如何将Series设为MultiIndex dataframe后，为什么会产生一列NaN？

Question

下面的代码说明了我的问题：

In [2]: idx = pd.date_range('1/1/2011', periods=5)

In [3]: idx
Out[3]:
DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04', '2011-01-05'],
    dtype='datetime64[ns]', freq='D')

In [4]: midx = pd.MultiIndex.from_product([['100', '200'], idx])

In [5]: midx
Out[5]: MultiIndex(levels=[['100', '200'],
    [2011-01-01 00:00:00, 2011-01-02 00:00:00, 2011-01-03 00:00:00, 2011-01-04 00:00:00, 2011-01-05 00:00:00]], 
    labels=[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]])

In [6]: test_data = pd.DataFrame(
            2*[[1, 2], [NaN, 3], [4, NaN], [5, 6], [7, 8]],
            index=midx, columns=['quant1', 'quant2']
        )

In [7]: test_data
Out[7]: 
                quant1  quant2
100 2011-01-01     1.0     2.0
    2011-01-02     NaN     3.0
    2011-01-03     4.0     NaN
    2011-01-04     5.0     6.0
    2011-01-05     7.0     8.0
200 2011-01-01     1.0     2.0
    2011-01-02     NaN     3.0
    2011-01-03     4.0     NaN
    2011-01-04     5.0     6.0
    2011-01-05     7.0     8.0

In [8]: new_data = pd.DataFrame([11, 12, 13, 14, 15], index=idx, columns=['quant1'])

In [9]: new_data
Out[9]: 
            quant1
2011-01-01      11
2011-01-02      12
2011-01-03      13
2011-01-04      14
2011-01-05      15

In [10]: test_data.loc['100', 'quant1'] = new_data

In [11]: test_data
Out[11]: 
                quant1  quant2
100 2011-01-01     NaN     2.0
    2011-01-02     NaN     3.0
    2011-01-03     NaN     NaN
    2011-01-04     NaN     6.0
    2011-01-05     NaN     8.0
200 2011-01-01     1.0     2.0
    2011-01-02     NaN     3.0
    2011-01-03     4.0     NaN
    2011-01-04     5.0     6.0
    2011-01-05     7.0     8.0

为什么['100', 'quant1']数据段用NaN而不是来自new_data的数字填充

我发现使用

test_data.loc['100', 'quant1'] = new_data.values

确实有效，但我想了解是什么使得Pandas这样做的。子切片具有与新数据相同的维度，甚至相同的索引，因此，尽管我确实怀疑这与索引/对齐有关，但我并不真正理解如何或为什么-我的期望是，只要您使用与所分配的索引完全相同的索引，就可以正常工作

Answer 1

因为Pandas将接收数据帧和提供新数据的序列的索引对齐。当它这样做时，它无法找到它正在寻找的相关索引

test_data.loc['100', 'quant2']的索引项为('100', '2011-01-01')，而new_data的索引项为'2011-01-01'。这些不一样

解决1

使用values属性并跳过尝试对齐的熊猫

test_data.loc['100', 'quant1'] = new_data.values

test_data

                quant1  quant2
100 2011-01-01    11.0     2.0
    2011-01-02    12.0     3.0
    2011-01-03    13.0     NaN
    2011-01-04    14.0     6.0
    2011-01-05    15.0     8.0
200 2011-01-01     1.0     2.0
    2011-01-02     NaN     3.0
    2011-01-03     4.0     NaN
    2011-01-04     5.0     6.0
    2011-01-05     7.0     8.0

---

解决2

使用pd.concat添加索引级别

test_data.loc['100', 'quant1'] = pd.concat({'100': new_data})

test_data

                quant1  quant2
100 2011-01-01    11.0     2.0
    2011-01-02    12.0     3.0
    2011-01-03    13.0     NaN
    2011-01-04    14.0     6.0
    2011-01-05    15.0     8.0
200 2011-01-01     1.0     2.0
    2011-01-02     NaN     3.0
    2011-01-03     4.0     NaN
    2011-01-04     5.0     6.0
    2011-01-05     7.0     8.0