GROUPING SQL function


1. Usage of GROUPING to show order total based on shipped country

SQL Server Query 1

            
-- Analyze order counts by customer and customer demographics  
SELECT
c.CustomerID,
c.CompanyName,
c.ContactName,
c.Country,
COUNT_BIG(o.OrderID) AS TotalOrders, -- Use COUNT_BIG here
AVG(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS AverageOrderValue,
MAX(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS MaxOrderValue,
MIN(CAST(od.Quantity * od.UnitPrice * (1 - od.Discount) AS DECIMAL(18, 2))) AS MinOrderValue
FROM Customers AS c
JOIN Orders AS o ON c.CustomerID = o.CustomerID
JOIN [Order Details] AS od ON o.OrderID = od.OrderID
GROUP BY c.CustomerID, c.CompanyName, c.ContactName, c.Country
ORDER BY TotalOrders DESC;

Create SQL query with SqlQueryBuilder 1

            
var (sql1, parameters1) = new SqlQueryBuilder()  
.Select()
.Columns("c.CustomerID","c.CompanyName","c.ContactName","c.Country")
.Column(new COUNT_BIG(new Column("o.OrderID")), "TotalOrders")
.Column(new AVG(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int,int>(18,2)))
, "AverageOrderValue")
.Column(new MAX(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int, int>(18, 2)))
, "MaxOrderValue")
.Column(new MIN(new CAST(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket(), SqlDataType.DECIMAL, new Tuple<int, int>(18, 2)))
, "MinOrderValue")
.From("Customers", "c")
.Join(new List<IJoin>()
{
new INNERJOIN().TableName("Orders", "o")
.On(new Column("c.CustomerID").Equale(new Column("o.CustomerID"))),
new INNERJOIN().TableName("[Order Details]", "od")
.On(new Column("o.OrderID").Equale(new Column("od.OrderID")))
})
.GroupBy(new GroupBy("c.CustomerID","c.CompanyName","c.ContactName","c.Country"))
.OrderBy(new OrderBy()
.SetColumnDescending("TotalOrders"))
.Build();

Query build by SqlQueryBuilder 1

            
WITH OrderDetailsWithGrouping
AS (SELECT ShipCountry AS ShipCountry,
           SUM(UnitPrice * Quantity * (@pMAIN_2507200140484164550 - Discount)) AS OrderTotal,
           GROUPING(ShipCountry) AS GroupingLevel
    FROM [Order Details]
         INNER JOIN
         Orders
         ON [Order Details].OrderID = Orders.OrderID
    GROUP BY ROLLUP(ShipCountry))
SELECT ShipCountry,
       OrderTotal,
       CASE WHEN GroupingLevel = @pMAIN_2507200140484164551 THEN @pMAIN_2507200140484164552 WHEN GroupingLevel = @pMAIN_2507200140484164553 THEN @pMAIN_2507200140484164554 ELSE @pMAIN_2507200140484164555 END AS GroupingSource
FROM OrderDetailsWithGrouping
WHERE GroupingLevel IN (@pMAIN_2507200140484164556, @pMAIN_2507200140484164557)
ORDER BY GroupingLevel ASC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200140484164550 1
@pMAIN_2507200140484164551 0
@pMAIN_2507200140484164552 Grouped by Country
@pMAIN_2507200140484164553 1
@pMAIN_2507200140484164554 Grand Total
@pMAIN_2507200140484164555 Invalid Grouping Level
@pMAIN_2507200140484164556 0
@pMAIN_2507200140484164557 1

Query Results 1:

  ShipCountry OrderTotal GroupingSource
1 Argentina 8119.09997558594 Grouped by Country
2 Austria 128003.838745117 Grouped by Country
3 Belgium 33824.8549804688 Grouped by Country
4 Brazil 106925.776360512 Grouped by Country
5 Canada 50196.2903213501 Grouped by Country
6 Denmark 32661.0223455429 Grouped by Country
7 Finland 18810.052570343 Grouped by Country
8 France 81358.322476387 Grouped by Country
9 Germany 230284.633333206 Grouped by Country
10 Ireland 49979.9050006866 Grouped by Country
11 Italy 15770.1549053192 Grouped by Country
12 Mexico 23582.0776252747 Grouped by Country
13 Norway 5735.14999961853 Grouped by Country
14 Poland 3531.94997596741 Grouped by Country
15 Portugal 11472.3626556396 Grouped by Country
16 Spain 17983.2001066208 Grouped by Country
17 Sweden 54495.140045166 Grouped by Country
18 Switzerland 31692.658903122 Grouped by Country
19 UK 58971.3100633621 Grouped by Country
20 USA 245584.610227585 Grouped by Country
21 Venezuela 56810.6290016174 Grouped by Country
22 1265793.03961849 Grand Total


