pandas - 具有非数字值的数据透视表？ （数据错误：没有要聚合的数字类型）

我正在尝试对包含字符串作为结果的表进行数据透视。

import pandas as pd

df1 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': ["on","off","off","on","on","off","off","on"]})

df1.pivot_table(values='result',rows='index',cols=['variable1','variable2','variable3'])

但我得到：DataError: No numeric types to aggregate.

当我将结果值更改为数字时，这将按预期工作：

df2 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': [1,0,0,1,1,0,0,1]})

df2.pivot_table(values='result',rows='index',cols=['variable1','variable2','variable3'])

我得到了我需要的东西：

variable1   A               B    
variable2   a       b       a   b
variable3   x   y   x   y   x   y
index                            
0           1 NaN NaN NaN NaN NaN
1         NaN NaN   0 NaN NaN NaN
2         NaN NaN NaN NaN   0 NaN
3         NaN NaN NaN NaN NaN   1
4         NaN   1 NaN NaN NaN NaN
5         NaN NaN NaN NaN NaN   0
6         NaN NaN NaN NaN   0 NaN
7         NaN NaN NaN   1 NaN NaN

我知道我可以将字符串映射到数值，然后反转操作，但也许有更优雅的解决方案？

我最初的回复是基于 Pandas 0.14.1，从那时起，pivot_table 函数中的许多事情都发生了变化（行 --> 索引，列 --> 列...）

此外，我发布的原始 lambda 技巧似乎不再适用于 Pandas 0.18。您必须提供一个减少函数（即使它是最小值、最大值或平均值）。但即使这样似乎也不合适 - 因为我们没有减少数据集，只是对其进行转换......所以我更加努力地研究了 unstack ......

import pandas as pd

df1 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': ["on","off","off","on","on","off","off","on"]})

# these are the columns to end up in the multi-index columns.
unstack_cols = ['variable1', 'variable2', 'variable3']

首先，使用索引 + 要堆叠的列在数据上设置索引，然后使用级别参数调用 unstack。

df1.set_index(['index'] + unstack_cols).unstack(level=unstack_cols)

生成的数据框如下。