python - 从 pandas df 核心面板中切片特定记录

Question

我有一个 pandas dataframe 核心面板 (data_r3000)，其中包含多个工业部门的库存数据...

{'capital_goods': <class 'pandas.core.panel.Panel'>
 Dimensions: 6 (items) x 13820 (major_axis) x 423 (minor_axis)
 Items axis: OPEN to ADJ_CLOSE
 Major_axis axis: 1962-01-02 00:00:00 to 2016-11-18 00:00:00
 Minor_axis axis: A to ZEUS, 'consumer': <class 'pandas.core.panel.Panel'>
 Dimensions: 6 (items) x 11832 (major_axis) x 94 (minor_axis)
 Items axis: OPEN to ADJ_CLOSE
 Major_axis axis: 1970-01-02 00:00:00 to 2016-11-18 00:00:00
 Minor_axis axis: ABG to WSO, 'consumer_non_durables': <class 'pandas.core.panel.Panel'>
 Dimensions: 6 (items) x 13819 (major_axis) x 138 (minor_axis)

等等。我隔离了其中一个扇区，我想在其中对 df 中的一些值进行一些修改。

x = data_r3000['capital_goods'].to_frame().unstack(level=1)

这会产生以下 df:

我在 pandas 中使用多索引的经验很少，并且在隔离“AA”的“CLOSE”和“ADJ_CLOSE”记录时遇到问题。如何隔离这些记录，以便创建一个仅包含 OPEN 和 ADJ_CLOSE 定时器系列的 AA_df？

我已经尝试过 x.xs(['CLOSE','ADJ_CLOSE'], axis=1), 它正确地隔离了我正在寻找的两个功能，但我仍然不知道如何仅隔离“AA”。谢谢

Answer 1

我认为你可以使用slicers :

idx = pd.IndexSlice
print (df.loc[:, idx[['CLOSE','ADJ_CLOSE'], 'AA']])

或者:

print (df.loc[:, (['CLOSE','ADJ_CLOSE'],'AA')])

示例:

cols = pd.MultiIndex.from_product((['ADJ','ADJ_CLOSE', 'CLOSE'],
                                   ['A','AA','AEPI']))
df = pd.DataFrame(np.arange(27).reshape(3,9),columns=cols)

print (df)
  ADJ          ADJ_CLOSE          CLOSE         
    A  AA AEPI         A  AA AEPI     A  AA AEPI
0   0   1    2         3   4    5     6   7    8
1   9  10   11        12  13   14    15  16   17
2  18  19   20        21  22   23    24  25   26

idx = pd.IndexSlice
print (df.loc[:, idx[['CLOSE','ADJ_CLOSE'], 'AA']])
  ADJ_CLOSE CLOSE
         AA    AA
0         4     7
1        13    16
2        22    25

print (df.loc[:, (['CLOSE','ADJ_CLOSE'],'AA')])
  ADJ_CLOSE CLOSE
         AA    AA
0         4     7
1        13    16
2        22    25

<小时/>

使用面板的解决方案:

np.random.seed(1234)
rng = pd.date_range('1/1/2013',periods=10,freq='D')

data = np.random.randn(10, 4)

cols = ['A','AA','AAON','ABAX']

df1, df2, df3 = pd.DataFrame(data, rng, cols), 
                pd.DataFrame(data, rng, cols), 
                pd.DataFrame(data, rng, cols)

pf = pd.Panel({'OPEN':df1,'ADJ':df2,'ADJ_CLOSE':df3});pf
print (pf)
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 10 (major_axis) x 4 (minor_axis)
Items axis: ADJ to OPEN
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-10 00:00:00
Minor_axis axis: A to ABAX

print (pf.loc[['OPEN', 'ADJ_CLOSE'], :,'AA'])
                OPEN  ADJ_CLOSE
2013-01-01 -1.190976  -1.190976
2013-01-02  0.887163   0.887163
2013-01-03 -2.242685  -2.242685
2013-01-04 -2.021255  -2.021255
2013-01-05  0.289092   0.289092
2013-01-06 -0.655969  -0.655969
2013-01-07 -0.469305  -0.469305
2013-01-08  1.058969   1.058969
2013-01-09  1.045938   1.045938
2013-01-10 -0.322795  -0.322795