Question I: These questions are based on Human Resources (HR) database given in site https://www.w3resource.com/python-exercises/pandas/index.php. This site includes Pandas exercises, practice facilities and solutions of some exercises. You can look at these exercises before solving the following questions. CSV files in HR database can be found in assignment's attachments (HRDatabase.rar). First, generate a data frame for each of tables in HR Database as follows: import pandas as pd pd.set option('display.max rows', 500) pd.set option ('display.max columns', 500) employees = pd. read csv ("EMPLOYEES CSV departments = pd. read csv ("DEPARTMENTS.CSV") job history = pd. read csv (r" JOB HISTORY.Cav") jobs = pd. read csv (r"JOBS.csv") countries = pd. read csv ("COUNTRIES.csv") regions = pd.read.csv ("REGIONS.css" locations = pd. read csv (r"LOCATIONS.css" a. Display the first name, last name, salary, and department number for those employees # who work in departments with ids 30, 50 or 80. b. Merge/Join data frames employees and departments using their common column department id. Store the result in a new data frame called emp_dept. Find the minimum, maximum and mean salaries of employees in each department (use empt dept). salary min max mean department_name Accounting 8300 12000 Administration 4400 4400 Executive 17000 24000 Finance 6900 12000 Human Resources 6500 6500 10150.000000 4400.000000 19333.333333 8600.000000 6500 000000 (5000, d. Find mean salaries of employees grouped by country_id, city, # in ranges (0, 5000] 10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.) salary
Question I: These questions are based on Human Resources (HR) database given in site https://www.w3resource.com/python-exercises/pandas/index.php. This site includes Pandas exercises, practice facilities and solutions of some exercises. You can look at these exercises before solving the following questions. CSV files in HR database can be found in assignment's attachments (HRDatabase.rar). First, generate a data frame for each of tables in HR Database as follows: import pandas as pd pd.set option('display.max rows', 500) pd.set option ('display.max columns', 500) employees = pd. read csv ("EMPLOYEES CSV departments = pd. read csv ("DEPARTMENTS.CSV") job history = pd. read csv (r" JOB HISTORY.Cav") jobs = pd. read csv (r"JOBS.csv") countries = pd. read csv ("COUNTRIES.csv") regions = pd.read.csv ("REGIONS.css" locations = pd. read csv (r"LOCATIONS.css" a. Display the first name, last name, salary, and department number for those employees # who work in departments with ids 30, 50 or 80. b. Merge/Join data frames employees and departments using their common column department id. Store the result in a new data frame called emp_dept. Find the minimum, maximum and mean salaries of employees in each department (use empt dept). salary min max mean department_name Accounting 8300 12000 Administration 4400 4400 Executive 17000 24000 Finance 6900 12000 Human Resources 6500 6500 10150.000000 4400.000000 19333.333333 8600.000000 6500 000000 (5000, d. Find mean salaries of employees grouped by country_id, city, # in ranges (0, 5000] 10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.) salary
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Using python (pycharm)
Please solve all parts i am in need please )
![10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.)
salary
salary
(0, 5000] (5000, 10000] (10000, 15000] (15000, 25000]
country_id
city
CA
Toronto
6000.000000 13000.000000
0.000000
DE
Munich
0 10000.000000
0.000000
0.000000
London
6500.000000
0.000000
0.000000
UK
Oxford
O
8096.153846
11750.000000
0.000000
Seattle
7983.333333
11666.666667
19333.333333
3050
3000 7280.000000
US South San Francisco
0.000000
0.000000
Southlake
4600
7500.000000
0.000000
0.000000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F83065055-7170-4985-b044-e96855d4403d%2Fed6fea68-4b81-4247-b92a-484fdc3469e7%2F355gg8_processed.png&w=3840&q=75)
Transcribed Image Text:10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.)
