尝试在DF1中创建一个新列，列出该队伍的主队数量。
DF1
Date Visitor V_PTS Home H_PTS \
0 2012-10-30 19:00:00 Washington Wizards 84 Cleveland Cavaliers 94
1 2012-10-30 19:30:00 Dallas Mavericks 99 Los Angeles Lakers 91
2 2012-10-30 20:00:00 Boston Celtics 107 Miami Heat 120
3 2012-10-31 19:00:00 Dallas Mavericks 94 Utah Jazz 113
4 2012-10-31 19:00:00 San Antonio Spurs 99 New Orleans Pelicans 95
Attendance Arena Location Capacity \
0 20562 Quicken Loans Arena Cleveland, Ohio 20562
1 18997 Staples Center Los Angeles, California 18997
2 20296 American Airlines Arena Miami, Florida 19600
3 17634 Vivint Smart Home Arena Salt Lake City, Utah 18303
4 15358 Smoothie King Center New Orleans, Louisiana 16867
Yr Arena Opened Season
0 1994 2012-13
1 1992 2012-13
2 1999 2012-13
3 1991 2012-13
4 1999 2012-13
DF2
2012-13 2013-14 2014-15 2015-16 2016-17
Cleveland Cavaliers 1 1 2 1 3
Los Angeles Lakers 2 1 1 1 0
Miami Heat 3 3 2 2 1
Chicago Bulls 2 1 2 2 1
Detroit Pistons 0 0 0 1 1
Los Angeles Clippers 2 2 2 1 1
New Orleans Pelicans 0 1 1 1 1
Philadelphia 76ers 1 0 0 0 0
Phoenix Suns 0 0 0 0 0
Portland Trail Blazers 1 2 2 0 0
Toronto Raptors 0 1 1 2 2
DF1['H_Allstars']=DF2[DF1['Season'],DF1['Home']])
导致TypeError：'Series'对象是可变的，因此它们不能被散列
我理解错误只是不确定如何做到这一点。
答案 0 :(得分：0)
您可以使用pandas.melt。将您的数据df2转换为长格式，即Home和Season作为列，Allstars作为值，然后合并到df1 on＆＃39; Home＆＃39;和＆＃39;季节＆＃39;。
import pandas as pd
df2['Home'] = df2.index
df2 = pd.melt(df2, id_vars = 'Home', value_vars = ['2012-13', '2013-14', '2014-15', '2015-16', '2016-17'], var_name = 'Season', value_name='H_Allstars')
df = df1.merge(df2, on=['Home','Season'], how='left')
答案 1 :(得分：0)
我删除了额外的列，只关注必要的列进行演示。
输入：
<强> DF1
Home 2012-13 2013-14 2014-15 2015-16 2016-17
0 Cleveland Cavaliers 1 1 2 1 3
1 Los Angeles Lakers 2 1 1 1 0
2 Miami Heat 3 3 2 2 1
3 Chicago Bulls 2 1 2 2 1
4 Detroit Pistons 0 0 0 1 1
5 Los Angeles Clippers 2 2 2 1 1
6 New Orleans Pelicans 0 1 1 1 1
7 Philadelphia 76ers 1 0 0 0 0
8 Phoenix Suns 0 0 0 0 0
9 Portland Trail Blazers 1 2 2 0 0
10 Toronto Raptors 0 1 1 2 2
<强> DF2
Visitor Home Season
0 Washington Wizards Cleveland Cavaliers 2012-13
1 Dallas Mavericks Los Angeles Lakers 2012-13
2 Boston Celtics Miami Heat 2012-13
3 Dallas Mavericks Utah Jazz 2012-13
4 San Antonio Spurs New Orleans Pelicans 2012-13
第1步：融化df1以获取allstars列
df3 = pd.melt(df1, id_vars='Home', value_vars = df1.columns[df.columns.str.contains('20')], var_name = 'Season', value_name='H_Allstars')
输出继电器：
Home Season H_Allstars
0 Cleveland Cavaliers 2012-13 1
1 Los Angeles Lakers 2012-13 2
2 Miami Heat 2012-13 3
3 Chicago Bulls 2012-13 2
4 Detroit Pistons 2012-13 0
5 Los Angeles Clippers 2012-13 2
6 New Orleans Pelicans 2012-13 0
7 Philadelphia 76ers 2012-13 1
8 Phoenix Suns 2012-13 0
...
第2步：将此新数据框与df2合并以获取H_Allstars和V_Allstars列
df4 = pd.merge(df2, df3, how='left', on=['Home', 'Season'])
输出：
Visitor Home Season H_Allstars
0 Washington Wizards Cleveland Cavaliers 2012-13 1.0
1 Dallas Mavericks Los Angeles Lakers 2012-13 2.0
2 Boston Celtics Miami Heat 2012-13 3.0
3 Dallas Mavericks Utah Jazz 2012-13 NaN
4 San Antonio Spurs New Orleans Pelicans 2012-13 0.0
第3步：添加V_Allstars列
# renaming column as required
df3.rename(columns={'Home': 'Visitor', 'H_Allstars': 'V_Allstars'}, inplace=True)
df5 = pd.merge(df4, df3, how='left', on=['Visitor', 'Season'])
输出：
Visitor Home Season H_Allstars V_Allstars
0 Washington Wizards Cleveland Cavaliers 2012-13 1.0 NaN
1 Dallas Mavericks Los Angeles Lakers 2012-13 2.0 NaN
2 Boston Celtics Miami Heat 2012-13 3.0 NaN
3 Dallas Mavericks Utah Jazz 2012-13 NaN NaN
4 San Antonio Spurs New Orleans Pelicans 2012-13 0.0 NaN