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question
stringlengths 46
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| query
stringlengths 50
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| schema
stringclasses 13
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int64 3.41k
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stringlengths 6
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display all the information about the department Marketing. | SELECT * FROM departments WHERE department_name = 'Marketing' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
``` | 3,483 | DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
20,Marketing,201,1800
|
What is the full name ( first name and last name ) for those employees who gets more salary than the employee whose id is 163? | SELECT first_name, last_name FROM employees WHERE salary > (SELECT salary FROM employees WHERE employee_id = 163) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,469 | FIRST_NAME,LAST_NAME
Steven,King
Neena,Kochhar
Lex,De Haan
Nancy,Greenberg
Den,Raphaely
John,Russell
Karen,Partners
Alberto,Errazuriz
Gerald,Cambrault
Eleni,Zlotkey
Peter,Tucker
Janette,King
Clara,Vishney
Lisa,Ozer
Harrison,Bloom
Tayler,Fox
Ellen,Abel
Michael,Hartstein
Hermann,Baer
Shelley,Higgins
|
Find the ids of the employees who does not work in those departments where some employees works whose manager id within the range 100 and 200. | SELECT * FROM employees WHERE NOT department_id IN (SELECT department_id FROM departments WHERE manager_id BETWEEN 100 AND 200) | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,497 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
178,Kimberely,Grant,KGRANT,011.44.1644.429263,1987-09-03,SA_REP,7000,0.15,149,0
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0.0,100,20
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0.0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0.0,205,110
|
What is the average salary for each job title? | SELECT job_title, AVG(salary) FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id = T2.job_id GROUP BY T2.job_title | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'job_id' in dataframe 'employees' has a foreign key relation to column 'job_id' in dataframe 'jobs'.
| 3,468 | JOB_TITLE,AVG(salary)
Accountant,7920.0
Accounting Manager,12000.0
Administration Assistant,4400.0
Administration Vice President,17000.0
Finance Manager,12000.0
Human Resources Representative,6500.0
Marketing Manager,13000.0
Marketing Representative,6000.0
President,24000.0
Programmer,5760.0
Public Accountant,8300.0
Public Relations Representative,10000.0
Purchasing Clerk,2780.0
Purchasing Manager,11000.0
Sales Manager,12200.0
Sales Representative,8350.0
Shipping Clerk,3215.0
Stock Clerk,2785.0
Stock Manager,7280.0
|
display the employee ID and job name for all those jobs in department 80. | SELECT T1.employee_id, T2.job_title FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id = T2.job_id WHERE T1.department_id = 80 | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'job_id' in dataframe 'employees' has a foreign key relation to column 'job_id' in dataframe 'jobs'.
| 3,491 | EMPLOYEE_ID,JOB_TITLE
145,Sales Manager
146,Sales Manager
147,Sales Manager
148,Sales Manager
149,Sales Manager
150,Sales Representative
151,Sales Representative
152,Sales Representative
153,Sales Representative
154,Sales Representative
155,Sales Representative
156,Sales Representative
157,Sales Representative
158,Sales Representative
159,Sales Representative
160,Sales Representative
161,Sales Representative
162,Sales Representative
163,Sales Representative
164,Sales Representative
165,Sales Representative
166,Sales Representative
167,Sales Representative
168,Sales Representative
169,Sales Representative
170,Sales Representative
171,Sales Representative
172,Sales Representative
173,Sales Representative
174,Sales Representative
175,Sales Representative
176,Sales Representative
177,Sales Representative
179,Sales Representative
|
What are the full name, hire data, salary and department id for employees without the letter M in their first name, ordered by ascending department id? | SELECT first_name, last_name, hire_date, salary, department_id FROM employees WHERE NOT first_name LIKE '%M%' ORDER BY department_id | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,420 | FIRST_NAME,LAST_NAME,HIRE_DATE,SALARY,DEPARTMENT_ID
Jennifer,Whalen,1987-09-25,4400,10
Pat,Fay,1987-09-27,6000,20
Den,Raphaely,1987-07-01,11000,30
Alexander,Khoo,1987-07-02,3100,30
Shelli,Baida,1987-07-03,2900,30
Sigal,Tobias,1987-07-04,2800,30
Guy,Himuro,1987-07-05,2600,30
Karen,Colmenares,1987-07-06,2500,30
Susan,Mavris,1987-09-28,6500,40
Shanta,Vollman,1987-07-10,6500,50
Kevin,Mourgos,1987-07-11,5800,50
Julia,Nayer,1987-07-12,3200,50
Irene,Mikkilineni,1987-07-13,2700,50
Steven,Markle,1987-07-15,2200,50
Laura,Bissot,1987-07-16,3300,50
TJ,Olson,1987-07-19,2100,50
Jason,Mallin,1987-07-20,3300,50
Ki,Gee,1987-07-22,2400,50
Hazel,Philtanker,1987-07-23,2200,50
Renske,Ladwig,1987-07-24,3600,50
Stephen,Stiles,1987-07-25,3200,50
John,Seo,1987-07-26,2700,50
Joshua,Patel,1987-07-27,2500,50
Trenna,Rajs,1987-07-28,3500,50
Curtis,Davies,1987-07-29,3100,50
Randall,Matos,1987-07-30,2600,50
Peter,Vargas,1987-07-31,2500,50
Winston,Taylor,1987-09-05,3200,50
Jean,Fleaur,1987-09-06,3100,50
Girard,Geoni,1987-09-08,2800,50
Nandita,Sarchand,1987-09-09,4200,50
Alexis,Bull,1987-09-10,4100,50
Julia,Dellinger,1987-09-11,3400,50
Anthony,Cabrio,1987-09-12,3000,50
Kelly,Chung,1987-09-13,3800,50
Jennifer,Dilly,1987-09-14,3600,50
Randall,Perkins,1987-09-16,2500,50
Sarah,Bell,1987-09-17,4000,50
Britney,Everett,1987-09-18,3900,50
Vance,Jones,1987-09-20,2800,50
Alana,Walsh,1987-09-21,3100,50
Kevin,Feeney,1987-09-22,3000,50
Donald,OConnell,1987-09-23,2600,50
Douglas,Grant,1987-09-24,2600,50
Alexander,Hunold,1987-06-20,9000,60
Bruce,Ernst,1987-06-21,6000,60
David,Austin,1987-06-22,4800,60
Valli,Pataballa,1987-06-23,4800,60
Diana,Lorentz,1987-06-24,4200,60
John,Russell,1987-08-01,14000,80
Karen,Partners,1987-08-02,13500,80
Alberto,Errazuriz,1987-08-03,12000,80
Gerald,Cambrault,1987-08-04,11000,80
Eleni,Zlotkey,1987-08-05,10500,80
Peter,Tucker,1987-08-06,10000,80
David,Bernstein,1987-08-07,9500,80
Peter,Hall,1987-08-08,9000,80
Christopher,Olsen,1987-08-09,8000,80
Nanette,Cambrault,1987-08-10,7500,80
Oliver,Tuvault,1987-08-11,7000,80
Janette,King,1987-08-12,10000,80
Patrick,Sully,1987-08-13,9500,80
Allan,McEwen,1987-08-14,9000,80
Lindsey,Smith,1987-08-15,8000,80
Louise,Doran,1987-08-16,7500,80
Sarath,Sewall,1987-08-17,7000,80
Clara,Vishney,1987-08-18,10500,80
Danielle,Greene,1987-08-19,9500,80
David,Lee,1987-08-21,6800,80
Sundar,Ande,1987-08-22,6400,80
Lisa,Ozer,1987-08-24,11500,80
Harrison,Bloom,1987-08-25,10000,80
Tayler,Fox,1987-08-26,9600,80
Elizabeth,Bates,1987-08-28,7300,80
Sundita,Kumar,1987-08-29,6100,80
Ellen,Abel,1987-08-30,11000,80
Alyssa,Hutton,1987-08-31,8800,80
Jonathon,Taylor,1987-09-01,8600,80
Jack,Livingston,1987-09-02,8400,80
Charles,Johnson,1987-09-04,6200,80
Steven,King,1987-06-17,24000,90
Neena,Kochhar,1987-06-18,17000,90
Lex,De Haan,1987-06-19,17000,90
Nancy,Greenberg,1987-06-25,12000,100
Daniel,Faviet,1987-06-26,9000,100
John,Chen,1987-06-27,8200,100
Luis,Popp,1987-06-30,6900,100
Shelley,Higgins,1987-09-30,12000,110
|
display the department name and number of employees in each of the department. | SELECT T2.department_name, COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id GROUP BY T2.department_name | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,463 | DEPARTMENT_NAME,COUNT(*)
Accounting,2
Administration,1
Executive,3
Finance,6
Human Resources,1
IT,5
Marketing,2
Public Relations,1
Purchasing,6
Sales,34
Shipping,45
|
What is all the job history info done by employees earning a salary greater than or equal to 12000? | SELECT * FROM job_history AS T1 JOIN employees AS T2 ON T1.employee_id = T2.employee_id WHERE T2.salary >= 12000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
```
Foreign Key constraints:
Column 'employee_id' in dataframe 'job_history' has a foreign key relation to column 'employee_id' in dataframe 'employees'.
| 3,466 | EMPLOYEE_ID,START_DATE,END_DATE,JOB_ID,DEPARTMENT_ID,EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
102,1993-01-13,1998-07-24,IT_PROG,60,102,Lex,De Haan,LDEHAAN,515.123.4569,1987-06-19,AD_VP,17000,0,100,90
101,1989-09-21,1993-10-27,AC_ACCOUNT,110,101,Neena,Kochhar,NKOCHHAR,515.123.4568,1987-06-18,AD_VP,17000,0,100,90
101,1993-10-28,1997-03-15,AC_MGR,110,101,Neena,Kochhar,NKOCHHAR,515.123.4568,1987-06-18,AD_VP,17000,0,100,90
201,1996-02-17,1999-12-19,MK_REP,20,201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0,100,20
|
What are the department names, cities, and state provinces for each department? | SELECT T1.department_name, T2.city, T2.state_province FROM departments AS T1 JOIN locations AS T2 ON T2.location_id = T1.location_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
| 3,522 | DEPARTMENT_NAME,CITY,STATE_PROVINCE
Administration,Seattle,Washington
Marketing,Toronto,Ontario
Purchasing,Seattle,Washington
Human Resources,London,
Shipping,South San Francisco,California
IT,Southlake,Texas
Public Relations,Munich,Bavaria
Sales,OX9 9ZB,Oxford
Executive,Seattle,Washington
Finance,Seattle,Washington
Accounting,Seattle,Washington
Treasury,Seattle,Washington
Corporate Tax,Seattle,Washington
Control And Credit,Seattle,Washington
Shareholder Services,Seattle,Washington
Benefits,Seattle,Washington
Manufacturing,Seattle,Washington
Construction,Seattle,Washington
Contracting,Seattle,Washington
Operations,Seattle,Washington
IT Support,Seattle,Washington
NOC,Seattle,Washington
IT Helpdesk,Seattle,Washington
Government Sales,Seattle,Washington
Retail Sales,Seattle,Washington
Recruiting,Seattle,Washington
Payroll,Seattle,Washington
|
display those employees who joined after 7th September, 1987. | SELECT * FROM employees WHERE hire_date > '1987-09-07' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,439 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0,120,50
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0,121,50
186,Julia,Dellinger,JDELLING,650.509.3876,1987-09-11,SH_CLERK,3400,0,121,50
187,Anthony,Cabrio,ACABRIO,650.509.4876,1987-09-12,SH_CLERK,3000,0,121,50
188,Kelly,Chung,KCHUNG,650.505.1876,1987-09-13,SH_CLERK,3800,0,122,50
189,Jennifer,Dilly,JDILLY,650.505.2876,1987-09-14,SH_CLERK,3600,0,122,50
190,Timothy,Gates,TGATES,650.505.3876,1987-09-15,SH_CLERK,2900,0,122,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0,122,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0,123,50
193,Britney,Everett,BEVERETT,650.501.2876,1987-09-18,SH_CLERK,3900,0,123,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0,123,50
195,Vance,Jones,VJONES,650.501.4876,1987-09-20,SH_CLERK,2800,0,123,50
196,Alana,Walsh,AWALSH,650.507.9811,1987-09-21,SH_CLERK,3100,0,124,50
197,Kevin,Feeney,KFEENEY,650.507.9822,1987-09-22,SH_CLERK,3000,0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0,124,50
200,Jennifer,Whalen,JWHALEN,515.123.4444,1987-09-25,AD_ASST,4400,0,101,10
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0,100,20
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0,205,110
|
What are the full names and salaries for any employees earning less than 6000? | SELECT first_name, last_name, salary FROM employees WHERE salary < 6000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,408 | FIRST_NAME,LAST_NAME,SALARY
David,Austin,4800
Valli,Pataballa,4800
Diana,Lorentz,4200
Alexander,Khoo,3100
Shelli,Baida,2900
Sigal,Tobias,2800
Guy,Himuro,2600
Karen,Colmenares,2500
Kevin,Mourgos,5800
Julia,Nayer,3200
Irene,Mikkilineni,2700
James,Landry,2400
Steven,Markle,2200
Laura,Bissot,3300
Mozhe,Atkinson,2800
James,Marlow,2500
TJ,Olson,2100
Jason,Mallin,3300
Michael,Rogers,2900
Ki,Gee,2400
Hazel,Philtanker,2200
Renske,Ladwig,3600
Stephen,Stiles,3200
John,Seo,2700
Joshua,Patel,2500
Trenna,Rajs,3500
Curtis,Davies,3100
Randall,Matos,2600
Peter,Vargas,2500
Winston,Taylor,3200
Jean,Fleaur,3100
Martha,Sullivan,2500
Girard,Geoni,2800
Nandita,Sarchand,4200
Alexis,Bull,4100
Julia,Dellinger,3400
Anthony,Cabrio,3000
Kelly,Chung,3800
Jennifer,Dilly,3600
Timothy,Gates,2900
Randall,Perkins,2500
Sarah,Bell,4000
Britney,Everett,3900
Samuel,McCain,3200
Vance,Jones,2800
Alana,Walsh,3100
Kevin,Feeney,3000
Donald,OConnell,2600
Douglas,Grant,2600
Jennifer,Whalen,4400
|
What is the first name and job id for all employees in the Finance department? | SELECT T1.first_name, T1.job_id FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id WHERE T2.department_name = 'Finance' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,493 | FIRST_NAME,JOB_ID
Nancy,FI_MGR
Daniel,FI_ACCOUNT
John,FI_ACCOUNT
Ismael,FI_ACCOUNT
Jose Manuel,FI_ACCOUNT
Luis,FI_ACCOUNT
|
What is all the information about employees with D or S in their first name, ordered by salary descending? | SELECT * FROM employees WHERE first_name LIKE '%D%' OR first_name LIKE '%S%' ORDER BY salary DESC | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,438 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
168,Lisa,Ozer,LOZER,011.44.1343.929268,1987-08-24,SA_REP,11500,0.25,148,80
114,Den,Raphaely,DRAPHEAL,515.127.4561,1987-07-01,PU_MAN,11000,0.0,100,30
148,Gerald,Cambrault,GCAMBRAU,011.44.1344.619268,1987-08-04,SA_MAN,11000,0.3,100,80
169,Harrison,Bloom,HBLOOM,011.44.1343.829268,1987-08-25,SA_REP,10000,0.2,148,80
151,David,Bernstein,DBERNSTE,011.44.1344.345268,1987-08-07,SA_REP,9500,0.25,145,80
163,Danielle,Greene,DGREENE,011.44.1346.229268,1987-08-19,SA_REP,9500,0.15,147,80
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60
109,Daniel,Faviet,DFAVIET,515.124.4169,1987-06-26,FI_ACCOUNT,9000,0.0,108,100
175,Alyssa,Hutton,AHUTTON,011.44.1644.429266,1987-08-31,SA_REP,8800,0.25,149,80
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50
153,Christopher,Olsen,COLSEN,011.44.1344.498718,1987-08-09,SA_REP,8000,0.2,145,80
159,Lindsey,Smith,LSMITH,011.44.1345.729268,1987-08-15,SA_REP,8000,0.3,146,80
112,Jose Manuel,Urman,JMURMAN,515.124.4469,1987-06-29,FI_ACCOUNT,7800,0.0,108,100
111,Ismael,Sciarra,ISCIARRA,515.124.4369,1987-06-28,FI_ACCOUNT,7700,0.0,108,100
160,Louise,Doran,LDORAN,011.44.1345.629268,1987-08-16,SA_REP,7500,0.3,146,80
161,Sarath,Sewall,SSEWALL,011.44.1345.529268,1987-08-17,SA_REP,7000,0.25,146,80
113,Luis,Popp,LPOPP,515.124.4567,1987-06-30,FI_ACCOUNT,6900,0.0,108,100
165,David,Lee,DLEE,011.44.1346.529268,1987-08-21,SA_REP,6800,0.1,147,80
123,Shanta,Vollman,SVOLLMAN,650.123.4234,1987-07-10,ST_MAN,6500,0.0,100,50
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
166,Sundar,Ande,SANDE,011.44.1346.629268,1987-08-22,SA_REP,6400,0.1,147,80
179,Charles,Johnson,CJOHNSON,011.44.1644.429262,1987-09-04,SA_REP,6200,0.1,149,80
173,Sundita,Kumar,SKUMAR,011.44.1343.329268,1987-08-29,SA_REP,6100,0.1,148,80
105,David,Austin,DAUSTIN,590.423.4569,1987-06-22,IT_PROG,4800,0.0,103,60
107,Diana,Lorentz,DLORENTZ,590.423.5567,1987-06-24,IT_PROG,4200,0.0,103,60
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0.0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0.0,121,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0.0,123,50
137,Renske,Ladwig,RLADWIG,650.121.1234,1987-07-24,ST_CLERK,3600,0.0,123,50
133,Jason,Mallin,JMALLIN,650.127.1934,1987-07-20,ST_CLERK,3300,0.0,122,50
138,Stephen,Stiles,SSTILES,650.121.2034,1987-07-25,ST_CLERK,3200,0.0,123,50
180,Winston,Taylor,WTAYLOR,650.507.9876,1987-09-05,SH_CLERK,3200,0.0,120,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0.0,123,50
115,Alexander,Khoo,AKHOO,515.127.4562,1987-07-02,PU_CLERK,3100,0.0,114,30
142,Curtis,Davies,CDAVIES,650.121.2994,1987-07-29,ST_CLERK,3100,0.0,124,50
116,Shelli,Baida,SBAIDA,515.127.4563,1987-07-03,PU_CLERK,2900,0.0,114,30
117,Sigal,Tobias,STOBIAS,515.127.4564,1987-07-04,PU_CLERK,2800,0.0,114,30
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0.0,120,50
143,Randall,Matos,RMATOS,650.121.2874,1987-07-30,ST_CLERK,2600,0.0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0.0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0.0,124,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0.0,121,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0.0,123,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0.0,122,50
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0.0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0.0,120,50
|
Display all the information about the department Marketing. | SELECT * FROM departments WHERE department_name = 'Marketing' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
``` | 3,413 | DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
20,Marketing,201,1800
|
display the employee ID for each employee and the date on which he ended his previous job. | SELECT employee_id, MAX(end_date) FROM job_history GROUP BY employee_id | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,447 | EMPLOYEE_ID,MAX(end_date)
0,0000-00-00
101,1997-03-15
102,1998-07-24
114,1999-12-31
122,1999-12-31
176,1999-12-31
200,1998-12-31
201,1999-12-19
|
What is all the information about the Marketing department? | SELECT * FROM departments WHERE department_name = 'Marketing' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
``` | 3,484 | DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
20,Marketing,201,1800
|
What are the full names and cities of employees who have the letter Z in their first names? | SELECT T1.first_name, T1.last_name, T3.city FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id WHERE T1.first_name LIKE '%z%' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,520 | FIRST_NAME,LAST_NAME,CITY
Mozhe,Atkinson,South San Francisco
Hazel,Philtanker,South San Francisco
Elizabeth,Bates,OX9 9ZB
|
display the full name (first and last), hire date, salary, and department number for those employees whose first name does not containing the letter M and make the result set in ascending order by department number. | SELECT first_name, last_name, hire_date, salary, department_id FROM employees WHERE NOT first_name LIKE '%M%' ORDER BY department_id | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,419 | FIRST_NAME,LAST_NAME,HIRE_DATE,SALARY,DEPARTMENT_ID
Jennifer,Whalen,1987-09-25,4400,10
Pat,Fay,1987-09-27,6000,20
Den,Raphaely,1987-07-01,11000,30
Alexander,Khoo,1987-07-02,3100,30
Shelli,Baida,1987-07-03,2900,30
Sigal,Tobias,1987-07-04,2800,30
Guy,Himuro,1987-07-05,2600,30
Karen,Colmenares,1987-07-06,2500,30
Susan,Mavris,1987-09-28,6500,40
Shanta,Vollman,1987-07-10,6500,50
Kevin,Mourgos,1987-07-11,5800,50
Julia,Nayer,1987-07-12,3200,50
Irene,Mikkilineni,1987-07-13,2700,50
Steven,Markle,1987-07-15,2200,50
Laura,Bissot,1987-07-16,3300,50
TJ,Olson,1987-07-19,2100,50
Jason,Mallin,1987-07-20,3300,50
Ki,Gee,1987-07-22,2400,50
Hazel,Philtanker,1987-07-23,2200,50
Renske,Ladwig,1987-07-24,3600,50
Stephen,Stiles,1987-07-25,3200,50
John,Seo,1987-07-26,2700,50
Joshua,Patel,1987-07-27,2500,50
Trenna,Rajs,1987-07-28,3500,50
Curtis,Davies,1987-07-29,3100,50
Randall,Matos,1987-07-30,2600,50
Peter,Vargas,1987-07-31,2500,50
Winston,Taylor,1987-09-05,3200,50
Jean,Fleaur,1987-09-06,3100,50
Girard,Geoni,1987-09-08,2800,50
Nandita,Sarchand,1987-09-09,4200,50
Alexis,Bull,1987-09-10,4100,50
Julia,Dellinger,1987-09-11,3400,50
Anthony,Cabrio,1987-09-12,3000,50
Kelly,Chung,1987-09-13,3800,50
Jennifer,Dilly,1987-09-14,3600,50
Randall,Perkins,1987-09-16,2500,50
Sarah,Bell,1987-09-17,4000,50
Britney,Everett,1987-09-18,3900,50
Vance,Jones,1987-09-20,2800,50
Alana,Walsh,1987-09-21,3100,50
Kevin,Feeney,1987-09-22,3000,50
Donald,OConnell,1987-09-23,2600,50
Douglas,Grant,1987-09-24,2600,50
Alexander,Hunold,1987-06-20,9000,60
Bruce,Ernst,1987-06-21,6000,60
David,Austin,1987-06-22,4800,60
Valli,Pataballa,1987-06-23,4800,60
Diana,Lorentz,1987-06-24,4200,60
John,Russell,1987-08-01,14000,80
Karen,Partners,1987-08-02,13500,80
Alberto,Errazuriz,1987-08-03,12000,80
Gerald,Cambrault,1987-08-04,11000,80
Eleni,Zlotkey,1987-08-05,10500,80
Peter,Tucker,1987-08-06,10000,80
David,Bernstein,1987-08-07,9500,80
Peter,Hall,1987-08-08,9000,80
Christopher,Olsen,1987-08-09,8000,80
Nanette,Cambrault,1987-08-10,7500,80
Oliver,Tuvault,1987-08-11,7000,80
Janette,King,1987-08-12,10000,80
Patrick,Sully,1987-08-13,9500,80
Allan,McEwen,1987-08-14,9000,80
Lindsey,Smith,1987-08-15,8000,80
Louise,Doran,1987-08-16,7500,80
Sarath,Sewall,1987-08-17,7000,80
Clara,Vishney,1987-08-18,10500,80
Danielle,Greene,1987-08-19,9500,80
David,Lee,1987-08-21,6800,80
Sundar,Ande,1987-08-22,6400,80
Lisa,Ozer,1987-08-24,11500,80
Harrison,Bloom,1987-08-25,10000,80
Tayler,Fox,1987-08-26,9600,80
Elizabeth,Bates,1987-08-28,7300,80
Sundita,Kumar,1987-08-29,6100,80
Ellen,Abel,1987-08-30,11000,80
Alyssa,Hutton,1987-08-31,8800,80
Jonathon,Taylor,1987-09-01,8600,80
Jack,Livingston,1987-09-02,8400,80
Charles,Johnson,1987-09-04,6200,80
Steven,King,1987-06-17,24000,90
Neena,Kochhar,1987-06-18,17000,90
Lex,De Haan,1987-06-19,17000,90
Nancy,Greenberg,1987-06-25,12000,100
Daniel,Faviet,1987-06-26,9000,100
John,Chen,1987-06-27,8200,100
Luis,Popp,1987-06-30,6900,100
Shelley,Higgins,1987-09-30,12000,110
|
Can you return all detailed info of jobs which was done by any of the employees who is presently earning a salary on and above 12000? | SELECT * FROM job_history AS T1 JOIN employees AS T2 ON T1.employee_id = T2.employee_id WHERE T2.salary >= 12000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
```
Foreign Key constraints:
Column 'employee_id' in dataframe 'job_history' has a foreign key relation to column 'employee_id' in dataframe 'employees'.
| 3,465 | EMPLOYEE_ID,START_DATE,END_DATE,JOB_ID,DEPARTMENT_ID,EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
102,1993-01-13,1998-07-24,IT_PROG,60,102,Lex,De Haan,LDEHAAN,515.123.4569,1987-06-19,AD_VP,17000,0,100,90
101,1989-09-21,1993-10-27,AC_ACCOUNT,110,101,Neena,Kochhar,NKOCHHAR,515.123.4568,1987-06-18,AD_VP,17000,0,100,90
101,1993-10-28,1997-03-15,AC_MGR,110,101,Neena,Kochhar,NKOCHHAR,515.123.4568,1987-06-18,AD_VP,17000,0,100,90
201,1996-02-17,1999-12-19,MK_REP,20,201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0,100,20
|
display the department id and the total salary for those departments which contains at least two employees. | SELECT department_id, SUM(salary) FROM employees GROUP BY department_id HAVING COUNT(*) >= 2 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,511 | DEPARTMENT_ID,SUM(salary)
20,19000
30,24900
50,156400
60,28800
80,304500
90,58000
100,51600
110,20300
|
Find the employee id for all employees who earn more than the average salary. | SELECT employee_id FROM employees WHERE salary > (SELECT AVG(salary) FROM employees) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,475 | EMPLOYEE_ID
100
101
102
103
108
109
110
111
112
113
114
120
121
122
123
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
168
169
170
171
172
174
175
176
177
178
201
203
204
205
206
|
What is all the information about the Marketing department? | SELECT * FROM departments WHERE department_name = 'Marketing' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
``` | 3,414 | DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
20,Marketing,201,1800
|
display the job title of jobs which minimum salary is greater than 9000. | SELECT job_title FROM jobs WHERE min_salary > 9000 | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
``` | 3,441 | JOB_TITLE
President
Administration Vice President
Sales Manager
|
Find the salary and manager number for those employees who is working under a manager. | SELECT salary, manager_id FROM employees WHERE manager_id <> "null" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,433 | SALARY,MANAGER_ID
24000,0
17000,100
17000,100
9000,102
6000,103
4800,103
4800,103
4200,103
12000,101
9000,108
8200,108
7700,108
7800,108
6900,108
11000,100
3100,114
2900,114
2800,114
2600,114
2500,114
8000,100
8200,100
7900,100
6500,100
5800,100
3200,120
2700,120
2400,120
2200,120
3300,121
2800,121
2500,121
2100,121
3300,122
2900,122
2400,122
2200,122
3600,123
3200,123
2700,123
2500,123
3500,124
3100,124
2600,124
2500,124
14000,100
13500,100
12000,100
11000,100
10500,100
10000,145
9500,145
9000,145
8000,145
7500,145
7000,145
10000,146
9500,146
9000,146
8000,146
7500,146
7000,146
10500,147
9500,147
7200,147
6800,147
6400,147
6200,147
11500,148
10000,148
9600,148
7400,148
7300,148
6100,148
11000,149
8800,149
8600,149
8400,149
7000,149
6200,149
3200,120
3100,120
2500,120
2800,120
4200,121
4100,121
3400,121
3000,121
3800,122
3600,122
2900,122
2500,122
4000,123
3900,123
3200,123
2800,123
3100,124
3000,124
2600,124
2600,124
4400,101
13000,100
6000,201
6500,101
10000,101
12000,101
8300,205
|
display the average salary of employees for each department who gets a commission percentage. | SELECT department_id, AVG(salary) FROM employees WHERE commission_pct <> "null" GROUP BY department_id | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,453 | DEPARTMENT_ID,AVG(salary)
0,7000.0
10,4400.0
20,9500.0
30,4150.0
40,6500.0
50,3475.5555555555557
60,5760.0
70,10000.0
80,8955.882352941177
90,19333.333333333332
100,8600.0
110,10150.0
|
Find the first name and last name and department id for those employees who earn such amount of salary which is the smallest salary of any of the departments. | SELECT first_name, last_name, department_id FROM employees WHERE salary IN (SELECT MIN(salary) FROM employees GROUP BY department_id) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,473 | FIRST_NAME,LAST_NAME,DEPARTMENT_ID
Neena,Kochhar,90
Lex,De Haan,90
Bruce,Ernst,60
Diana,Lorentz,60
Luis,Popp,100
Karen,Colmenares,30
Shanta,Vollman,50
James,Marlow,50
TJ,Olson,50
Joshua,Patel,50
Peter,Vargas,50
Peter,Tucker,80
Oliver,Tuvault,80
Janette,King,80
Sarath,Sewall,80
Harrison,Bloom,80
Sundita,Kumar,80
Kimberely,Grant,0
Martha,Sullivan,50
Nandita,Sarchand,50
Randall,Perkins,50
Jennifer,Whalen,10
Pat,Fay,20
Susan,Mavris,40
Hermann,Baer,70
William,Gietz,110
|
List the full name (first and last name), and salary for those employees who earn below 6000. | SELECT first_name, last_name, salary FROM employees WHERE salary < 6000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,407 | FIRST_NAME,LAST_NAME,SALARY
David,Austin,4800
Valli,Pataballa,4800
Diana,Lorentz,4200
Alexander,Khoo,3100
Shelli,Baida,2900
Sigal,Tobias,2800
Guy,Himuro,2600
Karen,Colmenares,2500
Kevin,Mourgos,5800
Julia,Nayer,3200
Irene,Mikkilineni,2700
James,Landry,2400
Steven,Markle,2200
Laura,Bissot,3300
Mozhe,Atkinson,2800
James,Marlow,2500
TJ,Olson,2100
Jason,Mallin,3300
Michael,Rogers,2900
Ki,Gee,2400
Hazel,Philtanker,2200
Renske,Ladwig,3600
Stephen,Stiles,3200
John,Seo,2700
Joshua,Patel,2500
Trenna,Rajs,3500
Curtis,Davies,3100
Randall,Matos,2600
Peter,Vargas,2500
Winston,Taylor,3200
Jean,Fleaur,3100
Martha,Sullivan,2500
Girard,Geoni,2800
Nandita,Sarchand,4200
Alexis,Bull,4100
Julia,Dellinger,3400
Anthony,Cabrio,3000
Kelly,Chung,3800
Jennifer,Dilly,3600
Timothy,Gates,2900
Randall,Perkins,2500
Sarah,Bell,4000
Britney,Everett,3900
Samuel,McCain,3200
Vance,Jones,2800
Alana,Walsh,3100
Kevin,Feeney,3000
Donald,OConnell,2600
Douglas,Grant,2600
Jennifer,Whalen,4400
|
Display the first name, and department number for all employees whose last name is "McEwen". | SELECT first_name, department_id FROM employees WHERE last_name = 'McEwen' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,409 | FIRST_NAME,DEPARTMENT_ID
Allan,80
|
Give the country id and corresponding count of cities in each country. | SELECT country_id, COUNT(*) FROM locations GROUP BY country_id | ``` yaml
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
``` | 3,456 | COUNTRY_ID,COUNT(*)
"""",1
AU,1
BR,1
CA,2
CH,2
CN,1
DE,1
IN,1
IT,2
JP,2
NL,1
Ox,1
SG,1
UK,2
US,4
|
What are the job ids corresponding to jobs with average salary above 8000? | SELECT job_id FROM employees GROUP BY job_id HAVING AVG(salary) > 8000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,490 | JOB_ID
AC_ACCOUNT
AC_MGR
AD_PRES
AD_VP
FI_MGR
MK_MAN
PR_REP
PU_MAN
SA_MAN
SA_REP
|
What are the employee ids for employees who have held two or more jobs? | SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*) >= 2 | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,460 | EMPLOYEE_ID
101
176
200
|
What are total salaries and department id for each department that has more than 2 employees? | SELECT department_id, SUM(salary) FROM employees GROUP BY department_id HAVING COUNT(*) >= 2 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,512 | DEPARTMENT_ID,SUM(salary)
20,19000
30,24900
50,156400
60,28800
80,304500
90,58000
100,51600
110,20300
|
display those departments where more than ten employees work who got a commission percentage. | SELECT department_id FROM employees GROUP BY department_id HAVING COUNT(commission_pct) > 10 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,449 | DEPARTMENT_ID
50
80
|
What are the emails of employees with null commission, salary between 7000 and 12000, and who work in department 50? | SELECT email FROM employees WHERE commission_pct = "null" AND salary BETWEEN 7000 AND 12000 AND department_id = 50 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,446 | EMAIL
|
What are the employee ids for employees who make more than the average? | SELECT employee_id FROM employees WHERE salary > (SELECT AVG(salary) FROM employees) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,476 | EMPLOYEE_ID
100
101
102
103
108
109
110
111
112
113
114
120
121
122
123
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
168
169
170
171
172
174
175
176
177
178
201
203
204
205
206
|
What are the employee ids, full names, and job ids for employees who make more than the highest earning employee with title PU_MAN? | SELECT employee_id, first_name, last_name, job_id FROM employees WHERE salary > (SELECT MAX(salary) FROM employees WHERE job_id = 'PU_MAN') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,510 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,JOB_ID
100,Steven,King,AD_PRES
101,Neena,Kochhar,AD_VP
102,Lex,De Haan,AD_VP
108,Nancy,Greenberg,FI_MGR
145,John,Russell,SA_MAN
146,Karen,Partners,SA_MAN
147,Alberto,Errazuriz,SA_MAN
168,Lisa,Ozer,SA_REP
201,Michael,Hartstein,MK_MAN
205,Shelley,Higgins,AC_MGR
|
Display the first and last name, and salary for those employees whose first name is ending with the letter m. | SELECT first_name, last_name, salary FROM employees WHERE first_name LIKE '%m' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,427 | FIRST_NAME,LAST_NAME,SALARY
Adam,Fripp,8200
Payam,Kaufling,7900
William,Smith,7400
William,Gietz,8300
|
What are the full names and hire dates for employees in the same department as someone with the first name Clara, not including Clara? | SELECT first_name, last_name, hire_date FROM employees WHERE department_id = (SELECT department_id FROM employees WHERE first_name = "Clara") AND first_name <> "Clara" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,502 | FIRST_NAME,LAST_NAME,HIRE_DATE
John,Russell,1987-08-01
Karen,Partners,1987-08-02
Alberto,Errazuriz,1987-08-03
Gerald,Cambrault,1987-08-04
Eleni,Zlotkey,1987-08-05
Peter,Tucker,1987-08-06
David,Bernstein,1987-08-07
Peter,Hall,1987-08-08
Christopher,Olsen,1987-08-09
Nanette,Cambrault,1987-08-10
Oliver,Tuvault,1987-08-11
Janette,King,1987-08-12
Patrick,Sully,1987-08-13
Allan,McEwen,1987-08-14
Lindsey,Smith,1987-08-15
Louise,Doran,1987-08-16
Sarath,Sewall,1987-08-17
Danielle,Greene,1987-08-19
Mattea,Marvins,1987-08-20
David,Lee,1987-08-21
Sundar,Ande,1987-08-22
Amit,Banda,1987-08-23
Lisa,Ozer,1987-08-24
Harrison,Bloom,1987-08-25
Tayler,Fox,1987-08-26
William,Smith,1987-08-27
Elizabeth,Bates,1987-08-28
Sundita,Kumar,1987-08-29
Ellen,Abel,1987-08-30
Alyssa,Hutton,1987-08-31
Jonathon,Taylor,1987-09-01
Jack,Livingston,1987-09-02
Charles,Johnson,1987-09-04
|
What are the department ids for which more than 10 employees had a commission? | SELECT department_id FROM employees GROUP BY department_id HAVING COUNT(commission_pct) > 10 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,450 | DEPARTMENT_ID
50
80
|
Return all the information for all employees without any department number. | SELECT * FROM employees WHERE department_id = "null" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,411 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
|
What is all the information about employees hired before June 21, 2002? | SELECT * FROM employees WHERE hire_date < '2002-06-21' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,436 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90
101,Neena,Kochhar,NKOCHHAR,515.123.4568,1987-06-18,AD_VP,17000,0.0,100,90
102,Lex,De Haan,LDEHAAN,515.123.4569,1987-06-19,AD_VP,17000,0.0,100,90
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60
104,Bruce,Ernst,BERNST,590.423.4568,1987-06-21,IT_PROG,6000,0.0,103,60
105,David,Austin,DAUSTIN,590.423.4569,1987-06-22,IT_PROG,4800,0.0,103,60
106,Valli,Pataballa,VPATABAL,590.423.4560,1987-06-23,IT_PROG,4800,0.0,103,60
107,Diana,Lorentz,DLORENTZ,590.423.5567,1987-06-24,IT_PROG,4200,0.0,103,60
108,Nancy,Greenberg,NGREENBE,515.124.4569,1987-06-25,FI_MGR,12000,0.0,101,100
109,Daniel,Faviet,DFAVIET,515.124.4169,1987-06-26,FI_ACCOUNT,9000,0.0,108,100
110,John,Chen,JCHEN,515.124.4269,1987-06-27,FI_ACCOUNT,8200,0.0,108,100
111,Ismael,Sciarra,ISCIARRA,515.124.4369,1987-06-28,FI_ACCOUNT,7700,0.0,108,100
112,Jose Manuel,Urman,JMURMAN,515.124.4469,1987-06-29,FI_ACCOUNT,7800,0.0,108,100
113,Luis,Popp,LPOPP,515.124.4567,1987-06-30,FI_ACCOUNT,6900,0.0,108,100
114,Den,Raphaely,DRAPHEAL,515.127.4561,1987-07-01,PU_MAN,11000,0.0,100,30
115,Alexander,Khoo,AKHOO,515.127.4562,1987-07-02,PU_CLERK,3100,0.0,114,30
116,Shelli,Baida,SBAIDA,515.127.4563,1987-07-03,PU_CLERK,2900,0.0,114,30
117,Sigal,Tobias,STOBIAS,515.127.4564,1987-07-04,PU_CLERK,2800,0.0,114,30
118,Guy,Himuro,GHIMURO,515.127.4565,1987-07-05,PU_CLERK,2600,0.0,114,30
119,Karen,Colmenares,KCOLMENA,515.127.4566,1987-07-06,PU_CLERK,2500,0.0,114,30
120,Matthew,Weiss,MWEISS,650.123.1234,1987-07-07,ST_MAN,8000,0.0,100,50
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50
122,Payam,Kaufling,PKAUFLIN,650.123.3234,1987-07-09,ST_MAN,7900,0.0,100,50
123,Shanta,Vollman,SVOLLMAN,650.123.4234,1987-07-10,ST_MAN,6500,0.0,100,50
124,Kevin,Mourgos,KMOURGOS,650.123.5234,1987-07-11,ST_MAN,5800,0.0,100,50
125,Julia,Nayer,JNAYER,650.124.1214,1987-07-12,ST_CLERK,3200,0.0,120,50
126,Irene,Mikkilineni,IMIKKILI,650.124.1224,1987-07-13,ST_CLERK,2700,0.0,120,50
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0.0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0.0,120,50
129,Laura,Bissot,LBISSOT,650.124.5234,1987-07-16,ST_CLERK,3300,0.0,121,50
130,Mozhe,Atkinson,MATKINSO,650.124.6234,1987-07-17,ST_CLERK,2800,0.0,121,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0.0,121,50
132,TJ,Olson,TJOLSON,650.124.8234,1987-07-19,ST_CLERK,2100,0.0,121,50
133,Jason,Mallin,JMALLIN,650.127.1934,1987-07-20,ST_CLERK,3300,0.0,122,50
134,Michael,Rogers,MROGERS,650.127.1834,1987-07-21,ST_CLERK,2900,0.0,122,50
135,Ki,Gee,KGEE,650.127.1734,1987-07-22,ST_CLERK,2400,0.0,122,50
136,Hazel,Philtanker,HPHILTAN,650.127.1634,1987-07-23,ST_CLERK,2200,0.0,122,50
137,Renske,Ladwig,RLADWIG,650.121.1234,1987-07-24,ST_CLERK,3600,0.0,123,50
138,Stephen,Stiles,SSTILES,650.121.2034,1987-07-25,ST_CLERK,3200,0.0,123,50
139,John,Seo,JSEO,650.121.2019,1987-07-26,ST_CLERK,2700,0.0,123,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0.0,123,50
141,Trenna,Rajs,TRAJS,650.121.8009,1987-07-28,ST_CLERK,3500,0.0,124,50
142,Curtis,Davies,CDAVIES,650.121.2994,1987-07-29,ST_CLERK,3100,0.0,124,50
143,Randall,Matos,RMATOS,650.121.2874,1987-07-30,ST_CLERK,2600,0.0,124,50
144,Peter,Vargas,PVARGAS,650.121.2004,1987-07-31,ST_CLERK,2500,0.0,124,50
145,John,Russell,JRUSSEL,011.44.1344.429268,1987-08-01,SA_MAN,14000,0.4,100,80
146,Karen,Partners,KPARTNER,011.44.1344.467268,1987-08-02,SA_MAN,13500,0.3,100,80
147,Alberto,Errazuriz,AERRAZUR,011.44.1344.429278,1987-08-03,SA_MAN,12000,0.3,100,80
148,Gerald,Cambrault,GCAMBRAU,011.44.1344.619268,1987-08-04,SA_MAN,11000,0.3,100,80
149,Eleni,Zlotkey,EZLOTKEY,011.44.1344.429018,1987-08-05,SA_MAN,10500,0.2,100,80
150,Peter,Tucker,PTUCKER,011.44.1344.129268,1987-08-06,SA_REP,10000,0.3,145,80
151,David,Bernstein,DBERNSTE,011.44.1344.345268,1987-08-07,SA_REP,9500,0.25,145,80
152,Peter,Hall,PHALL,011.44.1344.478968,1987-08-08,SA_REP,9000,0.25,145,80
153,Christopher,Olsen,COLSEN,011.44.1344.498718,1987-08-09,SA_REP,8000,0.2,145,80
154,Nanette,Cambrault,NCAMBRAU,011.44.1344.987668,1987-08-10,SA_REP,7500,0.2,145,80
155,Oliver,Tuvault,OTUVAULT,011.44.1344.486508,1987-08-11,SA_REP,7000,0.15,145,80
156,Janette,King,JKING,011.44.1345.429268,1987-08-12,SA_REP,10000,0.35,146,80
157,Patrick,Sully,PSULLY,011.44.1345.929268,1987-08-13,SA_REP,9500,0.35,146,80
158,Allan,McEwen,AMCEWEN,011.44.1345.829268,1987-08-14,SA_REP,9000,0.35,146,80
159,Lindsey,Smith,LSMITH,011.44.1345.729268,1987-08-15,SA_REP,8000,0.3,146,80
160,Louise,Doran,LDORAN,011.44.1345.629268,1987-08-16,SA_REP,7500,0.3,146,80
161,Sarath,Sewall,SSEWALL,011.44.1345.529268,1987-08-17,SA_REP,7000,0.25,146,80
162,Clara,Vishney,CVISHNEY,011.44.1346.129268,1987-08-18,SA_REP,10500,0.25,147,80
163,Danielle,Greene,DGREENE,011.44.1346.229268,1987-08-19,SA_REP,9500,0.15,147,80
164,Mattea,Marvins,MMARVINS,011.44.1346.329268,1987-08-20,SA_REP,7200,0.1,147,80
165,David,Lee,DLEE,011.44.1346.529268,1987-08-21,SA_REP,6800,0.1,147,80
166,Sundar,Ande,SANDE,011.44.1346.629268,1987-08-22,SA_REP,6400,0.1,147,80
167,Amit,Banda,ABANDA,011.44.1346.729268,1987-08-23,SA_REP,6200,0.1,147,80
168,Lisa,Ozer,LOZER,011.44.1343.929268,1987-08-24,SA_REP,11500,0.25,148,80
169,Harrison,Bloom,HBLOOM,011.44.1343.829268,1987-08-25,SA_REP,10000,0.2,148,80
170,Tayler,Fox,TFOX,011.44.1343.729268,1987-08-26,SA_REP,9600,0.2,148,80
171,William,Smith,WSMITH,011.44.1343.629268,1987-08-27,SA_REP,7400,0.15,148,80
172,Elizabeth,Bates,EBATES,011.44.1343.529268,1987-08-28,SA_REP,7300,0.15,148,80
173,Sundita,Kumar,SKUMAR,011.44.1343.329268,1987-08-29,SA_REP,6100,0.1,148,80
174,Ellen,Abel,EABEL,011.44.1644.429267,1987-08-30,SA_REP,11000,0.3,149,80
175,Alyssa,Hutton,AHUTTON,011.44.1644.429266,1987-08-31,SA_REP,8800,0.25,149,80
176,Jonathon,Taylor,JTAYLOR,011.44.1644.429265,1987-09-01,SA_REP,8600,0.2,149,80
177,Jack,Livingston,JLIVINGS,011.44.1644.429264,1987-09-02,SA_REP,8400,0.2,149,80
178,Kimberely,Grant,KGRANT,011.44.1644.429263,1987-09-03,SA_REP,7000,0.15,149,0
179,Charles,Johnson,CJOHNSON,011.44.1644.429262,1987-09-04,SA_REP,6200,0.1,149,80
180,Winston,Taylor,WTAYLOR,650.507.9876,1987-09-05,SH_CLERK,3200,0.0,120,50
181,Jean,Fleaur,JFLEAUR,650.507.9877,1987-09-06,SH_CLERK,3100,0.0,120,50
182,Martha,Sullivan,MSULLIVA,650.507.9878,1987-09-07,SH_CLERK,2500,0.0,120,50
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0.0,120,50
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0.0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0.0,121,50
186,Julia,Dellinger,JDELLING,650.509.3876,1987-09-11,SH_CLERK,3400,0.0,121,50
187,Anthony,Cabrio,ACABRIO,650.509.4876,1987-09-12,SH_CLERK,3000,0.0,121,50
188,Kelly,Chung,KCHUNG,650.505.1876,1987-09-13,SH_CLERK,3800,0.0,122,50
189,Jennifer,Dilly,JDILLY,650.505.2876,1987-09-14,SH_CLERK,3600,0.0,122,50
190,Timothy,Gates,TGATES,650.505.3876,1987-09-15,SH_CLERK,2900,0.0,122,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0.0,122,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0.0,123,50
193,Britney,Everett,BEVERETT,650.501.2876,1987-09-18,SH_CLERK,3900,0.0,123,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0.0,123,50
195,Vance,Jones,VJONES,650.501.4876,1987-09-20,SH_CLERK,2800,0.0,123,50
196,Alana,Walsh,AWALSH,650.507.9811,1987-09-21,SH_CLERK,3100,0.0,124,50
197,Kevin,Feeney,KFEENEY,650.507.9822,1987-09-22,SH_CLERK,3000,0.0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0.0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0.0,124,50
200,Jennifer,Whalen,JWHALEN,515.123.4444,1987-09-25,AD_ASST,4400,0.0,101,10
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0.0,100,20
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0.0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0.0,205,110
|
What is the average salary of employees who have a commission percentage that is not null? | SELECT department_id, AVG(salary) FROM employees WHERE commission_pct <> "null" GROUP BY department_id | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,454 | DEPARTMENT_ID,AVG(salary)
