Competition notes: numpy learning notes

ndarray Provides more array operations than ordinary lists , Accept the following parameters ：

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

Create array

import numpy as np	#  Import and stock in first 
a = np.array([1, 2, 3])

Create a 2D array

a = np.array([[1, 2], [3, 4]])

Create array , Customize the minimum dimension

a = np.array([1, 2, 3, 4, 5], ndmin =  3)  

here a As a three-dimensional array [ [ [1, 2, 3, 4, 5] ] ]

Set data type

import numpy as np 
a = np.array([1,  2,  3], dtype = complex)

here a The element in is stored in plural form , That is to say 1 + 0i, 2 + 0i, 3 + 0i

arange() Function to quickly create a one-dimensional array

arange(start, end, step)

start： Starting value , Included in the array
end： Final value , Not included in the array
step： step , Tolerance

arr = np.arange(1, 6, 1)	# [1, 2, 3, 4, 5]

linspace()

linspace(start, end, elements)

start： Starting value , Included in the array
end： Final value , Included in the array
elements： Number of elements in the array

arr = np.linspace(1, 11, 10) 
''' [ 1. 2.11111111 3.22222222 4.33333333 5.44444444 6.55555556 7.66666667 8.77777778 9.88888889 11. ] '''

ones()
Generate all 1 Array of

np.ones(shape = (row, col), dtype = type)

row： Array rows
col： Number of array columns
type： Array data type

zeros()
Generate all 0 Array of

np.zeros(shape = (row, col), dtype = type)

row： Array rows
col： Number of array columns
type： Array data type

Get array properties
ndim Dimension of array , Represents how many dimensional arrays , return 2, Then it represents a two-dimensional array
shape Dimension of array , Returns the number of rows and columns of the array
size Returns the number of elements in the array
dtype Returns the type of the element in the array

arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) 
print(arr)
print(arr.ndim)  # 2
print(arr.shape)  # (2, 5)
print(arr.size)  # 10
print(arr.dtype)  # int32

notes ：dtype Return the highest precision data , For example, if 2 Change it to 2.0,dtype Returns the float64, At this time, all the numbers are float64.Numpy Array requires all elements to be of the same type , It depends on the number with the highest accuracy

Array index
One dimensional array operation is the same as list ,arr[ i ] Get a single element ,arr[i : j] Get multiple elements （ Left closed right open interval , Include a[ i ], barring a[ j ]）

arr = np.array([1, 2, 3, 4, 5])
print(arr[1])  #  Array subscript from 0 Start , The return position is 1 The elements of 
# 2
 
print(arr[0:2])  #  Slicing operation , The middle is separated by a colon , The return position is 0,1 The elements of 
# [1 2]

Two dimensional array operations
arr[i, j] Get a single element ,arr[i1 : i2, j1 : j2] Get slice , The slice is still left closed and right open

arr2 = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr2[0:2, 0:2])  #  Slice operation of two-dimensional array , Row index before comma , Then the column index , Column index positions are separated by colons 
 [[1 2]
 [6 7]]

Non standard range , Just write : Represents selecting the whole row or column

print(arr2[:, 0:1])  #  The default row index is all rows of the array , The first 1 Column 
>>>
[[1.]
 [6.]]
 
print(arr2[0:1, :])  #  The default column index is all columns of the array , The first 1 That's ok 
>>>
[[1. 2. 3. 4. 5.]

Numpy Statistical function

sum Sum up
mean The average
var equation
std Standard deviation
max Maximum
min minimum value

Reference article
https://blog.csdn.net/qq_44039983/article/details/123369799?spm=1001.2101.3001.6650.16&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-16-123369799-blog-79580734.pc_relevant_multi_platform_whitelistv1_exp2&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-16-123369799-blog-79580734.pc_relevant_multi_platform_whitelistv1_exp2&utm_relevant_index=24