我该如何“取消透视"?从 pandas DataFrame的特定列? [英] How can I "unpivot" specific columns from a pandas DataFrame?

问题描述

我有一个熊猫DataFrame，例如:

I have a pandas DataFrame, eg:

x = DataFrame.from_dict({'farm' : ['A','B','A','B'], 
                         'fruit':['apple','apple','pear','pear'], 
                         '2014':[10,12,6,8], 
                         '2015':[11,13,7,9]})

即:

   2014  2015 farm  fruit
0    10    11    A  apple
1    12    13    B  apple
2     6     7    A   pear
3     8     9    B   pear

如何将其转换为:?

  farm  fruit  value  year
0    A  apple     10  2014
1    B  apple     12  2014
2    A   pear      6  2014
3    B   pear      8  2014
4    A  apple     11  2015
5    B  apple     13  2015
6    A   pear      7  2015
7    B   pear      9  2015

我尝试过stack和unstack，但无法使其正常工作.

I have tried stack and unstack but haven't been able to make it work.

谢谢！

推荐答案

这可以通过pd.melt()完成:

# value_name is 'value' by default, but setting it here to make it clear
pd.melt(x, id_vars=['farm', 'fruit'], var_name='year', value_name='value')

结果:

  farm  fruit  year  value
0    A  apple  2014     10
1    B  apple  2014     12
2    A   pear  2014      6
3    B   pear  2014      8
4    A  apple  2015     11
5    B  apple  2015     13
6    A   pear  2015      7
7    B   pear  2015      9

[8 rows x 4 columns]

我不确定融解"作为这种操作的名称有多常见，但这就是R的reshape2程序包中所称的名称，这可能启发了此名称.

I'm not sure how common "melt" is as the name for this kind of operation, but that's what it's called in R's reshape2 package, which probably inspired the name here.

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