Question

这是我的代码：

import pandas as pd
import numpy
from quilt.data.bussiere import test
ar = numpy.array([[1.1, 2, 3.3, 4], [2.7, 10, 5.4, 7], [5.3, 9, 1.5, 15]])
df = pd.DataFrame(ar, index = ['a1', 'a2', 'a3'], columns = ['A', 'B', 'C', 'D'])
df.loc[df['A'] == 5.3] = df.loc[df['A'] == 2.7] 
df

结果是一行NaN：

     A        B      C       D
a1  1.1     2.0     3.3     4.0
a2  2.7     10.0    5.4     7.0
a3  NaN     NaN     NaN     NaN

如何正确更换？

Answer 1

pandas是索引敏感的df.loc[df['A'] == 5.3]和df.loc[df['A'] == 2.7]都是pandas object，因此在您进行操作时将考虑index Assign，因为其中一个是索引a2另一个是a3，这就是为什么您收到NaN

df.loc[df['A'] == 5.3] = df.loc[df['A'] == 2.7] .values # using value here,without the index match assign
df
Out[137]: 
      A     B    C    D
a1  1.1   2.0  3.3  4.0
a2  2.7  10.0  5.4  7.0
a3  2.7  10.0  5.4  7.0

将值选择的行替换为数据框中的值选择的另一行

1 个答案: