下面是我有帮助的脚本。我想对其进行更改，以便为我提供2个新列以及3个可能的变量。 Date | gamePK | Home | Home Rest | Away | Away Rest
当前的matches.csv格式为Date | gamePK | Home | Away
Home Rest和Away Rest（如果前一天对阵没有踢的球队，则为-1；如果前一天对阵没有踢的球队，则为1；如果对手没有对阵，则为0；否则）
任何有关如何创建列并为其编写此语句的信息将不胜感激。
import csv
import requests
import datetime
from pprint import pprint
import time
import pandas as pd
kp = []
for i in range(20001,20070):
req = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?site=en_nhl&gamePk=20180' + str(i) + '&leaderGameTypes=R&expand=schedule.broadcasts.all,schedule.radioBroadcasts,schedule.teams,schedule.ticket,schedule.game.content.media.epg')
data = req.json()
for item in data['dates']:
date = item['date']
games = item['games']
for game in games:
gamePk = game['gamePk']
season = game['season']
teams = game['teams']
home = teams['home']
home_tm = home['team']['abbreviation']
away = teams['away']
away_tm = away['team']['abbreviation']
print (date, gamePk, away_tm, home_tm)
kp.append([date, gamePk, away_tm, home_tm])
pprint(kp)
df = pd.DataFrame(kp, columns=['Date','gamePk','Home', 'Away'])
df.to_csv('matches.csv', sep=',', header=True, index=False)
time.sleep(5)
def find_last(match_date, da, team):
home_play = da[da['Home'] == team].tail(1) #then find last matches played at home, select greatest
away_play = da[da['Away'] == team].tail(1) #" " find last matches played at away, select greatest
#then take the last match played, either home or away, whichever is more recent
if home_play.empty and away_play.empty:
print (team, "no_matches before this date")
last_match = 'NA'
elif home_play.empty:
last_match = away_play.Date.item()
elif away_play.empty:
last_match = home_play.Date.item()
else:
last_match = max([home_play.Date.item(), away_play.Date.item()])
if last_match != 'NA':
#And then subtract this from "todays" date (match_date)
duration_since_last = pd.to_datetime(match_date) - pd.to_datetime(last_match)
print ("Team:", team)
print ("Todays game date = ", match_date)
print ("Last match played = ", last_match)
print ("Rest Period = ", duration_since_last)
print()
return duration_since_last
df = pd.read_csv('matches.csv', sep=',')
for k in df.index:
home_team = df.Home[k]
away_team = df.Away[k]
match_date = df.Date[k]
gamePk = df.gamePk[k]
#we want to find all date values less than todays match date.
da = df[df['Date'] < match_date]
## if not da.empty:
for team in [home_team,away_team]:
print ("Record", k, home_team, 'vs', away_team)
find_last(match_date, da, team)
print ('________________________________________')
答案 0 :(得分：1)
您提供的脚本已分为几个小节，以进一步理解。 需要以下新部分来为DataFrame添加所需的内容：
这是该作品的一本Jupyter笔记本：nhl_stats_parsing
代码：
import csv
import requests
import datetime
from pprint import pprint
import time
import pandas as pd
from pprint import pprint as pp
import json
pd.set_option('max_columns', 100)
pd.set_option('max_rows', 300)
# ### make request to NHL stats server for data and save it to a file
address_p1 = 'https://statsapi.web.nhl.com/api/v1/schedule?site=en_nhl&gamePk=20180'
address_p2 = '&leaderGameTypes=R&expand=schedule.broadcasts.all,schedule.radioBroadcasts,schedule.teams,schedule.ticket,schedule.game.content.media.epg'
with open('data.json', 'w') as outfile:
data_list = []
for i in range(20001,20070): # end 20070
req = requests.get(address_p1 + str(i) + address_p2)
data = req.json()
data_list.append(data) # append each request to the data list; will be a list of dicts
json.dump(data_list, outfile) # save the json file so you don't have to keep hitting the nhl server with your testing
# ### read the json file back in
with open('data.json') as f:
data = json.load(f)
# ### this is what 1 record looks like
for i, x in enumerate(data):
if i == 0:
pp(x)
# ### parse each dict
kp = []
for json_dict in data:
for item in json_dict['dates']:
date = item['date']
games = item['games']
for game in games:
gamePk = game['gamePk']
season = game['season']
teams = game['teams']
home = teams['home']
home_tm = home['team']['abbreviation']
away = teams['away']
away_tm = away['team']['abbreviation']
print (date, gamePk, away_tm, home_tm)
