python数据透视表计数去除重复_Python Pandas：带有aggfunc的数据透视表=计数唯一唯一...

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')

Traceback (most recent call last): ... AttributeError: 'Index' object

has no attribute 'index'

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

EDIT: The output should be:

Z Z1 Z2 Z3

Y

Y1 1 1 NaN

Y2 NaN NaN 1

解决方案

Do you mean something like this?

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

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.