0,7000.0
10,4400.0
20,9500.0
30,4150.0
40,6500.0
50,3475.5555555555557
60,5760.0
70,10000.0
80,8955.882352941177
90,19333.333333333332
100,8600.0
110,10150.0
|
What is all the information about employees who have never had a job in the past? | SELECT * FROM employees WHERE NOT employee_id IN (SELECT employee_id FROM job_history) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,514 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60
104,Bruce,Ernst,BERNST,590.423.4568,1987-06-21,IT_PROG,6000,0.0,103,60
105,David,Austin,DAUSTIN,590.423.4569,1987-06-22,IT_PROG,4800,0.0,103,60
106,Valli,Pataballa,VPATABAL,590.423.4560,1987-06-23,IT_PROG,4800,0.0,103,60
107,Diana,Lorentz,DLORENTZ,590.423.5567,1987-06-24,IT_PROG,4200,0.0,103,60
108,Nancy,Greenberg,NGREENBE,515.124.4569,1987-06-25,FI_MGR,12000,0.0,101,100
109,Daniel,Faviet,DFAVIET,515.124.4169,1987-06-26,FI_ACCOUNT,9000,0.0,108,100
110,John,Chen,JCHEN,515.124.4269,1987-06-27,FI_ACCOUNT,8200,0.0,108,100
111,Ismael,Sciarra,ISCIARRA,515.124.4369,1987-06-28,FI_ACCOUNT,7700,0.0,108,100
112,Jose Manuel,Urman,JMURMAN,515.124.4469,1987-06-29,FI_ACCOUNT,7800,0.0,108,100
113,Luis,Popp,LPOPP,515.124.4567,1987-06-30,FI_ACCOUNT,6900,0.0,108,100
115,Alexander,Khoo,AKHOO,515.127.4562,1987-07-02,PU_CLERK,3100,0.0,114,30
116,Shelli,Baida,SBAIDA,515.127.4563,1987-07-03,PU_CLERK,2900,0.0,114,30
117,Sigal,Tobias,STOBIAS,515.127.4564,1987-07-04,PU_CLERK,2800,0.0,114,30
118,Guy,Himuro,GHIMURO,515.127.4565,1987-07-05,PU_CLERK,2600,0.0,114,30
119,Karen,Colmenares,KCOLMENA,515.127.4566,1987-07-06,PU_CLERK,2500,0.0,114,30
120,Matthew,Weiss,MWEISS,650.123.1234,1987-07-07,ST_MAN,8000,0.0,100,50
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50
123,Shanta,Vollman,SVOLLMAN,650.123.4234,1987-07-10,ST_MAN,6500,0.0,100,50
124,Kevin,Mourgos,KMOURGOS,650.123.5234,1987-07-11,ST_MAN,5800,0.0,100,50
125,Julia,Nayer,JNAYER,650.124.1214,1987-07-12,ST_CLERK,3200,0.0,120,50
126,Irene,Mikkilineni,IMIKKILI,650.124.1224,1987-07-13,ST_CLERK,2700,0.0,120,50
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0.0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0.0,120,50
129,Laura,Bissot,LBISSOT,650.124.5234,1987-07-16,ST_CLERK,3300,0.0,121,50
130,Mozhe,Atkinson,MATKINSO,650.124.6234,1987-07-17,ST_CLERK,2800,0.0,121,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0.0,121,50
132,TJ,Olson,TJOLSON,650.124.8234,1987-07-19,ST_CLERK,2100,0.0,121,50
133,Jason,Mallin,JMALLIN,650.127.1934,1987-07-20,ST_CLERK,3300,0.0,122,50
134,Michael,Rogers,MROGERS,650.127.1834,1987-07-21,ST_CLERK,2900,0.0,122,50
135,Ki,Gee,KGEE,650.127.1734,1987-07-22,ST_CLERK,2400,0.0,122,50
136,Hazel,Philtanker,HPHILTAN,650.127.1634,1987-07-23,ST_CLERK,2200,0.0,122,50
137,Renske,Ladwig,RLADWIG,650.121.1234,1987-07-24,ST_CLERK,3600,0.0,123,50
138,Stephen,Stiles,SSTILES,650.121.2034,1987-07-25,ST_CLERK,3200,0.0,123,50
139,John,Seo,JSEO,650.121.2019,1987-07-26,ST_CLERK,2700,0.0,123,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0.0,123,50
141,Trenna,Rajs,TRAJS,650.121.8009,1987-07-28,ST_CLERK,3500,0.0,124,50
142,Curtis,Davies,CDAVIES,650.121.2994,1987-07-29,ST_CLERK,3100,0.0,124,50
143,Randall,Matos,RMATOS,650.121.2874,1987-07-30,ST_CLERK,2600,0.0,124,50
144,Peter,Vargas,PVARGAS,650.121.2004,1987-07-31,ST_CLERK,2500,0.0,124,50
145,John,Russell,JRUSSEL,011.44.1344.429268,1987-08-01,SA_MAN,14000,0.4,100,80
146,Karen,Partners,KPARTNER,011.44.1344.467268,1987-08-02,SA_MAN,13500,0.3,100,80
147,Alberto,Errazuriz,AERRAZUR,011.44.1344.429278,1987-08-03,SA_MAN,12000,0.3,100,80
148,Gerald,Cambrault,GCAMBRAU,011.44.1344.619268,1987-08-04,SA_MAN,11000,0.3,100,80
149,Eleni,Zlotkey,EZLOTKEY,011.44.1344.429018,1987-08-05,SA_MAN,10500,0.2,100,80
150,Peter,Tucker,PTUCKER,011.44.1344.129268,1987-08-06,SA_REP,10000,0.3,145,80
151,David,Bernstein,DBERNSTE,011.44.1344.345268,1987-08-07,SA_REP,9500,0.25,145,80
152,Peter,Hall,PHALL,011.44.1344.478968,1987-08-08,SA_REP,9000,0.25,145,80
153,Christopher,Olsen,COLSEN,011.44.1344.498718,1987-08-09,SA_REP,8000,0.2,145,80
154,Nanette,Cambrault,NCAMBRAU,011.44.1344.987668,1987-08-10,SA_REP,7500,0.2,145,80
155,Oliver,Tuvault,OTUVAULT,011.44.1344.486508,1987-08-11,SA_REP,7000,0.15,145,80
156,Janette,King,JKING,011.44.1345.429268,1987-08-12,SA_REP,10000,0.35,146,80
157,Patrick,Sully,PSULLY,011.44.1345.929268,1987-08-13,SA_REP,9500,0.35,146,80
158,Allan,McEwen,AMCEWEN,011.44.1345.829268,1987-08-14,SA_REP,9000,0.35,146,80
159,Lindsey,Smith,LSMITH,011.44.1345.729268,1987-08-15,SA_REP,8000,0.3,146,80
160,Louise,Doran,LDORAN,011.44.1345.629268,1987-08-16,SA_REP,7500,0.3,146,80
161,Sarath,Sewall,SSEWALL,011.44.1345.529268,1987-08-17,SA_REP,7000,0.25,146,80
162,Clara,Vishney,CVISHNEY,011.44.1346.129268,1987-08-18,SA_REP,10500,0.25,147,80
163,Danielle,Greene,DGREENE,011.44.1346.229268,1987-08-19,SA_REP,9500,0.15,147,80
164,Mattea,Marvins,MMARVINS,011.44.1346.329268,1987-08-20,SA_REP,7200,0.1,147,80
165,David,Lee,DLEE,011.44.1346.529268,1987-08-21,SA_REP,6800,0.1,147,80
166,Sundar,Ande,SANDE,011.44.1346.629268,1987-08-22,SA_REP,6400,0.1,147,80
167,Amit,Banda,ABANDA,011.44.1346.729268,1987-08-23,SA_REP,6200,0.1,147,80
168,Lisa,Ozer,LOZER,011.44.1343.929268,1987-08-24,SA_REP,11500,0.25,148,80
169,Harrison,Bloom,HBLOOM,011.44.1343.829268,1987-08-25,SA_REP,10000,0.2,148,80
170,Tayler,Fox,TFOX,011.44.1343.729268,1987-08-26,SA_REP,9600,0.2,148,80
171,William,Smith,WSMITH,011.44.1343.629268,1987-08-27,SA_REP,7400,0.15,148,80
172,Elizabeth,Bates,EBATES,011.44.1343.529268,1987-08-28,SA_REP,7300,0.15,148,80
173,Sundita,Kumar,SKUMAR,011.44.1343.329268,1987-08-29,SA_REP,6100,0.1,148,80
174,Ellen,Abel,EABEL,011.44.1644.429267,1987-08-30,SA_REP,11000,0.3,149,80
175,Alyssa,Hutton,AHUTTON,011.44.1644.429266,1987-08-31,SA_REP,8800,0.25,149,80
177,Jack,Livingston,JLIVINGS,011.44.1644.429264,1987-09-02,SA_REP,8400,0.2,149,80
178,Kimberely,Grant,KGRANT,011.44.1644.429263,1987-09-03,SA_REP,7000,0.15,149,0
179,Charles,Johnson,CJOHNSON,011.44.1644.429262,1987-09-04,SA_REP,6200,0.1,149,80
180,Winston,Taylor,WTAYLOR,650.507.9876,1987-09-05,SH_CLERK,3200,0.0,120,50
181,Jean,Fleaur,JFLEAUR,650.507.9877,1987-09-06,SH_CLERK,3100,0.0,120,50
182,Martha,Sullivan,MSULLIVA,650.507.9878,1987-09-07,SH_CLERK,2500,0.0,120,50
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0.0,120,50
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0.0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0.0,121,50
186,Julia,Dellinger,JDELLING,650.509.3876,1987-09-11,SH_CLERK,3400,0.0,121,50
187,Anthony,Cabrio,ACABRIO,650.509.4876,1987-09-12,SH_CLERK,3000,0.0,121,50
188,Kelly,Chung,KCHUNG,650.505.1876,1987-09-13,SH_CLERK,3800,0.0,122,50
189,Jennifer,Dilly,JDILLY,650.505.2876,1987-09-14,SH_CLERK,3600,0.0,122,50
190,Timothy,Gates,TGATES,650.505.3876,1987-09-15,SH_CLERK,2900,0.0,122,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0.0,122,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0.0,123,50
193,Britney,Everett,BEVERETT,650.501.2876,1987-09-18,SH_CLERK,3900,0.0,123,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0.0,123,50
195,Vance,Jones,VJONES,650.501.4876,1987-09-20,SH_CLERK,2800,0.0,123,50
196,Alana,Walsh,AWALSH,650.507.9811,1987-09-21,SH_CLERK,3100,0.0,124,50
197,Kevin,Feeney,KFEENEY,650.507.9822,1987-09-22,SH_CLERK,3000,0.0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0.0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0.0,124,50
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0.0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0.0,205,110
|
Find the ids of the departments where any manager is managing 4 or more employees. | SELECT DISTINCT department_id FROM employees GROUP BY department_id, manager_id HAVING COUNT(employee_id) >= 4 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,451 | DEPARTMENT_ID
30
50
60
80
100
|
display the department name, city, and state province for each department. | SELECT T1.department_name, T2.city, T2.state_province FROM departments AS T1 JOIN locations AS T2 ON T2.location_id = T1.location_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
| 3,521 | DEPARTMENT_NAME,CITY,STATE_PROVINCE
Administration,Seattle,Washington
Marketing,Toronto,Ontario
Purchasing,Seattle,Washington
Human Resources,London,
Shipping,South San Francisco,California
IT,Southlake,Texas
Public Relations,Munich,Bavaria
Sales,OX9 9ZB,Oxford
Executive,Seattle,Washington
Finance,Seattle,Washington
Accounting,Seattle,Washington
Treasury,Seattle,Washington
Corporate Tax,Seattle,Washington
Control And Credit,Seattle,Washington
Shareholder Services,Seattle,Washington
Benefits,Seattle,Washington
Manufacturing,Seattle,Washington
Construction,Seattle,Washington
Contracting,Seattle,Washington
Operations,Seattle,Washington
IT Support,Seattle,Washington
NOC,Seattle,Washington
IT Helpdesk,Seattle,Washington
Government Sales,Seattle,Washington
Retail Sales,Seattle,Washington
Recruiting,Seattle,Washington
Payroll,Seattle,Washington
|
What are full names and salaries of employees working in the city of London? | SELECT first_name, last_name, salary FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id WHERE T3.city = 'London' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,528 | FIRST_NAME,LAST_NAME,SALARY
Susan,Mavris,6500
|
On what dates were employees without the letter M in their first names hired? | SELECT hire_date FROM employees WHERE NOT first_name LIKE '%M%' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,416 | HIRE_DATE
1987-06-17
1987-06-18
1987-06-19
1987-06-20
1987-06-21
1987-06-22
1987-06-23
1987-06-24
1987-06-25
1987-06-26
1987-06-27
1987-06-30
1987-07-01
1987-07-02
1987-07-03
1987-07-04
1987-07-05
1987-07-06
1987-07-10
1987-07-11
1987-07-12
1987-07-13
1987-07-15
1987-07-16
1987-07-19
1987-07-20
1987-07-22
1987-07-23
1987-07-24
1987-07-25
1987-07-26
1987-07-27
1987-07-28
1987-07-29
1987-07-30
1987-07-31
1987-08-01
1987-08-02
1987-08-03
1987-08-04
1987-08-05
1987-08-06
1987-08-07
1987-08-08
1987-08-09
1987-08-10
1987-08-11
1987-08-12
1987-08-13
1987-08-14
1987-08-15
1987-08-16
1987-08-17
1987-08-18
1987-08-19
1987-08-21
1987-08-22
1987-08-24
1987-08-25
1987-08-26
1987-08-28
1987-08-29
1987-08-30
1987-08-31
1987-09-01
1987-09-02
1987-09-04
1987-09-05
1987-09-06
1987-09-08
1987-09-09
1987-09-10
1987-09-11
1987-09-12
1987-09-13
1987-09-14
1987-09-16
1987-09-17
1987-09-18
1987-09-20
1987-09-21
1987-09-22
1987-09-23
1987-09-24
1987-09-25
1987-09-27
1987-09-28
1987-09-30
|
What are the job titles, and range of salaries for jobs with maximum salary between 12000 and 18000? | SELECT job_title, max_salary - min_salary FROM jobs WHERE max_salary BETWEEN 12000 AND 18000 | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
``` | 3,444 | JOB_TITLE,max_salary - min_salary
Finance Manager,7800
Accounting Manager,7800
Sales Representative,6000
Purchasing Manager,7000
Marketing Manager,6000
|
get the details of employees who manage a department. | SELECT DISTINCT * FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id WHERE T1.employee_id = T2.manager_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,481 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID,DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90,90,Executive,100,1700
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60,60,IT,103,1400
108,Nancy,Greenberg,NGREENBE,515.124.4569,1987-06-25,FI_MGR,12000,0.0,101,100,100,Finance,108,1700
114,Den,Raphaely,DRAPHEAL,515.127.4561,1987-07-01,PU_MAN,11000,0.0,100,30,30,Purchasing,114,1700
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50,50,Shipping,121,1500
145,John,Russell,JRUSSEL,011.44.1344.429268,1987-08-01,SA_MAN,14000,0.4,100,80,80,Sales,145,2500
200,Jennifer,Whalen,JWHALEN,515.123.4444,1987-09-25,AD_ASST,4400,0.0,101,10,10,Administration,200,1700
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0.0,100,20,20,Marketing,201,1800
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40,40,Human Resources,203,2400
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70,70,Public Relations,204,2700
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110,110,Accounting,205,1700
|
display the ID for those employees who did two or more jobs in the past. | SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*) >= 2 | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,485 | EMPLOYEE_ID
101
176
200
|
Give the distinct department ids of departments in which a manager is in charge of 4 or more employees? | SELECT DISTINCT department_id FROM employees GROUP BY department_id, manager_id HAVING COUNT(employee_id) >= 4 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,488 | DEPARTMENT_ID
30
50
60
80
100
|
What are the ids and full names for employees who work in a department that has someone with a first name that contains the letter T? | SELECT employee_id, first_name, last_name FROM employees WHERE department_id IN (SELECT department_id FROM employees WHERE first_name LIKE '%T%') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,504 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME
100,Steven,King
101,Neena,Kochhar
102,Lex,De Haan
120,Matthew,Weiss
121,Adam,Fripp
122,Payam,Kaufling
123,Shanta,Vollman
124,Kevin,Mourgos
125,Julia,Nayer
126,Irene,Mikkilineni
127,James,Landry
128,Steven,Markle
129,Laura,Bissot
130,Mozhe,Atkinson
131,James,Marlow
132,TJ,Olson
133,Jason,Mallin
134,Michael,Rogers
135,Ki,Gee
136,Hazel,Philtanker
137,Renske,Ladwig
138,Stephen,Stiles
139,John,Seo
140,Joshua,Patel
141,Trenna,Rajs
142,Curtis,Davies
