我在stackoverflow上找到了此脚本，以从低估状态获取一些数据。 （它是由@ chitown88制作或修改的，我认为这是他/她的功劳。）
输出是这样的
League Season Date Home team Away team 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
0 EPL 2018 Saturday, February 23, 2019 Newcastle United Huddersfield* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.087855 0.087855 0.087855 0.087855 0.474551 0.474551 0.474551 0.474551 0.52089 0.52089 0.52089 0.588242 0.588242 0.588242 0.588242 0.588242 0.588242 0.588242 0.650563 0.650563 0.650563 0.713521 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.780235 0.862191 0.862191 0.862191 0.972581 1.00803 1.00803 2.01324 2.01324 2.103931 2.103931 2.103931 2.103931 2.248354 2.248354 2.248354 2.278213 2.278213 2.278213 2.278213 2.278213 2.278213 2.397133 2.397133 2.397133 2.397133 2.397133 2.397133 2.484387 2.484387 2.624275 2.624275 2.755339 2.868987 2.868987 2.868987 2.868987 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.026651 3.026651 3.026651 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.133949
在多个URL上执行此操作时，时间数据不一致，即某些结束于83，某些结束于89、90、92等。
有什么方法可以修改此设置以使时间每次从0到90？
如果数据以83结尾，也许我们可以重复84-90之间的xG值？
这可能吗？
这是脚本：
import requests
import json
import re
from pandas.io.json import json_normalize
import pandas as pd
response = requests.get('https://understat.com/match/9458')
shotsData = re.search("shotsData\s+=\s+JSON.parse\('([^']+)", response.text)
decoded_string = bytes(shotsData.groups()[0], 'utf-8').decode('unicode_escape')
shotsObj = json.loads(decoded_string)
match_info = re.search("match_info\s+=\s+JSON.parse\('([^']+)", response.text)
decoded_string = bytes(match_info.groups()[0], 'utf-8').decode('unicode_escape')
matchObj = json.loads(decoded_string)
rostersData = re.search("rostersData\s+=\s+JSON.parse\('([^']+)", response.text)
decoded_string = bytes(rostersData.groups()[0], 'utf-8').decode('unicode_escape')
rostersObj = json.loads(decoded_string)
# Shots Data into a DataFrame
away_shots_df = json_normalize(shotsObj['a'])
home_shots_df = json_normalize(shotsObj['h'])
shots_df = away_shots_df.append(home_shots_df)
# Rosters Data into a DataFrame
away_rosters_df = pd.DataFrame()
for key, v in rostersObj['a'].items():
temp_df = pd.DataFrame.from_dict([v])
away_rosters_df = away_rosters_df.append(temp_df)
home_rosters_df = pd.DataFrame()
for key, v in rostersObj['h'].items():
temp_df = pd.DataFrame.from_dict([v])
home_rosters_df = home_rosters_df.append(temp_df)
rosters_df = away_rosters_df.append(home_rosters_df)
if away_rosters_df['red_card'].astype(int).sum() > 0:
a_red_card = '*'
else:
a_red_card = ''
if home_rosters_df['red_card'].astype(int).sum() > 0:
h_red_card = '*'
else:
h_red_card = ''
teams_dict = {'a':matchObj['team_a']+a_red_card, 'h':matchObj['team_h']+h_red_card}
match_title = matchObj['team_h'] + ' vs. ' + matchObj['team_a']
#########################################################################
# Timing Chart is an aggregation (running sum) of xG from the shotsData
#########################################################################
import numpy as np
# Convert 'minute' astype int and sort the dataframe by 'minute'
shots_df['minute'] = shots_df['minute'].astype(int)
shots_df['xG'] = shots_df['xG'].astype(float)
timing_chart_df = shots_df[['h_a', 'minute', 'xG']].sort_values('minute')
timing_chart_df['h_a'] = timing_chart_df['h_a'].map(teams_dict)
# Get max value of the 'minute' column to interpolate minute interval between that range
max_value = timing_chart_df['minute'].max()
