SQL: FIter rows with specyfic pattern - sql

I'm a bit new in sql.
I have the following table:
+-----+---------+------------------------+
| ID | ID_TEST | FILE_PATH |
+-----+---------+------------------------+
| 575 | 3 | Landscapes_001_h_A.jpg |
| 576 | 3 | Landscapes_001_h_B.jpg |
| 577 | 3 | Landscapes_001_h_C.jpg |
| 578 | 3 | Landscapes_001_h_D.jpg |
| 579 | 3 | Landscapes_001_h_E.jpg |
| 580 | 3 | Landscapes_002_h_A.jpg |
| 581 | 3 | Landscapes_002_h_B.jpg |
| 582 | 3 | Landscapes_002_h_C.jpg |
| 583 | 3 | Landscapes_002_h_D.jpg |
| 584 | 3 | Landscapes_002_h_E.jpg |
+-----+---------+------------------------+
The pattern for picture is Landscapes_XXX_h_Y.jpg
where
XXX is number from 1 to 185 and Y is quality version from A to E
I wanna select each image name with different quality.
The output should be
+-----+---------+------------------------+
| ID | ID_TEST | FILE_PATH |
+-----+---------+------------------------+
| 575 | 3 | Landscapes_001_h_A.jpg |
| 576 | 3 | Landscapes_002_h_E.jpg |
| 577 | 3 | Landscapes_003_h_C.jpg |
| 578 | 3 | Landscapes_004_h_B.jpg |
| 579 | 3 | Landscapes_005_h_D.jpg |
| 580 | 3 | Landscapes_006_h_A.jpg |
| 581 | 3 | Landscapes_007_h_E.jpg |
| 582 | 3 | Landscapes_008_h_C.jpg |
| 583 | 3 | Landscapes_009_h_B.jpg |
| 584 | 3 | Landscapes_010_h_E.jpg |
+-----+---------+------------------------+
but of course for 185 elements.
I'm using 5.5.60-MariaDB.
How to write SELECT statement? Using REGEXP?

Related

SQL subcategory total is not properly placed at the end of every parent category

I am having trouble in SQl query,The query result should be like this
+------------+------------+-----+------+-------+--+--+--+
| District | Tehsil | yes | no | Total | | | |
+------------+------------+-----+------+-------+--+--+--+
| ABBOTTABAD | ABBOTTABAD | 377 | 5927 | 6304 | | | |
| ABBOTTABAD | HAVELIAN | 112 | 2276 | 2388 | | | |
| ABBOTTABAD | Overall | 489 | 8203 | 8692 | | | |
| CHARSADDA | CHARSADDA | 289 | 3762 | 4051 | | | |
| CHARSADDA | SHABQADAR | 121 | 1376 | 1497 | | | |
| CHARSADDA | TANGI | 94 | 1703 | 1797 | | | |
| CHARSADDA | Overall | 504 | 6841 | 7345 | | | |
+------------+------------+-----+------+-------+--+--+--+
The overall total should be should be shown at the end of every parent category but now it is showing like this
+------------+------------+-----+------+-------+--+--+--+
| District | Tehsil | yes | no | Total | | | |
+------------+------------+-----+------+-------+--+--+--+
| ABBOTTABAD | ABBOTTABAD | 377 | 5927 | 6304 | | | |
| ABBOTTABAD | HAVELIAN | 112 | 2276 | 2388 | | | |
| ABBOTTABAD | Overall | 489 | 8203 | 8692 | | | |
| CHARSADDA | CHARSADDA | 289 | 3762 | 4051 | | | |
| CHARSADDA | Overall | 504 | 6841 | 7345 | | | |
| CHARSADDA | SHABQADAR | 121 | 1376 | 1497 | | | |
| CHARSADDA | TANGI | 94 | 1703 | 1797 | | | |
+------------+------------+-----+------+-------+--+--+--+
My query is sorting second column with respect to first column although order by query is applied on my first column. This is my query
select District as 'District', tName as 'tehsil',[1] as 'yes',[0] as 'no',ISNULL([1]+[0], 0) as "Total" from
(
select d.Name as 'District',
case when grouping (t.Name)=1 then 'Overall' else t.Name end as tName,
BoundaryWallAvailable,
count(*) as total from School s
INNER JOIN SchoolIndicator i ON (i.refSchoolID=s.SchoolID)
INNER JOIN Tehsil t ON (t.TehsilID=s.refTehsilID)
INNER JOIN district d ON (d.DistrictID=t.refDistrictID)
group by
GROUPING sets((d.Name, BoundaryWallAvailable), (d.Name,t.Name, BoundaryWallAvailable))
) B
PIVOT
(
max(total) for BoundaryWallAvailable in ([1],[0])
) as Pvt
order by District
P.S: BoundaryWall is one column through pivoting i am breaking it into Yes and No Column

