python ndarray Array Object Characteristics and Instance Sharing

1. numpy arrays are homogeneous arrays, that is, all elements must have the same data type.

2. ndarray Array 1 generally requires that all elements have the same data type, the subscript starts from 0, and the subscript of the last element is the array length minus 1.

Instances

import numpy as np
 
a = np.arange(0, 5, 1)
print(a)
b = np.arange(0, 10, 2)
print(b)

Expansion of knowledge points:

Defining Array

>>> import numpy as np
>>> m = np.array([[1,2,3], [2,3,4]])    # Defines a matrix, int64
>>> m
array([[1, 2, 3],
    [2, 3, 4]])
>>> m = np.array([[1,2,3], [2,3,4]], dtype=np.float)  # Definition Matrix ,float64
>>> m
array([[1., 2., 3.],
    [2., 3., 4.]])
>>> print(m.dtype)  # Data type   
float64
>>> print(m.shape)  # Shape 2 Row 3 Column 
(2, 3)
>>> print(m.ndim)   # Dimension 
2
>>> print(m.size)   # Number of elements 
6
>>> print(type(m))
<class 'numpy.ndarray'>

There are also one special way to define a matrix

>>> m = np.zeros((2,2))     # All 0
>>> m
array([[0., 0.],
    [0., 0.]])
>>> print(type(m))        # Also ndarray Type 
<class 'numpy.ndarray'>
>>> m = np.ones((2,2,3))    # All 1
>>> m = np.full((3,4), 7)    # All for 7
>>> np.eye(3)          # Unity matrix 
array([[1., 0., 0.],
    [0., 1., 0.],
    [0., 0., 1.]])
>>> np.arange(20).reshape(4,5)  # Generate 1 A 4 Row 5 Array of columns 
>>>
>>> np.random.random((2,3))    #[0,1) Random number 
array([[0.51123127, 0.40852721, 0.26159126],
    [0.42450279, 0.34763668, 0.06167501]])
>>> np.random.randint(1,10,(2,3))  #[1,10) Of random integers 2 Row 3 Column array 
array([[5, 4, 9],
    [2, 5, 7]])
>>> np.random.randn(2,3)       # Normal random distribution 
array([[-0.29538656, -0.50370707, -2.05627716],
    [-1.50126655, 0.41884067, 0.67306605]])
>>> np.random.choice([10,20,30], (2,3))   # Random selection 
array([[10, 20, 10],
    [30, 10, 20]])
>>> np.random.beta(1,10,(2,3))       # Beta distribution 
array([[0.01588963, 0.12635485, 0.22279098],
    [0.08950147, 0.02244569, 0.00953366]])

Operational array

>>> from numpy import *
>>> a1=array([1,1,1])  # Definition 1 Array of numbers 
>>> a2=array([2,2,2])
>>> a1+a2        # For addition of elements 
array([3, 3, 3])
>>> a1*2         # Multiplication 1 Number 
array([2, 2, 2])

##
>>> a1=np.array([1,2,3])
>>> a1
array([1, 2, 3])
>>> a1**3       # Indicates doing cubes for each number in an array 
array([ 1, 8, 27])

## Value, notice that it is based on the 0 Is the starting coordinate, no matlab Different 
>>> a1[1]
2

## Defining multidimensional arrays 
>>> a3=np.array([[1,2,3],[4,5,6]])
>>> a3
array([[1, 2, 3],
    [4, 5, 6]])
>>> a3[0]       # Take out the diaphragm 1 Row data 
array([1, 2, 3])
>>> a3[0,0]      # No. 1 1 Line number 1 Data 
1
>>> a3[0][0]     # It can also be used in this way 
1
>>> a3
array([[1, 2, 3],
    [4, 5, 6]])
>>> a3.sum(axis=0)   # Add by row, the columns remain unchanged 
array([5, 7, 9])
>>> a3.sum(axis=1)   # Add by column, row unchanged 
array([ 6, 15])