# Aggregate xG within the same minute
timing_chart_df = timing_chart_df.groupby(['h_a','minute'], as_index=False)['xG'].sum()
# Interpolate for each team/group
min_idx = np.arange(timing_chart_df['minute'].max() + 1)
m_idx = pd.MultiIndex.from_product([timing_chart_df['h_a'].unique(), min_idx], names=['h_a', 'minute'])
# Calculate the running sum
timing_chart_df = timing_chart_df.set_index(['h_a', 'minute']).reindex(m_idx, fill_value=0).reset_index()
timing_chart_df['running_sum_xG'] = timing_chart_df.groupby('h_a')['xG'].cumsum()
timing_chart_T_df = timing_chart_df.pivot(index='h_a', columns='minute', values='running_sum_xG')
timing_chart_T_df = timing_chart_T_df.reset_index().rename(columns={timing_chart_T_df.index.name:match_title})
from datetime import datetime
home_team = matchObj['team_h']+h_red_card
away_team = matchObj['team_a']+a_red_card
league = matchObj['league']
season = matchObj['season']
date = matchObj['date']
datetime_object = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
date = datetime_object.strftime('%A, %B %d, %Y')
results_df = pd.DataFrame([[league, season, date, home_team, away_team]], columns = ['League','Season','Date','Home team','Away team'])
home_xg_sum = timing_chart_df[timing_chart_df['h_a'] == home_team].pivot(index='h_a', columns='minute', values='running_sum_xG')
away_xg_sum = timing_chart_df[timing_chart_df['h_a'] == away_team].pivot(index='h_a', columns='minute', values='running_sum_xG')
data = [league, season, date, home_team, away_team] + home_xg_sum.values.tolist()[0] + away_xg_sum.values.tolist()[0]
cols = ['League','Season','Date','Home team','Away team'] + list(home_xg_sum.columns) + list(away_xg_sum.columns)
results_df = pd.DataFrame([data], columns = cols)
答案 0 :(得分：2)
实际上有人使用了我的代码！好的，此脚本的设置方式并不完全是我安排输出的方式，而是OP寻求帮助的内容。
至于更改该分钟列表以将其硬截止时间设置为90，更改的是1行（实际上这里是2行。我不知道我没有在应该更改的行中使用该变量使用过）。但要更改：
# Get max value of the 'minute' column to interpolate minute interval between that range
max_value = timing_chart_df['minute'].max()
到
# Get max value of the 'minute' column to interpolate minute interval between that range
max_value = timing_chart_df['minute'].max()
if max_value < 90:
max_value = 90
，然后下面几行应引用该max_value变量，而我最初没有这么做。所以改变：
# Interpolate for each team/group
min_idx = np.arange(timing_chart_df['minute'].max() + 1)
到
# Interpolate for each team/group
min_idx = np.arange(max_value + 1)
输出：
print (results_df.to_string())
League Season Date Home team Away team 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
0 EPL 2018 Saturday, February 23, 2019 Newcastle United Huddersfield* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.087855 0.087855 0.087855 0.087855 0.474551 0.474551 0.474551 0.474551 0.52089 0.52089 0.52089 0.588242 0.588242 0.588242 0.588242 0.588242 0.588242 0.588242 0.650563 0.650563 0.650563 0.713521 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.765269 0.780235 0.862191 0.862191 0.862191 0.972581 1.00803 1.00803 2.01324 2.01324 2.103931 2.103931 2.103931 2.103931 2.248354 2.248354 2.248354 2.278213 2.278213 2.278213 2.278213 2.278213 2.278213 2.397133 2.397133 2.397133 2.397133 2.397133 2.397133 2.484387 2.484387 2.624275 2.624275 2.755339 2.868987 2.868987 2.868987 2.868987 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.011753 3.026651 3.026651 3.026651 3.026651 3.026651 3.026651 3.026651 3.026651 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.110397 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.120421 0.133949 0.133949 0.133949 0.133949 0.133949 0.133949