python - 在 pivot 之后为多索引重新排序 pandas 数据帧数据

Question

我正在为公共(public)交通数据构建一个分析工具，并希望对 pandas 数据框中的数据重新排序，使用以下示例可以最好地解释这一点:

我的初始数据形状是:

            Population                                GDP per capita
date        2015          2016          2017          2015            2016            2017
country                        
France      66593366.0    66859768.0    67118648.0    40564.460707    41357.986933    42850.386280
Germany     81686611.0    82348669.0    82695000.0    47810.836011    48943.101805    50638.890964
Italy       60730582.0    60627498.0    60551416.0    36640.115578    38380.172412    39426.940797
Spain       46444832.0    46484062.0    46572028.0    34818.120507    36305.222132    37997.852337

我不想 reshape 数据框，使日期成为顶级索引，而当前较低级别的信息 Population 和 GDP per capital 位于较低级别。生成的数据框应如下所示:

            2015                            2016                            2017
date        Population    GDP per capita    Population    GDP per capita    Population    GDP per capita
country
France      66593366.0    40564.460707      66859768.0    41357.986933      67118648.0    42850.386280
Germany     81686611.0    47810.836011      82348669.0    48943.101805      82695000.0    50638.890964
Italy       60730582.0    36640.115578      60627498.0    38380.172412      60551416.0    39426.940797
Spain       46444832.0    34818.120507      46484062.0    36305.222132      46572028.0    37997.852337

如何使用 pandas 实现此目的？我一直在试验 swaplevel 但未能获得预期的结果。

数据帧是通过pivot操作从以下数据中获得的:

       country    date    Population    GDP per capita    GNI per capita

1      Germany    2017    82695000.0    50638.890964    51680.0
2      Germany    2016    82348669.0    48943.101805    49770.0
3      Germany    2015    81686611.0    47810.836011    48690.0
60     Spain      2017    46572028.0    37997.852337    37990.0
61     Spain      2016    46484062.0    36305.222132    36300.0
62     Spain      2015    46444832.0    34818.120507    34740.0
119    France     2017    67118648.0    42850.386280    43790.0
120    France     2016    66859768.0    41357.986933    42020.0
121    France     2015    66593366.0    40564.460707    41100.0
237    Italy      2017    60551416.0    39426.940797    39640.0
238    Italy      2016    60627498.0    38380.172412    38470.0
239    Italy      2015    60730582.0    36640.115578    36440.0

以及以下pivot:

df_p = df_small.pivot(
    index='country', 
    columns='date', 
    values=['Population', 'GDP per capita'])

Answer 1

交换级别和排序索引，

df_p.columns = df_p.columns.swaplevel(1,0)
df_p = df_p.sort_index(axis = 1)


date    2015                        2016                        2017
        GDP per capita  Population  GDP per capita  Population  GDP per capita  Population
country                     
France  40564.460707    66593366.0  41357.986933    66859768.0  42850.386280    67118648.0
Germany 47810.836011    81686611.0  48943.101805    82348669.0  50638.890964    82695000.0
Italy   36640.115578    60730582.0  38380.172412    60627498.0  39426.940797    60551416.0
Spain   34818.120507    46444832.0  36305.222132    46484062.0  37997.852337    46572028.0