143,Randall,Matos
144,Peter,Vargas
145,John,Russell
146,Karen,Partners
147,Alberto,Errazuriz
148,Gerald,Cambrault
149,Eleni,Zlotkey
150,Peter,Tucker
151,David,Bernstein
152,Peter,Hall
153,Christopher,Olsen
154,Nanette,Cambrault
155,Oliver,Tuvault
156,Janette,King
157,Patrick,Sully
158,Allan,McEwen
159,Lindsey,Smith
160,Louise,Doran
161,Sarath,Sewall
162,Clara,Vishney
163,Danielle,Greene
164,Mattea,Marvins
165,David,Lee
166,Sundar,Ande
167,Amit,Banda
168,Lisa,Ozer
169,Harrison,Bloom
170,Tayler,Fox
171,William,Smith
172,Elizabeth,Bates
173,Sundita,Kumar
174,Ellen,Abel
175,Alyssa,Hutton
176,Jonathon,Taylor
177,Jack,Livingston
179,Charles,Johnson
180,Winston,Taylor
181,Jean,Fleaur
182,Martha,Sullivan
183,Girard,Geoni
184,Nandita,Sarchand
185,Alexis,Bull
186,Julia,Dellinger
187,Anthony,Cabrio
188,Kelly,Chung
189,Jennifer,Dilly
190,Timothy,Gates
191,Randall,Perkins
192,Sarah,Bell
193,Britney,Everett
194,Samuel,McCain
195,Vance,Jones
196,Alana,Walsh
197,Kevin,Feeney
198,Donald,OConnell
199,Douglas,Grant
201,Michael,Hartstein
202,Pat,Fay
|
display the employee number and name( first name and last name ) for all employees who work in a department with any employee whose name contains a ’T’. | SELECT employee_id, first_name, last_name FROM employees WHERE department_id IN (SELECT department_id FROM employees WHERE first_name LIKE '%T%') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,503 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME
100,Steven,King
101,Neena,Kochhar
102,Lex,De Haan
120,Matthew,Weiss
121,Adam,Fripp
122,Payam,Kaufling
123,Shanta,Vollman
124,Kevin,Mourgos
125,Julia,Nayer
126,Irene,Mikkilineni
127,James,Landry
128,Steven,Markle
129,Laura,Bissot
130,Mozhe,Atkinson
131,James,Marlow
132,TJ,Olson
133,Jason,Mallin
134,Michael,Rogers
135,Ki,Gee
136,Hazel,Philtanker
137,Renske,Ladwig
138,Stephen,Stiles
139,John,Seo
140,Joshua,Patel
141,Trenna,Rajs
142,Curtis,Davies
143,Randall,Matos
144,Peter,Vargas
145,John,Russell
146,Karen,Partners
147,Alberto,Errazuriz
148,Gerald,Cambrault
149,Eleni,Zlotkey
150,Peter,Tucker
151,David,Bernstein
152,Peter,Hall
153,Christopher,Olsen
154,Nanette,Cambrault
155,Oliver,Tuvault
156,Janette,King
157,Patrick,Sully
158,Allan,McEwen
159,Lindsey,Smith
160,Louise,Doran
161,Sarath,Sewall
162,Clara,Vishney
163,Danielle,Greene
164,Mattea,Marvins
165,David,Lee
166,Sundar,Ande
167,Amit,Banda
168,Lisa,Ozer
169,Harrison,Bloom
170,Tayler,Fox
171,William,Smith
172,Elizabeth,Bates
173,Sundita,Kumar
174,Ellen,Abel
175,Alyssa,Hutton
176,Jonathon,Taylor
177,Jack,Livingston
179,Charles,Johnson
180,Winston,Taylor
181,Jean,Fleaur
182,Martha,Sullivan
183,Girard,Geoni
184,Nandita,Sarchand
185,Alexis,Bull
186,Julia,Dellinger
187,Anthony,Cabrio
188,Kelly,Chung
189,Jennifer,Dilly
190,Timothy,Gates
191,Randall,Perkins
192,Sarah,Bell
193,Britney,Everett
194,Samuel,McCain
195,Vance,Jones
196,Alana,Walsh
197,Kevin,Feeney
198,Donald,OConnell
199,Douglas,Grant
201,Michael,Hartstein
202,Pat,Fay
|
What are the ids for employees who do not work in departments with managers that have ids between 100 and 200? | SELECT * FROM employees WHERE NOT department_id IN (SELECT department_id FROM departments WHERE manager_id BETWEEN 100 AND 200) | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,498 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
178,Kimberely,Grant,KGRANT,011.44.1644.429263,1987-09-03,SA_REP,7000,0.15,149,0
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0.0,100,20
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0.0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0.0,205,110
|
What are all the employees without a department number? | SELECT * FROM employees WHERE department_id = "null" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,412 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
|
What are the employee ids for each employee and final dates of employment at their last job? | SELECT employee_id, MAX(end_date) FROM job_history GROUP BY employee_id | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,448 | EMPLOYEE_ID,MAX(end_date)
0,0000-00-00
101,1997-03-15
102,1998-07-24
114,1999-12-31
122,1999-12-31
176,1999-12-31
200,1998-12-31
201,1999-12-19
|
What are the names of departments that have at least one employee. | SELECT DISTINCT T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,480 | DEPARTMENT_NAME
Executive
IT
Finance
Purchasing
Shipping
Sales
Administration
Marketing
Human Resources
Public Relations
Accounting
|
display the employee id and salary of all employees who report to Payam (first name). | SELECT employee_id, salary FROM employees WHERE manager_id = (SELECT employee_id FROM employees WHERE first_name = 'Payam') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,477 | EMPLOYEE_ID,SALARY
133,3300
134,2900
135,2400
136,2200
188,3800
189,3600
190,2900
191,2500
|
What are the salaries and manager ids for employees who have managers? | SELECT salary, manager_id FROM employees WHERE manager_id <> "null" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,434 | SALARY,MANAGER_ID
24000,0
17000,100
17000,100
9000,102
6000,103
4800,103
4800,103
4200,103
12000,101
9000,108
8200,108
7700,108
7800,108
6900,108
11000,100
3100,114
2900,114
2800,114
2600,114
2500,114
8000,100
8200,100
7900,100
6500,100
5800,100
3200,120
2700,120
2400,120
2200,120
3300,121
2800,121
2500,121
2100,121
3300,122
2900,122
2400,122
2200,122
3600,123
3200,123
2700,123
2500,123
3500,124
3100,124
2600,124
2500,124
14000,100
13500,100
12000,100
11000,100
10500,100
10000,145
9500,145
9000,145
8000,145
7500,145
7000,145
10000,146
9500,146
9000,146
8000,146
7500,146
7000,146
10500,147
9500,147
7200,147
6800,147
6400,147
6200,147
11500,148
10000,148
9600,148
7400,148
7300,148
6100,148
11000,149
8800,149
8600,149
8400,149
7000,149
6200,149
3200,120
3100,120
2500,120
2800,120
4200,121
4100,121
3400,121
3000,121
3800,122
3600,122
2900,122
2500,122
4000,123
3900,123
3200,123
2800,123
3100,124
3000,124
2600,124
2600,124
4400,101
13000,100
6000,201
6500,101
10000,101
12000,101
8300,205
|
display the employee name ( first name and last name ) and hire date for all employees in the same department as Clara excluding Clara. | SELECT first_name, last_name, hire_date FROM employees WHERE department_id = (SELECT department_id FROM employees WHERE first_name = "Clara") AND first_name <> "Clara" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,501 | FIRST_NAME,LAST_NAME,HIRE_DATE
John,Russell,1987-08-01
Karen,Partners,1987-08-02
Alberto,Errazuriz,1987-08-03
Gerald,Cambrault,1987-08-04
Eleni,Zlotkey,1987-08-05
Peter,Tucker,1987-08-06
David,Bernstein,1987-08-07
Peter,Hall,1987-08-08
Christopher,Olsen,1987-08-09
Nanette,Cambrault,1987-08-10
Oliver,Tuvault,1987-08-11
Janette,King,1987-08-12
Patrick,Sully,1987-08-13
Allan,McEwen,1987-08-14
Lindsey,Smith,1987-08-15
Louise,Doran,1987-08-16
Sarath,Sewall,1987-08-17
Danielle,Greene,1987-08-19
Mattea,Marvins,1987-08-20
David,Lee,1987-08-21
Sundar,Ande,1987-08-22
Amit,Banda,1987-08-23
Lisa,Ozer,1987-08-24
Harrison,Bloom,1987-08-25
Tayler,Fox,1987-08-26
William,Smith,1987-08-27
Elizabeth,Bates,1987-08-28
Sundita,Kumar,1987-08-29
Ellen,Abel,1987-08-30
Alyssa,Hutton,1987-08-31
Jonathon,Taylor,1987-09-01
Jack,Livingston,1987-09-02
Charles,Johnson,1987-09-04
|
What are the job ids for jobs done more than once for a period of more than 300 days? | SELECT job_id FROM job_history WHERE end_date - start_date > 300 GROUP BY job_id HAVING COUNT(*) >= 2 | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,458 | JOB_ID
|
Which employees were hired after September 7th, 1987? | SELECT * FROM employees WHERE hire_date > '1987-09-07' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,440 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0,120,50
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0,121,50
186,Julia,Dellinger,JDELLING,650.509.3876,1987-09-11,SH_CLERK,3400,0,121,50
187,Anthony,Cabrio,ACABRIO,650.509.4876,1987-09-12,SH_CLERK,3000,0,121,50
188,Kelly,Chung,KCHUNG,650.505.1876,1987-09-13,SH_CLERK,3800,0,122,50
189,Jennifer,Dilly,JDILLY,650.505.2876,1987-09-14,SH_CLERK,3600,0,122,50
190,Timothy,Gates,TGATES,650.505.3876,1987-09-15,SH_CLERK,2900,0,122,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0,122,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0,123,50
193,Britney,Everett,BEVERETT,650.501.2876,1987-09-18,SH_CLERK,3900,0,123,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0,123,50
195,Vance,Jones,VJONES,650.501.4876,1987-09-20,SH_CLERK,2800,0,123,50
196,Alana,Walsh,AWALSH,650.507.9811,1987-09-21,SH_CLERK,3100,0,124,50
197,Kevin,Feeney,KFEENEY,650.507.9822,1987-09-22,SH_CLERK,3000,0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0,124,50
200,Jennifer,Whalen,JWHALEN,515.123.4444,1987-09-25,AD_ASST,4400,0,101,10
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0,100,20
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0,205,110
|
Display the first name and department name for each employee. | SELECT T1.first_name, T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,405 | FIRST_NAME,DEPARTMENT_NAME
Steven,Executive
Neena,Executive
Lex,Executive
Alexander,IT
Bruce,IT
David,IT
Valli,IT
Diana,IT
Nancy,Finance
Daniel,Finance
John,Finance
Ismael,Finance
Jose Manuel,Finance
Luis,Finance
Den,Purchasing
Alexander,Purchasing
Shelli,Purchasing
Sigal,Purchasing
Guy,Purchasing
Karen,Purchasing
Matthew,Shipping
Adam,Shipping
Payam,Shipping
Shanta,Shipping
Kevin,Shipping
Julia,Shipping
Irene,Shipping
James,Shipping
Steven,Shipping
Laura,Shipping
Mozhe,Shipping
James,Shipping
TJ,Shipping
Jason,Shipping
Michael,Shipping
Ki,Shipping
Hazel,Shipping
Renske,Shipping
Stephen,Shipping
John,Shipping
Joshua,Shipping
Trenna,Shipping
Curtis,Shipping
Randall,Shipping
Peter,Shipping
John,Sales
Karen,Sales
Alberto,Sales
Gerald,Sales
Eleni,Sales
Peter,Sales
David,Sales
Peter,Sales
Christopher,Sales
Nanette,Sales
Oliver,Sales
Janette,Sales
Patrick,Sales
Allan,Sales
Lindsey,Sales
Louise,Sales
Sarath,Sales
Clara,Sales
Danielle,Sales
Mattea,Sales
David,Sales
Sundar,Sales
Amit,Sales
Lisa,Sales
Harrison,Sales
Tayler,Sales
William,Sales
Elizabeth,Sales
Sundita,Sales
Ellen,Sales
Alyssa,Sales
Jonathon,Sales
Jack,Sales
Charles,Sales
Winston,Shipping
Jean,Shipping
Martha,Shipping
Girard,Shipping
Nandita,Shipping
Alexis,Shipping
Julia,Shipping
Anthony,Shipping
Kelly,Shipping
Jennifer,Shipping
Timothy,Shipping
Randall,Shipping
Sarah,Shipping
Britney,Shipping
Samuel,Shipping
Vance,Shipping
Alana,Shipping
Kevin,Shipping
Donald,Shipping
Douglas,Shipping
Jennifer,Administration
Michael,Marketing
Pat,Marketing
Susan,Human Resources
Hermann,Public Relations
Shelley,Accounting
William,Accounting
|
What are the department names and how many employees work in each of them? | SELECT department_name, COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id GROUP BY department_name | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,526 | DEPARTMENT_NAME,COUNT(*)
Accounting,2
Administration,1
Executive,3
Finance,6
Human Resources,1
IT,5
Marketing,2
Public Relations,1
Purchasing,6
Sales,34
Shipping,45
|
Return the full names and salaries for employees with first names that end with the letter m. | SELECT first_name, last_name, salary FROM employees WHERE first_name LIKE '%m' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,428 | FIRST_NAME,LAST_NAME,SALARY
Adam,Fripp,8200
Payam,Kaufling,7900
William,Smith,7400
William,Gietz,8300
|
What are the ids, full names, and salaries for employees making more than average and who work in a department with employees who have the letter J in their first name? | SELECT employee_id, first_name, last_name, salary FROM employees WHERE salary > (SELECT AVG(salary) FROM employees) AND department_id IN (SELECT department_id FROM employees WHERE first_name LIKE '%J%') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,506 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,SALARY
108,Nancy,Greenberg,12000
109,Daniel,Faviet,9000
110,John,Chen,8200
111,Ismael,Sciarra,7700
112,Jose Manuel,Urman,7800
113,Luis,Popp,6900
120,Matthew,Weiss,8000
121,Adam,Fripp,8200
122,Payam,Kaufling,7900
123,Shanta,Vollman,6500
145,John,Russell,14000
146,Karen,Partners,13500
147,Alberto,Errazuriz,12000
148,Gerald,Cambrault,11000
149,Eleni,Zlotkey,10500
150,Peter,Tucker,10000
151,David,Bernstein,9500
152,Peter,Hall,9000
153,Christopher,Olsen,8000
154,Nanette,Cambrault,7500
155,Oliver,Tuvault,7000
156,Janette,King,10000
157,Patrick,Sully,9500
158,Allan,McEwen,9000
159,Lindsey,Smith,8000
160,Louise,Doran,7500
161,Sarath,Sewall,7000
162,Clara,Vishney,10500
163,Danielle,Greene,9500
164,Mattea,Marvins,7200
165,David,Lee,6800
168,Lisa,Ozer,11500
169,Harrison,Bloom,10000
170,Tayler,Fox,9600
171,William,Smith,7400
172,Elizabeth,Bates,7300
174,Ellen,Abel,11000
175,Alyssa,Hutton,8800
176,Jonathon,Taylor,8600
177,Jack,Livingston,8400
|
What are all the employee ids and the names of the countries in which they work? | SELECT T1.employee_id, T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id JOIN countries AS T4 ON T3.country_id = T4.country_id | ``` yaml
countries: # name of dataframe
- COUNTRY_ID:
dtype: string
sample_values: AR, AU, BE
- COUNTRY_NAME:
dtype: string
sample_values: Argentina, Australia, Belgium
- REGION_ID:
dtype: int64
sample_values: 2, 3, 1
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
Column 'country_id' in dataframe 'locations' has a foreign key relation to column 'country_id' in dataframe 'countries'.
