转置和加宽数据

本文介绍了转置和加宽数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我的熊猫数据框如下所示:

My panda data frame looks like as follows:

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
...

如何将其转置为如下所示?

How can I transpose it that it looks like as follows?

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.

推荐答案

我认为需要 melt 与 sort_values:

I think need melt with sort_values:

df = (df.melt(['Country Code'], var_name='Year', value_name='Econometric_Metric')
        .sort_values(['Country Code','Year'])
        .reset_index(drop=True))

或 set_index 与 stack:

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

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