NumPy(Numerical Python) yes Python An extended library of language , Support a large number of dimension arrays and matrix operations , In addition, it also provides a large number of mathematical function libraries for array operation .
NumPy It's a scientific computing toolkit , It provides a set of functions for processing arrays , And functions for processing matrices .
NumPy It's a very fast math library , Mainly used for array calculation , contain ：
# numpy Use
# Import numpy
import numpy as np
from matplotlib import pyplot as plt
# establish 2 One dimensional array
a1 = np.array([1, 2, 3, 4, 5])
a2 = np.array([6, 7, 8, 9, 10])
# establish 2 Two dimensional array
b1 = np.array([[1, 5], [6, 7]])
b2 = np.array([[11, 13], [18, 19]])
# Create an empty array
c = np.empty((2, 3))
# Create a full 0 Array
d = np.zeros((2, 3))
# Create a full 1 Array
e = np.ones((2, 3))
# Create a random array
f = np.random.random((2, 3))
# Create an array of random integers
g = np.random.randint(0, 10, (2, 3))
# Create a random floating-point array
h = np.random.rand(2, 3)
# NumPy Matrix library (Matrix)
# NumPy Provides a matrix library , Can be used to deal with matrix operations .
# NumPy The matrix library provides a matrix object , It's a two-dimensional array , Each of these elements is a floating point number .
# NumPy Matrix library provides some functions of matrix operation , Include ：
# 1. The addition, subtraction, multiplication and division of matrices
# 2. Dot product of matrix
# 3. The transpose of the matrix
# 4. The matrix of the inverse
# 5. Determinant of matrix
# 6. The rank of a matrix
# 7. The inverse of a matrix
# 8. The inverse of a matrix （ Use SVD）
# 9. The inverse of a matrix （ Use QR）
# 10. The inverse of a matrix （ Use LU）
# 11. The inverse of a matrix （ Use Cholesky）
import numpy.matlib
print (np.matlib.empty((2,2)))
# NumPy Linear algebra usage
# NumPy A library of linear algebraic functions is provided linalg, This library contains all the functions needed for linear algebra
# dot Dot product of two arrays
np.dot(b1, b2)
print(np.dot(b1, b2))
# vdot Dot product of two vectors
np.vdot(b1, b2)
print(np.vdot(b1, b2))
# inner The inner product of two arrays
np.inner(b1, b2)
# matmul Matrix multiplication of two arrays
np.matmul(b1, b2)
# determinant Determinant of matrix
np.linalg.det(b1)
# solve Solution of matrix
np.linalg.solve(b1, b2)
# inv The matrix of the inverse
np.linalg.inv(b1)
# Matplotlib yes Python Drawing library of . It can be connected with NumPy Use it together , Provides an effective MatLab Open source alternatives .
# In the example ,np.arange() Function creation x The value on the axis .y The corresponding value on the axis is stored in another array object y in .
# These values use matplotlib Package's pyplot Sub module of plot() Function to draw . The figure is created by show() Function shows .
x = np.arange(1, 11)
y = 2 * x + 5
plt.title("Matplotlib demo")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
plt.plot(x, y)
plt.show()
# NumPy Sort 、 Conditional brush function
# NumPy Sort ：sort
# numpy.sort(a, axis, kind, order)
# a: Array to sort
# axis: Sort the axis of the array along it , If there is no array, it will be expanded , Sort along the last axis , axis=0 Sort by column ,axis=1 Sort by row
# kind: The default is 'quicksort'（ Quick sort ）
# order: If the array contains fields , The fields to be sorted
# numpy.sort() Function returns a sorted copy of the input array
a1 = np.array([7, 2, 0, 9, 3])
np.sort(a1)
print(np.sort(a1))
# numpy.argsort() Function returns the index value of the array value from small to large
np.argsort(a1)
print(np.argsort(a1))
# numpy.lexsort() Used to sort multiple sequences . Think of it as a sort of spreadsheet , Each column represents a sequence , When sorting, priority is given to the next column
# nm = ('raju','anil','ravi','amar')
# dv = ('f.y.', 's.y.', 's.y.', 'f.y.')
# ind = np.lexsort((dv,nm))
np.lexsort(a1)
print(np.lexsort(a1))
# numpy.argmax() and numpy.argmin() Function returns the index of the maximum and minimum elements along the given axis
np.argmax(a1)
np.argmin(a1)
print(np.argmax(a1))
print(np.argmin(a1))
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