How to make sql hive when i have input this?

input:
| a.user_id | a_stream_length | b_stream_length | subtract_inactive |
-----------------------------------------------------------------------------
| a | 11 | 1686 | 22 |
| a | 1686 | 328 | 12 |
| a | 328 | 732 | 22 |
| a | 732 | 11 | 1699 |
| a | 11 | 2123 | 18 |
| a | 2123 | 160 | 2 |
| a | 160 | 1358 | 0 |
| a | 1358 | 129 | 1 |
| a | 129 | 4042 | 109334 |
output:
| a | (1686+11+328+732) (if subtract_inactive < 1000) |
| a | 732(a_stream_length) if subtract_inactive > 1000) |

Find the highest and lowest value locations within an interval on a column?

Given this pandas dataframe with two columns, 'Values' and 'Intervals'. How do I get a third column 'MinMax' indicating whether the value is a maximum or a minimum within that interval? The challenge for me is that the interval length and the distance between intervals are not fixed, therefore I post the question.
import pandas as pd
import numpy as np
data = pd.DataFrame([
[1879.289,np.nan],[1879.281,np.nan],[1879.292,1],[1879.295,1],[1879.481,1],[1879.294,1],[1879.268,1],
[1879.293,1],[1879.277,1],[1879.285,1],[1879.464,1],[1879.475,1],[1879.971,1],[1879.779,1],
[1879.986,1],[1880.791,1],[1880.29,1],[1879.253,np.nan],[1878.268,np.nan],[1875.73,1],[1876.792,1],
[1875.977,1],[1876.408,1],[1877.159,1],[1877.187,1],[1883.164,1],[1883.171,1],[1883.495,1],
[1883.962,1],[1885.158,1],[1885.974,1],[1886.479,np.nan],[1885.969,np.nan],[1884.693,1],[1884.977,1],
[1884.967,1],[1884.691,1],[1886.171,1],[1886.166,np.nan],[1884.476,np.nan],[1884.66,1],[1882.962,1],
[1881.496,1],[1871.163,1],[1874.985,1],[1874.979,1],[1871.173,np.nan],[1871.973,np.nan],[1871.682,np.nan],
[1872.476,np.nan],[1882.361,1],[1880.869,1],[1882.165,1],[1881.857,1],[1880.375,1],[1880.66,1],
[1880.891,1],[1880.377,1],[1881.663,1],[1881.66,1],[1877.888,1],[1875.69,1],[1875.161,1],
[1876.697,np.nan],[1876.671,np.nan],[1879.666,np.nan],[1877.182,np.nan],[1878.898,1],[1878.668,1],[1878.871,1],
[1878.882,1],[1879.173,1],[1878.887,1],[1878.68,1],[1878.872,1],[1878.677,1],[1877.877,1],
[1877.669,1],[1877.69,1],[1877.684,1],[1877.68,1],[1877.885,1],[1877.863,1],[1877.674,1],
[1877.676,1],[1877.687,1],[1878.367,1],[1878.179,1],[1877.696,1],[1877.665,1],[1877.667,np.nan],
[1878.678,np.nan],[1878.661,1],[1878.171,1],[1877.371,1],[1877.359,1],[1878.381,1],[1875.185,1],
[1875.367,np.nan],[1865.492,np.nan],[1865.495,1],[1866.995,1],[1866.672,1],[1867.465,1],[1867.663,1],
[1867.186,1],[1867.687,1],[1867.459,1],[1867.168,1],[1869.689,1],[1869.693,1],[1871.676,1],
[1873.174,1],[1873.691,np.nan],[1873.685,np.nan]
])
In the third column below you can see where the max and min is for each interval.
+-------+----------+-----------+---------+
| index | Value | Intervals | Min/Max |
+-------+----------+-----------+---------+
| 0 | 1879.289 | np.nan | |
| 1 | 1879.281 | np.nan | |
| 2 | 1879.292 | 1 | |
| 3 | 1879.295 | 1 | |
