我有3D面板数据。
我无法将其重新索引为第2级的多索引。
我创建了多索引“ mind”。
import pandas as pd
mind = pd.MultiIndex.from_arrays([['Consumer,Cyclical','Industrial','Software'], ['Retail','MiscellaneousManufactur','Technology'], ['AZO','AZZ','AZPN']],names=['sec','sub','ticker'])
dfclose = pd.DataFrame([[1.1,2.1,3.1],[1.2,2.2,3.2]], index=['2013-09-02','2013-09-03'], columns=['AZO','AZZ','AZPN'])
dfmean = dfclose - dfclose.mean()
pdata2 = pd.Panel({'close':dfclose, 'mean':dfmean})
pdata2.minor_axis.name='ticker'
pdata3=pdata2.reindex_axis(mind,axis=2,level='ticker')
但是pdata3不会映射到新的多重索引并给出NaN。
最佳答案
这似乎是0.12中的错误(并将在0.13中修复)。
解决方法不是在此之后重新索引,而是在创建dfclose时使用MultiIndex:
dfclose = pd.DataFrame([[1.1, 2.1, 3.1], [1.2, 2.2, 3.2]],
index=['2013-09-02','2013-09-03'],
columns=mind)
dfmean = dfclose - dfclose.mean()
pdata2 = pd.Panel({'close':dfclose, 'mean':dfmean})
pdata2.minor_axis.name='ticker'
In [11]: pdata2.iloc[0]
Out[12]:
sec Consumer,Cyclical Industrial Software
sub Retail MiscellaneousManufactur Technology
ticker AZO AZZ AZPN
2013-09-02 1.1 2.1 3.1
2013-09-03 1.2 2.2 3.2
另一个选择是仅使用DataFrame:
In [12]: pd.concat([dfmean, dfclose], axis=1, keys=['dfmean' ,'dfclose'])
Out[12]:
dfmean dfclose
sec Consumer,Cyclical Industrial Software Consumer,Cyclical Industrial Software
sub Retail MiscellaneousManufactur Technology Retail MiscellaneousManufactur Technology
ticker AZO AZZ AZPN AZO AZZ AZPN
2013-09-02 -0.05 -0.05 -0.05 1.1 2.1 3.1
2013-09-03 0.05 0.05 0.05 1.2 2.2 3.2
关于python - 如何将Pandas面板reindex_axis转换为MultiIndex,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/18651142/