Pandas SQL Query: Exercises, Practice, Solution

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Pandas HR database Query [24 exercises with solution]

Click to see Structure of HR database

1. Write a Pandas program to display all the records of REGIONS file. Go to the editor

REGION.csv

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2. Write a Pandas program to display all the location id from locations file. Go to the editor

LOCATIONS.csv

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3. Write a Pandas program to extract first 7 records from employees file. Go to the editor

EMPLOYEES.csv

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4. Write a Pandas program to select distinct department id from employees file. Go to the editor

DEPARTMENTS.csv

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5. Write a Pandas program to display the first and last name, and department number for all employees whose last name is "McEwen". Go to the editor

EMPLOYEES.csv

DEPARTMENTS.csv

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6. Write a Pandas program to display the first, last name, salary and department number for those employees whose first name starts with the letter 'S'. Go to the editor

EMPLOYEES.csv

DEPARTMENTS.csv

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7. Write a Pandas program to display the first, last name, salary and department number for those employees whose first name does not contain the letter 'M'. Go to the editor

DEPARTMENTS.csv

EMPLOYEES.csv

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8. Write a Pandas program to display the first name, last name, salary and department number in ascending order by department number. Go to the editor

DEPARTMENTS.csv

EMPLOYEES.csv

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9. Write a Pandas program to display the first name, last name, salary and department number in descending order by first name. Go to the editor

DEPARTMENTS.csv

EMPLOYEES.csv

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10. Write a Pandas program to display the first name, last name, salary and manger id where manager ids are null. Go to the editor

EMPLOYEES.csv

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11. Write a Pandas program to display the first name, last name, salary and manger id where manager ids are not null. Go to the editor

EMPLOYEES.csv

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12. Write a Pandas program to create and display a boolean series, where True for not null and False for null values or missing values in state_province column of locations file. Go to the editor

LOCATIONS.csv

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13. Write a Pandas program to create a boolean series selecting rows with one or more nulls from locations file. Go to the editor

LOCATIONS.csv

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14. Write a Pandas program to count the NaN values of all the columns of locations file. Go to the editor

LOCATIONS.csv

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15. Write a Pandas program to display the first name, last name, salary and department number for those employees whose first name ends with the letter 'm'. Go to the editor

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16. Write a Pandas program to display the first name, last name, salary and department number for those employees whose first name ends with the letter 'd' or 'n' or 's' and also arrange the result in descending order by department id. Go to the editor

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17. Write a Pandas program to display the first name, last name, salary and department number for employees who works either in department 70 or 90. Go to the editor

EMPLOYEES.csv

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18. Write a Pandas program to display the first name, last name, salary and department number for those employees whose managers are hold the ID 120, 103 or 145. Go to the editor

EMPLOYEES.csv

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19. Write a Pandas program to display the first, last name, salary and department number for those employees who holds a letter n as a 3rd character in their first name. Go to the editor

EMPLOYEES.csv

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20. Write a Pandas program to display the first name, job id, salary and department for those employees not working in the departments 50,30 and 80. Go to the editor

EMPLOYEES.csv

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21. Write a Pandas program to display the ID for those employees who did two or more jobs in the past. Go to the editor

JOB_HISTORY.csv

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22. Write a Pandas program to calculate minimum, maximum and mean salary from employees file. Go to the editor

EMPLOYEES.csv

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23. Write a Pandas program to display the details of jobs in descending sequence on job title. Go to the editor

JOBS.csv

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24. Write a Pandas program to display the first and last name and date of joining of the employees who is either Sales Representative or Sales Man. Go to the editor

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Python Code Editor:

Structure of HR database :

More to Come !

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