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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

EMPLOYEES.csv

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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

EMPLOYEES.csv

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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

EMPLOYEES.csv

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Python Code Editor:


Structure of HR database :

HR database

More to Come !

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Python: Tips of the Day

Understanding slice notation:

It's pretty simple really:

a[start:stop]  # items start through stop-1
a[start:]      # items start through the rest of the array
a[:stop]       # items from the beginning through stop-1
a[:]           # a copy of the whole array

There is also the step value, which can be used with any of the above:

a[start:stop:step] # start through not past stop, by step

The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).

The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:

a[-1]    # last item in the array
a[-2:]   # last two items in the array
a[:-2]   # everything except the last two items

Similarly, step may be a negative number:

a[::-1]    # all items in the array, reversed
a[1::-1]   # the first two items, reversed
a[:-3:-1]  # the last two items, reversed
a[-3::-1]  # everything except the last two items, reversed

Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.

Relation to slice() object

The slicing operator [] is actually being used in the above code with a slice() object using the : notation (which is only valid within []), i.e.:

a[start:stop:step]

is equivalent to:

a[slice(start, stop, step)]

Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

While the : -based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.

Ref: https://bit.ly/2MHaTp7