NumPy > Aggregate operations

Name
Code
Output
array = numpy.random.randint(5, size=(3, 4))
print ("array \n",array)
result = numpy.average(array)
print ("average result:",result)
array 
 [[0 1 0 1]
 [0 4 2 1]
 [4 1 2 1]]
average result: 1.41666666667
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = numpy.var(array)
print ("variance result: \n",result)
array 
 [[3 4 1]
 [0 2 4]]
variance result: 2.22222222222
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = numpy.corrcoef(array)
print ("correlation coeff result \n",result)
array 
 [[0 4 3]
 [0 0 4]]
correlation coeff result 
 [[ 1.         0.2773501]
 [ 0.2773501  1.       ]]
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = array.cumsum()
print ("cumsum result \n",result)
array 
 [[4 2 0]
 [1 2 2]]
cumsum result 
[ 4  6  6  7  9 11]
array = numpy.random.randint(10, size=(2, 3))
print ("array \n",array)
result = array.max(axis=0)
print ("max result",result)
array 
 [[4 1 6]
 [5 7 0]]
max result [5 7 6]
array = numpy.random.randint(10, size=(2, 3))
print ("array \n",array)
result = array.max(axis=1)
print ("max row result \n",result)
array 
 [[2 7 4]
 [1 4 5]]
max row result 
 [7 5]
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = array.mean()
print ("mean result:",result)
array 
 [[4 3 4]
 [2 3 0]]
mean result: 2.66666666667
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = numpy.median(array)
print ("median result:",result)
array 
 [[4 3 0]
 [4 0 0]]
median result: 1.5
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = array.min()
print ("min result:",result)
array 
 [[2 0 4]
 [4 4 0]]
min result: 0
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = numpy.std(array)
print ("standard deviation result:",result)
array 
 [[3 4 2]
 [2 0 3]]
standard deviation result: 1.24721912892
array = numpy.random.randint(5, size=(2, 3))
print ("array \n",array)
result = array.sum()
print ("sum result:",result)
array 
 [[2 4 4]
 [2 1 4]]
sum result: 17
dataframe = pandas.DataFrame(numpy.random.randint(0,5,size=(5, 3)), columns=['C1','C2','C3'])
print ("dataframe")
print (dataframe)
aggregate = dataframe.groupby('C1').count()
print ("aggregated data")
print (aggregate)
dataframe
   C1  C2  C3
0   2   4   4
1   4   1   1
2   2   0   1
3   3   3   2
4   0   2   4
aggregated data
    C2  C3
C1        
0    1   1
2    2   2
3    1   1
4    1   1