pandas - pivot_table与非数字值？ （DataError：没有要聚合的数字类型）

Question

我正在尝试将包含字符串的表作为结果。

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'])

但我明白了：DataError: No numeric types to aggregate。

当我将结果值更改为数字时，这可以正常工作：

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？

Answer 1

我的原始回复基于Pandas 0.14.1，从那时起，pivot_table函数中的许多内容发生了变化（rows - ＆gt; index，cols - ＆gt; columns ......）

此外，我发布的原始lambda技巧似乎不再适用于Pandas 0.18。您必须提供减少功能（即使它是最小值，最大值或平均值）。但即使这样看起来也不合适 - 因为我们并没有减少数据集，只是改变它......所以我看起来更加难以置信......

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+要堆叠的列设置数据的索引，然后使用级别arg调用unstack。

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

结果数据框如下。

Answer 2

我认为最好的妥协是用True / False替换on / off，这将使pandas能够更好地“理解”数据，并以一种智能的，预期的方式行事。

df2 = df1.replace({'on': True, 'off': False})

你基本上在你的问题中承认了这一点。我的回答是，我不认为有更好的方法，你应该为接下来的任何事情取代'on'/'off'。

正如Andy Hayden在评论中指出的那样，如果用1/0替换开/关，你的表现会更好。