Installation and creation of numpy

install

1. Open cmd as an administrator

2. Enter the command to install numpy plug-in

pip install numpy
  •   Enter the above command in cmd interface

  3. After the installation is successful, enter the pip list command to check whether the installation is successful

pip list

  establish

Create using array

array syntax

numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
import numpy as np


​
#Create a one-dimensional array using array

list01 = [1,2,3,4]
np01 = np.array(list01)
print(np01)
print(type(np01))
#Creating a two-dimensional array using array

list02 = [[1,2,3,4],[5,6,7,8]]
np02 = np.array(list02)
print(np02)
print(type(np02))
#Create a 3D array using array

list03 = [[[1,2,3,4],[5,6,7,8],[9,10,11,12]]]
np03 = np.array(list03)
print(np03)
print(type(np03))

Understanding of dimension

Use of array parameters

parameterParameters and description
ObjectAny sequence type
dtypeRequired data type of array, optional.
copyOptional. The default value is true. Whether the object is copied.
orderC (by row), F (by column), or A (any, default).
subokBy default, the returned array is forced to be a base class array. If true, the subclass is returned.
ndiminSpecifies the minimum dimension of the returned array.
#Object any sequence type  

tuple01 = (1,2,3)
list01= [1,2,3]
set01 = {5,6,7}
dict01 ={"Zhang San":10,"Li Si":20}
np01= np.array(dict01)
print(np01)
​
#dtype      Required data type of array, optional.  

list01 = [1,2,3]
np01= np.array(list01,dtype=float)
print(np01)
​
​
"""
Note np02 is a copy of np01, and the two are different objects
 Therefore, the data of np02 and np01 are different
"""

list01 = [1,2,3]
np01= np.array(list01)
np02=np.array(np01,copy=True)
np02[0]=10
print(np01)
print(np02)
"""
You can see that the data is the same, indicating that np02 and np01 are the same object
"""

list01 = [1,2,3]
np01= np.array(list01)
np02=np.array(np01,copy=False)
np02[0]=10
print(np01)
print(np02)
​
#Since the order effect is not obvious and is not commonly used, it is used here as an understanding
​
#subok      By default, the returned array is forced to be a base class array. If true, the subclass is returned. 
#Matrix is a matrix. I'll explain it in detail later. I'll reuse it first

np01 = np.matrix('1 2 7; 3 4 8; 5 6 9')
print(type(np01))
print(np01)
np02 = np.array(np01, subok=True)
np03 = np.array(np01, subok=False)
print(type(np02)) #< class' numpy. Matrix '> if true, returns the matrix
print(type(np03)) #< class' numpy. Ndarray '> if it is False, it is forced to change to array
​
#ndimin      Specifies the minimum dimension of the returned array.  
list01 = [1,2,3]
np01= np.array(list01,dtype=float,ndmin=3)
print(np01)
​

Create with orange

Let's review the following range function first
"""
The range(start,stop,step) function creates a list of integers
 1. Do not write start, which starts from 0 by default
 2. Left opening right closing
 3.step step size. If not written, the default value is 1
"""
for i in range(10):
    print(i)
"""
arange(start,stop,step,dtype) 
1. Do not write start, which starts from 0 by default
 2. Left opening right closing
 3.step step size. If not written, the default value is 1
"""
#One dimensional array

a = np.arange(10) #[0 1 2 3 4 5 6 7 8 9]
a = np.arange(2,10) #[2 3 4 5 6 7 8 9]
a = np.arange(1,10,2) #[1 3 5 7 9]
a = np.arange(1,10,2,dtype=float)
print(a)
#Two dimensional array
 #Remember that the previous 12 must meet 3 * 4

np01  = np.arange(12).reshape(3, 4)
print(np01)

Creating arrays using random

Common random functions

functiondescribe
np.random.random(size)Generate random numbers between 0 and 1
np.random.randint(low,high=None,size=None,dtype="1")Generate random integers
np.random.randn(d0,d1,d2,d3.........)Generate a standard normal random number (expected 0, variance 1)
np.random.normal(loc,scale,size)Generate normal distribution (specify expectation and variance)
np.random.uniform(low,high,size)Generate uniformly distributed random numbers
np.random.shuffle()Random disorder order
np.random.seed()Set random number seed
np.random.sample(size)Generate random floating point numbers
# np.random.random()      Generate random numbers between 0 and 1  
#Create a one-dimensional array    size generates several data, which can be written directly to 4

np01= np.random.random(size=4) #[0.13475357 0.8088961  0.52055803 0.49706622]
#Create a two-dimensional array size=(3,4) 3 rows and 4 columns available () and [], the effect is the same

np01= np.random.random((3,4))
#Create two three-dimensional arrays, three rows and four columns
np01= np.random.random((2,3,4))
print(np01) 
"""
np.random.randint(low,high=None,size=None,dtype="1")     Generate random integers  
low: start
 high=None: end
 size: length
 Dtype data type. The default is int32 no01.dtype attribute
 1. Left opening right closing
 2. Do not write low. The default value is 0
​
"""
#Create a one-dimensional array

np01= np.random.randint(1,11,10)#1-10
#Create a 2D array

np01= np.random.randint(1,11,(3,4))#1-10
#Create 3D array
np01= np.random.randint(1,11,(2,3,4))#1-10
print(np01)
#Create a standard normal distribution
 #One dimensional array

np01=np.random.randn(4)
#Two dimensional array

np01=np.random.randn(2,4)
#3D array
np01=np.random.randn(3,2,4)
print(np01)
"""
np.random.normal(loc,scale,size) generates a normal distribution (specify expectation and variance)
loc: expected, default 0
 scale: variance, default 1.0
 size: length
"""