| 3,462 | EMPLOYEE_ID,COUNTRY_NAME
100,United States of America
101,United States of America
102,United States of America
103,United States of America
104,United States of America
105,United States of America
106,United States of America
107,United States of America
108,United States of America
109,United States of America
110,United States of America
111,United States of America
112,United States of America
113,United States of America
114,United States of America
115,United States of America
116,United States of America
117,United States of America
118,United States of America
119,United States of America
120,United States of America
121,United States of America
122,United States of America
123,United States of America
124,United States of America
125,United States of America
126,United States of America
127,United States of America
128,United States of America
129,United States of America
130,United States of America
131,United States of America
132,United States of America
133,United States of America
134,United States of America
135,United States of America
136,United States of America
137,United States of America
138,United States of America
139,United States of America
140,United States of America
141,United States of America
142,United States of America
143,United States of America
144,United States of America
180,United States of America
181,United States of America
182,United States of America
183,United States of America
184,United States of America
185,United States of America
186,United States of America
187,United States of America
188,United States of America
189,United States of America
190,United States of America
191,United States of America
192,United States of America
193,United States of America
194,United States of America
195,United States of America
196,United States of America
197,United States of America
198,United States of America
199,United States of America
200,United States of America
201,Canada
202,Canada
203,United Kingdom
204,Germany
205,United States of America
206,United States of America
|
What are the full name, hire date, salary, and department id for employees without the letter M in their first name? | SELECT first_name, last_name, hire_date, salary, department_id FROM employees WHERE NOT first_name LIKE '%M%' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,418 | FIRST_NAME,LAST_NAME,HIRE_DATE,SALARY,DEPARTMENT_ID
Steven,King,1987-06-17,24000,90
Neena,Kochhar,1987-06-18,17000,90
Lex,De Haan,1987-06-19,17000,90
Alexander,Hunold,1987-06-20,9000,60
Bruce,Ernst,1987-06-21,6000,60
David,Austin,1987-06-22,4800,60
Valli,Pataballa,1987-06-23,4800,60
Diana,Lorentz,1987-06-24,4200,60
Nancy,Greenberg,1987-06-25,12000,100
Daniel,Faviet,1987-06-26,9000,100
John,Chen,1987-06-27,8200,100
Luis,Popp,1987-06-30,6900,100
Den,Raphaely,1987-07-01,11000,30
Alexander,Khoo,1987-07-02,3100,30
Shelli,Baida,1987-07-03,2900,30
Sigal,Tobias,1987-07-04,2800,30
Guy,Himuro,1987-07-05,2600,30
Karen,Colmenares,1987-07-06,2500,30
Shanta,Vollman,1987-07-10,6500,50
Kevin,Mourgos,1987-07-11,5800,50
Julia,Nayer,1987-07-12,3200,50
Irene,Mikkilineni,1987-07-13,2700,50
Steven,Markle,1987-07-15,2200,50
Laura,Bissot,1987-07-16,3300,50
TJ,Olson,1987-07-19,2100,50
Jason,Mallin,1987-07-20,3300,50
Ki,Gee,1987-07-22,2400,50
Hazel,Philtanker,1987-07-23,2200,50
Renske,Ladwig,1987-07-24,3600,50
Stephen,Stiles,1987-07-25,3200,50
John,Seo,1987-07-26,2700,50
Joshua,Patel,1987-07-27,2500,50
Trenna,Rajs,1987-07-28,3500,50
Curtis,Davies,1987-07-29,3100,50
Randall,Matos,1987-07-30,2600,50
Peter,Vargas,1987-07-31,2500,50
John,Russell,1987-08-01,14000,80
Karen,Partners,1987-08-02,13500,80
Alberto,Errazuriz,1987-08-03,12000,80
Gerald,Cambrault,1987-08-04,11000,80
Eleni,Zlotkey,1987-08-05,10500,80
Peter,Tucker,1987-08-06,10000,80
David,Bernstein,1987-08-07,9500,80
Peter,Hall,1987-08-08,9000,80
Christopher,Olsen,1987-08-09,8000,80
Nanette,Cambrault,1987-08-10,7500,80
Oliver,Tuvault,1987-08-11,7000,80
Janette,King,1987-08-12,10000,80
Patrick,Sully,1987-08-13,9500,80
Allan,McEwen,1987-08-14,9000,80
Lindsey,Smith,1987-08-15,8000,80
Louise,Doran,1987-08-16,7500,80
Sarath,Sewall,1987-08-17,7000,80
Clara,Vishney,1987-08-18,10500,80
Danielle,Greene,1987-08-19,9500,80
David,Lee,1987-08-21,6800,80
Sundar,Ande,1987-08-22,6400,80
Lisa,Ozer,1987-08-24,11500,80
Harrison,Bloom,1987-08-25,10000,80
Tayler,Fox,1987-08-26,9600,80
Elizabeth,Bates,1987-08-28,7300,80
Sundita,Kumar,1987-08-29,6100,80
Ellen,Abel,1987-08-30,11000,80
Alyssa,Hutton,1987-08-31,8800,80
Jonathon,Taylor,1987-09-01,8600,80
Jack,Livingston,1987-09-02,8400,80
Charles,Johnson,1987-09-04,6200,80
Winston,Taylor,1987-09-05,3200,50
Jean,Fleaur,1987-09-06,3100,50
Girard,Geoni,1987-09-08,2800,50
Nandita,Sarchand,1987-09-09,4200,50
Alexis,Bull,1987-09-10,4100,50
Julia,Dellinger,1987-09-11,3400,50
Anthony,Cabrio,1987-09-12,3000,50
Kelly,Chung,1987-09-13,3800,50
Jennifer,Dilly,1987-09-14,3600,50
Randall,Perkins,1987-09-16,2500,50
Sarah,Bell,1987-09-17,4000,50
Britney,Everett,1987-09-18,3900,50
Vance,Jones,1987-09-20,2800,50
Alana,Walsh,1987-09-21,3100,50
Kevin,Feeney,1987-09-22,3000,50
Donald,OConnell,1987-09-23,2600,50
Douglas,Grant,1987-09-24,2600,50
Jennifer,Whalen,1987-09-25,4400,10
Pat,Fay,1987-09-27,6000,20
Susan,Mavris,1987-09-28,6500,40
Shelley,Higgins,1987-09-30,12000,110
|
display all the information of those employees who did not have any job in the past. | SELECT * FROM employees WHERE NOT employee_id IN (SELECT employee_id FROM job_history) | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,513 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60
104,Bruce,Ernst,BERNST,590.423.4568,1987-06-21,IT_PROG,6000,0.0,103,60
105,David,Austin,DAUSTIN,590.423.4569,1987-06-22,IT_PROG,4800,0.0,103,60
106,Valli,Pataballa,VPATABAL,590.423.4560,1987-06-23,IT_PROG,4800,0.0,103,60
107,Diana,Lorentz,DLORENTZ,590.423.5567,1987-06-24,IT_PROG,4200,0.0,103,60
108,Nancy,Greenberg,NGREENBE,515.124.4569,1987-06-25,FI_MGR,12000,0.0,101,100
109,Daniel,Faviet,DFAVIET,515.124.4169,1987-06-26,FI_ACCOUNT,9000,0.0,108,100
110,John,Chen,JCHEN,515.124.4269,1987-06-27,FI_ACCOUNT,8200,0.0,108,100
111,Ismael,Sciarra,ISCIARRA,515.124.4369,1987-06-28,FI_ACCOUNT,7700,0.0,108,100
112,Jose Manuel,Urman,JMURMAN,515.124.4469,1987-06-29,FI_ACCOUNT,7800,0.0,108,100
113,Luis,Popp,LPOPP,515.124.4567,1987-06-30,FI_ACCOUNT,6900,0.0,108,100
115,Alexander,Khoo,AKHOO,515.127.4562,1987-07-02,PU_CLERK,3100,0.0,114,30
116,Shelli,Baida,SBAIDA,515.127.4563,1987-07-03,PU_CLERK,2900,0.0,114,30
117,Sigal,Tobias,STOBIAS,515.127.4564,1987-07-04,PU_CLERK,2800,0.0,114,30
118,Guy,Himuro,GHIMURO,515.127.4565,1987-07-05,PU_CLERK,2600,0.0,114,30
119,Karen,Colmenares,KCOLMENA,515.127.4566,1987-07-06,PU_CLERK,2500,0.0,114,30
120,Matthew,Weiss,MWEISS,650.123.1234,1987-07-07,ST_MAN,8000,0.0,100,50
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50
123,Shanta,Vollman,SVOLLMAN,650.123.4234,1987-07-10,ST_MAN,6500,0.0,100,50
124,Kevin,Mourgos,KMOURGOS,650.123.5234,1987-07-11,ST_MAN,5800,0.0,100,50
125,Julia,Nayer,JNAYER,650.124.1214,1987-07-12,ST_CLERK,3200,0.0,120,50
126,Irene,Mikkilineni,IMIKKILI,650.124.1224,1987-07-13,ST_CLERK,2700,0.0,120,50
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0.0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0.0,120,50
129,Laura,Bissot,LBISSOT,650.124.5234,1987-07-16,ST_CLERK,3300,0.0,121,50
130,Mozhe,Atkinson,MATKINSO,650.124.6234,1987-07-17,ST_CLERK,2800,0.0,121,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0.0,121,50
132,TJ,Olson,TJOLSON,650.124.8234,1987-07-19,ST_CLERK,2100,0.0,121,50
133,Jason,Mallin,JMALLIN,650.127.1934,1987-07-20,ST_CLERK,3300,0.0,122,50
134,Michael,Rogers,MROGERS,650.127.1834,1987-07-21,ST_CLERK,2900,0.0,122,50
135,Ki,Gee,KGEE,650.127.1734,1987-07-22,ST_CLERK,2400,0.0,122,50
136,Hazel,Philtanker,HPHILTAN,650.127.1634,1987-07-23,ST_CLERK,2200,0.0,122,50
137,Renske,Ladwig,RLADWIG,650.121.1234,1987-07-24,ST_CLERK,3600,0.0,123,50
138,Stephen,Stiles,SSTILES,650.121.2034,1987-07-25,ST_CLERK,3200,0.0,123,50
139,John,Seo,JSEO,650.121.2019,1987-07-26,ST_CLERK,2700,0.0,123,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0.0,123,50
141,Trenna,Rajs,TRAJS,650.121.8009,1987-07-28,ST_CLERK,3500,0.0,124,50
142,Curtis,Davies,CDAVIES,650.121.2994,1987-07-29,ST_CLERK,3100,0.0,124,50
143,Randall,Matos,RMATOS,650.121.2874,1987-07-30,ST_CLERK,2600,0.0,124,50
144,Peter,Vargas,PVARGAS,650.121.2004,1987-07-31,ST_CLERK,2500,0.0,124,50
145,John,Russell,JRUSSEL,011.44.1344.429268,1987-08-01,SA_MAN,14000,0.4,100,80
146,Karen,Partners,KPARTNER,011.44.1344.467268,1987-08-02,SA_MAN,13500,0.3,100,80
147,Alberto,Errazuriz,AERRAZUR,011.44.1344.429278,1987-08-03,SA_MAN,12000,0.3,100,80
148,Gerald,Cambrault,GCAMBRAU,011.44.1344.619268,1987-08-04,SA_MAN,11000,0.3,100,80
149,Eleni,Zlotkey,EZLOTKEY,011.44.1344.429018,1987-08-05,SA_MAN,10500,0.2,100,80
150,Peter,Tucker,PTUCKER,011.44.1344.129268,1987-08-06,SA_REP,10000,0.3,145,80
151,David,Bernstein,DBERNSTE,011.44.1344.345268,1987-08-07,SA_REP,9500,0.25,145,80
152,Peter,Hall,PHALL,011.44.1344.478968,1987-08-08,SA_REP,9000,0.25,145,80
153,Christopher,Olsen,COLSEN,011.44.1344.498718,1987-08-09,SA_REP,8000,0.2,145,80
154,Nanette,Cambrault,NCAMBRAU,011.44.1344.987668,1987-08-10,SA_REP,7500,0.2,145,80
155,Oliver,Tuvault,OTUVAULT,011.44.1344.486508,1987-08-11,SA_REP,7000,0.15,145,80
156,Janette,King,JKING,011.44.1345.429268,1987-08-12,SA_REP,10000,0.35,146,80
157,Patrick,Sully,PSULLY,011.44.1345.929268,1987-08-13,SA_REP,9500,0.35,146,80
158,Allan,McEwen,AMCEWEN,011.44.1345.829268,1987-08-14,SA_REP,9000,0.35,146,80
159,Lindsey,Smith,LSMITH,011.44.1345.729268,1987-08-15,SA_REP,8000,0.3,146,80
160,Louise,Doran,LDORAN,011.44.1345.629268,1987-08-16,SA_REP,7500,0.3,146,80
161,Sarath,Sewall,SSEWALL,011.44.1345.529268,1987-08-17,SA_REP,7000,0.25,146,80
162,Clara,Vishney,CVISHNEY,011.44.1346.129268,1987-08-18,SA_REP,10500,0.25,147,80
163,Danielle,Greene,DGREENE,011.44.1346.229268,1987-08-19,SA_REP,9500,0.15,147,80
164,Mattea,Marvins,MMARVINS,011.44.1346.329268,1987-08-20,SA_REP,7200,0.1,147,80
165,David,Lee,DLEE,011.44.1346.529268,1987-08-21,SA_REP,6800,0.1,147,80
166,Sundar,Ande,SANDE,011.44.1346.629268,1987-08-22,SA_REP,6400,0.1,147,80
167,Amit,Banda,ABANDA,011.44.1346.729268,1987-08-23,SA_REP,6200,0.1,147,80
168,Lisa,Ozer,LOZER,011.44.1343.929268,1987-08-24,SA_REP,11500,0.25,148,80
169,Harrison,Bloom,HBLOOM,011.44.1343.829268,1987-08-25,SA_REP,10000,0.2,148,80
170,Tayler,Fox,TFOX,011.44.1343.729268,1987-08-26,SA_REP,9600,0.2,148,80
171,William,Smith,WSMITH,011.44.1343.629268,1987-08-27,SA_REP,7400,0.15,148,80
172,Elizabeth,Bates,EBATES,011.44.1343.529268,1987-08-28,SA_REP,7300,0.15,148,80
173,Sundita,Kumar,SKUMAR,011.44.1343.329268,1987-08-29,SA_REP,6100,0.1,148,80
174,Ellen,Abel,EABEL,011.44.1644.429267,1987-08-30,SA_REP,11000,0.3,149,80
