Pandas dataframe 或 SQLite 模糊搜索
Pandas dataframe or SQLite fuzzy search
我正在抓取多个体育博彩网站，以便比较网站上每场比赛的赔率。
我的问题是如何从数据库中已存在但团队名称以不同方式书写的比赛中识别 match_id。
请随意添加任何方法，即使它们不使用数据框或 SQLite。
matches table 的列是：
match_id: int, sport: string, home_team: string, away_team: string, date: string (dd/mm/YYY)
因此，对于每个新匹配项，我都想验证它是否已存在于数据库中。
新匹配 = (sport_to_check, home_team_to_check, away_team_to_check, date_to_check)
我的伪代码是这样的：
SELECT match_id FROM matches
WHERE sport = (sport_to_check)
AND date = (date_to_check)
AND (fuzz(home_team, home_team_to_check) > 80 OR fuzz(away_team, away_team_to_check) > 80) //the fuzzy scores evaluation
如果未找到匹配项，将插入新行。
我相信没有办法像这样将 python 与 SQL 混合，所以这就是我将其称为“伪代码”的原因。
我还可以将匹配项 table 拉入 Pandas 数据框并使用它进行评估，如果可行的话（如何？...）。
在任何给定时间，匹配 table 都不会超过几千条记录。
让我举一些预期输出的例子。其中解决方案由“find(row)”
表示
在数据库中匹配 table 为：
+----------+------------+-----------------------------+----------------------+------------+
| match_id | sport | home_team | visitor_team | date |
+----------+------------+-----------------------------+----------------------+------------+
| 84 | football | confianca | cuiaba esporte clube | 24/11/2020 |
| 209 | football | cs alagoana | operario pr | 24/11/2020 |
| 184 | football | grenoble foot 38 | as nancy lorraine | 24/11/2020 |
| 7 | football | sv turkgucu-ataspor munchen | saarbrucken | 24/11/2020 |
| 414 | handball | dinamo bucareste | usam nimes | 24/11/2020 |
| 846 | handball | benidorm | naturhouse la rioja | 25/11/2020 |
| 874 | handball | cegledi | ferencvarosi tc | 25/11/2020 |
| 418 | handball | lemvig-thyboron | kif kolding | 25/11/2020 |
| 740 | ice hockey | tps | kookoo | 25/11/2020 |
| 385 | football | stevenage | hull | 29/11/2020 |
+----------+------------+-----------------------------+----------------------+------------+
以及要评估的新匹配项：
+----------------+------------+---------------------+---------------------+------------+
| row (for demo) | sport | home_team | visitor_team | date |
+----------------+------------+---------------------+---------------------+------------+
| A | football | confianca-se | cuiaba mt | 24/11/2020 |
| B | football | csa | operario | 24/11/2020 |
| C | football | grenoble | nancy | 24/11/2020 |
| D | football | sv turkgucu ataspor | 1 fc saarbrucken | 24/11/2020 |
| E | handball | dinamo bucuresti | nimes | 24/11/2020 |
| F | handball | bm benidorm | bm logrono la rioja | 25/11/2020 |
| G | handball | cegledi kkse | ftc budapest | 25/11/2020 |
| H | handball | lemvig | kif kobenhavn | 25/11/2020 |
| I | ice hockey | turku ps | kookoo kouvola | 25/11/2020 |
| J | football | stevenage borough | hull city | 29/11/2020 |
| K | football | west brom | sheffield united | 28/11/2020 |
+----------------+------------+---------------------+---------------------+------------+
输出：
find(A) returns: 84
find(B) returns: 209
find(C) returns: 184
find(D) returns: 7
find(E) returns: 414
find(F) returns: 846
find(G) returns: 874
find(H) returns: 418
find(I) returns: 740
find(J) returns: 385
find(K) returns: (something like "not found" => I would then insert the new row)
谢谢！
基本上我按给定的日期和运动过滤掉原始的table。然后使用 fuzzywuzzy 在剩余行之间找到家庭和访客之间的最佳匹配：
设置：
import pandas as pd
cols = ['match_id','sport','home_team','visitor_team','date']
df1 = pd.DataFrame([
['84','football','confianca','cuiaba esporte clube','24/11/2020'],
['209','football','cs alagoana','operario pr','24/11/2020'],
['184','football','grenoble foot 38','as nancy lorraine','24/11/2020'],
['7','football','sv turkgucu-ataspor munchen','saarbrucken','24/11/2020'],
['414','handball','dinamo bucareste','usam nimes','24/11/2020'],
['846','handball','benidorm','naturhouse la rioja','25/11/2020'],
['874','handball','cegledi','ferencvarosi tc','25/11/2020'],
['418','handball','lemvig-thyboron','kif kolding','25/11/2020'],
['740','ice hockey','tps','kookoo','25/11/2020'],
['385','football','stevenage','hull','29/11/2020']], columns=cols)
cols = ['row','sport','home_team','visitor_team','date']
df2 = pd.DataFrame([
['A','football','confianca-se','cuiaba mt','24/11/2020'],
['B','football','csa','operario','24/11/2020'],
['C','football','grenoble','nancy','24/11/2020'],
['D','football','sv turkgucu ataspor','1 fc saarbrucken','24/11/2020'],
['E','handball','dinamo bucuresti','nimes','24/11/2020'],
['F','handball','bm benidorm','bm logrono la rioja','25/11/2020'],
['G','handball','cegledi kkse','ftc budapest','25/11/2020'],
['H','handball','lemvig','kif kobenhavn','25/11/2020'],
['I','ice hockey','turku ps','kookoo kouvola','25/11/2020'],
['J','football','stevenage borough','hull city','29/11/2020'],
['K','football','west brom','sheffield united','28/11/2020']], columns=cols)
代码：
import pandas as pd
from fuzzywuzzy import fuzz
import string
def calculate_ratio(row):
return fuzz.token_set_ratio(row['col1'],row['col2'] )
def find(df1, df2, row_search):
alpha = df2.query('row == "{row_search}"'.format(row_search=row_search))
sport = alpha.iloc[0]['sport']
date = alpha.iloc[0]['date']
home_team = alpha.iloc[0]['home_team']
visitor_team = alpha.iloc[0]['visitor_team']
beta = df1.query('sport == "{sport}" & date == "{date}"'.format(sport=sport,date=date))
if len(beta) == 0:
return 'Not found.'
else:
temp = pd.DataFrame({'match_id':list(beta['match_id']),'col1':list(beta['home_team'] + ' ' + beta['visitor_team']), 'col2':[home_team + ' ' + visitor_team]*len(beta)})
temp['score'] = temp.apply(calculate_ratio, axis=1)
temp = temp.sort_values('score', ascending=False)
outcome = temp.head(1).iloc[0]['match_id']
return outcome
for row_alpha in string.ascii_uppercase[0:11]:
outcome = find(df1, df2, row_alpha)
print ('{row_alpha} --> {outcome}'.format(row_alpha=row_alpha, outcome=outcome))
输出：
A --> 84
B --> 209
C --> 184
D --> 7
E --> 414
F --> 846
G --> 874
H --> 418
I --> 740
J --> 385
K --> Not found.
我正在抓取多个体育博彩网站，以便比较网站上每场比赛的赔率。
我的问题是如何从数据库中已存在但团队名称以不同方式书写的比赛中识别 match_id。
请随意添加任何方法，即使它们不使用数据框或 SQLite。
matches table 的列是：
match_id: int, sport: string, home_team: string, away_team: string, date: string (dd/mm/YYY)
因此，对于每个新匹配项，我都想验证它是否已存在于数据库中。
新匹配 = (sport_to_check, home_team_to_check, away_team_to_check, date_to_check)
我的伪代码是这样的：
SELECT match_id FROM matches
WHERE sport = (sport_to_check)
AND date = (date_to_check)
AND (fuzz(home_team, home_team_to_check) > 80 OR fuzz(away_team, away_team_to_check) > 80) //the fuzzy scores evaluation
如果未找到匹配项，将插入新行。
我相信没有办法像这样将 python 与 SQL 混合，所以这就是我将其称为“伪代码”的原因。
我还可以将匹配项 table 拉入 Pandas 数据框并使用它进行评估，如果可行的话（如何？...）。
在任何给定时间，匹配 table 都不会超过几千条记录。
让我举一些预期输出的例子。其中解决方案由“find(row)”
表示
在数据库中匹配 table 为：
+----------+------------+-----------------------------+----------------------+------------+
| match_id | sport | home_team | visitor_team | date |
+----------+------------+-----------------------------+----------------------+------------+
| 84 | football | confianca | cuiaba esporte clube | 24/11/2020 |
| 209 | football | cs alagoana | operario pr | 24/11/2020 |
| 184 | football | grenoble foot 38 | as nancy lorraine | 24/11/2020 |
| 7 | football | sv turkgucu-ataspor munchen | saarbrucken | 24/11/2020 |
| 414 | handball | dinamo bucareste | usam nimes | 24/11/2020 |
| 846 | handball | benidorm | naturhouse la rioja | 25/11/2020 |
| 874 | handball | cegledi | ferencvarosi tc | 25/11/2020 |
| 418 | handball | lemvig-thyboron | kif kolding | 25/11/2020 |
| 740 | ice hockey | tps | kookoo | 25/11/2020 |
| 385 | football | stevenage | hull | 29/11/2020 |
+----------+------------+-----------------------------+----------------------+------------+
以及要评估的新匹配项：
+----------------+------------+---------------------+---------------------+------------+
| row (for demo) | sport | home_team | visitor_team | date |