np01= np.random.normal(size=5)
np01= np.random.normal(loc= 2,scale=3,size=5)
np01= np.random.normal(size=(2,3,5))
print(np01)
"""
np.random.uniform(low,high,size) generates uniformly distributed random numbers
 low: do not write. The default is 0,
high: end,
: length
 1. Left opening right closing
"""

np01= np.random.uniform(size= 4)#The four data hardly differ much and are relatively uniform
np01= np.random.uniform(size= (2,4))
np01= np.random.uniform(high=3)
print(np01)
"""
np.random.shuffle(ArrayLike) randomly scrambles the order
"""

np01 = np.array([2,5,6,7,3])
print(np01)
np.random.shuffle(np01)
print(np01)
"""
np.random.seed() sets the random number seed
 The number selected from each pile of seeds will not change. Selecting random seeds from different piles is different every time. If you want to get the same random number every time, you need to call seed() every time before generating a random number
"""

np.random.seed(1)
np01= np.random.randint(1,10,size= 5)
np.random.seed(1)
np02= np.random.randint(1,10,size = 5)
print(np01)
print(np02)
"""
np.random.sample(size) generates random floating-point numbers
"""

np01= np.random.sample(size=2)
print(np01)
np02= np.random.sample(size=(2,3))
print(np02)

Creating arrays using zeros

Numpy. Zeros (shapes, dtype = float, order = "C") # creates an array of the specified size, and the array is filled with 0
"""
Numpy. Zeros (shapes, dtype = float, order = "C") # creates an array of the specified size, and the array is filled with 0
 Shapes: Dimensions
 dtype: data type
 order: by row and column
"""

np01= np.zeros(5) #[0. 0. 0. 0. 0.]
np01= np.zeros(5,dtype="int32")  #[0 0 0 0 0]
np01= np.zeros((2,5),dtype="int32")
 
"""
[[0 0 0 0 0]
 [0 0 0 0 0]]
"""
print(np01)

Creates a multidimensional array of specific shapes

functiondescribe
np.zeros((3, 4))Create 3 × An array whose elements of 4 are all 0
np.ones((3, 4))Create 3 × An array whose elements of 4 are all 1
np.empty((2, 3))Create 2 × 3. The value in the empty data is not 0, but an uninitialized garbage value
np.zeros_like(ndarr)Create an array with all 0 elements in the same dimension of ndarr
np.ones_like(ndarr)Create an array with all 1 elements in the same dimension of ndarr
np.empty_like(ndarr)Create an empty array with ndarr the same dimension
np.eye(5)This function is used to create a 5 × 5 matrix, diagonal is 1, and the rest is 0
np.full((3,5), 10)Create 3 × The elements of 5 are all arrays of 10, and 10 is the specified value
np01= np.ones((2,5),dtype="int32")
np01= np.empty((2,5),dtype="int32")
print(np01)
list01= [
    [1,2,3],
    [4,5,6]
]
np01= np.array(list01)
print(np01.shape)
np02= np.zeros_like(np01,dtype=float)
print(np02)
print(np02.shape)
#  np.eye(5)      This function is used to create a 5 × 5 matrix, diagonal is 1, and the rest is 0  

np01= np.eye(5)
print(np01)
# np.full((3,5), 10)      Create 3 × The elements of 5 are all arrays of 10, and 10 is the specified value  
#10 is specified. You can specify any value
np01= np.full((3,5),5)
print(np01)

Creating arrays using linspace

np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
parameterdescribe
startStarting value of the sequence
stopThe termination value of the sequence. If endpoint=True, it proves that the array is contained in the sequence
numThe number of generated samples is 50 by default
endpointIf true, stop is included, otherwise it is not included
retstepIf retstep=Ture, the generated array will display the spacing, otherwise it will not be displayed
dtypedata type
"""
np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
"""

np01= np.linspace(1,10,5)#From 1-10, 5 data are generated
np01= np.linspace(1,10,5,endpoint=True)#Include 10
np01= np.linspace(1,10,5,endpoint=False)#Exclude 10
np01= np.linspace(1,10,5,retstep=True)#Display spacing
np01= np.linspace(1,10,5,dtype="int32")
print(np01)

Create an array using logspace

np.logspace(start,stop,num=50,endpoint=Ture,base=10.0,dtype=None)

parameterdescribe
startStarting value of the sequence
stopThe termination value of the sequence. If endpoint=True, it proves that the array is contained in the sequence
numThe number of generated samples is 50 by default
endpointIf true, stop is included, otherwise it is not included
baseThe base of log. The default value is 10.0
dtypedata type

practice

Create a one-dimensional all 0 Darry object with a length of 10, and then make the fifth element equal to
 Create a array object with elements from 10 to 49.
Create a 4 * 4 two-dimensional array and output the array element type.
Create an array that can completely transpose coordinate positions from (0, 1, 3) to (3, 0, 1).
Convert the data type in question 4 to float64.

catalogue

install

1. Open cmd as an administrator

2. Enter the command to install numpy plug-in

  3. After the installation is successful, enter the pip list command to check whether the installation is successful

  establish

Create using array

array syntax

Understanding of dimension

Create with orange

Creating arrays using random

Creating arrays using zeros

Creates a multidimensional array of specific shapes

Creating arrays using linspace

Create an array using logspace

practice

Tags: Python Back-end

Posted on Tue, 23 Nov 2021 11:24:26 -0500 by sbacelic