Python 绘制 groupbys 时 Seaborn 的“无法解释输入”错误
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/32908315/
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
'Could not interpret input' error with Seaborn when plotting groupbys
提问by marshallbanana
Say I have this dataframe
说我有这个数据框
d = { 'Path' : ['abc', 'abc', 'ghi','ghi', 'jkl','jkl'],
'Detail' : ['foo', 'bar', 'bar','foo','foo','foo'],
'Program': ['prog1','prog1','prog1','prog2','prog3','prog3'],
'Value' : [30, 20, 10, 40, 40, 50],
'Field' : [50, 70, 10, 20, 30, 30] }
df = DataFrame(d)
df.set_index(['Path', 'Detail'], inplace=True)
df
Field Program Value
Path Detail
abc foo 50 prog1 30
bar 70 prog1 20
ghi bar 10 prog1 10
foo 20 prog2 40
jkl foo 30 prog3 40
foo 30 prog3 50
I can aggregate it no problem (if there's a better way to do this, by the way, I'd like to know!)
我可以汇总它没问题（如果有更好的方法来做到这一点，顺便说一下，我想知道！）
df_count = df.groupby('Program').count().sort(['Value'], ascending=False)[['Value']]
df_count
Program Value
prog1 3
prog3 2
prog2 1
df_mean = df.groupby('Program').mean().sort(['Value'], ascending=False)[['Value']]
df_mean
Program Value
prog3 45
prog2 40
prog1 20
I can plot it from Pandas no problem...
我可以从 Pandas 绘制它没问题...
df_mean.plot(kind='bar')
But why do I get this error when I try it in seaborn?
但是为什么我在 seaborn 中尝试时会收到此错误？
sns.factorplot('Program',data=df_mean)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-23c2921627ec> in <module>()
----> 1 sns.factorplot('Program',data=df_mean)
C:\Anaconda3\lib\site-packages\seaborn\categorical.py in factorplot(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, size, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)
2673 # facets to ensure representation of all data in the final plot
2674 p = _CategoricalPlotter()
-> 2675 p.establish_variables(x_, y_, hue, data, orient, order, hue_order)
2676 order = p.group_names
2677 hue_order = p.hue_names
C:\Anaconda3\lib\site-packages\seaborn\categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
143 if isinstance(input, string_types):
144 err = "Could not interperet input '{}'".format(input)
--> 145 raise ValueError(err)
146
147 # Figure out the plotting orientation
ValueError: Could not interperet input 'Program'
采纳答案by lrnzcig
The reason for the exception you are getting is that Programbecomes an index of the dataframes df_meanand df_countafter your group_byoperation.
您获得异常的原因是它Program成为数据帧的索引df_mean并df_count在您的group_by操作之后。
If you wanted to get the factorplotfrom df_mean, an easy solution is to add the index as a column,
如果您想获取factorplotfrom df_mean，一个简单的解决方案是将索引添加为列，
In [7]:
df_mean['Program'] = df_mean.index
In [8]:
%matplotlib inline
import seaborn as sns
sns.factorplot(x='Program', y='Value', data=df_mean)
However you could even more simply let factorplotdo the calculations for you,
然而，你甚至可以更简单地让factorplot你为你做计算，
sns.factorplot(x='Program', y='Value', data=df)
You'll obtain the same result. Hope it helps.
您将获得相同的结果。希望能帮助到你。
EDIT after comments
评论后编辑
Indeed you make a very good point about the parameter as_index; by default it is set to True, and in that case Programbecomes part of the index, as in your question.
确实，您对参数提出了很好的观点as_index；默认情况下，它设置为 True，在这种情况下，它Program会成为索引的一部分，就像您的问题一样。
In [14]:
df_mean = df.groupby('Program', as_index=True).mean().sort(['Value'], ascending=False)[['Value']]
df_mean
Out[14]:
Value
Program
prog3 45
prog2 40
prog1 20
Just to be clear, this way Programis not column anymore, but it becomes the index. the trick df_mean['Program'] = df_mean.indexactually keeps the index as it is, and adds a new column for the index, so that Programis duplicated now.
需要明确的是，这种方式Program不再是列，而是成为索引。这个技巧df_mean['Program'] = df_mean.index实际上保持索引原样，并为索引添加一个新列，以便Program现在复制。
In [15]:
df_mean['Program'] = df_mean.index
df_mean
Out[15]:
Value Program
Program
prog3 45 prog3
prog2 40 prog2
prog1 20 prog1
However, if you set as_indexto False, you get Programas a column, plus a new autoincrement index,
然而，如果你设置as_index为 False，你会得到Program一个列，加上一个新的自动增量索引，
In [16]:
df_mean = df.groupby('Program', as_index=False).mean().sort(['Value'], ascending=False)[['Program', 'Value']]
df_mean
Out[16]:
Program Value
2 prog3 45
1 prog2 40
0 prog1 20
This way you could feed it directly to seaborn. Still, you could use dfand get the same result.
这样你就可以直接把它喂给seaborn. 不过，您可以使用df并获得相同的结果。
Hope it helps.
希望能帮助到你。