| 4 | 1879.481 | 1 | |
| 5 | 1879.294 | 1 | |
| 6 | 1879.268 | 1 | min |
| 7 | 1879.293 | 1 | |
| 8 | 1879.277 | 1 | |
| 9 | 1879.285 | 1 | |
| 10 | 1879.464 | 1 | |
| 11 | 1879.475 | 1 | |
| 12 | 1879.971 | 1 | |
| 13 | 1879.779 | 1 | |
| 17 | 1879.986 | 1 | |
| 18 | 1880.791 | 1 | max |
| 19 | 1880.29 | 1 | |
| 55 | 1879.253 | np.nan | |
| 56 | 1878.268 | np.nan | |
| 57 | 1875.73 | 1 | |
| 58 | 1876.792 | 1 | |
| 59 | 1875.977 | 1 | min |
| 60 | 1876.408 | 1 | |
| 61 | 1877.159 | 1 | |
| 62 | 1877.187 | 1 | |
| 63 | 1883.164 | 1 | |
| 64 | 1883.171 | 1 | |
| 65 | 1883.495 | 1 | |
| 66 | 1883.962 | 1 | |
| 67 | 1885.158 | 1 | |
| 68 | 1885.974 | 1 | max |
| 69 | 1886.479 | np.nan | |
| 70 | 1885.969 | np.nan | |
| 71 | 1884.693 | 1 | |
| 72 | 1884.977 | 1 | |
| 73 | 1884.967 | 1 | |
| 74 | 1884.691 | 1 | min |
| 75 | 1886.171 | 1 | max |
| 76 | 1886.166 | np.nan | |
| 77 | 1884.476 | np.nan | |
| 78 | 1884.66 | 1 | max |
| 79 | 1882.962 | 1 | |
| 80 | 1881.496 | 1 | |
| 81 | 1871.163 | 1 | min |
| 82 | 1874.985 | 1 | |
| 83 | 1874.979 | 1 | |
| 84 | 1871.173 | np.nan | |
| 85 | 1871.973 | np.nan | |
| 86 | 1871.682 | np.nan | |
| 87 | 1872.476 | np.nan | |
| 88 | 1882.361 | 1 | max |
| 89 | 1880.869 | 1 | |
| 90 | 1882.165 | 1 | |
| 91 | 1881.857 | 1 | |
| 92 | 1880.375 | 1 | |
| 93 | 1880.66 | 1 | |
| 94 | 1880.891 | 1 | |
| 95 | 1880.377 | 1 | |
| 96 | 1881.663 | 1 | |
| 97 | 1881.66 | 1 | |
| 98 | 1877.888 | 1 | |
| 99 | 1875.69 | 1 | |
| 100 | 1875.161 | 1 | min |
| 101 | 1876.697 | np.nan | |
| 102 | 1876.671 | np.nan | |
| 103 | 1879.666 | np.nan | |
| 111 | 1877.182 | np.nan | |
| 112 | 1878.898 | 1 | |
| 113 | 1878.668 | 1 | |
| 114 | 1878.871 | 1 | |
| 115 | 1878.882 | 1 | |
| 116 | 1879.173 | 1 | max |
| 117 | 1878.887 | 1 | |
| 118 | 1878.68 | 1 | |
| 119 | 1878.872 | 1 | |
| 120 | 1878.677 | 1 | |
| 121 | 1877.877 | 1 | |
| 122 | 1877.669 | 1 | |
| 123 | 1877.69 | 1 | |
| 124 | 1877.684 | 1 | |
| 125 | 1877.68 | 1 | |
| 126 | 1877.885 | 1 | |
| 127 | 1877.863 | 1 | |
| 128 | 1877.674 | 1 | |
| 129 | 1877.676 | 1 | |
| 130 | 1877.687 | 1 | |
| 131 | 1878.367 | 1 | |
| 132 | 1878.179 | 1 | |
| 133 | 1877.696 | 1 | |
| 134 | 1877.665 | 1 | min |
| 135 | 1877.667 | np.nan | |
| 136 | 1878.678 | np.nan | |
| 137 | 1878.661 | 1 | max |
| 138 | 1878.171 | 1 | |
| 139 | 1877.371 | 1 | |
| 140 | 1877.359 | 1 | |
| 141 | 1878.381 | 1 | |
| 142 | 1875.185 | 1 | min |
| 143 | 1875.367 | np.nan | |
| 144 | 1865.492 | np.nan | |
| 145 | 1865.495 | 1 | max |
| 146 | 1866.995 | 1 | |
| 147 | 1866.672 | 1 | |
| 148 | 1867.465 | 1 | |
| 149 | 1867.663 | 1 | |
| 150 | 1867.186 | 1 | |
| 151 | 1867.687 | 1 | |
| 152 | 1867.459 | 1 | |
| 153 | 1867.168 | 1 | |
| 154 | 1869.689 | 1 | |
| 155 | 1869.693 | 1 | |
| 156 | 1871.676 | 1 | |
| 157 | 1873.174 | 1 | min |
| 158 | 1873.691 | np.nan | |
| 159 | 1873.685 | np.nan | |
+-------+----------+-----------+---------+

