi = commonplayerinfo.CommonPlayerInfo(
player_id).get_normalized_dict()
# initialize response with the specified database
r = Response(cpi, database=database)
# extract the response for player common info
r.extract_player_common_info(player_id)
def __init__(self, id):
self.id = id
self.player_info = commonplayerinfo.CommonPlayerInfo(
self.id).common_player_info.get_dict().get('data')[0]
self.name = self.player_info[3]
self.number = self.player_info[13]
self.height = self.player_info[10]
self.weight = self.player_info[11]
self.team = self.player_info[17]
self.birth_year = int(self.player_info[6][0:4])
#self.stock_value = #get stock value
self.performances = []
def printHeadlineStats(self, full_name="Stephen Curry"):
id = self.getPlayerId(full_name)
player_info = commonplayerinfo.CommonPlayerInfo(id)
headline_stats = player_info.player_headline_stats.get_dict()
with open('headline_stats.txt', 'w') as fp:
fp.write("")
for i in range(1, 6):
print(headline_stats['headers'][i] + ": " +
str(headline_stats['data'][0][i]))
with open('headline_stats.txt', 'a') as fp:
fp.write(headline_stats['headers'][i] + ": " +
str(headline_stats['data'][0][i]) + " | ")
def get_player_information(playerid):
playerinfo = {}
# Retrieve player 1 information and stats
##player_summary = commonplayerinfo.CommonPlayerInfo(player_id=playerid, proxy=proxy, timeout=100)
player_summary = commonplayerinfo.CommonPlayerInfo(player_id=playerid,
timeout=500)
player_summary_info = player_summary.get_data_frames()[0]
headline_stats = player_summary.get_data_frames()[1]
playerinfo['headline_stats'] = headline_stats
playerinfo['player_summary_info'] = player_summary_info
# Get last year that he played in
to_year = int(player_summary_info.iloc[0]["TO_YEAR"])
next_year = to_year + 1
season = str(to_year) + "-" + str(next_year)[2:4]
playerinfo['season'] = season
time.sleep(5)
# Get birth date for the player 1
birth_datestring = player_summary_info.iloc[0]["BIRTHDATE"][:10]
birth_date = datetime.datetime.strptime(birth_datestring, "%Y-%m-%d")
playerinfo['pretty_birth_date'] = birth_date.strftime("%m-%d-%Y")
playerinfo['age'] = calculate_age(birth_date)
#career = playercareerstats.PlayerCareerStats(player_id=playerid, proxy=proxy, timeout=100)
career = playercareerstats.PlayerCareerStats(player_id=playerid,
timeout=100)
player_career_regular_season_totals = career.get_data_frames()[1].to_dict(
'records')
player_career_post_season_totals = career.get_data_frames()[3].to_dict(
'records')
player_career_regular_season = career.get_data_frames()[0].to_dict(
'records')
player_career_post_season = career.get_data_frames()[2].to_dict('records')
playerinfo['player_career_regular_season_averages'] = get_average_stats(
player_career_regular_season)
playerinfo['player_career_post_season_averages'] = get_average_stats(
player_career_post_season)
playerinfo['player_career_regular_season'] = player_career_regular_season
playerinfo['player_career_post_season'] = player_career_post_season
playerinfo[
'player_career_regular_season_totals'] = get_shooting_percentage(
player_career_regular_season_totals)
playerinfo['player_career_post_season_totals'] = get_shooting_percentage(
player_career_post_season_totals)
player_headshot = "https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/latest/260x190/" + playerid + ".png"
if (not test_link(player_headshot)):
player_headshot = False
playerinfo['player_headshot'] = player_headshot
return playerinfo
def printDraftInfoForPlayer(self, full_name="Stephen Curry"):
