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