Python numpy, pythonnumpy

Python numpy, pythonnumpy

import numpya=numpy.array([1,2,3,4])b=numpy.array([[1,2,3],[4,5,6],[7,8,9]])print(a.shape)print(b.shape)

Creates a one-dimensional vector and a three-row matrix.

Note: Data must be in the same structure. The shape function is used to specify rows and columns.

 

Valid value:

Import numpyb = numpy. array ([[, 3], [, 6], [, 9]) print (B [:, 1]) # print the second column of the Matrix print (B [:, 0: 2]) # obtain the first and second columns here.

 

Modify the value in the matrix:

Change the values of 5 and 7 to 10.

import numpyb=numpy.array([[1,2,3],[4,5,6],[7,8,9]])b[(b==5)|(b==7)] = 10print(b)

 

Conversion Type:

Convert int type to str type

import numpyb=numpy.array([[1,2,3],[4,5,6],[7,8,9]])c = b.astype(str)print(c)

 

Other operations:

Import numpyb = numpy. array ([[1, 2, 3], [4, 5, 6], [7, 8, 9]) print (B. min () # Calculate the minimum value print (B. max (axis = 1) # calculate the maximum print (B. sum (axis = 0) # sum by Column

 

Import numpy as npa = np. arange (10 ). reshape () print (a) ''' create a matrix: [[0 1 2 3 4] [5 6 7 8 9] ''' print (. ndim) # evaluate the Dimension print (. shape) # print (. dtype. name) # print (. size) # Number of Elements

 

Matrix initialization:

Import numpy as np # matrix initialization method: np. zeros (3, 4) # initialize the 3-row 4-column matrix to 0 (float type by default) np. ones (3, 4), dtype = np. int32) # Three rows, four columns, int type with an initial value of 1

 

Create a matrix:

Import numpy as npnp. arange (10, 30, 5) # from 10 to 30, every 5 # array ([10, 15, 20, 25]) np. random. random (2, 3) ''' is randomly created: two rows and three columns, between-1 and 1. Note: It must be two randomarray ([0.20925672, 0.09790786, 0.00158854], [0.73711854, 0.83033327, 0.22525092]) ''' np. linspace (100, 100) # obtain an average of numbers from 1 to 3 (float type)

 

 

Operation:

Import numpy as npa = np. array ([[1, 2, 3], [4, 5, 6], [7, 8, 9]) print (np. hstack (a, a) print (np. vstack (a, a) print (. t) print (a + a) print (a * a) print (. dot (a) print (np. dot (a, a) print (np. exp (a) print (np. sqrt (a) print (. shape) print (. ravel () ''' is not explained, [1 2 3 1 2 3] [4 5 6 4 5 6] [7 8 9 7 8 9] [[1 2 3] [4 5 6] [7 8 9] [1 2 3] [4 5 6] [7 8 9] [[1 4 7] [2 5 8] [3 6 9] [2 4 6] [8 10 12] [14 16 18] [[1 4 9] [16 25 36] [49 64 81] [[30 36 42] [66 81 96] [102 126 150] [[30 36 42] [66 81 96] [102 126 150] [[2.71828183e + 00 7.38905610e + 00 2.00855366e + 01] [5.45981500e + 01 1.48413159e + 02 4.03428793e + 02] [1.09663316e + 03 2.98095799e + 03 8.10308393e + 03] [[1. 1.41421356 1.73205081] [2. 2.23606798 2.44948974] [2.64575131 2.82842712 3.] (3, 3) [1 2 3 4 5 6 7 8 9] '''

 

Import numpy as npa = np. array ([[1, 2, 3], [4, 5, 6], [7, 8, 9]) print (. argmax (axis = 0) #[2 2 2] maximum column index value print (. argmin (axis = 1) #[0 0 0] minimum index value of the row

 

import numpy as npa=np.arange(0,40,10)print(a)b=np.tile(a,(3,2))c=np.tile(a,(2,3))print(b)print(c)'''[ 0 10 20 30][[ 0 10 20 30  0 10 20 30] [ 0 10 20 30  0 10 20 30] [ 0 10 20 30  0 10 20 30]]  [[ 0 10 20 30  0 10 20 30  0 10 20 30] [ 0 10 20 30  0 10 20 30  0 10 20 30]]'''

 

Sort:

Import numpy as npa = np. array ([[, 6], [, 7], [, 8]) print () ''' [[1 4 6] [2 9 7] [5 3 8] ''' B = np. sort (a, axis = 1) # print (B) by row) ''' [[1 4 6] [2 7 9] [3 5 8] ''' c = np. sort (a, axis = 0) # print (c) by Column) ''' [[1 3 6] [2 4 7] [5 9 8] ''' d = np. argsort (a) # Sort index values print (d) ''' [[0 1 2] [0 2 1] [1 0 2] '''

 

 

Note:

Import numpy as npa = np. array ([[, 3], [, 6], [, 9]) c =. view () print (c is a) # false (c and a point to the memory address is different) # copy a and assign it to c # If c =, then c and a are the same (pointing to the same address) # print (c is a), The truec [100] = print (a) will be printed) ''' [[1 2 3] [4 5 100] [7 8 9] ''' # Here c is modified, then a is modified. # c and a share a group of data, even though their addresses are different. d =. copy () print (d is a) # falsed [100] = # The aprint (a) is not changed here)

 

 

Read txt files:

Import numpy # The first parameter is the path, the second parameter is the separator, and the third parameter is the read type # The last parameter indicates whether to remove the first line a = numpy. genfromtxt ("d:/a.txt", delimiter = ",", dtype = "str", skip_header = 1) print ()