python - 如何从 pandas 多索引数据框中选择此类数据

Question

我正在处理 future 市场数据，这是一个多索引数据框示例:

date_index = pd.date_range('2018-03-20', periods = 10)
contract = ['ZN1805', 'ZN1806', 'ZN1807']
price = ['open', 'close']
columns = pd.MultiIndex.from_product([contract, price], names=['contract', 'price'])
df1 = pd.DataFrame(data=np.random.randint(100, 150, (10, columns.shape[0])), index=date_index, columns=columns)

df2 = pd.DataFrame(columns=['contract', 'close'], index=df1.index)
# Set the data in contract column randomly here for illustration
df2.contract = np.random.choice(contract, 10)

这是 df1 的样子，

df1
Out[357]: 
contract   ZN1805       ZN1806       ZN1807      
price        open close   open close   open close
2018-03-20    145   144    116   127    107   128
2018-03-21    116   143    114   103    114   148
2018-03-22    101   135    143   125    140   129
2018-03-23    106   139    100   127    116   100
2018-03-24    104   101    148   132    102   140
2018-03-25    125   141    106   136    128   134
2018-03-26    148   146    142   143    108   137
2018-03-27    110   123    128   128    124   127
2018-03-28    144   143    117   116    112   140
2018-03-29    143   114    115   105    124   118

和df2将是:

df2
Out[364]: 
           contract close
2018-03-20   ZN1805   NaN
2018-03-21   ZN1807   NaN
2018-03-22   ZN1806   NaN
2018-03-23   ZN1807   NaN
2018-03-24   ZN1807   NaN
2018-03-25   ZN1806   NaN
2018-03-26   ZN1807   NaN
2018-03-27   ZN1806   NaN
2018-03-28   ZN1805   NaN
2018-03-29   ZN1807   NaN

我的问题是如何“Python方式”填写具有相同日期的df1中df2的close列 索引和合约 值？

我尝试过这个:

from pandas import IndexSlice as idx
df2['close'] = df1.loc[df2.index, idx[df2.contract.values.tolist(), 'close']]

但是我得到了一个错误:

UnsortedIndexError: 'MultiIndex Slicing requires the index to be fully lexsorted tuple len (2), lexsort depth (1)'

我知道我可以采用迭代的方式来过滤每一行，但是有任何Pythonic方式可以做到这一点吗？

Answer 1

使用join由 xs 创建的 2 列选择关闭级别和unstack reshape :

s = df1.xs('close', axis=1, level=1).unstack().rename('close')

df2 = (df2.drop('close', 1)
          .reset_index()
          .join(s, on=['contract', 'index'])
          .set_index('index')
          .rename_axis(None))

print (df2)
           contract  close
2018-03-20   ZN1805    124
2018-03-21   ZN1805    112
2018-03-22   ZN1807    118
2018-03-23   ZN1807    136
2018-03-24   ZN1805    103
2018-03-25   ZN1805    135
2018-03-26   ZN1805    138
2018-03-27   ZN1805    109
2018-03-28   ZN1805    129
2018-03-29   ZN1805    104