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
|