isnull = data.iloc[:, 1].isnull()
minmax = data.groupby(isnull.cumsum()[~isnull])[0].agg(['idxmax', 'idxmin'])
data.loc[minmax['idxmax'], 'MinMax'] = 'max'
data.loc[minmax['idxmin'], 'MinMax'] = 'min'
data.MinMax = data.MinMax.fillna('')
print(data)
0 1 MinMax
0 1879.289 NaN
1 1879.281 NaN
2 1879.292 1.0
3 1879.295 1.0
4 1879.481 1.0
5 1879.294 1.0
6 1879.268 1.0 min
7 1879.293 1.0
8 1879.277 1.0
9 1879.285 1.0
10 1879.464 1.0
11 1879.475 1.0
12 1879.971 1.0
13 1879.779 1.0
14 1879.986 1.0
15 1880.791 1.0 max
16 1880.290 1.0
17 1879.253 NaN
18 1878.268 NaN
19 1875.730 1.0 min
20 1876.792 1.0
21 1875.977 1.0
22 1876.408 1.0
23 1877.159 1.0
24 1877.187 1.0
25 1883.164 1.0
26 1883.171 1.0
27 1883.495 1.0
28 1883.962 1.0
29 1885.158 1.0
.. ... ... ...
85 1877.687 1.0
86 1878.367 1.0
87 1878.179 1.0
88 1877.696 1.0
89 1877.665 1.0 min
90 1877.667 NaN
91 1878.678 NaN
92 1878.661 1.0 max
93 1878.171 1.0
94 1877.371 1.0
95 1877.359 1.0
96 1878.381 1.0
97 1875.185 1.0 min
98 1875.367 NaN
99 1865.492 NaN
100 1865.495 1.0 min
101 1866.995 1.0
102 1866.672 1.0
103 1867.465 1.0
104 1867.663 1.0
105 1867.186 1.0
106 1867.687 1.0
107 1867.459 1.0
108 1867.168 1.0
109 1869.689 1.0
110 1869.693 1.0
111 1871.676 1.0
112 1873.174 1.0 max
113 1873.691 NaN
114 1873.685 NaN
[115 rows x 3 columns]

