Pandas HR database: Count the NaN values of all the columns of locations file

Pandas HR database Queries: Exercise-14 with Solution

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

Sample Solution :

Python Code :

import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
employees = 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(r"COUNTRIES.csv")
regions = pd.read_csv(r"REGIONS.csv")
locations = pd.read_csv(r"LOCATIONS.csv")
print("\nNaN values of all the columns of locations file:" )

Sample Output:

NaN values of all the columns of locations file:
location_id       0
street_address    0
postal_code       1
city              0
state_province    6
country_id        0
dtype: int64

Click to view the table contain:

Employees Table

Departments Table

Countries Table

Job_History Table

Jobs Table

Locations Table

Regions Table

Python Code Editor:

Structure of HR database :

HR database

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Previous: Write a Pandas program to create a boolean series selecting rows with one or more nulls from locations file.
Next: 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'.

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Python: Tips of the Day

Python: The Zip() Function

>>> students = ('John', 'Mary', 'Mike')
>>> ages = (15, 17, 16)
>>> scores = (90, 88, 82, 17, 14)
>>> for student, age, score in zip(students, ages, scores):
...     print(f'{student}, age: {age}, score: {score}')
John, age: 15, score: 90
Mary, age: 17, score: 88
Mike, age: 16, score: 82
>>> zipped = zip(students, ages, scores)
>>> a, b, c = zip(*zipped)
>>> print(b)
(15, 17, 16)