salary
salary
(0, 5000] (5000, 10000] (10000, 15000] (15000, 25000]
country_id
city
CA
Toronto
6000.000000 13000.000000
0.000000
DE
Munich
0 10000.000000
0.000000
0.000000
London
6500.000000
0.000000
0.000000
UK
Oxford
O
8096.153846
11750.000000
0.000000
Seattle
7983.333333
11666.666667
19333.333333
3050
3000 7280.000000
US South San Francisco
0.000000
0.000000
Southlake
4600
7500.000000
0.000000
0.000000
![Question I: These questions are based on Human Resources (HR) database given in site
https://www.w3resource.com/python-exercises/pandas/index.php. This site includes Pandas exercises,
practice facilities and solutions of some exercises. You can look at these exercises before solving the
following questions. CSV files in HR database can be found in assignment's attachments
(HRDatabase.rar). First, generate a data frame for each of tables in HR Database as follows:
import pandas as pd
pd.set option('display.max rows', 500)
employees
=
pd.set option ('display.max columns', 500)
pd. read csv (r"EMPLOYEES.csv")
departments = pd. read csv (r"DEPARTMENTS.CSV")
job history pd. read csv (r"JOB HISTORY.CSV")
jobs = pd. read csv (r"JOBS.csv") countries =
pd. read csv ("COUNTRIES.csv") regions =
pd. read csv (r"REGIONS.csv")
=
locations = pd. read csv (r"LOCATIONS.csv")
a. Display the first name, last name, salary, and department number for those employees #who
work in departments with ids 30, 50 or 80.
b. Merge/Join data frames employees and departments using their common column
department id. Store the result in a new data frame called emp_dept.
Find the minimum, maximum and mean salaries of employees in each department (use
empt dept).
salary
min
max
mean
department_name
Accounting
Administration
Executive
Finance
Human Resources
8300 12000
4400 4400
17000 24000
6900 12000
6500
6500
10150.000000
4400.000000
19333.333333
8600.000000
6500.000000
(5000,
d. Find mean salaries of employees grouped by country_id, city, # in ranges (0, 5000]
10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.)
salary
ة](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F83065055-7170-4985-b044-e96855d4403d%2Fed6fea68-4b81-4247-b92a-484fdc3469e7%2Fj9np3r_processed.png&w=3840&q=75)
Transcribed Image Text:Question I: These questions are based on Human Resources (HR) database given in site
https://www.w3resource.com/python-exercises/pandas/index.php. This site includes Pandas exercises,
practice facilities and solutions of some exercises. You can look at these exercises before solving the
following questions. CSV files in HR database can be found in assignment's attachments
(HRDatabase.rar). First, generate a data frame for each of tables in HR Database as follows:
import pandas as pd
pd.set option('display.max rows', 500)
employees
=
pd.set option ('display.max columns', 500)
pd. read csv (r"EMPLOYEES.csv")
departments = pd. read csv (r"DEPARTMENTS.CSV")
job history pd. read csv (r"JOB HISTORY.CSV")
jobs = pd. read csv (r"JOBS.csv") countries =
pd. read csv ("COUNTRIES.csv") regions =
pd. read csv (r"REGIONS.csv")
=
locations = pd. read csv (r"LOCATIONS.csv")
a. Display the first name, last name, salary, and department number for those employees #who
work in departments with ids 30, 50 or 80.
b. Merge/Join data frames employees and departments using their common column
department id. Store the result in a new data frame called emp_dept.
Find the minimum, maximum and mean salaries of employees in each department (use
empt dept).
salary
min
max
mean
department_name
Accounting
Administration
Executive
Finance
Human Resources
8300 12000
4400 4400
17000 24000
6900 12000
6500
6500
10150.000000
4400.000000
19333.333333
8600.000000
6500.000000
(5000,
d. Find mean salaries of employees grouped by country_id, city, # in ranges (0, 5000]
10000] (10000, 15000] (15000, 25000]. (First, merge/join locations and empt dept.)
salary
ة
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