175,Alyssa,Hutton,AHUTTON,011.44.1644.429266,1987-08-31,SA_REP,8800,0.25,149,80
177,Jack,Livingston,JLIVINGS,011.44.1644.429264,1987-09-02,SA_REP,8400,0.2,149,80
178,Kimberely,Grant,KGRANT,011.44.1644.429263,1987-09-03,SA_REP,7000,0.15,149,0
179,Charles,Johnson,CJOHNSON,011.44.1644.429262,1987-09-04,SA_REP,6200,0.1,149,80
180,Winston,Taylor,WTAYLOR,650.507.9876,1987-09-05,SH_CLERK,3200,0.0,120,50
181,Jean,Fleaur,JFLEAUR,650.507.9877,1987-09-06,SH_CLERK,3100,0.0,120,50
182,Martha,Sullivan,MSULLIVA,650.507.9878,1987-09-07,SH_CLERK,2500,0.0,120,50
183,Girard,Geoni,GGEONI,650.507.9879,1987-09-08,SH_CLERK,2800,0.0,120,50
184,Nandita,Sarchand,NSARCHAN,650.509.1876,1987-09-09,SH_CLERK,4200,0.0,121,50
185,Alexis,Bull,ABULL,650.509.2876,1987-09-10,SH_CLERK,4100,0.0,121,50
186,Julia,Dellinger,JDELLING,650.509.3876,1987-09-11,SH_CLERK,3400,0.0,121,50
187,Anthony,Cabrio,ACABRIO,650.509.4876,1987-09-12,SH_CLERK,3000,0.0,121,50
188,Kelly,Chung,KCHUNG,650.505.1876,1987-09-13,SH_CLERK,3800,0.0,122,50
189,Jennifer,Dilly,JDILLY,650.505.2876,1987-09-14,SH_CLERK,3600,0.0,122,50
190,Timothy,Gates,TGATES,650.505.3876,1987-09-15,SH_CLERK,2900,0.0,122,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0.0,122,50
192,Sarah,Bell,SBELL,650.501.1876,1987-09-17,SH_CLERK,4000,0.0,123,50
193,Britney,Everett,BEVERETT,650.501.2876,1987-09-18,SH_CLERK,3900,0.0,123,50
194,Samuel,McCain,SMCCAIN,650.501.3876,1987-09-19,SH_CLERK,3200,0.0,123,50
195,Vance,Jones,VJONES,650.501.4876,1987-09-20,SH_CLERK,2800,0.0,123,50
196,Alana,Walsh,AWALSH,650.507.9811,1987-09-21,SH_CLERK,3100,0.0,124,50
197,Kevin,Feeney,KFEENEY,650.507.9822,1987-09-22,SH_CLERK,3000,0.0,124,50
198,Donald,OConnell,DOCONNEL,650.507.9833,1987-09-23,SH_CLERK,2600,0.0,124,50
199,Douglas,Grant,DGRANT,650.507.9844,1987-09-24,SH_CLERK,2600,0.0,124,50
202,Pat,Fay,PFAY,603.123.6666,1987-09-27,MK_REP,6000,0.0,201,20
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110
206,William,Gietz,WGIETZ,515.123.8181,1987-10-01,AC_ACCOUNT,8300,0.0,205,110
|
What are the first names and department numbers for employees with last name McEwen? | SELECT first_name, department_id FROM employees WHERE last_name = 'McEwen' | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,410 | FIRST_NAME,DEPARTMENT_ID
Allan,80
|
What is all the information regarding employees with salaries above the minimum and under 2500? | SELECT * FROM employees WHERE salary BETWEEN (SELECT MIN(salary) FROM employees) AND 2500 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,496 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
119,Karen,Colmenares,KCOLMENA,515.127.4566,1987-07-06,PU_CLERK,2500,0,114,30
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0,120,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0,121,50
132,TJ,Olson,TJOLSON,650.124.8234,1987-07-19,ST_CLERK,2100,0,121,50
135,Ki,Gee,KGEE,650.127.1734,1987-07-22,ST_CLERK,2400,0,122,50
136,Hazel,Philtanker,HPHILTAN,650.127.1634,1987-07-23,ST_CLERK,2200,0,122,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0,123,50
144,Peter,Vargas,PVARGAS,650.121.2004,1987-07-31,ST_CLERK,2500,0,124,50
182,Martha,Sullivan,MSULLIVA,650.507.9878,1987-09-07,SH_CLERK,2500,0,120,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0,122,50
|
What are the employee ids of employees who report to Payam, and what are their salaries? | SELECT employee_id, salary FROM employees WHERE manager_id = (SELECT employee_id FROM employees WHERE first_name = 'Payam') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,478 | EMPLOYEE_ID,SALARY
133,3300
134,2900
135,2400
136,2200
188,3800
189,3600
190,2900
191,2500
|
display the emails of the employees who have no commission percentage and salary within the range 7000 to 12000 and works in that department which number is 50. | SELECT email FROM employees WHERE commission_pct = "null" AND salary BETWEEN 7000 AND 12000 AND department_id = 50 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,445 | EMAIL
|
display job ID for those jobs that were done by two or more for more than 300 days. | SELECT job_id FROM job_history WHERE end_date - start_date > 300 GROUP BY job_id HAVING COUNT(*) >= 2 | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,457 | JOB_ID
|
What are department ids for departments with managers managing more than 3 employees? | SELECT DISTINCT department_id FROM employees GROUP BY department_id, manager_id HAVING COUNT(employee_id) >= 4 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,452 | DEPARTMENT_ID
30
50
60
80
100
|
display the employee name ( first name and last name ) and hire date for all employees in the same department as Clara. | SELECT first_name, last_name, hire_date FROM employees WHERE department_id = (SELECT department_id FROM employees WHERE first_name = "Clara") | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,499 | FIRST_NAME,LAST_NAME,HIRE_DATE
John,Russell,1987-08-01
Karen,Partners,1987-08-02
Alberto,Errazuriz,1987-08-03
Gerald,Cambrault,1987-08-04
Eleni,Zlotkey,1987-08-05
Peter,Tucker,1987-08-06
David,Bernstein,1987-08-07
Peter,Hall,1987-08-08
Christopher,Olsen,1987-08-09
Nanette,Cambrault,1987-08-10
Oliver,Tuvault,1987-08-11
Janette,King,1987-08-12
Patrick,Sully,1987-08-13
Allan,McEwen,1987-08-14
Lindsey,Smith,1987-08-15
Louise,Doran,1987-08-16
Sarath,Sewall,1987-08-17
Clara,Vishney,1987-08-18
Danielle,Greene,1987-08-19
Mattea,Marvins,1987-08-20
David,Lee,1987-08-21
Sundar,Ande,1987-08-22
Amit,Banda,1987-08-23
Lisa,Ozer,1987-08-24
Harrison,Bloom,1987-08-25
Tayler,Fox,1987-08-26
William,Smith,1987-08-27
Elizabeth,Bates,1987-08-28
Sundita,Kumar,1987-08-29
Ellen,Abel,1987-08-30
Alyssa,Hutton,1987-08-31
Jonathon,Taylor,1987-09-01
Jack,Livingston,1987-09-02
Charles,Johnson,1987-09-04
|
display the full name (first and last name ) of employee with ID and name of the country presently where (s)he is working. | SELECT T1.first_name, T1.last_name, T1.employee_id, T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id JOIN countries AS T4 ON T3.country_id = T4.country_id | ``` yaml
countries: # name of dataframe
- COUNTRY_ID:
dtype: string
sample_values: AR, AU, BE
- COUNTRY_NAME:
dtype: string
sample_values: Argentina, Australia, Belgium
- REGION_ID:
dtype: int64
sample_values: 2, 3, 1
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
Column 'country_id' in dataframe 'locations' has a foreign key relation to column 'country_id' in dataframe 'countries'.
| 3,523 | FIRST_NAME,LAST_NAME,EMPLOYEE_ID,COUNTRY_NAME
Steven,King,100,United States of America
Neena,Kochhar,101,United States of America
Lex,De Haan,102,United States of America
Alexander,Hunold,103,United States of America
Bruce,Ernst,104,United States of America
David,Austin,105,United States of America
Valli,Pataballa,106,United States of America
Diana,Lorentz,107,United States of America
Nancy,Greenberg,108,United States of America
Daniel,Faviet,109,United States of America
John,Chen,110,United States of America
Ismael,Sciarra,111,United States of America
Jose Manuel,Urman,112,United States of America
Luis,Popp,113,United States of America
Den,Raphaely,114,United States of America
Alexander,Khoo,115,United States of America
Shelli,Baida,116,United States of America
Sigal,Tobias,117,United States of America
Guy,Himuro,118,United States of America
Karen,Colmenares,119,United States of America
Matthew,Weiss,120,United States of America
Adam,Fripp,121,United States of America
Payam,Kaufling,122,United States of America
Shanta,Vollman,123,United States of America
Kevin,Mourgos,124,United States of America
Julia,Nayer,125,United States of America
Irene,Mikkilineni,126,United States of America
James,Landry,127,United States of America
Steven,Markle,128,United States of America
Laura,Bissot,129,United States of America
Mozhe,Atkinson,130,United States of America
James,Marlow,131,United States of America
TJ,Olson,132,United States of America
Jason,Mallin,133,United States of America
Michael,Rogers,134,United States of America
Ki,Gee,135,United States of America
Hazel,Philtanker,136,United States of America
Renske,Ladwig,137,United States of America
Stephen,Stiles,138,United States of America
John,Seo,139,United States of America
Joshua,Patel,140,United States of America
Trenna,Rajs,141,United States of America
Curtis,Davies,142,United States of America
Randall,Matos,143,United States of America
Peter,Vargas,144,United States of America
Winston,Taylor,180,United States of America
Jean,Fleaur,181,United States of America
Martha,Sullivan,182,United States of America
Girard,Geoni,183,United States of America
Nandita,Sarchand,184,United States of America
Alexis,Bull,185,United States of America
Julia,Dellinger,186,United States of America
Anthony,Cabrio,187,United States of America
Kelly,Chung,188,United States of America
Jennifer,Dilly,189,United States of America
Timothy,Gates,190,United States of America
Randall,Perkins,191,United States of America
Sarah,Bell,192,United States of America
Britney,Everett,193,United States of America
Samuel,McCain,194,United States of America
Vance,Jones,195,United States of America
Alana,Walsh,196,United States of America
Kevin,Feeney,197,United States of America
Donald,OConnell,198,United States of America
Douglas,Grant,199,United States of America
Jennifer,Whalen,200,United States of America
Michael,Hartstein,201,Canada
Pat,Fay,202,Canada
Susan,Mavris,203,United Kingdom
Hermann,Baer,204,Germany
Shelley,Higgins,205,United States of America
William,Gietz,206,United States of America
|
Give the first name and job id for all employees in the Finance department. | SELECT T1.first_name, T1.job_id FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id WHERE T2.department_name = 'Finance' | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,494 | FIRST_NAME,JOB_ID
Nancy,FI_MGR
Daniel,FI_ACCOUNT
John,FI_ACCOUNT
Ismael,FI_ACCOUNT
Jose Manuel,FI_ACCOUNT
Luis,FI_ACCOUNT
|
What are the unique ids of those departments where any manager is managing 4 or more employees. | SELECT DISTINCT department_id FROM employees GROUP BY department_id, manager_id HAVING COUNT(employee_id) >= 4 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,487 | DEPARTMENT_ID
30
50
60
80
100
|
Return the full names and salaries of employees with null commissions. | SELECT first_name, last_name, salary FROM employees WHERE commission_pct = "null" | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,426 | FIRST_NAME,LAST_NAME,SALARY
|
display job title and average salary of employees. | SELECT job_title, AVG(salary) FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id = T2.job_id GROUP BY T2.job_title | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'job_id' in dataframe 'employees' has a foreign key relation to column 'job_id' in dataframe 'jobs'.
