Python Pandas:带有aggfunc的数据透视表=计数唯一唯一 [英] Python Pandas : pivot table with aggfunc = count unique distinct

问题描述

df2 = pd.DataFrame({'X' : ['X1', 'X1', 'X1', 'X1'], 'Y' : ['Y2','Y1','Y1','Y1'], 'Z' : ['Z3','Z1','Z1','Z2']})

    X   Y   Z
0  X1  Y2  Z3
1  X1  Y1  Z1
2  X1  Y1  Z1
3  X1  Y1  Z2

g=df2.groupby('X')

pd.pivot_table(g, values='X', rows='Y', cols='Z', margins=False, aggfunc='count')

回溯(最近一次通话最近):... AttributeError:索引"对象 没有属性索引"

Traceback (most recent call last): ... AttributeError: 'Index' object has no attribute 'index'

如何获得一个数据帧表，其中一个数据帧列具有唯一值计数，而另外两个列又具有此值?
是否有aggfunc个唯一计数?我应该使用np.bincount()吗?

How do I get a Pivot Table with counts of unique values of one DataFrame column for two other columns?
Is there aggfunc for count unique? Should I be using np.bincount()?

NB.我知道'Series'values_counts()，但是我需要一个数据透视表.

NB. I am aware of 'Series' values_counts() however I need a pivot table.

输出应为:

Z   Z1  Z2  Z3
Y             
Y1   1   1 NaN
Y2 NaN NaN   1

推荐答案

您的意思是这样的吗?

In [39]: df2.pivot_table(values='X', rows='Y', cols='Z', 
                         aggfunc=lambda x: len(x.unique()))
Out[39]: 
Z   Z1  Z2  Z3
Y             
Y1   1   1 NaN
Y2 NaN NaN   1

请注意，使用len假定您的DataFrame中没有NA.否则，您可以执行x.value_counts().count()或len(x.dropna().unique()).

Note that using len assumes you don't have NAs in your DataFrame. You can do x.value_counts().count() or len(x.dropna().unique()) otherwise.

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