id = self.getPlayerId(full_name)
with open('draft_player.txt', 'w') as fp:
fp.write("")
player = commonplayerinfo.CommonPlayerInfo(id)
common_info = player.common_player_info.get_dict()
print("Draft Year: " + str(common_info['data'][0][27]))
print("Round Number: " + str(common_info['data'][0][28]))
print("Pick Number: " + str(common_info['data'][0][29]))
with open('draft_player.txt', 'a') as fp:
fp.write("Draft Year: " + str(common_info['data'][0][27]) + '\n')
fp.write("Round Number: " + str(common_info['data'][0][28]) + '\n')
fp.write("Pick Number: " + str(common_info['data'][0][29]) + '\n')
def get_player_common_info(player):
player_info = commonplayerinfo.CommonPlayerInfo(
player).get_data_frames()[0]
name = player_info['DISPLAY_FIRST_LAST'][0]
prior = 'Prior: ' + player_info['LAST_AFFILIATION'][0]
height = 'Height: ' + player_info['HEIGHT'][0]
weight = 'Weight: ' + player_info['WEIGHT'][0] + 'lbs'
exp = 'Exp: ' + str(player_info['SEASON_EXP'][0])
jersey = '#' + player_info['JERSEY'][0] + ' '
team = ' | ' + player_info['TEAM_CITY'][0] + ' ' + player_info[
'TEAM_NAME'][0]
position = player_info['POSITION'][0]
abrv = player_info['TEAM_ABBREVIATION'][0]
team_logo = f'https://stats.nba.com/media/img/teams/logos/{abrv}_logo.svg'
birthDate = date(int(player_info['BIRTHDATE'][0][0:4]),
int(player_info['BIRTHDATE'][0][5:7]),
int(player_info['BIRTHDATE'][0][8:10]))
today = date.today()
age = 'Age: ' + str(today.year - birthDate.year - (
(today.month, today.day) < (birthDate.month, birthDate.day)))
born = 'Born: ' + player_info['BIRTHDATE'][0][5:7] + '/' + player_info[
'BIRTHDATE'][0][8:10] + '/' + player_info['BIRTHDATE'][0][0:4]
draft = 'Drafted: ' + player_info['DRAFT_YEAR'][0] + ' Rnd ' + player_info[
'DRAFT_ROUND'][0] + ' Pick ' + player_info['DRAFT_NUMBER'][0]
player_stats = commonplayerinfo.CommonPlayerInfo(
player).get_data_frames()[1]
timeframe = player_stats['TimeFrame'][0].capitalize() + ' Stats'
if list(player_stats.columns)[-1] == 'PIE':
pie = str(player_stats['PIE'][0])
else:
pie = str(player_stats['ALL_STAR_APPEARANCES'][0])
pts = str(player_stats['PTS'][0])
reb = str(player_stats['REB'][0])
ast = str(player_stats['AST'][0])
return [jersey, name], height, weight, prior, age, born, draft, exp, [
position, team
], team_logo, pts, reb, ast, pie
def printCommonPlayerInfo(self, full_name="Stephen Curry"):
id = self.getPlayerId(full_name)
player = commonplayerinfo.CommonPlayerInfo(id)
common_info = player.common_player_info.get_dict()
with open('general.txt', 'w') as fp:
fp.write("")
for i in range(0, 30):
if (i == 0 or i == 3 or i == 4 or i == 5 or i == 15 or i == 16
or i == 19 or i == 21 or i == 24 or i == 25 or i == 26):
continue
print(common_info['headers'][i] + ": " +
str(common_info['data'][0][i]))
with open('general.txt', 'a') as fp:
fp.write(common_info['headers'][i] + ": " +
str(common_info['data'][0][i]) + '\n')
def getIds(fullName):
# find player id by full name
try:
pd = players.find_players_by_full_name(fullName)
player_id = pd[0]['id']
except Exception:
return "error"
# get the player's current team (seems quite extensive to do
# it like this, but I don't want ppl to have to enter in
# a team name as well just to get a shot chart)
info = commonplayerinfo.CommonPlayerInfo(player_id, headers=headers)
team_id = info.common_player_info.get_data_frame()['TEAM_ID'][0]
# return id's as key-value pair
return {'player_id': player_id, 'team_id': team_id}
def get_team_id(self, player_id):
"""Match player id to team id which is necessary for shot info.
