更有效的方式来编写这个 for 循环？

Question

import pandas as pd

sim = [['Matthew Stafford', 15, 13, 12], ['Dalvin Cook', 18, 16, 17], ['Daniel Jones', 17, 17, 15], ['Joe Mixon', 16, 15, 15]]
col = ['Player', 1 , 2, 3]
NFL_Sim = pd.DataFrame(sim, columns=col)
list = [['Matthew Stafford', 'Dalvin Cook'], ['Daniel Jones', 'Joe Mixon']]
col = ['QB', 'RB']
output_lines = pd.DataFrame(list, columns=col)

for x in range(1, 4):
    output_lines[x] = output_lines.QB.map(NFL_Sim.set_index('Player')[x].to_dict()) + output_lines.RB.map(NFL_Sim.set_index('Player')[x].to_dict())

print(output_lines)

                 QB           RB   1   2   3
0  Matthew Stafford  Dalvin Cook  33  29  29
1      Daniel Jones    Joe Mixon  33  32  30

所需的输出是正确的，但是随着我的扩展，我在 NFL_Sim 数据框中有数千列，这使得映射非常慢。有没有更有效的方法来编写这个 for 循环？还是先将 output_lines 转换为列表？我真的不确定什么是最好的。

Answer 1

使用系列创建映射：（QB 和 RB 的配对在 output_lines 中处理，我们希望使用索引位置连接到 NFL_Sim）

mapping = output_lines.T.stack()
mapping = pd.Series(mapping.index.droplevel(0), mapping)

获取每个位置的总和：

mapping = (NFL_Sim.assign(positions = lambda df: df.Player.map(mapping))
           # we do not need the Player column anymore,
           # since we have our mapping
                 .select_dtypes('number')
                 .groupby('positions')
                 .sum()
           )

将映射重新连接回 output_lines

output_lines.join(mapping)

                 QB           RB   1   2   3
0  Matthew Stafford  Dalvin Cook  33  29  29
1      Daniel Jones    Joe Mixon  33  32  30

Answer 2

首先，我建议您在创建时设置一次NFL_Sim 的索引。这样您就不必在循环内执行两次。

其次，如果您有一个四分卫列表和一个跑卫列表，我建议您创建两个矩阵：一个用于四分卫，一个用于跑卫。然后你可以把这两个加在一起。

import pandas as pd

sim = [['Matthew Stafford', 15, 13, 12], ['Dalvin Cook', 18, 16, 17], ['Daniel Jones', 17, 17, 15], ['Joe Mixon', 16, 15, 15]]
col = ['Player', 1 , 2, 3]
NFL_Sim = pd.DataFrame(sim, columns=col).set_index('Player')
qbs = ['Matthew Stafford', 'Daniel Jones']
rbs = ['Dalvin Cook', 'Joe Mixon']

qb_scores = NFL_Sim.loc[qbs, :]
rb_scores = NFL_Sim.loc[rbs, :]
# We need to reset the index because otherwise the addition
# of qb_scores and rb_scores will not be compatible; they have
# different indexes
output = qb_scores.reset_index(drop=True) + rb_scores.reset_index(drop=True)
output = output.assign(QB=qbs, RB=rbs)

Answer 3

melt 是一种更加动态的方式：

>>> x = output_lines.melt(value_name='Player', ignore_index=False).merge(NFL_Sim, on='Player')
>>> output_lines = output_lines.join(x.loc[[*x.index[::2], *x.index[1::2]]].groupby(x.index // 2).sum())
>>> output_lines
                 RB           QB   1   2   3
0  Matthew Stafford  Dalvin Cook  33  29  29
1      Daniel Jones    Joe Mixon  33  32  30
>>>