我正在处理有关大联盟棒球比赛出勤率的数据。
我正在尝试在数据框中创建一个新列，以返回指定对手球队比赛的最接近日期（但不能在给定日期之后）。
例如，对于包含有关洛杉矶天使游戏数据的行：
Game_Num Date Team Win Attendance Net Wins
23 2010-04-05 LAA 1 43504 12
我想查找洛杉矶道奇（'LAD'）游戏的最近日期并将其附加在新列中。
我的最终目标是创建另一个列，该列显示比赛中对手球队的净胜利是什么，以便我可以查看是否另一支球队在影响门票销售的情况下表现出色。
到目前为止，这是我尝试过的：
for index,row in bbattend.iterrows():
if row['Team'] == 'LAA':
basedate = row['Date']
tempdf = bbattend.loc[(bbattend['Team'] == 'LAD') & (bbattend['Date'] < basedate)]
tempdf['Datediff'] = abs(basedate-tempdf['Date']).days
mindiff = tempdf['Datediff'].min()
bbattend['CloseRivalDate'] = tempdf[tempdf['Date']==mindiff]['Date']
bbattend['RivalNetWins'] = tempdf[tempdf['Date']==mindiff]['Net_Wins']
bbattend['RivalWinPer'] = tempdf[tempdf['Date']==mindiff]['Win_Per']
这是我从中得到的错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-128-f2be88528772> in <module>
3 basedate = row['Date']
4 tempdf = bbattend.loc[(bbattend['Team'] == 'LAD') & (bbattend['Date'] < basedate)]
----> 5 tempdf['Datediff'] = abs(basedate-tempdf['Date']).days
6 mindiff = tempdf['Datediff'].min()
7 bbattend['CloseRivalDate'] = tempdf[tempdf['Date']==mindiff]['Date']
~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self,name)
5065 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5066 return self[name]
-> 5067 return object.__getattribute__(self,name)
5068
5069 def __setattr__(self,name,value):
AttributeError: 'Series' object has no attribute 'days'
这是我的数据框代码，以防万一：
import requests
import pandas as pd
import numpy as np
from datetime import datetime
import re
Teams = ['LAA','LAD','NYY','NYM','CHC','CHW','OAK','SFG']
Years = []
for year in range(2010,2020):
Years.append(str(year))
list_of_df = list()
for team in Teams:
for year in Years:
url = 'https://www.baseball-reference.com/teams/' + team + '/' + year +'-schedule-scores.shtml'
dfname = team + '_' + year
html = requests.get(url).content
df_list = pd.read_html(html)
df = df_list[-1]
#Formatting data table
df.rename(columns={"Gm#": "GM_Num","Unnamed: 4": "Home","Tm": "Team","D/N": "Night"},inplace = True)
df['Home'] = df['Home'].apply(lambda x: 0 if x == '@' else 1)
df['Game_Win'] = df['W/L'].astype(str).str[0]
df['Game_Win'] = df['Game_Win'].apply(lambda x: 0 if x == 'L' else 1)
df['Night'] = df['Night'].apply(lambda x: 1 if x == 'N' else 0)
df['Streak'] = df['Streak'].apply(lambda x: -1*len(x) if '-' in x else len(x))
df.drop('Unnamed: 2',axis=1,inplace = True)
df.drop('Orig. Scheduled',inplace = True)
df.drop('Win',inplace = True)
df.drop('Loss',inplace = True)
df.drop('Save',inplace = True)
#Drop rows that do not have data
df = df[df['GM_Num'].str.isdigit()]
WL = df["W-L"].str.split("-",n = 1,expand = True)
df["Wins"] = WL[0].astype(dtype=np.int64)
df["Losses"] = WL[1].astype(dtype=np.int64)
df['Net_Wins'] = df['Wins'] - df['Losses']
df['Win_Per'] = df['Wins']/(df['Wins']+df['Losses'])
DayDate = df['Date'].str.split(",",expand = True)
df['DayOfWeek'] = DayDate[0]
df['Date'] = DayDate[1] + ',' + year
df['Date'] = [re.sub("\s\(\d+\)","",str(x)) for x in df['Date']]
df['Date'] = pd.to_datetime(df['Date'],format='%b %d,%Y')
list_of_df.append(df)
bbattend = pd.concat(list_of_df)
bbattend
我知道这绝对不是最有效的方法，但是可以得到我想要的结果。