Numpy Foundation

        NumPy(Numerical Python) yes Python An extended library of language , Support a large number of dimension arrays and matrix operations , In addition, it also provides a large number of mathematical function libraries for array operation , Mainly used for array calculation .

        NumPy install ： stay cmd The input terminal pip install numpy. In the use of Numpy Before , adopt import The order will numpy Library import , And named it np, for example ：import numpy as np.

Catalog

Create an array from a list

One dimensional array

Two dimensional array

Create an array by function

Operation of array

Arrays and scalars ( The set of real Numbers ) Operation between

Operations between arrays of the same dimension

Operations between arrays of different dimensions

  Index of array

Axis

One dimensional array index

  Two dimensional array index

  Boolean index

Ternary operator

function

The generic function

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Create an array from a list

One dimensional array

We can first define a list , And then through np.array() Function to convert data types and define a one-dimensional array .

data=[6,7.5,8,0,1]     #  Create a list of   
arr=np.array(data)     #  Convert to a one-dimensional array  

Two dimensional array

First define a list of nested list elements , And then through np.array() Function to perform data type conversion and define a two-dimensional array .

data=[[1,2,3,4],[5,6,7,8]]    #  Create nested sequences 
arr=np.array(data)            #  Convert to 2D array 

Create an array by function

utilize numpy The functions provided are convenient to create some specific arrays , The details are as follows ：

function	meaning
np.zeros()	Create specified dimension full 0 Multidimensional arrays .
np.ones()	Create specified dimension full 1 Multidimensional arrays .
np.eye()	Create the identity matrix of the specified dimension .
np.arange()	Creates a specified number of sequential arrays ( Default 0 Start ).
linspace()	Create a one-dimensional array with a specified range , And divided into several equal parts .
reshape()	Change the dimension of the array

Operation of array

Arrays and scalars ( The set of real Numbers ) Operation between

arr=np.array([[1,2,3],[4,5,6]])    # Create a 2D array

arr+2	arr-2	arr*2	arr/2
array([[3,4,5],        [6,7,8]])	array([[-1,0,1],        [2,3,4]])	array([[2,4,6],        [8,10,12]])	array([[0.5,1.,1.5],        [2.,2.5,3.]])

Operations between arrays of the same dimension

arr1=np.array([[1,2,3],[4,5,6]])

arr2=np.array([[2,2,2],[2,2,2]])

arr1+arr2	arr1-arr2	arr1*arr2	arr1/arr2
array([[3,4,5],        [6,7,8]])	array([[-1,0,1],        [2,3,4]])	array([[2,4,6],        [8,10,12]])	array([[0.5,1.,1.5],        [2.,2.5,3.]])

Operations between arrays of different dimensions

When calculating arrays of different dimensions , Arrays have broadcast properties , That is, automatically expand the dimension .

  Index of array

contain n The index of the array of elements is 0,1,…,n-1.

Axis

stay numpy Can be understood as direction , Use 0,1,2... Digital representation , For a one-dimensional array , only one 0 Axis , about 2 Dimension group (shape(2,2)), Yes 0 Axis and 1 Axis , For three-dimensional arrays (shape(2,2, 3)), Yes 0,1,2 Axis .

One dimensional array index

  Two dimensional array index

  Boolean index

Ternary operator

Ternary operator np.where( expression , Count 1, Count 2), When the expression is true , The result is a number 1, Otherwise, the result is a number 2.

function

The generic function

A general function is a function of array A function that performs element-level operations on the data in . You can think of it as a vectorization wrapper for simple functions .

One variable function

function	explain
Abs、fabs	Calculate the whole number 、 The absolute value of a floating-point number or negative number , For non complex values , You can use faster fabs
sqrt	Calculate the square root of each element , amount to arr**0.5
Square	Calculate the square of each element . amount to arr **2
exp	Calculate the index of each element e*
log、log10、log2、log1p	They are natural logarithm （ The base number is e)、 The base number is 10 Of log、 The base number is 2 Of log、log(1+x)
sign	Calculate the sign of each element :1( Positive numbers )、o( zero )、 One 1( negative )
Cell	Calculate the ceiling value , That is, the smallest integer greater than or equal to the value
floor	Calculate the floor value , That is, the maximum integer less than or equal to this value

Dual function

function	explain
add	Add the corresponding elements in the array
subtract	Subtracts the elements of the second array from the first array
multiply	Array element multiplication
divide、floor_dvide	Division or downward rounding Division （ Discard the remainder )
power	For the elements in the first array A, According to the corresponding element in the second array B, meter . count AB
maximum、 fmax	Calculation of the maximum value at the element level .fmax Will ignore NaN
minimum、fmin	Calculation of minimum value at element level .fmin Will ignore NaN
mod	Modular computation at the element level （ The remainder of division )
copysign	Assign the value in the second array to the value in the first array