按索引选择MultiIndex数据框中的行而不会丢失任何级别

时间:2017-12-19 11:52:09

标签: python pandas dataframe multi-index

我想选择一个名为“Mid'”的行,而不会丢失它的索引' Site'

以下代码显示了数据框:

m.commodity
                         price  max  maxperstep
Site  Commodity Type
Mid   Biomass   Stock     6.0  inf         inf
      CO2       Env       0.0  inf         inf
      Coal      Stock     7.0  inf         inf
      Elec      Demand    NaN  NaN         NaN
      Gas       Stock    27.0  inf         inf
      Hydro     SupIm     NaN  NaN         NaN
      Lignite   Stock     4.0  inf         inf
      Slack     Stock   999.0  inf         inf
      Solar     SupIm     NaN  NaN         NaN
      Wind      SupIm     NaN  NaN         NaN
North Biomass   Stock     6.0  inf         inf
      CO2       Env       0.0  inf         inf
      Coal      Stock     7.0  inf         inf
      Elec      Demand    NaN  NaN         NaN
      Gas       Stock    27.0  inf         inf
      Hydro     SupIm     NaN  NaN         NaN
      Lignite   Stock     4.0  inf         inf
      Slack     Stock   999.0  inf         inf
      Solar     SupIm     NaN  NaN         NaN
      Wind      SupIm     NaN  NaN         NaN
South Biomass   Stock     6.0  inf         inf
      CO2       Env       0.0  inf         inf
      Coal      Stock     7.0  inf         inf
      Elec      Demand    NaN  NaN         NaN
      Gas       Stock    27.0  inf         inf
      Hydro     SupIm     NaN  NaN         NaN
      Lignite   Stock     4.0  inf         inf
      Slack     Stock   999.0  inf         inf
      Solar     SupIm     NaN  NaN         NaN
      Wind      SupIm     NaN  NaN         NaN

期望的结果如下:

                         price  max  maxperstep
Site  Commodity Type
Mid   Biomass   Stock     6.0  inf         inf
      CO2       Env       0.0  inf         inf
      Coal      Stock     7.0  inf         inf
      Elec      Demand    NaN  NaN         NaN
      Gas       Stock    27.0  inf         inf
      Hydro     SupIm     NaN  NaN         NaN
      Lignite   Stock     4.0  inf         inf
      Slack     Stock   999.0  inf         inf
      Solar     SupIm     NaN  NaN         NaN
      Wind      SupIm     NaN  NaN         NaN

以下答案给出了预期的结果:

m.commodity.xs('Mid', drop_level=False)
m.commodity.loc[['Mid']]
m.commodity.loc['Mid', :, :]

ty MaxU,COLDSPEED和jezrael的答案:)

3 个答案:

答案 0 :(得分:6)

您也可以使用双括号loc

df.loc[['Mid']]

                       price  max  maxperstep
Site Commodity Type
Mid  Biomass   Stock     6.0  inf         inf
     CO2       Env       0.0  inf         inf
     Coal      Stock     7.0  inf         inf
     Elec      Demand    NaN  NaN         NaN
     Gas       Stock    27.0  inf         inf
     Hydro     SupIm     NaN  NaN         NaN
     Lignite   Stock     4.0  inf         inf
     Slack     Stock   999.0  inf         inf
     Solar     SupIm     NaN  NaN         NaN
     Wind      SupIm     NaN  NaN         NaN

在你的情况下,我认为它是m.commodity.loc[['Mid']]

在谈论locix时,不推荐使用后者,请使用loc / iloc / iat / xs进行索引

ix对传递的内容做出假设,并接受标签或位置。 loc纯粹基于标签,而iloc纯粹是索引(基于位置)

答案 1 :(得分:5)

In [59]: df.loc['Mid', :, :]
Out[59]:
                       price  max  maxperstep
Site Commodity Type
Mid  Biomass   Stock     6.0  inf         inf
     CO2       Env       0.0  inf         inf
     Coal      Stock     7.0  inf         inf
     Elec      Demand    NaN  NaN         NaN
     Gas       Stock    27.0  inf         inf
     Hydro     SupIm     NaN  NaN         NaN
     Lignite   Stock     4.0  inf         inf
     Slack     Stock   999.0  inf         inf
     Solar     SupIm     NaN  NaN         NaN
     Wind      SupIm     NaN  NaN         NaN

答案 2 :(得分:3)

我相信你需要xs

df = m.commodity.xs('Mid', drop_level=False)

print (df)
b                       price  max  maxperstep
Site Commodity Type                          
Mid  Biomass   Stock     6.0  inf         inf
     CO2       Env       0.0  inf         inf
     Coal      Stock     7.0  inf         inf
     Elec      Demand    NaN  NaN         NaN
     Gas       Stock    27.0  inf         inf
     Hydro     SupIm     NaN  NaN         NaN
     Lignite   Stock     4.0  inf         inf
     Slack     Stock   999.0  inf         inf
     Solar     SupIm     NaN  NaN         NaN
     Wind      SupIm     NaN  NaN         NaN

对于您,另一个问题是最好检查pandas iloc vs ix vs loc explanationLoc vs. iloc vs. ix vs. at vs. iat