According to Wikipedia, a Pivot Table is a useful summarization tool in data processing. Your task is to implement a Pivot tableaccording to given requirements
Requirements
We explain the aggregation functions using example for better understanding of the specifications Lets’ say, the data is as follows
A

B

C

D

Match1

Sachin

2

12.678

Match4

Rahul

23

14

Match5

Sonal

13

35.333

Match6

Rajiv

6

56.33

Match2

Manish

87

37

Match6

Nakul

34

45

Match2

Mukul

48

12.87

Match7

Srinath

24

29.625

Match1

Dora

83

51.667

Match8

James

79

59

Match4

Munna

45

53.333

Match6

Sachin

53

52.5

Match3

Sonal

21

58.93

Match2

Rajiv

69

61.5

Match7

Nakul

36

63.75

Match9

Mukul

96

56.222

Match5

Dora

43

51.714

Match3

Munna

80

49.417

Sum Function
 Can only be applied to Numeric columns i.e. C and D in this case
 Let’s say we are grouping on column B and applying Sum function on column C then, the output will look as follows :Dora 126
James 79
Manish 87
Mukul 144
Munna 125
Nakul 70
Rahul 23
Rajiv 75
Sachin 55
Sonal 34
Srinath 24
Count Function
 Let’s say we are grouping on column A and applying Count function on column A then, the output will look as follows :Match1 2
Match2 3
Match3 2
Match4 2
Match5 2
Match6 3
Match7 2
Match8 1
Match9 1
Average Function
 Can only be applied to Numeric columns i.e. C and D in this case
 Let’s say we are grouping on column B and applying Average function on column D then, the output will look as follows :Dora 52
James 59
Manish 37
Mukul 35
Munna 52
Nakul 55
Rahul 14
Rajiv 59
Sachin 33
Sonal 48
Srinath 30
Invert Function
 Let’s say we are grouping on column B and applying Invert function on column D then, the output will look as follows :Dora 51.667 # # # 51.714 # # # #
James # # # # # # # 59 #
Manish # 37 # # # # # # #
Mukul # 12.87 # # # # # # 56.222
Munna # # 49.417 53.333 # # # # #
Nakul # # # # # 45 63.75 # #
Rahul # # # 14 # # # # #
Rajiv # 61.5 # # # 56.33 # # #
Sachin 12.678 # # # # 52.5 # # #
Sonal # # 58.93 # 35.333 # # # #
Srinath # # # # # # 29.625 # #
The first column of output is column B in ascending order. The column A in the data table becomes column headers in the output (which is not printed in output). The nonhash values are values of column D for a combination of column A and column B in the original data table. Column A and column B combination in test cases for Invert function are unique. ‘#’ character is a place holder for null values.
Now that the workings of the four functions have been explained, let us understand the input and output specifications.
Input Format:
First line of input contains total number of test cases, denoted by N
Each test case comprises of 5 parts
 First line of a test case contains a function name (Sum , Average, Invert and Count)
 Second line contains the column no. on which grouping will be applied.(Column index starts from 1)
 Third line contains the column no. on which the aggregation function will be applied
 Next variable number of lines contain the actual data delimited by space
 A test case is terminated by 1
Output Format:
For each test case print the output of the appropriate function as explained above.
 Perform rounding up when printing output for Sum and Average functions.
 Do not perform rounding when printing output for Invert function.
SNo.

Input

Output

1

4 Sum 2 3 idx 49.865 30.071 blkqt ywhw 17.909 96.138 nqng odb 69.900 34.593 hcf 1 Count 3 2 79.424 bwipx eqmz 15.800 61.570 uhaci fifo 36.933 9.881 alt cjven 63.373 31.110 mpcq pdg 9.170 1 Average 3 2 76.114 69.394 nuvr 1.992 80.233 5.676 kxf 2.783 1.940 85.761 zlnv 5.537 74.016 79.417 atp 89.162 1 Invert 2 3 cky foq 57.193 80.148 mkf kjqs 15.449 43.623 ohu turnf 95.211 57.271 1 
17.909 97 49.865 31 69.9 35 cjven 1 eqmz 1 fifo 1 pdg 1 atp 80 kxf 6 nuvr 70 zlnv 86 foq 57.193 # # kjqs # 15.449 # turnf # # 95.211 