[array creation in numpy]

Catalog

One 、NumPy What is it? ？

Two 、 utilize array Create array

3、 ... and 、 utilize arange Create array

Four 、 Random number creation array

5、 ... and 、ndarray object

6、 ... and 、 Other ways to create arrays

7、 ... and 、 Slicing and indexing of arrays

One 、NumPy What is it? ？

1.NumPy It is the basic library of Scientific Computing , Provide a large number of scientific computing related functions , Such as data statistics , Random number generation , Its core type is multidimensional array （ndarray）, Support a large number of dimension arrays and matrix operations , Support vector processing ndarray object , Improve the operation speed of the program .

2.NumPy install

pip install numpy

Two 、 utilize array Create array

1.numpy Module array Function can generate multidimensional arrays , If you generate a two-dimensional array , You need to array Function passes a list type parameter , Each list element is one-dimensional ndarray Type array , As rows of a two-dimensional array ,numpy in shape[n] attribute , You can get the number of elements in each dimension ,n from 0 Start

# The import module 
import numpy as np
# Use array Function to create a one-dimensional array 
a=np.array[1,2,3,4]
print(a)
# Use array Function to create a two-dimensional array 
b=np.array([[1,2,3],[4,5,6],[7,8,9]])
print(b)
# Use array Function to create a three-dimensional array 
c=np.array([[[1,2,3],[4,5,6],[7,8,9]]])
print(c)
#array Function dtype Use , Specify the type of the element 
d=np.array([1,2,3],dtype=float)
print(d)
#array in ndmin Use , Specify the dimension of the array 
m=np.array([1,2,3],dtype=float,ndmin=3)
print(m)

3、 ... and 、 utilize arange Create array

Use arange Function to create a numeric range and return ndarray object , Format ：

numpy.arange(start,end,step)

Parameters	explain
start	Starting value （ The default is 0）
end	Termination value （ It doesn't contain ）
step	step , The default is 1
dtype	return ndarray Data type of

# Import numpy
import numpy as np
# review range Use (start,end,step)  [start,end)
a=list(range(1,10)) # Step length is 1
print(a)
b=list(range(10))
print(b)
c=list(range(1,10,3))
print(c)
#arange Create array (start,end,step)
m=np.arange(1,11)
print(m)
# Set step size 
n=np,arange(1,11,2)
print(n)
# Set up dtype
e=np.arange(10,20,2,dtype=float)
print(e)

[1, 2, 3, 4, 5, 6, 7, 8, 9]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[1, 4, 7]
[1 2 3 4 5 6 7 8 9 10]

[1 3 5 7 9]

[10. 12. 14. 16. 18.]

Four 、 Random number creation array

numpy.random.random(size=None)

This method returns [0.0~1.0) Between random floating-point numbers

# The import module 
import numpy as np
# Use random Create a one-dimensional array 
a=np.random.random(size=10)
# Create a 2D array 
b=np.random.random(size=(2,3))  #2 That's ok 3 Array of columns 
# Create a three-dimensional array 
c=np.random.random(size=(2,3,4)) # Two 3 That's ok 4 Column 

Create random integer method ：

numpy.random.randint(low,high,size)

This method returns a random integer , If only low, The return range is [0,low), If there is high, The return range is [low,high)

# The import module 
import numpy as np
# establish 0~10 One dimensional random integer 
a=np.random.randint(11,size=10)
# establish 0~10 Two dimensional random integer 
b=np.random.randint(11,size=(2,3)) #2 That's ok 3 Column 
# establish 5~10 Three dimensional random integer 
c=np.random.ramdint(5,11,size=(2,3,4)) #2 individual 3 That's ok 4 Column 

Create a standard normal distribution ( The expectation is 0, The variance of 1)：

numpy.random.randn(d0,d1...dn):dn Represents each dimension

# The import module 
import numpy as np
# Create one dimension 
a=np.random.randn(3)
# Create 2D 
b=np.random.randn(2,3)
# Create 3D 
c=np.random.randn(2,3,4)

Creates a normal distribution of specified expectations and variances ：

numpy.random.normal(loc,scale,size)

loc: expect ,scale： variance

5、 ... and 、ndarray object

1.ndarray object , Is a collection of data of the same type , By subscript 0 Start indexing the elements in the collection

2.ndarray Object properties of

attribute	explain
.ndim	Rank , Number of axes or dimensions
.shape	Dimension of array , For matrices （n,m）
.size	The total number of array elements
.dtype	Element type
.itemsize	The size of each element , Bytes are units
.flags	Object's memory information
.real	ndarray The real part of the element
.imag	ndarray The imaginary part of the element

6、 ... and 、 Other ways to create arrays

 1.zeros establish , Element use 0.0 fill ：

numpy.zeros((5,),dtype=int)  #5 individual 0 Array of elements

numpy.zeros((2,3)) #2 That's ok 3 Column 0.0 Array of elements

2.ones establish , Element use 1.0 fill

3.empty Create the specified shape 、 Uninitialized array of data types , The value of the element is the value of the previous memory

4.linspace Create a sequence of equal differences

numpy.linspace(5,20,5,endpoint=False)  # barring 20

[5. 8. 11. 14. 17.]

5.logspace Create an equal ratio sequence

numpy.logspace(start,stop,num,endpoint,base,dtype)

among , Sequence start value base**start, Termination value base**stop, Generate num individual

7、 ... and 、 Slicing and indexing of arrays

One dimensional array ：

1. Indexes ：x=numpy.arange(10)

Forward index ：x[0]~x[9]

Reverse index ：x[-9]~x[-1]

2. section ：[start,end,step]

Forward operation in slice ：

x[:]  ： From start to end

x[start:]  ：  from start From the beginning to the end

x[:end]  : From the beginning to end-1

x[start:end] : from start Start to end-1

x[start:end:step] : from start Start to end-1, In steps of step

Negative operation in slice ：

x[::-1]: Reverse output  

x[-5:-2]: Last but not least 5 individual ~ Last but not least 3 individual

Two dimensional array ：

1. Indexes ：

x=numpy.arange(1,13)

a=x.reshape((4,3)

# Index the second row and the third column

print(a[1,2]) perhaps print(a[1][2])

# Get the second row, the third column, and the third row, the first column

print(a[1,2],a[2,0]) perhaps print(a[(1,2),(2,0)]

2. section ：[ Slice rows , Slice columns ] [start:end:step,start:end:step]

x=numpy.arange(1,13)

a=x.reshape((4,3)

print(a(:,:))  # Get all rows and columns

print(a[:,1])# The second column of all rows

print(a[1,:]) # Second row of all columns

print(a[::2,:]) # All odd rows