+----------------+------------+---------------------+---------------------+------------+
| A | football | confianca-se | cuiaba mt | 24/11/2020 |
| B | football | csa | operario | 24/11/2020 |
| C | football | grenoble | nancy | 24/11/2020 |
| D | football | sv turkgucu ataspor | 1 fc saarbrucken | 24/11/2020 |
| E | handball | dinamo bucuresti | nimes | 24/11/2020 |
| F | handball | bm benidorm | bm logrono la rioja | 25/11/2020 |
| G | handball | cegledi kkse | ftc budapest | 25/11/2020 |
| H | handball | lemvig | kif kobenhavn | 25/11/2020 |
| I | ice hockey | turku ps | kookoo kouvola | 25/11/2020 |
| J | football | stevenage borough | hull city | 29/11/2020 |
| K | football | west brom | sheffield united | 28/11/2020 |
+----------------+------------+---------------------+---------------------+------------+
输出：
find(A) returns: 84
find(B) returns: 209
find(C) returns: 184
find(D) returns: 7
find(E) returns: 414
find(F) returns: 846
find(G) returns: 874
find(H) returns: 418
find(I) returns: 740
find(J) returns: 385
find(K) returns: (something like "not found" => I would then insert the new row)
谢谢！
基本上我按给定的日期和运动过滤掉原始的table。然后使用 fuzzywuzzy 在剩余行之间找到家庭和访客之间的最佳匹配：
设置：
import pandas as pd
cols = ['match_id','sport','home_team','visitor_team','date']
df1 = pd.DataFrame([
['84','football','confianca','cuiaba esporte clube','24/11/2020'],
['209','football','cs alagoana','operario pr','24/11/2020'],
['184','football','grenoble foot 38','as nancy lorraine','24/11/2020'],
['7','football','sv turkgucu-ataspor munchen','saarbrucken','24/11/2020'],
['414','handball','dinamo bucareste','usam nimes','24/11/2020'],
['846','handball','benidorm','naturhouse la rioja','25/11/2020'],
['874','handball','cegledi','ferencvarosi tc','25/11/2020'],
['418','handball','lemvig-thyboron','kif kolding','25/11/2020'],
['740','ice hockey','tps','kookoo','25/11/2020'],
['385','football','stevenage','hull','29/11/2020']], columns=cols)
cols = ['row','sport','home_team','visitor_team','date']
df2 = pd.DataFrame([
['A','football','confianca-se','cuiaba mt','24/11/2020'],
['B','football','csa','operario','24/11/2020'],
['C','football','grenoble','nancy','24/11/2020'],
['D','football','sv turkgucu ataspor','1 fc saarbrucken','24/11/2020'],
['E','handball','dinamo bucuresti','nimes','24/11/2020'],
['F','handball','bm benidorm','bm logrono la rioja','25/11/2020'],
['G','handball','cegledi kkse','ftc budapest','25/11/2020'],
['H','handball','lemvig','kif kobenhavn','25/11/2020'],
['I','ice hockey','turku ps','kookoo kouvola','25/11/2020'],
['J','football','stevenage borough','hull city','29/11/2020'],
['K','football','west brom','sheffield united','28/11/2020']], columns=cols)
代码：
import pandas as pd
from fuzzywuzzy import fuzz
import string
def calculate_ratio(row):
return fuzz.token_set_ratio(row['col1'],row['col2'] )
def find(df1, df2, row_search):
alpha = df2.query('row == "{row_search}"'.format(row_search=row_search))
sport = alpha.iloc[0]['sport']
date = alpha.iloc[0]['date']
home_team = alpha.iloc[0]['home_team']
visitor_team = alpha.iloc[0]['visitor_team']
beta = df1.query('sport == "{sport}" & date == "{date}"'.format(sport=sport,date=date))
if len(beta) == 0:
return 'Not found.'
else:
temp = pd.DataFrame({'match_id':list(beta['match_id']),'col1':list(beta['home_team'] + ' ' + beta['visitor_team']), 'col2':[home_team + ' ' + visitor_team]*len(beta)})
temp['score'] = temp.apply(calculate_ratio, axis=1)
temp = temp.sort_values('score', ascending=False)
outcome = temp.head(1).iloc[0]['match_id']
return outcome
for row_alpha in string.ascii_uppercase[0:11]:
outcome = find(df1, df2, row_alpha)
print ('{row_alpha} --> {outcome}'.format(row_alpha=row_alpha, outcome=outcome))
输出：
A --> 84
B --> 209
C --> 184
D --> 7
E --> 414
F --> 846
G --> 874
H --> 418
I --> 740
J --> 385
K --> Not found.