:param int player_id:
Player ID that is associated with player user provided.
:returns team_id
:rtype int
"""
try:
current_team_id = commonplayerinfo.CommonPlayerInfo(
player_id=player_id)
except requests.exceptions.ConnectionError:
print("Request failed.")
else:
return current_team_id.common_player_info.data['data'][0][16]
def parse_common_player_info(player_id: str):
"""
Parse player info from CommonPlayerInfo endpoint (when player has not been insert in database)
"""
player_json = json.loads(
commonplayerinfo.CommonPlayerInfo(player_id=player_id).get_json())
headers_player, row_set_player = get_result_set(player_json,
'CommonPlayerInfo')
if row_set_player:
data_player = row_set_player[0]
player = {
'personId': data_player[headers_player['PERSON_ID']],
'firstName': data_player[headers_player['FIRST_NAME']],
'lastName': data_player[headers_player['LAST_NAME']],
'teamId': format_team(data_player[headers_player['TEAM_ID']])
}
parsed_player = parse_player(player)
if parsed_player is not None:
return parsed_player
else:
# in case player is not registered by data.nba.net api
stats_json = json.loads(
playercareerstats.PlayerCareerStats(
player_id=player_id,
per_mode36='PerGame',
league_id_nullable=APIProperty('LeagueID')).get_json())
headers_stats, row_set_stats = get_result_set(
stats_json, 'SeasonTotalsRegularSeason')
# get stats for current season
data_stats = row_set_stats[-1]
return Player(id=data_player[headers_player['PERSON_ID']],
first_name=data_player[headers_player['FIRST_NAME']],
last_name=data_player[headers_player['LAST_NAME']],
team_id=format_team(
data_player[headers_player['TEAM_ID']]),
mpg=data_stats[headers_stats['MIN']],
ppg=data_stats[headers_stats['MIN']],
rpg=data_stats[headers_stats['REB']],
apg=data_stats[headers_stats['AST']],
spg=data_stats[headers_stats['STL']],
bpg=data_stats[headers_stats['BLK']])
else:
return None
def playerinfo(id=None):
player_info = commonplayerinfo.CommonPlayerInfo(player_id=id)
df = player_info.get_data_frames()[0]
player_data = []
for index, row in df.iterrows():
data = {}
data["a_Name"] = row["DISPLAY_FIRST_LAST"]
data["e_School"] = row["SCHOOL"]
data["f_Country"] = row["COUNTRY"]
data["b_Height"] = row["HEIGHT"]
data["c_Weight"] = row["WEIGHT"]
data["d_Seasons"] = row["SEASON_EXP"]
data["g_Draft_year"] = row["DRAFT_YEAR"]
data["h_Draft_round"] = row["DRAFT_ROUND"]
data["i_draft_number"] = row["DRAFT_NUMBER"]
player_data.append(data)
return jsonify(player_data)
def insert_new_players(**kwargs):
date_format_str = '%Y-%m-%d'
date = kwargs['ds']
staged_player_box_tuple = session.query(PlayerBoxStage.PLAYER_ID).join(
LeagueGameLog.__table__).filter(LeagueGameLog.GAME_DATE == date).all()
staged_players = [player[0] for player in staged_player_box_tuple]
for player in staged_players:
if not session.query(
Player.PLAYER_ID).filter(Player.PLAYER_ID == player).first():
new_player = commonplayerinfo.CommonPlayerInfo(
player_id=player).get_dict()['resultSets'][0]['rowSet'][0]
for n, col in enumerate(new_player):
if col in ['', ' ']:
new_player[n] = None
new_player[player_cols['DOB']] = datetime.strptime(
new_player[player_cols['DOB']][0:10],
date_format_str).strftime('%Y-%m-%d %H:%M:%S')
if new_player[player_cols['HEIGHT']]:
height_split = [
int(i)
for i in new_player[player_cols['HEIGHT']].split('-')
]
new_player[player_cols[
'HEIGHT']] = height_split[0] * 12 + height_split[1]
for col in ['WEIGHT', 'DRAFT_YEAR', 'DRAFT_ROUND', 'DRAFT_NUMBER']:
if new_player[player_cols[col]] and new_player[
