This article is used to learn the basic usage of the Python toolkit Numpy! ! !
import numpy as np
#from numpy import *
# store data in txt file
arr=np.arange(3)
np.savetxt("array.txt",arr)
#Read the data in the txt file in the form of a matrix or array
arr=np.loadtxt('array.txt')
print(arr)
#arange function creates an array of 0-n integers, accepts 1, 2, 3 arguments similar to range()
print( np.arange(1,5,2) )
# Array the list
print( np.array([0,1,2,3,4]) )
#Create a three-dimensional array, similar to the relationship between sheet and row and column in Execel
print( np.arange(24).reshape(2,3,4) )
#Get the data type in the array, the number of array elements, and get the dimension of the array
arr = np.array([np.arange(6),np.arange(6)])
print( arr.dtype )
print( arr.size )
print( arr.shape )
#Customize the array of heterogeneous data types, customize the data type of each element of the array
np.dtype([('name',np.str_,40),('age',np.int32),('weight',np.float32)])
print( np.array([('liyu',23,68.1)]) )
#Slice operation of one-dimensional array, select elements from subscript 0-5, step size is 2, negative subscript flips the array
arr = np.arange(6)
print( arr[:5:2] )
print( arr[::-1] )
#Multidimensional array slicing operation, select all rows and columns in the first sheet
arr = np.arange(24).reshape(2,3,4)
print( arr[0,::] )
print( arr[0,...] )
# Flatten multidimensional array with ravel function or flatten function
arr = np.arange(9).reshape(3,3)
print( arr.ravel() )
print( arr.flatten() )
#Transpose of multidimensional array 4x3x2
print( arr.transpose() )
print( arr.T )
#Array of horizontal combination, vertical combination, depth combination (3,3) -> (3,3,2)
print( np.hstack((arr,arr)) )
print( np.c_[arr,arr] )
print( np.concatenate((arr,arr),axis=1) )
print( np.vstack((arr,arr)) )
print( np.concatenate((arr,arr),axis=0) )
print( np.dstack((arr,arr)) )
#Horizontal division, vertical division, depth division of the array
print( np.hsplit(arr,3) )
print( np.split(arr,3,axis=1) )
print( np.vsplit(arr,3) )
print( np.split(arr,3,axis=0) )
#NumPy array converted to Python list
print( arr.tolist() )
#Array weighted average, each element has a corresponding weight
arr = np.arange(3)
print( np.average(arr) )
print( np.average(arr,weights=arr) )
#arithmetic mean, max, min,
print( np.mean(arr) )
print( arr.mean() )
print( np.max(arr) )
print (np.min (arr))
#range, median, variance, covariance, logarithm,
print( np.ptp(arr) )
print( np.median(arr) )
print (np.var (arr))
print( np.mean((arr-arr.mean())**2) )
print (np.cov (arr, arr))
print (np.log (arr))
#Sort from small to large, difference between adjacent elements, square root, exponent, factorial, sign function
print( np.msort(arr) )
print( np.diff(arr) )
print( np.sqrt(arr) )
print( np.exp(arr) )
print (arr.cumprod ())
print( np.sign(arr) )
The #where function returns the index in the vector that is greater than the threshold, and the take function extracts the corresponding element
for i in range(len(arr)):
indices=np.where(arr>0)
np.take(arr,indices)
for i,j in enumerate(arr):
if i>0:
print(j)
print( indices )
print( np.take(arr,0) )
#linspace function, returns an array evenly distributed in the specified range
print( np.linspace(0,1,5) )
#argmax function returns the subscript corresponding to the maximum value in the array, the minimum value
print( np.argmax(arr) )
print( np.argmin(arr) )
#extract function extracts elements from an array based on generated conditions
condition=(arr%2)==0
print( np.extract(condition,arr) )
# extract non-zero elements from the array
print( np.nonzero(arr) )
# Normalize processing
print( arr/arr.sum() )
#Create identity matrix, zero matrix, 1 matrix
print( np.eye(3) )
print( np.zeros(3) )
print( np.ones(3) )
#Create a matrix with spaces between elements and semicolons between rows
Mat=np.mat('1 2 3;4 5 6;7 8 9')
print( type(Mat) )
#View the diagonal elements of the matrix, calculate the trace, and calculate the correlation coefficient
print( Mat.diagonal() )
print( Mat.trace() )
print (np.corrcoef (Mat, Mat))
#Transpose matrix, inverse matrix, compound matrix, create the same zero matrix as Mat
print( Mat.T )
print( Mat.I )
print( np.linalg.inv(Mat) )
print (np.bmat ('Mat Mat; Mat Mat'))
print( np.zeros_like(Mat) )
#Column sum, store the result of the intermediate operation
print( np.add.reduce(Mat) )
print( np.add.accumulate(Mat) )
#Division operation, division only retains integers, division retains decimals
print (np.divide (Mat, Mat))
print (np.true_divide (Mat, Mat))
#modulo operation, the remaining function mode function% is equivalent, the fmod function makes the positive and negative of the remainder determined by the dividend
print( np.remainder(Mat,Mat) )
print( np.fmod(Mat,2) )#The second parameter is the divisor
#Bit operation, xor inequality operator, and operator
print( -Mat )
print (Mat ^ Mat)
print (np.less (np.bitwise_xor (Mat, Mat), 0))
print( (Mat&Mat)==0 )
print( np.equal(np.bitwise_and(Mat,Mat),0) )
The #np.random module generates an array of random numbers
print( np.random.random(5) )
print( np.random.randint(5,size=5) )
#Get random numbers in the interval [0,1) and return an array with 2 rows and 4 columns
print( np.random.rand(2,4) )
Function of the #np.linalg module to solve a system of linear equations
A = Matt
b=np.ones(Mat.shape[0])
x=np.linalg.solve(A,b)
print( x )
print( np.dot(A,x) )
# Solve for eigenvalues and eigenvectors, returned as a tuple
eigenvalues,eigenvectors=np.linalg.eig(A)
# Solve singular value decomposition
U,Sigma,V=np.linalg.svd(A,full_matrices=False)
print( U*np.diag(Sigma)*V )
# Solve the generalized inverse matrix
print( np.linalg.pinv(A) )
# determinant
print (np.linalg.det (A))
References:
1. "Numpy Learning Guide"