Python 将列中的值匹配的不同行中的1个值相加

我目前有两个数据集。 第一部分是我制定的足球队价值观清单。

我还有第二个数据集，它列出了今天比赛的球队

我想做的是将两支球队相互比赛的平均人数添加到数据集2中，这样结果会更准确

我已经查看了堆栈溢出，但没有找到任何能够提供帮助的内容。我对与熊猫合作相当陌生，所以我不确定这是否可行

作为示例数据集：

data1 = {
         'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
    }
data2 = {
         'Team': ['Hull', 'Leeds','Everton', 'Man City'],
         'Home0-0': ['80', '78','80', '66'],
         'Home1-0': ['81', '100','90', '70'],
    'Away0-1': ['88', '42','75', '69'],
    }

for i, row in data1.iterrows():
    team1 = data2[data2['Team']==row['TXTECHIPA1']]
    team2 = data2[data2['Team']==row['TXTECHIPA2']]
    for col in data2.columns[1:]:
        data1.loc[i, col]=(np.mean([int(team1[col].values[0]), int(team2[col].values[0])]))

print(data1)

期望输出为

Desired = {
    'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
             'Home0-0': ['80', '72'],
         'Home1-0': ['86', '85'],
    'Away0-1': ['86', '56',],
    }

---

感谢您添加数据。下面是一个使用循环的简单方法。循环通过

df2

（即将到来的比赛）。从

df1

（团队统计）中查找参与团队的等效行。现在，您将有两行来自

df1

。求所需列的平均值，并将其添加到

df2

。 考虑到与您的数据集结构相似，下面是一个示例：

df1 = pd.DataFrame({'team': ['one', 'two', 'three', 'four', 'five'],
                    'home0-0': [86, 78, 65, 67, 100],
                    'home1-0': [76, 86, 67, 100, 0],
                    'home0-1': [91, 88, 75, 100, 67],
                    'home1-1': [75, 67, 67, 100, 100],
                    'away0-0': [57, 86, 71, 91, 50],
                    'away1-0': [73, 50, 71, 100, 100],
                    'away0-1': [78, 62, 40, 80, 0],
                    'away1-1': [50, 71, 33, 100, 0]})
df2 = pd.DataFrame({'date': ['2020-06-17', '2020-06-17', '2020-06-17', '2020-06-17', '2020-06-17', '2020-06-17', '2020-06-17'],
                    'time': [1800, 1200, 1100, 2005, 1000, 1800, 1800],
                    'team1': ['one', 'two', 'three', 'four', 'five', 'one', 'three'],
                    'team2': ['five', 'four', 'two', 'one', 'three', 'two', 'four']})

for i, row in df2.iterrows():
    team1 = df1[df1['team']==row['team1']]
    team2 = df1[df1['team']==row['team2']]
    for col in df1.columns[1:]:
        df2.loc[i, col]=(np.mean([team1[col].values[0], team2[col].values[0]]))

print(df2)

对于示例数据集：

data1 = {
         'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
    }
data2 = {
         'Team': ['Hull', 'Leeds','Everton', 'Man City'],
         'Home0-0': ['80', '78','80', '66'],
         'Home1-0': ['81', '100','90', '70'],
    'Away0-1': ['88', '42','75', '69'],
    }

for i, row in data1.iterrows():
    team1 = data2[data2['Team']==row['TXTECHIPA1']]
    team2 = data2[data2['Team']==row['TXTECHIPA2']]
    for col in data2.columns[1:]:
        data1.loc[i, col]=(np.mean([int(team1[col].values[0]), int(team2[col].values[0])]))

print(data1)

结果:

     DATAMECI ORAMECI TXTECHIPA1 TXTECHIPA2  Home0-0  Home1-0  Away0-1
0  17/06/2020   11:30    Everton       Hull     80.0     85.5     81.5
1  17/06/2020   15:30   Man City      Leeds     72.0     85.0     55.5

---

与对行进行迭代相反的另一种选择是，您可以合并数据集，然后对列进行迭代

我也注意到你想要的输出是四舍五入的，所以我也有

示例：

data1 = pd.DataFrame({
         'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
    })
data2 = pd.DataFrame({
         'Team': ['Hull', 'Leeds','Everton', 'Man City'],
         'Home0-0': ['80', '78','80', '66'],
         'Home1-0': ['81', '100','90', '70'],
    'Away0-1': ['88', '42','75', '69'],
    })