player_cols[col]] != 'Undrafted':
new_player[player_cols[col]] = int(
new_player[player_cols[col]])
else:
new_player[player_cols[col]] = None
limited_content = [
new_player[value] for key, value in player_cols.items()
]
for n, item in enumerate(limited_content):
if type(item) == str:
if "'" in item:
limited_content[n] = item.replace("'", "''")
insert_data(limited_content, ["prod.players"])
time.sleep(4)
def get_height(player_id): response = commonplayerinfo.CommonPlayerInfo(player_id) time.sleep(0.5) content = json.loads(response.get_json()) # transform contents into dataframe playersinfo_raw = content['resultSets'][0] headers = playersinfo_raw['headers'] rows = playersinfo_raw['rowSet'] playersinfo_df = pd.DataFrame(rows) playersinfo_df.columns = headers height = playersinfo_df['HEIGHT'].tolist() return height
def player_average_stats(player_name):
player_search = players.find_players_by_full_name(player_name)
plural = ""
if len(player_search) == 0:
print("No player with name '%s' found." % name)
sys.exit(0)
if len(player_search) > 1:
plural = "s"
print("Found player%s matching the name '%s'!" % (plural, name))
for player in player_search:
player_id = player['id']
player_info = commonplayerinfo.CommonPlayerInfo(
player_id=player_id).get_normalized_dict()
# Display the players' information
#print("----General Information----")
#for info_key, info_value in player_info['CommonPlayerInfo'][0].items():
# print(info_key + ": " + str(info_value))
for stats_key, stats_value in player_info["PlayerHeadlineStats"][
0].items():
if stats_key in ["PLAYER_ID", "PLAYER_NAME"]:
continue
print(stats_key + ": " + str(stats_value))
def get_player_info(self):
# downlading information about the player from the NBA Stats API
player_info = commonplayerinfo.CommonPlayerInfo(player_id=self.id)
player_df = player_info.get_data_frames()[0]
# choosing columns which are interesting to us
player_columns = [
'DISPLAY_FIRST_LAST', 'BIRTHDATE', 'SCHOOL', 'COUNTRY', 'HEIGHT',
'WEIGHT', 'POSITION', 'TEAM_NAME', 'FROM_YEAR', 'TO_YEAR'
]
player_df = player_df[player_columns]
# changing these columns' names
col = [
'Name', 'Birthdate', 'School', 'Country', 'Height', 'Weight',
'Position', 'Team', 'Played from', 'Played to'
]
player_df.columns = col
# formatting player's birthdate
player_df['Birthdate'] = player_df['Birthdate'][0][:10]
# changing data format to dictionary so it's easier to handle with jinja2
player_df = player_df.to_dict()
return player_df
import json
from nba_api.stats.endpoints import commonplayerinfo as api
from pprint import pprint
players = json.loads(open("./All_Players.json").read())
header = {
'Host': 'stats.nba.com',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
'User-Agent':
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36',
'Accept':
'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'en-US,en;q=0.9',
'Referer': 'https://stats.nba.com',
}
teams = []
for player in players:
print("Fetching team for {}".format(player["full_name"]))
info = api.CommonPlayerInfo(player["id"], headers=header)
team_id = info.get_normalized_dict(
)["CommonPlayerInfo"][0]["TEAM_ABBREVIATION"]
if team_id not in teams:
teams.append(team_id)
print("{} team is {}".format(player["full_name"], team_id))
pprint(teams)
json.dumps(open("./team.json").write())
#print('Number of players fetched: {}'.format(len(nba_players)))
nba_players[:5]
#print(nba_players)
spurs = [
team for team in nba_teams if team['full_name'] == 'San Antonio Spurs'
][0]
#print(type(spurs))
