Question

我的数据框为：

dfAll = match ID    Team A Hero 1.1 Team A Hero 1.2   Team A Hero 1.3
     123124140         (1, 2)            (2, 3)            (1, 3)
     123123124         (4, 1)            (3, 4)            (1, 5)

以此类推。

我有一个交叉表：

dfEloCross =     1     2     3     4     5
             1  NaN  -1.0  +2.0  -8.0   +5.0

             2  +1.0  NaN  +2.5  +3.0    0

             3  -2.0 -2.5  NaN   +5.5   -3.5

             4  +8.0 -3.0  -5.5  NaN   +2.8

             5  -5.0   0   +3.5  -2.8    NaN

我希望每次比赛都按原样返回一个值，该值垂直于水平。

df1 = matchups 1    matchups 2    matchups 3
       +1.0           -2.5         -2.0
       -8.0           -5.5         -5.0

我尝试使用此代码：

for uv in range(1, 6):
for xv in range(1,6):
    dfAll['Matchup' + ' ' + str(uv) + '.' + str(xv)] = dfAll['Team A Hero' + ' ' + str(uv) + '.' + str(xv)].apply(lambda x: dfEloCross.lookup([x[0]],[x[1]])[0])

它对Team A Hero 1.1

的第一列工作正常

但是返回其他人的键错误 KeyError: 'One or more column labels was not found

我是一个初学者，所以我很想获得我所能获得的所有帮助。预先感谢！

编辑

作为参考，这是我的数据框的一个片段：

dfAll = {'Team A Hero 1.1': {0: '(22, 21)', 1: '(12, 3)', 2: '(6, 7)', 3: '(13, 18)', 4: '(28, 7)', 5: '(9, 36)', 6: '(36, 7)', 7: '(9, 7)', 8: '(4, 61)'}, 'Team A Hero 1.2': {0: '(22, 43)', 1: '(12, 15)', 2: '(6, 31)', 3: '(13, 49)', 4: '(28, 13)', 5: '(9, 58)', 6: '(36, 13)', 7: '(9, 8)', 8: '(4, 64)'}, 'Team A Hero 1.3': {0: '(22, 71)', 1: '(12, 30)', 2: '(6, 40)', 3: '(13, 64)', 4: '(28, 56)', 5: '(9, 78)', 6: '(36, 30)', 7: '(9, 13)', 8: '(4, 72)'}, 'Team A Hero 1.4': {0: '(22, 77)', 1: '(12, 65)', 2: '(6, 43)', 3: '(13, 65)', 4: '(28, 87)', 5: '(9, 95)', 6: '(36, 80)', 7: '(9, 15)', 8: '(4, 76)'}, 'Team A Hero 1.5': {0: '(22, 85)', 1: '(12, 103)', 2: '(6, 69)', 3: '(13, 107)', 4: '(28, 106)', 5: '(9, 107)', 6: '(36, 98)', 7: '(9, 112)', 8: '(4, 84)'}, 'Team A Hero 2.1': {0: '(28, 21)', 1: '(58, 3)', 2: '(20, 7)', 3: '(54, 18)', 4: '(44, 7)', 5: '(28, 36)', 6: '(49, 7)', 7: '(52, 7)', 8: '(51, 61)'}}

Answer 1

您不需要使用嵌套循环。相反，您可以使用Pandas可用的pd.DataFrame.lookup方法。

在这种情况下，您可以迭代“团队”列，解压缩坐标并一次执行一系列查找：

from ast import literal_eval

df1 = pd.DataFrame({'matchID': [123124140, 123123124],
                    'TeamA_1.1': ['(1, 2)', '(4, 1)'],
                    'TeamA_1.2': ['(2, 3)', '(3, 4)']})

# convert mapping table columns to integer type
dfEloCross.columns = dfEloCross.columns.astype(int)

# calculate columns which need mapping
team_cols = df1.columns[df1.columns.str.startswith('Team')]

# cycle each column, strip column & row coordinates and use with lookup
for col in team_cols:
    c, r = zip(*df1[col].apply(literal_eval))
    df1[col] = dfEloCross.lookup(r, c)

print(df1)

   TeamA_1.1  TeamA_1.2    matchID
0        1.0       -2.5  123124140
1       -8.0       -5.5  123123124

查找期间的KeyError以引用交叉表

1 个答案: