How to use two different functions in a / pivot_table crosstab in pandas?

Question

Using pandas, is it possible to compute one crosstab (or pivot table) containing values ​​computed from two different functions?

import pandas as pd import numpy as np c1 = np.repeat(['a','b'], [50, 50], axis=0) c2 = list('xy'*50) c3 = np.repeat(['G1','G2'], [50, 50], axis=0) np.random.shuffle(c3) c4=np.repeat([1,2], [50,50],axis=0) np.random.shuffle(c4) val = np.random.rand(100) df = pd.DataFrame({'c1':c1, 'c2':c2, 'c3':c3, 'c4':c4, 'val':val}) frequencyTable = pd.crosstab([df.c1,df.c2],[df.c3,df.c4]) meanVal = pd.crosstab([df.c1,df.c2],[df.c3,df.c4],values=df.val,aggfunc=np.mean) 

So, both rows and columns are the same in both tables, but I would really like a table with two frequencies and average values:

 c3 G1 G2 c4 1 2 1 2 c1 c2 freq val freq val freq val freq val ax 6 0.624931 5 0.582268 8 0.528231 6 0.362804 y 7 0.493890 8 0.465741 3 0.613126 7 0.312894 bx 9 0.488255 5 0.804015 6 0.722640 5 0.369480 y 6 0.462653 4 0.506791 5 0.583695 10 0.517954 

Answer 1

You can specify a list of functions:

 pd.crosstab([df.c1,df.c2], [df.c3,df.c4], values=df.val, aggfunc=[len, np.mean]) 

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If you need a table, as shown in your question, you will have to change the levels a bit:

 In [42]: table = pd.crosstab([df.c1,df.c2], [df.c3,df.c4], values=df.val, aggfunc=[len, np.mean]) In [43]: table Out[43]: len mean c3 G1 G2 G1 G2 c4 1 2 1 2 1 2 1 2 c1 c2 ax 4 6 8 7 0.303036 0.414474 0.624900 0.425234 y 5 5 8 7 0.543363 0.480419 0.583499 0.637657 bx 10 6 4 5 0.400279 0.436929 0.442924 0.287572 y 6 8 5 6 0.400427 0.623319 0.764506 0.408708 In [44]: table.reorder_levels([1, 2, 0], axis=1).sort_index(axis=1) Out[44]: c3 G1 G2 c4 1 2 1 2 len mean len mean len mean len mean c1 c2 ax 4 0.303036 6 0.414474 8 0.624900 7 0.425234 y 5 0.543363 5 0.480419 8 0.583499 7 0.637657 bx 10 0.400279 6 0.436929 4 0.442924 5 0.287572 y 6 0.400427 8 0.623319 5 0.764506 6 0.408708