big_fundamental = [
player for player in nba_players if player['full_name'] == 'Tim Duncan'
][0]
#print(big_fundamental['id'])
#print(players.find_players_by_first_name('lebron'))
#print(teams.find_teams_by_state('ohio'))
#print(big_fundamental)
# Basic Request
from nba_api.stats.endpoints import commonplayerinfo
player_info = commonplayerinfo.CommonPlayerInfo(player_id=2544)
player_dic = player_info.available_seasons.get_dict()
#print(player_dic)
print(player_info.common_player_info.get_data_frame())
async def career_stats(self, ctx, *, object_search):
try:
object_row = (sheet.find(object_search).row)
object_column = (sheet.find(object_search).col)
object_id = sheet.cell(object_row, object_column - 1).value
object_id = str(object_id)
career_stats = playercareerstats.PlayerCareerStats(
player_id=object_id)
df_career_stats = career_stats.get_data_frames()
career_totals = df_career_stats[1].sum()
# same process as before but this time all the columns are added up to later find career averages
def get_per_game(metric):
return round(
(career_totals.get(f'{metric}') / career_totals.get('GP')),
1)
def get_percent(metric, attemped_metric):
return round((career_totals.get(f'{metric}') /
career_totals.get(f'{attemped_metric}') * 100),
1)
# these functions calculate per-game stats and field goal efficiences respectively
career_ppg = get_per_game('PTS')
career_fgp = get_percent('FGM', 'FGA')
career_tpp = get_percent('FG3M', 'FG3A')
career_ftp = get_percent('FTM', 'FTA')
career_rpg = get_per_game('REB')
career_apg = get_per_game('AST')
career_spg = get_per_game('STL')
career_bpg = get_per_game('BLK')
career_tpg = get_per_game('TOV')
# all major stats are stored as variables to be used in embedded message later
career_stats_embed = discord.Embed(title=object_search,
colour=discord.Colour(0x0e1cf5))
player_info = commonplayerinfo.CommonPlayerInfo(
player_id=object_id)
df_player_info = player_info.get_data_frames()
t_id = df_player_info[0].iat[0, 16]
to_year = df_player_info[0].iat[0, 23]
# this data is only being pulled for the sake of finding the player's picture for the thumbnail
career_stats_embed.set_footer(text='gimme help | RIP Kobe')
career_stats_embed.set_image(
url=
'https://cdn.vox-cdn.com/thumbor/v1jR5XEgcDrnwTq_uSZt4ApiIqg=/1400x1400/filters:format(jpeg)/cdn.vox-cdn.com/uploads/chorus_asset/file/20025173/nba_covid_3_ringer_illo.jpg'
)
career_stats_embed.set_thumbnail(
url=
f'https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/{t_id}/{to_year}/260x190/{object_id}.png'
)
career_stats_embed.set_author(
name=f'Career stat averages for `{object_search}')
career_stats_embed.add_field(name='PPG', value=career_ppg)
career_stats_embed.add_field(name='FG%', value=f'{career_fgp}%')
career_stats_embed.add_field(name='3P%', value=f'{career_tpp}%')
career_stats_embed.add_field(name='FT%', value=f'{career_ftp}%')
career_stats_embed.add_field(name='RPG', value=career_rpg)
career_stats_embed.add_field(name='APG', value=career_apg)
career_stats_embed.add_field(name='SPG', value=career_spg)
career_stats_embed.add_field(name='BPG', value=career_bpg)
career_stats_embed.add_field(name='TPG', value=career_tpg)
# career stat averages added embedded message
await ctx.send(embed=career_stats_embed)
except (UnboundLocalError, gspread.exceptions.CellNotFound):
await ctx.send("""
I think you spelled it wrong hombre, make sure you're using this format: `gimme career_stats Steph Curry`.
Also, make sure you're not using shortened versions of names like `Steph Curry`.