2. Usage of GROUPING to Total sales with ROLLUP

SQL Server Query 2

            
SELECT  
o.ShipCountry,
c.CustomerID,
SUM(od.Quantity * od.UnitPrice * (1 - od.Discount)) AS TotalSales,
GROUPING(o.ShipCountry) AS CountryGrouping,
GROUPING(c.CustomerID) AS CustomerGrouping
FROM Orders AS o
JOIN Customers AS c ON o.CustomerID = c.CustomerID
JOIN [Order Details] AS od ON o.OrderID = od.OrderID
GROUP BY ROLLUP (o.ShipCountry, c.CustomerID) -- Using ROLLUP
ORDER BY o.ShipCountry, c.CustomerID;

Create SQL query with SqlQueryBuilder 2

            
var (sql2, parameters2) = new SqlQueryBuilder()  
.Select()
.Columns("o.ShipCountry","c.CustomerID")
.Column(new SUM(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket()), "TotalSales")
.Column(new GROUPING(new Column("o.ShipCountry")), "CountryGrouping")
.Column(new GROUPING(new Column("c.CustomerID")), "CustomerGrouping")
.From("Orders","o")
.Join(new List<IJoin>()
{
new INNERJOIN().TableName("Customers", "c")
.On(new Column("o.CustomerID").Equale(new Column("c.CustomerID"))),
new INNERJOIN().TableName("[Order Details]", "od")
.On(new Column("o.OrderID").Equale(new Column("od.OrderID")))
})
.GroupBy(new GroupBy("o.ShipCountry","c.CustomerID").WithRollUp())
.OrderBy(new OrderBy()
.SetColumnAscending("o.ShipCountry")
.SetColumnAscending("c.CustomerID"))
.Build();

Query build by SqlQueryBuilder 2

            
SELECT o.ShipCountry,
       c.CustomerID,
       SUM(od.Quantity * od.UnitPrice * (@pMAIN_2507200140484380360 - od.Discount)) AS TotalSales,
       GROUPING(o.ShipCountry) AS CountryGrouping,
       GROUPING(c.CustomerID) AS CustomerGrouping
FROM Orders AS o
     INNER JOIN
     Customers AS c
     ON o.CustomerID = c.CustomerID
     INNER JOIN
     [Order Details] AS od
     ON o.OrderID = od.OrderID
GROUP BY ROLLUP(o.ShipCountry, c.CustomerID)
ORDER BY o.ShipCountry ASC, c.CustomerID ASC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200140484380360 1

Query Results 2:

  SHipCountry CustomerID TotalSales CountryGrouping CustomerGrouping
1 1265793.03961849 1 1
2 Argentina 8119.09997558594 0 1
3 Argentina CACTU 1814.79998779297 0 0
4 Argentina OCEAN 3460.19999694824 0 0
5 Argentina RANCH 2844.09999084473 0 0
6 Austria 128003.838745117 0 1
7 Austria ERNSH 104874.978713989 0 0
8 Austria PICCO 23128.8600311279 0 0
9 Belgium 33824.8549804688 0 1
10 Belgium MAISD 9736.07500457764 0 0
11 Belgium SUPRD 24088.7799758911 0 0
12 Brazil 106925.776360512 0 1
13 Brazil COMMI 3810.75 0 0
14 Brazil FAMIA 4107.55003166199 0 0
15 Brazil GOURL 8414.13500213623 0 0
16 Brazil HANAR 32841.3699417114 0 0
17 Brazil QUEDE 6664.80997276306 0 0
18 Brazil QUEEN 25717.4975166321 0 0
19 Brazil RICAR 12450.8000183105 0 0
20 Brazil TRADH 6850.66397094726 0 0
21 Brazil WELLI 6068.19990634918 0 0
22 Canada 50196.2903213501 0 1
23 Canada BOTTM 20801.6000213623 0 0
24 Canada LAUGB 522.5 0 0
25 Canada MEREP 28872.1902999878 0 0
26 Denmark 32661.0223455429 0 1
27 Denmark SIMOB 16817.0975255966 0 0
28 Denmark VAFFE 15843.9248199463 0 0
29 Finland 18810.052570343 0 1
30 Finland WARTH 15648.7025642395 0 0
31 Finland WILMK 3161.35000610352 0 0
32 France 81358.322476387 0 1
33 France BLONP 18534.0799789429 0 0
34 France BONAP 21963.2524261475 0 0
35 France DUMON 1615.90000915527 0 0
36 France FOLIG 11666.9000015259 0 0
37 France FRANR 3172.16006469727 0 0
38 France LACOR 1992.04999542236 0 0
39 France LAMAI 9328.20000362396 0 0
40 France SPECD 2423.34999847412 0 0
41 France VICTE 9182.42999076843 0 0
42 France VINET 1480.00000762939 0 0
43 Germany 230284.633333206 0 1
44 Germany ALFKI 4272.99999809265 0 0
45 Germany BLAUS 3239.80000305176 0 0
46 Germany DRACD 3763.21001434326 0 0
47 Germany FRANK 26656.559387207 0 0
48 Germany KOENE 30908.3839836121 0 0
49 Germany LEHMS 19261.4100112915 0 0
50 Germany MORGK 5042.19998168945 0 0
51 Germany OTTIK 12496.1999893188 0 0
52 Germany QUICK 110277.304977417 0 0
53 Germany TOMSP 4778.13998413086 0 0
54 Germany WANDK 9588.42500305176 0 0
55 Ireland 49979.9050006866 0 1
56 Ireland HUNGO 49979.9050006866 0 0
57 Italy 15770.1549053192 0 1
58 Italy FRANS 1545.69999885559 0 0
59 Italy MAGAA 7176.21500205994 0 0
60 Italy REGGC 7048.23990440369 0 0
61 Mexico 23582.0776252747 0 1
62 Mexico ANATR 1402.95000076294 0 0
63 Mexico ANTON 7023.97755432129 0 0
64 Mexico CENTC 100.799999237061 0 0
65 Mexico PERIC 4242.20002746582 0 0
66 Mexico TORTU 10812.1500434875 0 0
67 Norway 5735.14999961853 0 1
68 Norway SANTG 5735.14999961853 0 0
69 Poland 3531.94997596741 0 1
70 Poland WOLZA 3531.94997596741 0 0
71 Portugal 11472.3626556396 0 1
72 Portugal FURIB 6427.42259216309 0 0
73 Portugal PRINI 5044.94006347656 0 0
74 Spain 17983.2001066208 0 1
75 Spain BOLID 4232.85009765625 0 0
76 Spain GALED 836.699996948242 0 0
77 Spain GODOS 11446.3600158691 0 0
78 Spain ROMEY 1467.28999614716 0 0
79 Sweden 54495.140045166 0 1
80 Sweden BERGS 24927.5774688721 0 0
81 Sweden FOLKO 29567.5625762939 0 0
82 Switzerland 31692.658903122 0 1
83 Switzerland CHOPS 12348.8800125122 0 0
84 Switzerland RICSU 19343.7788906097 0 0
85 UK 58971.3100633621 0 1
86 UK AROUT 13390.6500091553 0 0
87 UK BSBEV 6089.89999008179 0 0
88 UK CONSH 1719.10000324249 0 0
89 UK EASTC 14761.0350036621 0 0
90 UK ISLAT 6146.29999542236 0 0
91 UK NORTS 649 0 0
92 UK SEVES 16215.3250617981 0 0
93 USA 245584.610227585 0 1
94 USA GREAL 18507.4499664307 0 0
95 USA HUNGC 3063.20000076294 0 0
96 USA LAZYK 357 0 0
97 USA LETSS 3076.47247505188 0 0
98 USA LONEP 4258.60001373291 0 0
99 USA OLDWO 15177.4624938965 0 0
100 USA RATTC 51097.8003330231 0 0
101 USA SAVEA 104361.949920654 0 0
102 USA SPLIR 11441.6299972534 0 0
103 USA THEBI 3361 0 0
104 USA THECR 1947.23999023438 0 0
105 USA TRAIH 1571.19999313354 0 0
106 USA WHITC 27363.6050434113 0 0
107 Venezuela 56810.6290016174 0 1
108 Venezuela GROSR 1488.69999694824 0 0
109 Venezuela HILAA 22768.7639884949 0 0
110 Venezuela LILAS 16076.5999908447 0 0
111 Venezuela LINOD 16476.5650253296 0 0