data.columns=['Value','Interval']
data['Ingroup'] = (data['Interval'].notnull() + 0)
Use data['Interval'].notnull() to separate the groups...
Use cumsum() to number them with `groupno`...
Use groupby(groupno)..
Finally you want something using apply/idxmax/idxmin to label the max/min
But of course a for-loop as you suggested is the non-Pythonic but possibly simpler hack.

Transpose data using sql oracle

I have following data in a table
+---------+-----+----------+
| Zip_ cd | id | assignmt.|
|---------+-----+----------+
| 1812 | 777 | S |
| 1812 | 111 | P |
| 1451 | 878 | S |
| 55 | 45 | x |
| 55 | 646 | T |
| 55 | 455 | Z |
+---------+-----+----------+
I want to transpose it as following
+---------+-----+----+---------+-----+----------+---------+-----+----------+
| Zip_ cd | id | ass| Zip_cd1 | id1 |assignmt1 | Zip_cd2 | id2 |assignmt3 |
+---------+-----+----+---------+-----+----------+---------+-----+----------+
| 1812 | 777 | S | 1812 | 111 | P | 1812 | 111 | P |
| 1451 | 878 | S | | | | | | |
| 55 | 45 | X | 55 | 646 | T | 55 | 455 | Z |
+---------+-----+----------+---------+----------+---------+-----+----------+
So, I basically want to transpose based on the zip code. If 2 rows have same zip code its needs to be in single row.

Another query using pivot functionality:
select *
from table_name
pivot ( max(id) id
, max(assignmt) assignmt
FOR assignmt IN ('S' S,
'P' P));
+---------+-------+------------+--------+------------+
| ZIP_ CD | S_ID | S_ASSIGNMT.| P_ID | P_ASSIGNMT.|
+---------+-------+------------+--------+------------+
| 1812 | 777 | S | 111 | P |
| 1451 | 878 | S | NULL | NULL |
+---------+-------+------------+--------+------------+