| 3,467 | JOB_TITLE,AVG(salary)
Accountant,7920.0
Accounting Manager,12000.0
Administration Assistant,4400.0
Administration Vice President,17000.0
Finance Manager,12000.0
Human Resources Representative,6500.0
Marketing Manager,13000.0
Marketing Representative,6000.0
President,24000.0
Programmer,5760.0
Public Accountant,8300.0
Public Relations Representative,10000.0
Purchasing Clerk,2780.0
Purchasing Manager,11000.0
Sales Manager,12200.0
Sales Representative,8350.0
Shipping Clerk,3215.0
Stock Clerk,2785.0
Stock Manager,7280.0
|
display the employee number, name( first name and last name ), and salary for all employees who earn more than the average salary and who work in a department with any employee with a 'J' in their first name. | SELECT employee_id, first_name, last_name, salary FROM employees WHERE salary > (SELECT AVG(salary) FROM employees) AND department_id IN (SELECT department_id FROM employees WHERE first_name LIKE '%J%') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,505 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,SALARY
108,Nancy,Greenberg,12000
109,Daniel,Faviet,9000
110,John,Chen,8200
111,Ismael,Sciarra,7700
112,Jose Manuel,Urman,7800
113,Luis,Popp,6900
120,Matthew,Weiss,8000
121,Adam,Fripp,8200
122,Payam,Kaufling,7900
123,Shanta,Vollman,6500
145,John,Russell,14000
146,Karen,Partners,13500
147,Alberto,Errazuriz,12000
148,Gerald,Cambrault,11000
149,Eleni,Zlotkey,10500
150,Peter,Tucker,10000
151,David,Bernstein,9500
152,Peter,Hall,9000
153,Christopher,Olsen,8000
154,Nanette,Cambrault,7500
155,Oliver,Tuvault,7000
156,Janette,King,10000
157,Patrick,Sully,9500
158,Allan,McEwen,9000
159,Lindsey,Smith,8000
160,Louise,Doran,7500
161,Sarath,Sewall,7000
162,Clara,Vishney,10500
163,Danielle,Greene,9500
164,Mattea,Marvins,7200
165,David,Lee,6800
168,Lisa,Ozer,11500
169,Harrison,Bloom,10000
170,Tayler,Fox,9600
171,William,Smith,7400
172,Elizabeth,Bates,7300
174,Ellen,Abel,11000
175,Alyssa,Hutton,8800
176,Jonathon,Taylor,8600
177,Jack,Livingston,8400
|
display all the information of the employees whose salary if within the range of smallest salary and 2500. | SELECT * FROM employees WHERE salary BETWEEN (SELECT MIN(salary) FROM employees) AND 2500 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,495 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID
119,Karen,Colmenares,KCOLMENA,515.127.4566,1987-07-06,PU_CLERK,2500,0,114,30
127,James,Landry,JLANDRY,650.124.1334,1987-07-14,ST_CLERK,2400,0,120,50
128,Steven,Markle,SMARKLE,650.124.1434,1987-07-15,ST_CLERK,2200,0,120,50
131,James,Marlow,JAMRLOW,650.124.7234,1987-07-18,ST_CLERK,2500,0,121,50
132,TJ,Olson,TJOLSON,650.124.8234,1987-07-19,ST_CLERK,2100,0,121,50
135,Ki,Gee,KGEE,650.127.1734,1987-07-22,ST_CLERK,2400,0,122,50
136,Hazel,Philtanker,HPHILTAN,650.127.1634,1987-07-23,ST_CLERK,2200,0,122,50
140,Joshua,Patel,JPATEL,650.121.1834,1987-07-27,ST_CLERK,2500,0,123,50
144,Peter,Vargas,PVARGAS,650.121.2004,1987-07-31,ST_CLERK,2500,0,124,50
182,Martha,Sullivan,MSULLIVA,650.507.9878,1987-09-07,SH_CLERK,2500,0,120,50
191,Randall,Perkins,RPERKINS,650.505.4876,1987-09-16,SH_CLERK,2500,0,122,50
|
What are the employee ids for those who had two or more jobs. | SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*) >= 2 | ``` yaml
job_history: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 102, 101, 101
- START_DATE:
dtype: datetime
sample_values: 1993-01-13, 1989-09-21, 1993-10-28
- END_DATE:
dtype: datetime
sample_values: 1998-07-24, 1993-10-27, 1997-03-15
- JOB_ID:
dtype: string
sample_values: IT_PROG, AC_ACCOUNT, AC_MGR
- DEPARTMENT_ID:
dtype: int64
sample_values: 60, 110, 110
``` | 3,486 | EMPLOYEE_ID
101
176
200
|
Return the phone numbers of employees with salaries between 8000 and 12000. | SELECT phone_number FROM employees WHERE salary BETWEEN 8000 AND 12000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,422 | PHONE_NUMBER
590.423.4567
515.124.4569
515.124.4169
515.124.4269
515.127.4561
650.123.1234
650.123.2234
011.44.1344.429278
011.44.1344.619268
011.44.1344.429018
011.44.1344.129268
011.44.1344.345268
011.44.1344.478968
011.44.1344.498718
011.44.1345.429268
011.44.1345.929268
011.44.1345.829268
011.44.1345.729268
011.44.1346.129268
011.44.1346.229268
011.44.1343.929268
011.44.1343.829268
011.44.1343.729268
011.44.1644.429267
011.44.1644.429266
011.44.1644.429265
011.44.1644.429264
515.123.8888
515.123.8080
515.123.8181
|
display the employee number and job id for all employees whose salary is smaller than any salary of those employees whose job title is MK_MAN. | SELECT employee_id, job_id FROM employees WHERE salary < (SELECT MIN(salary) FROM employees WHERE job_id = 'MK_MAN') | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,507 | EMPLOYEE_ID,JOB_ID
103,IT_PROG
104,IT_PROG
105,IT_PROG
106,IT_PROG
107,IT_PROG
108,FI_MGR
109,FI_ACCOUNT
110,FI_ACCOUNT
111,FI_ACCOUNT
112,FI_ACCOUNT
113,FI_ACCOUNT
114,PU_MAN
115,PU_CLERK
116,PU_CLERK
117,PU_CLERK
118,PU_CLERK
119,PU_CLERK
120,ST_MAN
121,ST_MAN
122,ST_MAN
123,ST_MAN
124,ST_MAN
125,ST_CLERK
126,ST_CLERK
127,ST_CLERK
128,ST_CLERK
129,ST_CLERK
130,ST_CLERK
131,ST_CLERK
132,ST_CLERK
133,ST_CLERK
134,ST_CLERK
135,ST_CLERK
136,ST_CLERK
137,ST_CLERK
138,ST_CLERK
139,ST_CLERK
140,ST_CLERK
141,ST_CLERK
142,ST_CLERK
143,ST_CLERK
144,ST_CLERK
147,SA_MAN
148,SA_MAN
149,SA_MAN
150,SA_REP
151,SA_REP
152,SA_REP
153,SA_REP
154,SA_REP
155,SA_REP
156,SA_REP
157,SA_REP
158,SA_REP
159,SA_REP
160,SA_REP
161,SA_REP
162,SA_REP
163,SA_REP
164,SA_REP
165,SA_REP
166,SA_REP
167,SA_REP
168,SA_REP
169,SA_REP
170,SA_REP
171,SA_REP
172,SA_REP
173,SA_REP
174,SA_REP
175,SA_REP
176,SA_REP
177,SA_REP
178,SA_REP
179,SA_REP
180,SH_CLERK
181,SH_CLERK
182,SH_CLERK
183,SH_CLERK
184,SH_CLERK
185,SH_CLERK
186,SH_CLERK
187,SH_CLERK
188,SH_CLERK
189,SH_CLERK
190,SH_CLERK
191,SH_CLERK
192,SH_CLERK
193,SH_CLERK
194,SH_CLERK
195,SH_CLERK
196,SH_CLERK
197,SH_CLERK
198,SH_CLERK
199,SH_CLERK
200,AD_ASST
202,MK_REP
203,HR_REP
204,PR_REP
205,AC_MGR
206,AC_ACCOUNT
|
Find the job ID for those jobs which average salary is above 8000. | SELECT job_id FROM employees GROUP BY job_id HAVING AVG(salary) > 8000 | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,489 | JOB_ID
AC_ACCOUNT
AC_MGR
AD_PRES
AD_VP
FI_MGR
MK_MAN
PR_REP
PU_MAN
SA_MAN
SA_REP
|
What is all the information regarding employees who are managers? | SELECT DISTINCT * FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id WHERE T1.employee_id = T2.manager_id | ``` yaml
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
| 3,482 | EMPLOYEE_ID,FIRST_NAME,LAST_NAME,EMAIL,PHONE_NUMBER,HIRE_DATE,JOB_ID,SALARY,COMMISSION_PCT,MANAGER_ID,DEPARTMENT_ID,DEPARTMENT_ID,DEPARTMENT_NAME,MANAGER_ID,LOCATION_ID
100,Steven,King,SKING,515.123.4567,1987-06-17,AD_PRES,24000,0.0,0,90,90,Executive,100,1700
103,Alexander,Hunold,AHUNOLD,590.423.4567,1987-06-20,IT_PROG,9000,0.0,102,60,60,IT,103,1400
108,Nancy,Greenberg,NGREENBE,515.124.4569,1987-06-25,FI_MGR,12000,0.0,101,100,100,Finance,108,1700
114,Den,Raphaely,DRAPHEAL,515.127.4561,1987-07-01,PU_MAN,11000,0.0,100,30,30,Purchasing,114,1700
121,Adam,Fripp,AFRIPP,650.123.2234,1987-07-08,ST_MAN,8200,0.0,100,50,50,Shipping,121,1500
145,John,Russell,JRUSSEL,011.44.1344.429268,1987-08-01,SA_MAN,14000,0.4,100,80,80,Sales,145,2500
200,Jennifer,Whalen,JWHALEN,515.123.4444,1987-09-25,AD_ASST,4400,0.0,101,10,10,Administration,200,1700
201,Michael,Hartstein,MHARTSTE,515.123.5555,1987-09-26,MK_MAN,13000,0.0,100,20,20,Marketing,201,1800
203,Susan,Mavris,SMAVRIS,515.123.7777,1987-09-28,HR_REP,6500,0.0,101,40,40,Human Resources,203,2400
204,Hermann,Baer,HBAER,515.123.8888,1987-09-29,PR_REP,10000,0.0,101,70,70,Public Relations,204,2700
205,Shelley,Higgins,SHIGGINS,515.123.8080,1987-09-30,AC_MGR,12000,0.0,101,110,110,Accounting,205,1700
|
display the country ID and number of cities for each country. | SELECT country_id, COUNT(*) FROM locations GROUP BY country_id | ``` yaml
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
``` | 3,455 | COUNTRY_ID,COUNT(*)
"""",1
AU,1
BR,1
CA,2
CH,2
CN,1
DE,1
IN,1
IT,2
JP,2
NL,1
Ox,1
SG,1
UK,2
US,4
|
What the full names, ids of each employee and the name of the country they are in? | SELECT T1.first_name, T1.last_name, T1.employee_id, T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id JOIN countries AS T4 ON T3.country_id = T4.country_id | ``` yaml
countries: # name of dataframe
- COUNTRY_ID:
dtype: string
sample_values: AR, AU, BE
- COUNTRY_NAME:
dtype: string
sample_values: Argentina, Australia, Belgium
- REGION_ID:
dtype: int64
sample_values: 2, 3, 1
departments: # name of dataframe
- DEPARTMENT_ID:
dtype: int64
sample_values: 10, 20, 30
- DEPARTMENT_NAME:
dtype: string
sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
dtype: int64
sample_values: 200, 201, 114
- LOCATION_ID:
dtype: int64
sample_values: 1700, 1800, 1700
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
locations: # name of dataframe
- LOCATION_ID:
dtype: int64
sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
dtype: string
sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
dtype: numeric
sample_values: 989, 10934, 1689
- CITY:
dtype: string
sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
dtype: string
sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
dtype: string
sample_values: IT, IT, JP
```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
Column 'country_id' in dataframe 'locations' has a foreign key relation to column 'country_id' in dataframe 'countries'.
| 3,524 | FIRST_NAME,LAST_NAME,EMPLOYEE_ID,COUNTRY_NAME
Steven,King,100,United States of America
Neena,Kochhar,101,United States of America
Lex,De Haan,102,United States of America
Alexander,Hunold,103,United States of America
Bruce,Ernst,104,United States of America
David,Austin,105,United States of America
Valli,Pataballa,106,United States of America
Diana,Lorentz,107,United States of America
Nancy,Greenberg,108,United States of America
Daniel,Faviet,109,United States of America
John,Chen,110,United States of America
Ismael,Sciarra,111,United States of America
Jose Manuel,Urman,112,United States of America
Luis,Popp,113,United States of America
Den,Raphaely,114,United States of America
Alexander,Khoo,115,United States of America
Shelli,Baida,116,United States of America
Sigal,Tobias,117,United States of America
Guy,Himuro,118,United States of America
Karen,Colmenares,119,United States of America
Matthew,Weiss,120,United States of America
Adam,Fripp,121,United States of America
Payam,Kaufling,122,United States of America
Shanta,Vollman,123,United States of America
Kevin,Mourgos,124,United States of America
Julia,Nayer,125,United States of America
Irene,Mikkilineni,126,United States of America
James,Landry,127,United States of America
Steven,Markle,128,United States of America
Laura,Bissot,129,United States of America
Mozhe,Atkinson,130,United States of America
James,Marlow,131,United States of America
TJ,Olson,132,United States of America
Jason,Mallin,133,United States of America
Michael,Rogers,134,United States of America
Ki,Gee,135,United States of America
Hazel,Philtanker,136,United States of America
Renske,Ladwig,137,United States of America
Stephen,Stiles,138,United States of America
John,Seo,139,United States of America
Joshua,Patel,140,United States of America
Trenna,Rajs,141,United States of America
Curtis,Davies,142,United States of America
Randall,Matos,143,United States of America
Peter,Vargas,144,United States of America
Winston,Taylor,180,United States of America
Jean,Fleaur,181,United States of America
Martha,Sullivan,182,United States of America
Girard,Geoni,183,United States of America
Nandita,Sarchand,184,United States of America
Alexis,Bull,185,United States of America
Julia,Dellinger,186,United States of America
Anthony,Cabrio,187,United States of America
Kelly,Chung,188,United States of America
Jennifer,Dilly,189,United States of America
Timothy,Gates,190,United States of America
Randall,Perkins,191,United States of America
Sarah,Bell,192,United States of America
Britney,Everett,193,United States of America
Samuel,McCain,194,United States of America
Vance,Jones,195,United States of America
Alana,Walsh,196,United States of America
Kevin,Feeney,197,United States of America
Donald,OConnell,198,United States of America
Douglas,Grant,199,United States of America
Jennifer,Whalen,200,United States of America
Michael,Hartstein,201,Canada
Pat,Fay,202,Canada
Susan,Mavris,203,United Kingdom
Hermann,Baer,204,Germany
Shelley,Higgins,205,United States of America
William,Gietz,206,United States of America
|
display the department ID, full name (first and last name), salary for those employees who is highest salary in every department. | SELECT first_name, last_name, salary, department_id, MAX(salary) FROM employees GROUP BY department_id | ``` yaml
employees: # name of dataframe
- EMPLOYEE_ID:
dtype: int64
sample_values: 100, 101, 102
- FIRST_NAME:
dtype: string
sample_values: Steven, Neena, Lex
- LAST_NAME:
dtype: string
sample_values: King, Kochhar, De Haan
- EMAIL:
dtype: string
sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
dtype: string
sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
dtype: datetime
sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
dtype: int64
sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
dtype: float64
sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
dtype: int64
sample_values: 0, 100, 100
- DEPARTMENT_ID:
dtype: int64
sample_values: 90, 90, 90
``` | 3,515 | FIRST_NAME,LAST_NAME,SALARY,DEPARTMENT_ID,MAX(salary)
Kimberely,Grant,7000,0,7000
Jennifer,Whalen,4400,10,4400
Michael,Hartstein,13000,20,13000
Den,Raphaely,11000,30,11000
Susan,Mavris,6500,40,6500
Adam,Fripp,8200,50,8200
Alexander,Hunold,9000,60,9000
Hermann,Baer,10000,70,10000
John,Russell,14000,80,14000
Steven,King,24000,90,24000
Nancy,Greenberg,12000,100,12000
Shelley,Higgins,12000,110,12000
|
Which job titles correspond to jobs with salaries over 9000? | SELECT job_title FROM jobs WHERE min_salary > 9000 | ``` yaml
jobs: # name of dataframe
- JOB_ID:
dtype: string
sample_values: AD_PRES, AD_VP, AD_ASST
- JOB_TITLE:
dtype: string
sample_values: President, Administration Vice President, Administration Assistant
- MIN_SALARY:
dtype: int64
sample_values: 20000, 15000, 3000
- MAX_SALARY:
dtype: int64
sample_values: 40000, 30000, 6000
``` | 3,442 | JOB_TITLE
President
Administration Vice President
Sales Manager
|
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