a) Write SQL queries for the following. 1) Display details of those patients whose age is either 25 or 36 or 29 without using comparison operator. 2) Display first name and last name of those doctors...





a) Write SQL queries for the following.




1) Display details of those patients whose age is either 25 or 36 or 29 without using comparison operator.
2) Display first name and last name of those doctors whose salary is less than or equal to 77,000 and greater than or equal to 50,000 using between operator.
3) Display details of those doctors who neither belong to Isb nor Rwp nor Multan without using in and like operator.
4) Display full name of doctors whose first name is other than Tania.
5) Display first name of doctors whose first name contains any of vowels (a,e,i,o,u).
6) Display names of those patients whose name’s second letter is neither ‘a’ nor ‘d’
7) Display details of those male patients whose age is 25 or 36.
8) Display details of those patients whose name contains less than 5 characters.
9) Display first 4 doctors who are earning least salary.
10) Display details of all patients in such a way that female patients are shown before male patients.
11) Display the unique cities from doctor table.
12) Display all patients.
13) Display details of all those doctors whose salary is greater than 50000 or live in Rwp
14) Display names of those patients whose names starts with either ‘a’ or ‘b’ or‘d’ and don’t ends on‘s’.
15) Display 1/4 records of patients.







b) Assume the schema given above and identify the following queries are executable or not. If yes then draw their results, if not then mention their errors.




1) Select * from patient where age between not 25 and 31;
2) Select D_Fname+’ ‘+D_Lname as [name] from doctor where D_Fname like ‘_[^ubs]%’ ;
3) Select P_Name, gender from patient where age in(25,29,38) or gender=’Mle’ ;
4) Select D_Id from doctor where salary >60000 and <>
5) Select * from doctor where city=’sib’ or city=’wrp’ ;


Patient<br>Doctor<br>P ld<br>PName<br>Gender Age<br>1<br>Aqib<br>Male<br>25<br>D ld<br>DEname D Lname Salary City<br>Saima<br>Female 36<br>1<br>Laiba<br>Hafeez<br>50000 | Rwp<br>3<br>Fatima<br>Female<br>29<br>2<br>Sameer<br>Khan<br>80000 Isb<br>4<br>Nouman<br>Male<br>31<br>Muneeb<br>Male<br>40<br>3<br>Talib<br>Ali<br>62000 Işb<br>6.<br>Tariq<br>Male<br>38<br>Saleem<br>Iqbal<br>77000 Multan<br>7<br>Ikram<br>Male<br>26<br>Tania<br>Abbas<br>85000 Lahore<br>8<br>Sadia<br>Female 27<br>Huda<br>Ahmad<br>68000 | Rwp<br>4.<br>9,<br>

Extracted text: Patient Doctor P ld PName Gender Age 1 Aqib Male 25 D ld DEname D Lname Salary City Saima Female 36 1 Laiba Hafeez 50000 | Rwp 3 Fatima Female 29 2 Sameer Khan 80000 Isb 4 Nouman Male 31 Muneeb Male 40 3 Talib Ali 62000 Işb 6. Tariq Male 38 Saleem Iqbal 77000 Multan 7 Ikram Male 26 Tania Abbas 85000 Lahore 8 Sadia Female 27 Huda Ahmad 68000 | Rwp 4. 9,
Jun 11, 2022
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