Answer:
d. integrity
Explanation:
Data integrity is defined as the condition in which all of the data in the database are consistent with the real-world events and conditions.
Data integrity can be used to describe a state, a process or a function – and is often used as a proxy for “data quality”. Data with “integrity” is said to have a complete or whole structure. Data integrity is imposed within a database when it is designed and is authenticated through the ongoing use of error checking and validation routines. As a simple example, to maintain data integrity numeric columns/cells should not accept alphabetic data.
Answer:
# the dog dataframe has been loaded as mpr
# select the dogs where Age is greater than 2
greater_than_2 = mpr [mpr. age > 2]
print(greater_than_2)
# select the dogs whose status is equal to 'still missing'
still_missing = mpr[mpr. status == 'Still Missing']
print(still_missing)
# select all dogs whose dog breed is not equal to Poodle
not_poodle = mpr [mpr.breed != 'Poodle']
print(not_poodle)
Explanation:
The pandas dataframe is a tabular data structure that holds data in rows and columns like a spreadsheet. It is used for statistical data analysis and visualization.
The three program statements above use python conditional statements and operators to retrieve rows matching a given value or condition.