kp.append([date, gamePk, away_tm, home_tm])
# ### create DataFrame and save to csv
df = pd.DataFrame(kp, columns=['Date','gamePk','Home', 'Away'])
df.to_csv('matches.csv', sep=',', header=True, index=False)
# ### read in csv into DataFrame
df = pd.read_csv('matches.csv', sep=',')
print(df.head()) # first 5
## On Game Day, What is the Previous Day
def yesterday(date):
today = datetime.datetime.strptime(date, '%Y-%m-%d')
return datetime.datetime.strftime(today - datetime.timedelta(1), '%Y-%m-%d')
def yesterday_apply(df):
df['previous_day'] = df.apply(lambda row: yesterday(date=row['Date']), axis=1)
yesterday_apply(df)
## Did We Play on the Previous Day
def played_previous_day(df, date, team):
filter_t = f'(Date == "{date}") & ((Home == "{team}") | (Away == "{team}"))'
filtered_df = df.loc[df.eval(filter_t)]
if filtered_df.empty:
return False # didn't play previous day
else:
return True # played previous day
def played_previous_day_apply(df):
df['home_played_previous_day'] = df.apply(lambda row: played_previous_day(df, date=row['previous_day'], team=row['Home']), axis=1)
df['away_played_previous_day'] = df.apply(lambda row: played_previous_day(df, date=row['previous_day'], team=row['Away']), axis=1)
played_previous_day_apply(df)
# # Determine Game Day Handicap
# Home Rest & Away Rest (-1 if the team played the day prior vs a team that didn't, 1 if the team didn't play the day prior vs an opponent who did, 0 otherwise)
def handicap(team, home, away):
if (team == 'home') and not home and away:
return 1
elif (team == 'away') and not home and away:
return -1
elif (team == 'home') and home and not away:
return -1
elif (team == 'away') and home and not away:
return 1
else:
return 0
def handicap_apply(df):
df['home_rest'] = df.apply(lambda row: handicap(team='home', home=row['home_played_previous_day'], away=row['away_played_previous_day']), axis=1)
df['away_rest'] = df.apply(lambda row: handicap(team='away', home=row['home_played_previous_day'], away=row['away_played_previous_day']), axis=1)
handicap_apply(df)
print(df)
# ### data presentation method
def find_last(match_date, da, team):
home_play = da[da['Home'] == team].tail(1) # then find last matches played at home, select greatest
away_play = da[da['Away'] == team].tail(1) # " " find last matches played at away, select greatest
#then take the last match played, either home or away, whichever is more recent
if home_play.empty and away_play.empty:
print (team, "no_matches before this date")
last_match = 'NA'
elif home_play.empty:
last_match = away_play.Date.item()
elif away_play.empty:
last_match = home_play.Date.item()
else:
last_match = max([home_play.Date.item(), away_play.Date.item()])
if last_match != 'NA':
#And then subtract this from "todays" date (match_date)
duration_since_last = pd.to_datetime(match_date) - pd.to_datetime(last_match)
print ("Team:", team)
print ("Todays game date = ", match_date)
print ("Last match played = ", last_match)
print ("Rest Period = ", duration_since_last)
print()
return duration_since_last
# ### produce your output
for k in df.index:
home_team = df.Home[k]
away_team = df.Away[k]
match_date = df.Date[k]
gamePk = df.gamePk[k]
#we want to find all date values less than todays match date.
da = df[df['Date'] < match_date]
## if not da.empty:
for team in [home_team, away_team]:
print ("Record", k, home_team, 'vs', away_team)
find_last(match_date, da, team) # call your method
print('_' * 40)
输出：
Date gamePk Home Away previous_day home_played_previous_day away_played_previous_day home_rest away_rest
0 2018-10-03 2018020001 MTL TOR 2018-10-02 False False 0 0
1 2018-10-03 2018020002 BOS WSH 2018-10-02 False False 0 0
2 2018-10-03 2018020003 CGY VAN 2018-10-02 False False 0 0
3 2018-10-03 2018020004 ANA SJS 2018-10-02 False False 0 0
4 2018-10-04 2018020005 BOS BUF 2018-10-03 True False -1 1
5 2018-10-04 2018020006 NSH NYR 2018-10-03 False False 0 0
6 2018-10-04 2018020007 WSH PIT 2018-10-03 True False -1 1
7 2018-10-04 2018020008 NYI CAR 2018-10-03 False False 0 0
8 2018-10-04 2018020009 CHI OTT 2018-10-03 False False 0 0