2. Usage of GROUPING to Total sales with CUBE

SQL Server Query 3

            
SELECT  
o.ShipCountry,
c.CustomerID,
SUM(od.Quantity * od.UnitPrice * (1 - od.Discount)) AS TotalSales,
GROUPING(o.ShipCountry) AS CountryGrouping,
GROUPING(c.CustomerID) AS CustomerGrouping
FROM Orders AS o
JOIN Customers AS c ON o.CustomerID = c.CustomerID
JOIN [Order Details] AS od ON o.OrderID = od.OrderID
GROUP BY ROLLUP (o.ShipCountry, c.CustomerID) -- Using CUBE
ORDER BY o.ShipCountry, c.CustomerID;

Create SQL query with SqlQueryBuilder 3

            
var (sql3, parameters3) = new SqlQueryBuilder()  
.Select()
.Columns("o.ShipCountry","c.CustomerID")
.Column(new SUM(new ColumnArithmatic("od.Quantity").MULTIPLY("od.UnitPrice").MULTIPLY()
.StartBracket(1).SUBTRACT("od.Discount").EndBracket()), "TotalSales")
.Column(new GROUPING(new Column("o.ShipCountry")), "CountryGrouping")
.Column(new GROUPING(new Column("c.CustomerID")), "CustomerGrouping")
.From("Orders","o")
.Join(new List<IJoin>()
{
new INNERJOIN().TableName("Customers", "c")
.On(new Column("o.CustomerID").Equale(new Column("c.CustomerID"))),
new INNERJOIN().TableName("[Order Details]", "od")
.On(new Column("o.OrderID").Equale(new Column("od.OrderID")))
})
.GroupBy(new GroupBy("o.ShipCountry","c.CustomerID").WithCube())
.OrderBy(new OrderBy()
.SetColumnAscending("o.ShipCountry")
.SetColumnAscending("c.CustomerID"))
.Build();

Query build by SqlQueryBuilder 3

            
SELECT o.ShipCountry,
       c.CustomerID,
       SUM(od.Quantity * od.UnitPrice * (@pMAIN_2507200140484617970 - od.Discount)) AS TotalSales,
       GROUPING(o.ShipCountry) AS CountryGrouping,
       GROUPING(c.CustomerID) AS CustomerGrouping
FROM Orders AS o
     INNER JOIN
     Customers AS c
     ON o.CustomerID = c.CustomerID
     INNER JOIN
     [Order Details] AS od
     ON o.OrderID = od.OrderID
GROUP BY CUBE(o.ShipCountry, c.CustomerID)
ORDER BY o.ShipCountry ASC, c.CustomerID ASC;


            
        

Parameters (If used)

Name Value
@pMAIN_2507200140484617970 1

Query Results 3:

  SHipCountry CustomerID TotalSales CountryGrouping CustomerGrouping
1 1265793.03961849 1 1
2 ALFKI 4272.99999809265 1 0
3 ANATR 1402.95000076294 1 0
4 ANTON 7023.97755432129 1 0
5 AROUT 13390.6500091553 1 0
6 BERGS 24927.5774688721 1 0
7 BLAUS 3239.80000305176 1 0
8 BLONP 18534.0799789429 1 0
9 BOLID 4232.85009765625 1 0
10 BONAP 21963.2524261475 1 0
11 BOTTM 20801.6000213623 1 0
12 BSBEV 6089.89999008179 1 0
13 CACTU 1814.79998779297 1 0
14 CENTC 100.799999237061 1 0
15 CHOPS 12348.8800125122 1 0
16 COMMI 3810.75 1 0
17 CONSH 1719.10000324249 1 0
18 DRACD 3763.21001434326 1 0
19 DUMON 1615.90000915527 1 0
20 EASTC 14761.0350036621 1 0
21 ERNSH 104874.978713989 1 0
22 FAMIA 4107.55003166199 1 0
23 FOLIG 11666.9000015259 1 0
24 FOLKO 29567.5625762939 1 0
25 FRANK 26656.559387207 1 0
26 FRANR 3172.16006469727 1 0
27 FRANS 1545.69999885559 1 0
28 FURIB 6427.42259216309 1 0
29 GALED 836.699996948242 1 0
30 GODOS 11446.3600158691 1 0
31 GOURL 8414.13500213623 1 0
32 GREAL 18507.4499664307 1 0
33 GROSR 1488.69999694824 1 0
34 HANAR 32841.3699417114 1 0
35 HILAA 22768.7639884949 1 0
36 HUNGC 3063.20000076294 1 0
37 HUNGO 49979.9050006866 1 0
38 ISLAT 6146.29999542236 1 0
39 KOENE 30908.3839836121 1 0
40 LACOR 1992.04999542236 1 0
41 LAMAI 9328.20000362396 1 0
42 LAUGB 522.5 1 0
43 LAZYK 357 1 0
44 LEHMS 19261.4100112915 1 0
45 LETSS 3076.47247505188 1 0
46 LILAS 16076.5999908447 1 0
47 LINOD 16476.5650253296 1 0
48 LONEP 4258.60001373291 1 0
49 MAGAA 7176.21500205994 1 0
50 MAISD 9736.07500457764 1 0
51 MEREP 28872.1902999878 1 0
52 MORGK 5042.19998168945 1 0
53 NORTS 649 1 0
54 OCEAN 3460.19999694824 1 0
55 OLDWO 15177.4624938965 1 0
56 OTTIK 12496.1999893188 1 0
57 PERIC 4242.20002746582 1 0
58 PICCO 23128.8600311279 1 0
59 PRINI 5044.94006347656 1 0
60 QUEDE 6664.80997276306 1 0
61 QUEEN 25717.4975166321 1 0
62 QUICK 110277.304977417 1 0
63 RANCH 2844.09999084473 1 0
64 RATTC 51097.8003330231 1 0
65 REGGC 7048.23990440369 1 0
66 RICAR 12450.8000183105 1 0
67 RICSU 19343.7788906097 1 0
68 ROMEY 1467.28999614716 1 0
69 SANTG 5735.14999961853 1 0
70 SAVEA 104361.949920654 1 0
71 SEVES 16215.3250617981 1 0
72 SIMOB 16817.0975255966 1 0
73 SPECD 2423.34999847412 1 0
74 SPLIR 11441.6299972534 1 0
75 SUPRD 24088.7799758911 1 0
76 THEBI 3361 1 0
77 THECR 1947.23999023438 1 0
78 TOMSP 4778.13998413086 1 0
79 TORTU 10812.1500434875 1 0
80 TRADH 6850.66397094726 1 0
81 TRAIH 1571.19999313354 1 0
82 VAFFE 15843.9248199463 1 0
83 VICTE 9182.42999076843 1 0
84 VINET 1480.00000762939 1 0
85 WANDK 9588.42500305176 1 0
86 WARTH 15648.7025642395 1 0
87 WELLI 6068.19990634918 1 0
88 WHITC 27363.6050434113 1 0
89 WILMK 3161.35000610352 1 0
90 WOLZA 3531.94997596741 1 0
91 Argentina 8119.09997558594 0 1
92 Argentina CACTU 1814.79998779297 0 0
93 Argentina OCEAN 3460.19999694824 0 0
94 Argentina RANCH 2844.09999084473 0 0
95 Austria 128003.838745117 0 1
96 Austria ERNSH 104874.978713989 0 0
97 Austria PICCO 23128.8600311279 0 0
98 Belgium 33824.8549804688 0 1
99 Belgium MAISD 9736.07500457764 0 0
100 Belgium SUPRD 24088.7799758911 0 0
101 Brazil 106925.776360512 0 1
102 Brazil COMMI 3810.75 0 0
103 Brazil FAMIA 4107.55003166199 0 0
104 Brazil GOURL 8414.13500213623 0 0
105 Brazil HANAR 32841.3699417114 0 0
106 Brazil QUEDE 6664.80997276306 0 0
107 Brazil QUEEN 25717.4975166321 0 0