""")
async def player_info(self, ctx, *, object_search):
try:
object_row = (sheet.find(object_search).row)
object_column = (sheet.find(object_search).col)
# finds the coordinates of the cell containing the player name
object_id = sheet.cell(object_row, object_column - 1).value
# assigns the data in the cell to the left of the player name as a variable
# this is the ID itself
object_id = str(object_id)
# NBA API cannot read the ID unless it's in string form
player_info = commonplayerinfo.CommonPlayerInfo(
player_id=object_id)
df_player_info = player_info.get_data_frames()
# Data appended to a DataFrame, from which it can easily be extracted
t_id = df_player_info[0].iat[0, 16]
to_year = df_player_info[0].iat[0, 23]
jersey_number = df_player_info[0].iat[0, 13]
position = df_player_info[0].iat[0, 14]
height = df_player_info[0].iat[0, 10]
weight = df_player_info[0].iat[0, 11]
date_of_birth = df_player_info[0].iat[0, 6]
date_of_birth = date_of_birth.rstrip('T00:00:00')
draft_position = df_player_info[0].iat[0, 29]
draft_round = df_player_info[0].iat[0, 28]
# Pulled various pieces of data regarding the player
# These will later be used by the bot
player_embed = discord.Embed(title=object_search,
colour=discord.Colour(0x0e1cf5))
player_embed.set_footer(text='gimme help | RIP Kobe')
player_embed.set_image(
url=
'https://cdn.vox-cdn.com/thumbor/v1jR5XEgcDrnwTq_uSZt4ApiIqg=/1400x1400/filters:format(jpeg)/cdn.vox-cdn.com/uploads/chorus_asset/file/20025173/nba_covid_3_ringer_illo.jpg'
)
player_embed.set_thumbnail(
url=
f'https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/{t_id}/{to_year}/260x190/{object_id}.png'
)
# thumbnail is used to display the player picture
player_embed.set_author(name=f'Player info for `{object_search}')
player_embed.add_field(name='Jersey Number', value=jersey_number)
player_embed.add_field(name='Position', value=position)
player_embed.add_field(name='Height', value=height)
player_embed.add_field(name='Weight', value=f'{weight} lbs')
player_embed.add_field(name='Date of Birth', value=date_of_birth)
player_embed.add_field(
name='Drafted',
value=f'Round: {draft_round} Pick: {draft_position}')
# all data is added to embedded message to be more visually appealing
await ctx.send(embed=player_embed)
except (UnboundLocalError, gspread.exceptions.CellNotFound):
await ctx.send("""
I think you spelled it wrong hombre, make sure you're using this format: `gimme player_info Steph Curry`.
Also, make sure you're not using shortened versions of names like `Steph Curry`.
""")
from nba_api.stats.endpoints import commonplayerinfo
from nba_api.stats.endpoints import playerdashboardbylastngames
from nba_api.stats.static import players
player_info = commonplayerinfo.CommonPlayerInfo(2544, "00")
lebron_dict = player_info.common_player_info.get_dict()
# lebron_log = playergamelog.PlayerGameLog(2544, 2017-18, "Regular Season")
# print(lebron_log)
lebron_last_5_games = playerdashboardbylastngames.PlayerDashboardByLastNGames(2544, 0, "Base", 0, 0, "N", "PerGame", 0, "N", "N", "2017-18", "Regular Season")
#lebron_last_5_games_2 = playerdashboardbylastngames.PlayerDashboardByLastNGames(2544, 0, "Base", 0, 0, "N", "PerGame", 0, "N", "N", "2017-18", "Regular Season", "03-20-2018", "03-10-2018")
lebron_5_game_dict = lebron_last_5_games.last5_player_dashboard.get_dict()
#lebron_5_game_dict_2 = lebron_last_5_games_2.last5_player_dashboard.get_dict()