Desired = pd.DataFrame({
    'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
             'Home0-0': ['80', '72'],
         'Home1-0': ['86', '85'],
    'Away0-1': ['86', '56',],
    })

import pandas as pd

cols = [ x for x in data2 if 'Home' in x or 'Away' in x ]
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA1'}), how='left', on=['TXTECHIPA1'])
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA2'}), how='left', on=['TXTECHIPA2'])

for col in cols:
    data1[col] = data1[[col + '_x', col + '_y']].astype(int).mean(axis=1).round(0)
    data1 = data1.drop([col + '_x', col + '_y'], axis=1)

print(data1)
     DATAMECI ORAMECI TXTECHIPA1 TXTECHIPA2  Home0-0  Home1-0  Away0-1
0  17/06/2020   11:30    Everton       Hull     80.0     86.0     82.0
1  17/06/2020   15:30   Man City      Leeds     72.0     85.0     56.0

代码：

data1 = pd.DataFrame({
         'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
    })
data2 = pd.DataFrame({
         'Team': ['Hull', 'Leeds','Everton', 'Man City'],
         'Home0-0': ['80', '78','80', '66'],
         'Home1-0': ['81', '100','90', '70'],
    'Away0-1': ['88', '42','75', '69'],
    })





Desired = pd.DataFrame({
    'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
             'Home0-0': ['80', '72'],
         'Home1-0': ['86', '85'],
    'Away0-1': ['86', '56',],
    })

import pandas as pd

cols = [ x for x in data2 if 'Home' in x or 'Away' in x ]
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA1'}), how='left', on=['TXTECHIPA1'])
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA2'}), how='left', on=['TXTECHIPA2'])

for col in cols:
    data1[col] = data1[[col + '_x', col + '_y']].astype(int).mean(axis=1).round(0)
    data1 = data1.drop([col + '_x', col + '_y'], axis=1)

print(data1)
     DATAMECI ORAMECI TXTECHIPA1 TXTECHIPA2  Home0-0  Home1-0  Away0-1
0  17/06/2020   11:30    Everton       Hull     80.0     86.0     82.0
1  17/06/2020   15:30   Man City      Leeds     72.0     85.0     56.0

输出：

data1 = pd.DataFrame({
         'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
    })
data2 = pd.DataFrame({
         'Team': ['Hull', 'Leeds','Everton', 'Man City'],
         'Home0-0': ['80', '78','80', '66'],
         'Home1-0': ['81', '100','90', '70'],
    'Away0-1': ['88', '42','75', '69'],
    })





Desired = pd.DataFrame({
    'DATAMECI': ['17/06/2020', '17/06/2020'],
         'ORAMECI': ['11:30', '15:30'],
         'TXTECHIPA1': ['Everton', 'Man City'],
    'TXTECHIPA2': ['Hull', 'Leeds'],
             'Home0-0': ['80', '72'],
         'Home1-0': ['86', '85'],
    'Away0-1': ['86', '56',],
    })

import pandas as pd

cols = [ x for x in data2 if 'Home' in x or 'Away' in x ]
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA1'}), how='left', on=['TXTECHIPA1'])
data1 = data1.merge(data2.rename(columns={'Team':'TXTECHIPA2'}), how='left', on=['TXTECHIPA2'])

for col in cols:
    data1[col] = data1[[col + '_x', col + '_y']].astype(int).mean(axis=1).round(0)
    data1 = data1.drop([col + '_x', col + '_y'], axis=1)

print(data1)
     DATAMECI ORAMECI TXTECHIPA1 TXTECHIPA2  Home0-0  Home1-0  Away0-1
0  17/06/2020   11:30    Everton       Hull     80.0     86.0     82.0
1  17/06/2020   15:30   Man City      Leeds     72.0     85.0     56.0

---

请不要发布数据的截图，而是共享一个样本数据集，我们可以从中复制并处理它。屏幕截图不会吸引人们回答您的问题，因为创建样本数据集需要时间。另外，这两个数据框没有共同的团队，您可以在示例数据中更具体一些吗？谢谢您的建议。我添加了示例数据，包括一个输出示例