Iv. NumPy Basics: Array Operations

First, the vector operation

• Arrays perform bulk arithmetic operations on each element without looping, a process called vectorization, which is the practice of replacing loops with array expressions.
• The vectorized array performs one or two data levels faster than a pure Python method.
• Any arithmetic operation between two arrays of equal size will apply their operations to the action at the element level.

element-level operations: in NumPy, an operation between an array of equal size, an element-level operation, which is used only between elements of the same position, results in a new array, with the result of the operation having the same position as the operand.

Type	Function	Operator	Description
Base operations	Add (A,b[,c])	C=a+b	Addition
	Subtract (A,b[,c])	C=a-b	Subtraction
	Multiply (A,b[,c])	C=a*b	Multiplication
	Divide (A,b[,c])	C=a/b	Division (in Python3, divide is the same as true_divide usage)
	Floor_divide (A,b[,c])	c=a//b	Division, the result is rounded down
	Negative (A[,c])	C=-a	Take counter
	Power (A,b[,c])	C=a**b	A's B-square
	Remainder (A,b[,c])	C=a%b	Modulo (can also be written mod (A,B[,C)))
	Fmod (A,b[,c])	None	Signed modulo, plus or minus a decision
Comparison operation	Equal (A,b[,c])	C=a==b	Comparison result C is a Boolean array
	Not_equal (A,b[,c])	C=a!=b
	Less (A,b[,c])	C=a<b
	Less_equal (A,b[,c])	C=a<=b
	Greater (A,b[,c])	C=a>b
	Greater_equal (A,b[,c])	C=a>=b
Statistical operations	SUM (A[,axis,c])	None	Sum: Statistical dimensions can be defined by axis parameters
	Min (A[,axis,c])	None	Minimum: Statistical dimensions can be defined by axis parameters
	Max (A[,axis,c])	None	Maximum: Statistical dimensions can be defined by axis parameters
	Mean (A[,axis,c])	None	Average: Statistical dimensions can be defined by axis parameters
	STD (A[,axis,c])	None	Standard deviation: Statistical dimensions can be defined by axis parameters
	var (a[,axis,c])	None	Variance: Statistical dimensions can be defined by axis parameters
	Median (A[,axis,c])	None	Median: Statistical dimensions can be defined by axis parameters
	PTP (A[,axis,c])	None	Extreme difference: The difference between the minimum value of the maximum value
	diff (A[,axis,c])	None	Discrete difference: An array of difference values of adjacent array elements
Trigonometric operations	Sin (a[,axis,c]), Arcsin (A[,axis,c])	None	Sine (inverse chord)
	cos (a[,axis,c]), Arccos (A[,axis,c])	None	Cosine (Inverse cosine)
	Tan (A[,axis,c]), arctan (A[,axis,c])	None	Tangent (Arc tangent)
	Sinh (A[,axis,c]), Arcsinh (A[,axis,c])	None	Hyperbolic sine (inverse hyperbolic sine)
	Cosh (A[,axis,c]), Arccosh (A[,axis,c])	None	Hyperbolic cosine (inverse hyperbolic cosine)
	Tanh (A[,axis,c]), Arctanh (A[,axis,c])	None	Hyperbolic tangent (inverse hyperbolic tangent)
Logarithmic operations	Log (A[,axis,c])	None	The pair value of base a of E
	LOG2 (A[,axis,c])	None	Pairs of values with base a of 2
	LOG10 (A[,axis,c])	None	Pairs of values with base a of 10
	LOG1P (A[,axis,c])	None	The pairs of values with E as the base a+1

Second, built-in functions

Iv. NumPy Basics: Array Operations