print("Player:" + lebron_dict.get('data')[0][3] + ", Number: " + lebron_dict.get('data')[0][13] + "\n")
print(lebron_5_game_dict.get('data')[0][28])
print(players.find_players_by_full_name('LeBron James')[0].get('id'))
#print(lebron_5_game_dict_2.get('data')[0][28])
#print(lebron_5_game_dict_2)
# format data for dataframe
df = pandas.DataFrame(result_sorted)
df.columns = ["Percent Change in Opp. RA FGA", "Player"]
cols = df.columns.tolist()
cols = cols[::-1]
df = df[cols]
print(df)
# format data for html table
print(
"<table><tr><td>Player</td><td>Percent Change in <br />Opp. RA FGA</td></tr>"
)
for entry in result_sorted:
print("\n<tr><td>" + entry[1] + "</td><td>" + str(entry[0]) + "</td></tr>")
print("\n</table>")
# correlation with height
# api calls
results_with_height = []
for entry in result_sorted:
playerid = players.find_players_by_full_name(entry[1])[0]['id']
height = commonplayerinfo.CommonPlayerInfo(
player_id=playerid).common_player_info.data['data'][0][10]
height_inches = height.split("-")[0] * 12 + height.split("-")[
1] #convert to inches
results_with_height.append((entry[1], entry[0], height_inches))
#correlation
df = pandas.DataFrame(results_with_height)
df.corr()
print(df.corr())
Team2_PlayerIds = Team2_Roster[['PLAYER_ID']]
Team2_PlayerIds = convert(Team2_PlayerIds, 'PLAYER_ID')
#determining the lenght of each team
Team1_length = len(Team1_PlayerIds)
Team2_length = len(Team2_PlayerIds)
#getting info on all the players on each team for how succesfull each player is
Team1_PlayerInfo = []
Team2_PlayerInfo = []
Team1_PlayerCumeInfo = []
Team2_PlayerCumeInfo = []
for i in range(Team1_length):
print(i)
Team1_PlayerPIE = commonplayerinfo.CommonPlayerInfo(
Team1_PlayerIds[i]).get_data_frames()[1][['PIE']]
Team1_PlayerCumeInfo.append(
playercareerstats.PlayerCareerStats(
Team1_PlayerIds[i]).get_data_frames()[1])
time.sleep(.8)
Team1_PlayerPIE_List = Team1_PlayerPIE.values.tolist()
Team1_PlayerInfo.append(Team1_PlayerPIE_List)
#sorts the data from best to worst player
Team1_PlayerPIE_Sorted = sorted(Team1_PlayerInfo, reverse=True)
print(Team1_PlayerPIE_Sorted)
for k in range(Team2_length):
print(k)
Team2_PlayerPIE = commonplayerinfo.CommonPlayerInfo(
Team2_PlayerIds[k]).get_data_frames()[1][['PIE']]
df_all_playerprofile = pd.DataFrame()
df_active_playerprofile = pd.DataFrame()
df_retire_playerprofile = pd.DataFrame()
df_playerID_mapping = pd.DataFrame()
pair = list()
# person_id = [893, 977, 202689]
for id in person_id:
# Create id - name pair.
pair.append([id, df_playerList.loc[id]['DISPLAY_FIRST_LAST']])
playerprofile = playercareerstats.PlayerCareerStats(player_id=id)
df_playerprofile = playerprofile.career_totals_regular_season.get_data_frame()
df_playerprofile.insert(1,
"POSITION",
commonplayerinfo.CommonPlayerInfo(player_id=id)
.get_normalized_dict()['CommonPlayerInfo'][0]['POSITION'],
True)
# player_name = df_playerList.loc[id]['DISPLAY_FIRST_LAST']
# df_playerprofile.insert(0, "NAME", player_name, True)
print(df_playerprofile)
df_all_playerprofile = pd.concat(
[df_all_playerprofile, df_playerprofile], axis=0, ignore_index=True)
if df_playerList.loc[id]['ROSTERSTATUS'] == 1:
df_active_playerprofile = pd.concat(
[df_active_playerprofile, df_playerprofile], axis=0, ignore_index=True)
else:
df_retire_playerprofile = pd.concat(
[df_retire_playerprofile, df_playerprofile], axis=0, ignore_index=True)
