在不丢失任何级别的情况下按索引选择MultiIndex数据框中的行 [英] Selecting rows in a MultiIndex dataframe by index without losing any levels

问题描述

我想选择一个名为"Mid"的行，而不会丢失其索引"Site"

I would like to select a row called 'Mid', without losing it's index 'Site'

以下代码显示了数据框:

Following code shows the dataframe:

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

以下答案给出了理想的结果:

following answers give the desired outcome:

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

ty MaxU，COLDSPEED和jezrael给出的答案:)

ty MaxU, COLDSPEED and jezrael for the answers :)

推荐答案

您也可以将loc与大括号一起使用.

You can also use loc with double braces.

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

在您的情况下，我想它应该是m.commodity.loc[['Mid']].

In your case, I'd suppose it would be m.commodity.loc[['Mid']].

当谈论loc与ix是因为后者已被弃用时，请使用loc/iloc/iat/xs进行索引.

When talking about loc versus ix is that the latter is deprecated, use loc/iloc/iat/xs for indexing.

ix对传递的内容进行假设，并接受标签或位置. loc完全基于标签，而iloc完全基于索引(基于位置)

ix makes assumptions about what is passed, and accepts either labels or positions. loc is purely label based, while iloc is purely index (positional based)

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