Chapter 9 Larsen &Alteryx Appendix F
Joins and Unions
Join combines 2 datasets
Union is based on assumption that there are multiple columns between A and C(example)
tables->matracies
Inner Join
Outer Join
Left outer
right outer
full outer join
Dealing with carefully curated databases
as much info about a customer as possible
Customers table
Reduces the version of data so that is can be used for machine learning
combines data into one data set and not just adding it to the end
sharing similar data
testing for duplicate records
imputation is replacing missing data of a column with reasonable assumptions
Critical for high preforming machine learning
Boolean Expressions
A = B is normal equals expression
A != B is a doesn't equal N
A > B a is greater than B
A >+ B A is greater than or equal to B
A, A is true
NOT A, A is false
A IN B A is in the set of B things
A NOT IN B, A is not in the set of B things
A OR B, Expression A is True or expression B is True or both expressions are True
A AND B, expression A is True and expression B is True
A OR (B AND C), expression A is True or expression B and C are both Tue or expression A and expression B and C are True
A AND (B OR C), Expression A is True and expression B, expression C, or expressions B and C are True