108 Brazil RICAR 12450.8000183105 0 0
109 Brazil TRADH 6850.66397094726 0 0
110 Brazil WELLI 6068.19990634918 0 0
111 Canada 50196.2903213501 0 1
112 Canada BOTTM 20801.6000213623 0 0
113 Canada LAUGB 522.5 0 0
114 Canada MEREP 28872.1902999878 0 0
115 Denmark 32661.0223455429 0 1
116 Denmark SIMOB 16817.0975255966 0 0
117 Denmark VAFFE 15843.9248199463 0 0
118 Finland 18810.052570343 0 1
119 Finland WARTH 15648.7025642395 0 0
120 Finland WILMK 3161.35000610352 0 0
121 France 81358.322476387 0 1
122 France BLONP 18534.0799789429 0 0
123 France BONAP 21963.2524261475 0 0
124 France DUMON 1615.90000915527 0 0
125 France FOLIG 11666.9000015259 0 0
126 France FRANR 3172.16006469727 0 0
127 France LACOR 1992.04999542236 0 0
128 France LAMAI 9328.20000362396 0 0
129 France SPECD 2423.34999847412 0 0
130 France VICTE 9182.42999076843 0 0
131 France VINET 1480.00000762939 0 0
132 Germany 230284.633333206 0 1
133 Germany ALFKI 4272.99999809265 0 0
134 Germany BLAUS 3239.80000305176 0 0
135 Germany DRACD 3763.21001434326 0 0
136 Germany FRANK 26656.559387207 0 0
137 Germany KOENE 30908.3839836121 0 0
138 Germany LEHMS 19261.4100112915 0 0
139 Germany MORGK 5042.19998168945 0 0
140 Germany OTTIK 12496.1999893188 0 0
141 Germany QUICK 110277.304977417 0 0
142 Germany TOMSP 4778.13998413086 0 0
143 Germany WANDK 9588.42500305176 0 0
144 Ireland 49979.9050006866 0 1
145 Ireland HUNGO 49979.9050006866 0 0
146 Italy 15770.1549053192 0 1
147 Italy FRANS 1545.69999885559 0 0
148 Italy MAGAA 7176.21500205994 0 0
149 Italy REGGC 7048.23990440369 0 0
150 Mexico 23582.0776252747 0 1
151 Mexico ANATR 1402.95000076294 0 0
152 Mexico ANTON 7023.97755432129 0 0
153 Mexico CENTC 100.799999237061 0 0
154 Mexico PERIC 4242.20002746582 0 0
155 Mexico TORTU 10812.1500434875 0 0
156 Norway 5735.14999961853 0 1
157 Norway SANTG 5735.14999961853 0 0
158 Poland 3531.94997596741 0 1
159 Poland WOLZA 3531.94997596741 0 0
160 Portugal 11472.3626556396 0 1
161 Portugal FURIB 6427.42259216309 0 0
162 Portugal PRINI 5044.94006347656 0 0
163 Spain 17983.2001066208 0 1
164 Spain BOLID 4232.85009765625 0 0
165 Spain GALED 836.699996948242 0 0
166 Spain GODOS 11446.3600158691 0 0
167 Spain ROMEY 1467.28999614716 0 0
168 Sweden 54495.140045166 0 1
169 Sweden BERGS 24927.5774688721 0 0
170 Sweden FOLKO 29567.5625762939 0 0
171 Switzerland 31692.658903122 0 1
172 Switzerland CHOPS 12348.8800125122 0 0
173 Switzerland RICSU 19343.7788906097 0 0
174 UK 58971.3100633621 0 1
175 UK AROUT 13390.6500091553 0 0
176 UK BSBEV 6089.89999008179 0 0
177 UK CONSH 1719.10000324249 0 0
178 UK EASTC 14761.0350036621 0 0
179 UK ISLAT 6146.29999542236 0 0
180 UK NORTS 649 0 0
181 UK SEVES 16215.3250617981 0 0
182 USA 245584.610227585 0 1
183 USA GREAL 18507.4499664307 0 0
184 USA HUNGC 3063.20000076294 0 0
185 USA LAZYK 357 0 0
186 USA LETSS 3076.47247505188 0 0
187 USA LONEP 4258.60001373291 0 0
188 USA OLDWO 15177.4624938965 0 0
189 USA RATTC 51097.8003330231 0 0
190 USA SAVEA 104361.949920654 0 0
191 USA SPLIR 11441.6299972534 0 0
192 USA THEBI 3361 0 0
193 USA THECR 1947.23999023438 0 0
194 USA TRAIH 1571.19999313354 0 0
195 USA WHITC 27363.6050434113 0 0
196 Venezuela 56810.6290016174 0 1
197 Venezuela GROSR 1488.69999694824 0 0
198 Venezuela HILAA 22768.7639884949 0 0
199 Venezuela LILAS 16076.5999908447 0 0
200 Venezuela LINOD 16476.5650253296 0 0