def df_api_lideres(temporada, tipo):
response = leagueleaders.LeagueLeaders(season=temporada,
season_type_all_star=tipo)
df = response.get_data_frames()[0]
#df.head(10)
df1 = df[['PLAYER_ID', 'RANK', 'PLAYER', 'TEAM', 'AST', 'PTS']]
df1 = df1.head(20)
return df1
def league_leaders():
leaders = leagueleaders.LeagueLeaders()
leaders_df = leaders.get_data_frames()[0]
ranking_df = leaders_df[['PLAYER', 'RANK']]
#LeagueLeader rank added to df
ll_joined = pd.merge(df,
ranking_df,
left_on=['full_name'],
right_on=['PLAYER'])
ll_joined.to_csv('NBARANK.csv')
def perGame():
data = leagueleaders.LeagueLeaders()
df = data.league_leaders.get_data_frame()
was, stats = df[df.TEAM == 'WAS'], ['PTS', 'REB', 'AST', 'STL']
wash_per = was[stats].div(was['GP'], axis=0).round(1)
wash_per = pd.concat([was['PLAYER'], wash_per], axis=1)
# wash_per.PLAYER = wash_per.PLAYER.str.split(n=1).str[1]
wash_per = wash_per.set_index('PLAYER')
return tabulate(wash_per,
tablefmt='pipe',
headers='keys',
colalign=("left", "center", "center", "center", "center"),
floatfmt=".1f")
def getLeagueLeaderInfo(self,
league_num="00",
mode="PerGame",
my_scope="S",
my_season="2018-19",
my_type="Regular Season",
category="PTS"):
leader_info = leagueleaders.LeagueLeaders(
league_id=league_num,
per_mode48=mode,
scope=my_scope,
season=my_season,
season_type_all_star=my_type,
stat_category_abbreviation=category)
with open('nba.json', 'w') as fp:
json.dump(leader_info.get_dict(), fp, indent=3)
return leader_info.get_dict()
#this file uses the nba_api to create and then concatenate dataframes of each seasons' league leaders for major statistics only
#import endpoints and necessary parameters for the api
from nba_api.stats.endpoints import leagueleaders
from nba_api.stats.library.parameters import PerMode48
import pandas as pd
#reads in seasons and formats as a list
seasons_df = pd.read_excel(io='years_nba.xlsx')
seasons_list = [row['seasons'] for index, row in seasons_df.iterrows()]
#obtain most recent year's league leaders data as pergame averages data
league_leaders_upto_2020_raw = leagueleaders.LeagueLeaders(
per_mode48=PerMode48.per_game, season=seasons_list[0])
stats_upto_2020_df = league_leaders_upto_2020_raw.get_data_frames()[0]
#add column of year
num_rows = stats_upto_2020_df.shape[0]
year = [seasons_list[0]] * num_rows
stats_upto_2020_df['Year'] = year
#obtain each years' data, format as dataframe, and then concatenate with original dataframe
for nba_season in seasons_list:
if nba_season == seasons_list[0]:
continue
stats_raw = leagueleaders.LeagueLeaders(per_mode48=PerMode48.per_game,
season=nba_season)
stats_df = stats_raw.get_data_frames()[0]
num_rows = stats_df.shape[0]
year = [nba_season] * num_rows
stats_df['Year'] = year
from nba_api.stats.endpoints import commonteamroster
import json
roster_info = commonteamroster.CommonTeamRoster(season=2017,
team_id=1610612739)
roster_cavs = roster_info.get_dict()
with open('nba.json', 'w') as fp:
json.dump(roster_cavs, fp, indent=3)
a = roster_cavs['resultSets']
b = a[0]
c = b['rowSet']
for player in c:
print(player[3])
from nba_api.stats.endpoints import leagueleaders
league_info = leagueleaders.LeagueLeaders(
league_id="00",
per_mode48="PerGame",
scope="S",
season="2018-19",
season_type_all_star="Regular Season",
stat_category_abbreviation="PTS")
print(league_info.get_dict())
with open('nba.json', 'w') as fp:
json.dump(league_info.get_dict(), fp, indent=3)
player_or_team="Player", pt_measure_type="SpeedDistance")
stats_drives = leaguedashptstats.LeagueDashPtStats(player_or_team="Player",
pt_measure_type="Drives")
stats_catchshoot = leaguedashptstats.LeagueDashPtStats(
player_or_team="Player", pt_measure_type="CatchShoot")
stats_pullup = leaguedashptstats.LeagueDashPtStats(
player_or_team="Player", pt_measure_type="PullUpShot")
stats_postup = leaguedashptstats.LeagueDashPtStats(player_or_team="Player",
pt_measure_type="PostTouch")
stats_passing = leaguedashptstats.LeagueDashPtStats(player_or_team="Player",
pt_measure_type="Passing")
stats_possession = leaguedashptstats.LeagueDashPtStats(
player_or_team="Player", pt_measure_type="Possessions")
stats_rebounding = leaguedashptstats.LeagueDashPtStats(
player_or_team="Player", pt_measure_type="Rebounding")
stats_leagueleaders = leagueleaders.LeagueLeaders()
player_ids = set() #[player['id'] for player in players.get_active_players()]
player_ids_ignore = set()
# Heights and weights were causing issues trying to connect to the commonplayerinfo endpoint, so they have been omitted
#player_heights = []
#player_weights = []
#for player_id in player_ids:
# try:
# height_string = commonplayerinfo.CommonPlayerInfo(player_id=player_id).common_player_info.data['data'][0][10].split('-')
# if len(height_string) is 2:
# player_heights.append(int(height_string[0]) * 12 + int(height_string[1])) # convert height to inches
# else: #No data available
# player_ids_ignore.append(player_id)
# except:#connection error
# player_ids_ignore.append(player_id)
# fetchdata.py
# Gets league leader statistics from NBA-API and writes to text file "stats.txt"
import pandas as pd
from nba_api.stats.endpoints import leagueleaders # importing league leaders
lleaders2020 = leagueleaders.LeagueLeaders(league_id="00")
lleaders_df = lleaders2020.get_data_frames()[0] # creating data frame
#sort stats
counter = 0 # count amount of players in list
PlayerList = []
for index, row in lleaders_df.iterrows(): # iterate through dataframe
statlist = []
counter += 1
if counter <= 40:
PPG = round(row["PTS"] / row["GP"], 1) # calculate stat/game
RPG = round(row["REB"] / row["GP"], 1)
APG = round(row["AST"] / row["GP"], 1)
BPG = round(row["BLK"] / row["GP"], 1)
SPG = round(row["STL"] / row["GP"], 1)
statlist.append(row["PLAYER"])
statlist.append(row["TEAM"])
statlist.append(PPG) # append to list
statlist.append(RPG)
statlist.append(APG)
statlist.append(BPG)
statlist.append(SPG)
PlayerList.append(statlist)
#write to file
stats = open("stats.txt", "w") # open file
for p in PlayerList: