Question

我有以下功能。这里我有lambda，它接受多个参数row和float值。我正在跑步时，我发现错误的原因是什么？

def func():
    Top15 = create_frame()
    total_citations = Top15['Citations'].sum()
    ratioof_selfcitations_to_totalcitations = Top15.apply(lambda (row, total): (row['Self-citations']/total),
                                                          total_citations)
    return ratioof_selfcitations_to_totalcitations

FUNC

Answer 1

似乎你需要：

ratioof_selfcitations_to_totalcitations = Top15.apply(lambda row: row['Self-citations']/total_citations, axis=1)

但更好更快的是Series除以scalar：

ratioof_selfcitations_to_totalcitations = Top15['Self-citations']/total_citations

样品：

Top15 = pd.DataFrame({'Self-citations': [1,2,3,6],
                      'Citations': range(4)})
print (Top15)
   Citations  Self-citations
0          0               1
1          1               2
2          2               3
3          3               6

total_citations = Top15['Citations'].sum()
ratioof_selfcitations_to_totalcitations = Top15.apply(lambda row: row['Self-citations']/total_citations, axis=1)
print (ratioof_selfcitations_to_totalcitations)
0    0.166667
1    0.333333
2    0.500000
3    1.000000
dtype: float64

ratioof_selfcitations_to_totalcitations = Top15['Self-citations']/total_citations
print (ratioof_selfcitations_to_totalcitations)
0    0.166667
1    0.333333
2    0.500000
3    1.000000
Name: Self-citations, dtype: float64

带有多个参数的lambda用于应用函数

1 个答案: