另一个 Pandas 问题:

我有这个带有分层索引的表:

In [51]:
from pandas import DataFrame
f = DataFrame({'a': ['1','2','3'], 'b': ['2','3','4']})
f.columns = [['level1 item1', 'level1 item2'],['', 'level2 item2'], ['level3 item1', 'level3 item2']]
f
Out[51]:
    level1 item1    level1 item2
                    level2 item2
    level3 item1    level3 item2
0         1              2
1         2              3
2         3              4

碰巧选择level1 item1会产生如下错误:

In [58]: f['level1 item1']
AssertionError: Index length did not match values

不过，这好像和关卡的多少有些关系。当我将级别数减少到只有两个时，没有这样的错误:

from pandas import DataFrame
f = DataFrame({'a': ['1','2','3'], 'b': ['2','3','4']})
f.columns = [['level1 item1', 'level1 item2'],['', 'level1 item2']]
f
Out[59]:
     level1 item1   level1 item2
                    level1 item2
0          1              2
1          2              3
2          3              4

相反，之前的 DataFrame 给出了预期的系列:

In [63]:
f['level1 item1']
Out[63]:
0    1
1    2
2    3
Name: level1 item1

用虚拟字符填充 level1 item1 下面的空白“修复”了这个问题，但这不是一个好的解决方案。

如何在不使用虚拟名称填充这些列的情况下解决此问题？

非常感谢!

原始示例:

此表是使用以下索引生成的:

index = [np.array(['Size and accumulated size of adjusted gross income', 'All returns', 'All returns', 'All returns', 'All returns', 'All returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns']),
np.array(['', 'Number of returns', 'Percent of total', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Number of returns', 'Percent of total', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Taxable income', 'Taxable income', 'Taxable income', 'Income tax after credits', 'Income tax after credits', 'Income tax after credits', 'Total income tax', 'Total income tax', 'Total income tax', 'Total income tax', 'Total income tax']),
np.array(['', '', '', '', '', '', '', '','', '', 'Number of returns', 'Amount', 'Percent of total', 'Number of returns', 'Amount', 'Percent of total', 'Amount', 'Percent of', 'Percent of', 'Percent of', 'Average total income tax (dollars)']),
np.array(['', '', '', 'Amount', 'Percent of total', 'Average (dollars)', 'Average (dollars)', 'Average (dollars)', 'Amount', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Total', 'Taxable income', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit'])]

df.columns = index

这是 CSV 文件中一些数据的近乎完美的副本，但您可以看到在“返回数量”、“占总数的百分比”和“调整后的总收​​入减去赤字”下方存在差距。当我尝试选择返回数量时，该差距会产生此错误:

In [68]: df['Taxable returns']['Number of returns']
AssertionError: Index length did not match values

我不明白这个错误。所以一个很好的解释将不胜感激。无论如何，当我使用这个索引填补这个空白时(注意第三个 numpy 数组中的第一个元素):

index = [np.array(['Size and accumulated size of adjusted gross income', 'All returns', 'All returns', 'All returns', 'All returns', 'All returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns', 'Taxable returns']),
np.array(['', 'Number of returns', 'Percent of total', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Number of returns', 'Percent of total', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit', 'Taxable income', 'Taxable income', 'Taxable income', 'Income tax after credits', 'Income tax after credits', 'Income tax after credits', 'Total income tax', 'Total income tax', 'Total income tax', 'Total income tax', 'Total income tax']),
np.array(['1', '2', '3', '4', '5', '6', '7', '8','9', '10', 'Number of returns', 'Amount', 'Percent of total', 'Number of returns', 'Amount', 'Percent of total', 'Amount', 'Percent of', 'Percent of', 'Percent of', 'Average total income tax (dollars)']),
np.array(['', '', '', 'Amount', 'Percent of total', 'Average (dollars)', 'Average (dollars)', 'Average (dollars)', 'Amount', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Percent of total', 'Total', 'Taxable income', 'Adjusted gross income less deficit', 'Adjusted gross income less deficit'])]

df.columns = index

我得到了正确的结果:

In [71]: df['Taxable returns']['Number of returns']
Out[71]:
7
Average (dollars)
0    90,660,104
1    3,495
...

最佳答案

我昨天推送了一个修复程序。这是 github master 上的新行为:

In [1]: paste
from pandas import DataFrame
f = DataFrame({'a': ['1','2','3'], 'b': ['2','3','4']})
f.columns = [['level1 item1', 'level1 item2'],['', 'level2 item2'], ['level3 item1', 'level3 item2']]
f

## -- End pasted text --
Out[1]: 
  level1 item1 level1 item2
               level2 item2
  level3 item1 level3 item2
0            1            2
1            2            3
2            3            4

In [2]: f['level1 item1']
Out[2]: 
  level3 item1
0            1
1            2
2            3

关于python - 具有分层索引的 DataFrame 中的列断言错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/14025879/