Informatics Practices
Assertion. In a dataset, there can be missing values that cannot contribute to any computation.
Reason. In a dataset, NULL, NaN or None are considered the missing values.
- Both A and R are true and R is the correct explanation of A.
- Both A and R are true but R is not the correct explanation of A.
- A is true but R is false.
- A is false but R is true.
Python Pandas
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Answer
Both A and R are true and R is the correct explanation of A.
Explanation
Missing values are the values that cannot contribute to any computation or we can say that missing values are the values that carry no computational significance. In a dataset, NULL, NaN or None are considered the missing values.
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Related Questions
Assertion. A quantile refers to equally distributed portion of a data set.
Reason. A median divides a distribution in 2 quantiles while a quartile divides a distribution in 4 quantiles.
- Both A and R are true and R is the correct explanation of A.
- Both A and R are true but R is not the correct explanation of A.
- A is true but R is false.
- A is false but R is true.
Assertion. Data aggregation produces a summary statistics of a dataset.
Reason. Data aggregation summarizes data using statistical aggregation functions.
- Both A and R are true and R is the correct explanation of A.
- Both A and R are true but R is not the correct explanation of A.
- A is true but R is false.
- A is false but R is true.
Assertion (A). The output of addition of two series will be NaN, if one of the elements or both the elements have no value(s).
Reason (R). While performing mathematical operations on a series, by default all missing values are filled in with 0.
- Both A and R are true and R is the correct explanation of A.
- Both A and R are true but R is not the correct explanation of A.
- A is true but R is false.
- A is false but R is true.
Name the function to iterate over a DataFrame horizontally.