Python 转置和扩展数据

Python 转置和扩展数据,python,pandas,dataframe,transpose,Python,Pandas,Dataframe,Transpose,我的panda数据框如下所示: Country Code 1960 1961 1962 1963 1964 1965 1966 1967 1968 ... 2015 ABW 2.615300 2.734390 2.678430 2.929920 2.963250 3.060540 ... 4.349760 AFG 0.249760 0.218480 0.210840 0.217240 0.211410 0.209910 ... 0.671330

我的panda数据框如下所示:

Country Code  1960  1961  1962  1963  1964  1965  1966  1967  1968  ... 2015
ABW  2.615300  2.734390  2.678430  2.929920  2.963250  3.060540 ...  4.349760
AFG  0.249760  0.218480  0.210840  0.217240  0.211410  0.209910 ...  0.671330  
ALB  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ... 1.12214
...
我如何将它转换为如下所示

Country_Code  Year  Econometric_Metric
ABW  1960  2.615300
ABW  1961  2.734390
ABW  1962  2.678430
...
ABW  2015  4.349760
AFG  1960  0.249760
AFG  1961  0.218480
AFG  1962  0.210840
...
AFG  2015  0.671330
ALB  1960  NaN
ALB  1961  NaN
ALB  1962  NaN 
ALB  2015  1.12214
...
谢谢。

我想我需要:

或与:


df = (df.melt(['Country Code'], var_name='Year', value_name='Econometric_Metric')
        .sort_values(['Country Code','Year'])
        .reset_index(drop=True))
df = (df.set_index(['Country Code'])
        .stack(dropna=False)
        .reset_index(name='Econometric_Metric')
        .rename(columns={'level_1':'Year'}))
print (df.head(10))
  Country Code  Year  Econometric_Metric
0          ABW  1960             2.61530
1          ABW  1961             2.73439
2          ABW  1962             2.67843
3          ABW  1963             2.92992
4          ABW  1964             2.96325
5          ABW  1965             3.06054
6          ABW  1966                 NaN
7          ABW  1967                 NaN
8          ABW  1968                 NaN
9          ABW  2015             4.34976