逆透视 Pandas 数据
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/18259067/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Unpivot Pandas Data
提问by Alex Rothberg
I currently have a DataFramelaid out as:
我目前有一个DataFrame布局为：
Jan Feb Mar Apr ...
2001 1 12 12 19
2002 9 ...
2003 ...
and I would like to "unpivot" the data to look like:
我想“反透视”数据看起来像：
Date Value
Jan 2001 1
Feb 2001 1
Mar 2001 12
...
Jan 2002 9
What is the best way to accomplish this using pandas/NumPy?
使用 pandas/NumPy 完成此任务的最佳方法是什么？
回答by Viktor Kerkez
You just have to do df.unstack()and that will create a MultiIndexed Series with month as a first level and the year as the second level index. If you want them to be columns then just call reset_index()after that.
你只需要这样做df.unstack()，这将创建一个多索引系列，以月份为第一级，年份为第二级索引。如果您希望它们成为列，则只需reset_index()在此之后调用。
>>> df
Jan Feb
2001 3 4
2002 2 7
>>> df.unstack()
Jan 2001 3
2002 2
Feb 2001 4
2002 7
>>> df = df.unstack().reset_index(name='value')
>>> df
level_0 level_1 value
0 Jan 2001 3
1 Jan 2002 2
2 Feb 2001 4
3 Feb 2002 7
>>> df.rename(columns={'level_0': 'month', 'level_1': 'year'}, inplace=True)
>>> df
month year value
0 Jan 2001 3
1 Jan 2002 2
2 Feb 2001 4
3 Feb 2002 7
回答by Phillip Cloud
Another solution would be to use pandas.meltto avoid unnecessary creation of a MultiIndex, though this isn't thatexpensive if your frame is small and with my solution you still have to create a temporary for the "molten" data. The guts of meltsuggest that both id_varsand valueare copied since id_varscreation uses tileand valuecreation uses df.values.ravel('F')which I believe makes a copy if your data are not in Fortran order.
另一种解决方案是pandas.melt避免不必要地创建 a MultiIndex，尽管如果您的框架很小，这并不是那么昂贵，并且使用我的解决方案您仍然必须为“熔化”数据创建一个临时数据。胆量melt表明，id_vars和value都被复制，因为id_vars创建使用tile和value创建使用df.values.ravel('F')，如果您的数据不是 Fortran 顺序，我相信会复制。
EDIT:I'm not exactly sure when a copy is made when ravelis called since the orderparameter only indicates how you want your data readand the docstring says a copy is made only when needed.
编辑：我不确定何时在ravel调用时制作副本，因为该order参数仅指示您希望如何读取数据，并且文档字符串表示仅在需要时制作副本。
In [99]: mons
Out[99]:
['Jan',
'Feb',
'Mar',
'Apr',
'May',
'Jun',
'Jul',
'Aug',
'Sep',
'Oct',
'Nov',
'Dec']
In [100]: df = DataFrame(randn(201, len(mons)), columns=mons, index=map(str, arange(1901, 2102)))
In [101]: df.head()
Out[101]:
Jan Feb Mar Apr May Jun Jul Aug Sep Oct \
1901 1.141 -0.270 0.329 0.214 -1.030 0.324 -1.448 2.003 -0.061 0.477
1902 0.136 0.151 0.447 -0.493 1.329 1.410 0.020 -0.705 0.870 0.478
1903 -0.000 0.689 1.768 -0.057 -1.471 0.515 -0.315 0.703 2.511 0.592
1904 1.199 1.246 -0.255 0.182 -0.454 -0.452 1.074 0.178 2.495 -0.543
1905 1.073 1.375 -1.837 1.048 -0.139 -0.273 -0.958 -1.164 -1.012 0.950
Nov Dec
1901 0.102 0.122
1902 2.941 0.654
1903 0.347 -1.636
1904 -0.047 0.457
1905 1.277 -0.284
In [102]: df.reset_index(inplace=True)
In [103]: df.head()
Out[103]:
index Jan Feb Mar Apr May Jun Jul Aug Sep Oct \
0 1901 1.141 -0.270 0.329 0.214 -1.030 0.324 -1.448 2.003 -0.061 0.477
1 1902 0.136 0.151 0.447 -0.493 1.329 1.410 0.020 -0.705 0.870 0.478
2 1903 -0.000 0.689 1.768 -0.057 -1.471 0.515 -0.315 0.703 2.511 0.592
3 1904 1.199 1.246 -0.255 0.182 -0.454 -0.452 1.074 0.178 2.495 -0.543
4 1905 1.073 1.375 -1.837 1.048 -0.139 -0.273 -0.958 -1.164 -1.012 0.950
Nov Dec
0 0.102 0.122
1 2.941 0.654
2 0.347 -1.636
3 -0.047 0.457
4 1.277 -0.284
In [104]: res = pd.melt(df, id_vars=['index'], var_name=['months'])
In [105]: res['date'] = res['months'] + ' ' + res['index']
In [106]: res.head()
Out[106]:
index months value date
0 1901 Jan 1.141 Jan 1901
1 1902 Jan 0.136 Jan 1902
2 1903 Jan -0.000 Jan 1903
3 1904 Jan 1.199 Jan 1904
4 1905 Jan 1.073 Jan 1905