SQL Performance multiple exclusion from the same table

I have a table where I have a list of people, lets say i have 100 people listed in that table
I need to filter out the people using different criteria's and put them in groups, problem is when i start excluding on the 4th-5th level, performance issues come up and it becomes slow
with lst_tous_movements as (
select
t1.refid_eClinibase
t1.[dthrfinmouvement]
t1.[unite_service_id]
t1.[unite_service_suiv_id]
from sometable t1
)
,lst_patients_hospitalisés as (
select distinct
t1.refid_eClinibase
from lst_tous_movements t1
where
t1.[dthrfinmouvement] = '4000-01-01'
)
,lst_patients_admisUIB_transferes as (
select distinct
t1.refid_eClinibase
from lst_tous_movements t1
left join lst_patients_hospitalisés t2 on t1.refid_eClinibase = t2.refid_eClinibase
where
t1.[unite_service_id] = 4
and t1.[unite_service_suiv_id] <> 0
and t2.refid_eClinibase is null
)
,lst_patients_admisUIB_nonTransferes as (
select distinct
t1.refid_eClinibase
from lst_tous_movements t1
left join lst_patients_admisUIB_transferes t2 on t1.refid_eClinibase = t2.refid_eClinibase
left join lst_patients_hospitalisés t3 on t1.refid_eClinibase = t3.refid_eClinibase
where
t1.[unite_service_id] = 4
and t1.[unite_service_suiv_id] = 0
and t2.refid_eClinibase is null
and t3.refid_eClinibase is null
)
,lst_patients_autres as (
select distinct
t1.refid_eClinibase
from lst_patients t1
left join lst_patients_admisUIB_transferes t2 on t1.refid_eClinibase = t2.refid_eClinibase
left join lst_patients_hospitalisés t3 on t1.refid_eClinibase = t3.refid_eClinibase
left join lst_patients_admisUIB_nonTransferes t4 on t1.refid_eClinibase = t4.refid_eClinibase
where
t2.refid_eClinibase is null
and t3.refid_eClinibase is null
and t4.refid_eClinibase is null
)
as you can see i have a multi level filtering out going on here...
1st i get the people where t1.[dthrfinmouvement] = '4000-01-01'
2nd i get the people with another criteria EXCLUDING the 1st group
3rd i get the people with yet another criteria EXCLUDING the 1st and
the 2nd group
etc..
when i get to the 4th level, my query takes 6 - 10 seconds to complete
is there any way to speed this up ?
this is my dataset i'm working with:
+------------------+-------------------------------+------------------+------------------+-----------------------+
| refid_eClinibase | nodossierpermanent_eClinibase | dthrfinmouvement | unite_service_id | unite_service_suiv_id |
+------------------+-------------------------------+------------------+------------------+-----------------------+
| 25611 | P0017379 | 2013-04-27 | 58 | 0 |
| 25611 | P0017379 | 2013-05-02 | 4 | 2 |
| 25611 | P0017379 | 2013-05-18 | 2 | 0 |
| 85886 | P0077918 | 2013-04-10 | 58 | 0 |
| 85886 | P0077918 | 2013-05-06 | 6 | 12 |
| 85886 | P0077918 | 4000-01-01 | 12 | 0 |
| 91312 | P0083352 | 2013-07-24 | 3 | 14 |
| 91312 | P0083352 | 2013-07-24 | 14 | 3 |
| 91312 | P0083352 | 2013-07-30 | 3 | 8 |
| 91312 | P0083352 | 4000-01-01 | 8 | 0 |
| 93835 | P0085879 | 2013-04-30 | 58 | 0 |
| 93835 | P0085879 | 2013-05-07 | 4 | 2 |
| 93835 | P0085879 | 2013-05-16 | 2 | 0 |
| 93835 | P0085879 | 2013-05-22 | 58 | 0 |
| 93835 | P0085879 | 2013-05-24 | 4 | 0 |
| 93835 | P0085879 | 2013-05-31 | 58 | 0 |
| 93836 | P0085880 | 2013-05-20 | 58 | 0 |
| 93836 | P0085880 | 2013-05-22 | 4 | 2 |
