Hiding/masking personal identifiers from a data set, so that the data set can never identify an individual, even if it is correlated with other data sets is known as anonymization.
<h3>What is anonymization?</h3>
The term anonymization is known as data masking and it is the standard solution in the case of data pseudonymisation. It is generally recognised by using masking and data is de- sensitised also that privacy could be maintained and private information remains safe for the support.
Data is generally identified by using masking and data is de- sensitised also that privacy could be maintained and private information remains safe for the support.
Therefore, Hiding/masking personal identifiers from a data set, so that the data set can never identify an individual, even if it is correlated with other data sets is known as anonymization.
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Answer:
import numpy as np
def sample_median(n, P):
return np.median( np.random.choice ( np.arange (1, len(P) + 1 ), n, p = P ) )
print(sample_median(10,[0.1 0.2 0.1 0.3 0.1 0.2]))
print(sample_median(10,[0.1 0.2 0.1 0.3 0.1 0.2]))
print(sample_median(5, [0.3,0.7])
print(sample_median(5, [0.3,0.7])
Explanation:
- Bring in the numpy library to use the median function provided by the numpy library.
- Define the sample_median function that takes in 2 parameters and returns the median with the help of built-in random, choice and arrange functions.
- Call the sample_median function by providing some values to test and then display the results.
Output:
4.5
4.0
2.0
1.0
Answer:
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The SQL keyword that is often used to retrieve a minimum value from an attribute in a table is the MIN function.
<h3>What is the MIN Function? </h3>
The MIN Function is known to be a group found under Excel Statistical functions.
Note that the MIN often return the minimum value in any list of arguments and as such The SQL keyword that is often used to retrieve a minimum value from an attribute in a table is the MIN function.
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