| 93836 | P0085880 | 2013-05-31 | 2 | 0 |
| 97509 | P0089576 | 2013-04-09 | 58 | 0 |
| 97509 | P0089576 | 2013-04-11 | 4 | 0 |
| 102787 | P0094886 | 2013-04-08 | 58 | 0 |
| 102787 | P0094886 | 2013-04-11 | 4 | 2 |
| 102787 | P0094886 | 2013-05-21 | 2 | 0 |
| 103029 | P0095128 | 2013-04-04 | 58 | 0 |
| 103029 | P0095128 | 2013-04-10 | 4 | 1 |
| 103029 | P0095128 | 2013-05-03 | 1 | 0 |
| 103813 | P0095922 | 2013-07-02 | 58 | 0 |
| 103813 | P0095922 | 2013-07-03 | 4 | 6 |
| 103813 | P0095922 | 2013-08-14 | 6 | 0 |
| 105106 | P0097215 | 2013-08-09 | 58 | 0 |
| 105106 | P0097215 | 2013-08-13 | 4 | 0 |
| 105106 | P0097215 | 2013-08-14 | 58 | 0 |
| 105106 | P0097215 | 4000-01-01 | 4 | 0 |
| 106223 | P0098332 | 2013-06-11 | 1 | 0 |
| 106223 | P0098332 | 2013-08-01 | 58 | 0 |
| 106223 | P0098332 | 4000-01-01 | 1 | 0 |
| 106245 | P0098354 | 2013-04-02 | 58 | 0 |
| 106245 | P0098354 | 2013-05-24 | 58 | 0 |
| 106245 | P0098354 | 2013-05-29 | 4 | 1 |
| 106245 | P0098354 | 2013-07-12 | 1 | 0 |
| 106280 | P0098389 | 2013-04-07 | 58 | 0 |
| 106280 | P0098389 | 2013-04-09 | 4 | 0 |
| 106416 | P0098525 | 2013-04-19 | 58 | 0 |
| 106416 | P0098525 | 2013-04-23 | 4 | 0 |
| 106444 | P0098553 | 2013-04-22 | 58 | 0 |
| 106444 | P0098553 | 2013-04-25 | 4 | 0 |
| 106609 | P0098718 | 2013-05-08 | 58 | 0 |
| 106609 | P0098718 | 2013-05-10 | 4 | 11 |
| 106609 | P0098718 | 2013-07-24 | 11 | 12 |
| 106609 | P0098718 | 4000-01-01 | 12 | 0 |
| 106616 | P0098725 | 2013-05-09 | 58 | 0 |
| 106616 | P0098725 | 2013-05-09 | 4 | 1 |
| 106616 | P0098725 | 2013-07-27 | 1 | 0 |
| 106698 | P0098807 | 2013-05-16 | 58 | 0 |
| 106698 | P0098807 | 2013-05-22 | 4 | 6 |
| 106698 | P0098807 | 2013-06-14 | 6 | 1 |
| 106698 | P0098807 | 2013-06-28 | 1 | 0 |
| 106714 | P0098823 | 2013-05-20 | 58 | 0 |
| 106714 | P0098823 | 2013-05-21 | 58 | 0 |
| 106714 | P0098823 | 2013-05-24 | 58 | 0 |
| 106729 | P0098838 | 2013-05-21 | 58 | 0 |
| 106729 | P0098838 | 2013-05-23 | 4 | 1 |
| 106729 | P0098838 | 2013-06-03 | 1 | 0 |
| 107038 | P0099147 | 2013-06-25 | 58 | 0 |
| 107038 | P0099147 | 2013-06-28 | 4 | 1 |
| 107038 | P0099147 | 2013-07-04 | 1 | 0 |
| 107038 | P0099147 | 2013-08-13 | 58 | 0 |
| 107038 | P0099147 | 2013-08-15 | 4 | 6 |
| 107038 | P0099147 | 4000-01-01 | 6 | 0 |
| 107082 | P0099191 | 2013-06-29 | 58 | 0 |
| 107082 | P0099191 | 2013-07-04 | 4 | 6 |
| 107082 | P0099191 | 2013-07-19 | 6 | 0 |
| 107157 | P0099267 | 4000-01-01 | 13 | 0 |
| 107336 | P0099446 | 4000-01-01 | 6 | 0 |
+------------------+-------------------------------+------------------+------------------+-----------------------+
thanks.

It is hard to understand exactly what all your rules are from the question, but the general approach should be to add a "Grouping" column to a singl query that uses a CASE statement to categorize the people.
The conditions in a CASE are evaluated in order, so that if the first criteria is met, then the subsequent criteria are not even evaluated for that row.
Here is some code to get you started....
select t1.refid_eClinibase
,t1.[dthrfinmouvement]
,t1.[unite_service_id]
,t1.[unite_service_suiv_id]
CASE WHEN [dthrfinmouvement] = '4000-01-01' THEN 'Group1 Label'
WHEN condition2 = something THEN 'Group2 Label'
....
WHEN conditionN = something THEN 'GroupN Label'
ELSE 'Catch All Label'
END as person_category
from sometable t1