各类速查表汇总-PythonForDataScienceCheatSheetSciPy-LinearAlgebra

需积分: 12 234 浏览量更新于2023-05-28 评论收藏 145KB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

Python For Data Science Cheat Sheet

SciPy - Linear Algebra

Learn More Python for Data Science Interactively at www.datacamp.com

SciPy

DataCamp

Learn Python for Data Science Interactively

Interacting With NumPy

Also see NumPy

The SciPy library is one of the core packages for

scientiﬁc computing that provides mathematical

algorithms and convenience functions built on the

NumPy extension of Python.

Index Tricks

>>> np.mgrid[0:5,0:5] Create a dense meshgrid

>>> np.ogrid[0:2,0:2] Create an open meshgrid

>>> np.r_[[3,[0]*5,-1:1:10j] Stack arrays vertically (row-wise)

>>> np.c_[b,c] Create stacked column-wise arrays

Shape Manipulation

Polynomials

Vectorizing Functions

Type Handling

>>> np.angle(b,deg=True) Return the angle of the complex argument

>>> g = np.linspace(0,np.pi,num=5) Create an array of evenly spaced values

(number of samples)

>>> g [3:] += np.pi

>>> np.unwrap(g) Unwrap

>>> np.logspace(0,10,3) Create an array of evenly spaced values (log scale)

>>> np.select([c<4],[c*2]) Return values from a list of arrays depending on

conditions

>>> misc.factorial(a) Factorial

>>> misc.comb(10,3,exact=True) Combine N things taken at k time

>>> misc.central_diff_weights(3) Weights for Np-point central derivative

>>> misc.derivative(myfunc,1.0) Find the n-th derivative of a function at a point

Other Useful Functions

>>> np.real(c) Return the real part of the array elements

>>> np.imag(c) Return the imaginary part of the array elements

>>> np.real_if_close(c,tol=1000) Return a real array if complex parts close to 0

>>> np.cast['f'](np.pi) Cast object to a data type

>>> def myfunc(a):

if a < 0:

return a*2

else:

return a/2

>>> np.vectorize(myfunc) Vectorize functions

>>> from numpy import poly1d

>>> p = poly1d([3,4,5]) Create a polynomial object

>>> np.transpose(b)

Permute array dimensions

>>> b.atten() Flaen the array

>>> np.hstack((b,c)) Stack arrays horizontally (column-wise)

>>> np.vstack((a,b)) Stack arrays vertically (row-wise)

>>> np.hsplit(c,2) Split the array horizontally at the 2nd index

>>> np.vpslit(d,2) Split the array vertically at the 2nd index

>>> import numpy as np

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

>>> b = np.array([(1+5j,2j,3j), (4j,5j,6j)])

>>> c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]])

>>> help(scipy.linalg.diagsvd)

>>> np.info(np.matrix)

Linear Algebra

You’ll use the linalg and sparse modules. Note that scipy.linalg contains and expands on numpy.linalg.

>>> from scipy import linalg, sparse

Creating Matrices

>>> A = np.matrix(np.random.random((2,2)))

>>> B = np.asmatrix(b)

>>> C = np.mat(np.random.random((10,5)))

>>> D = np.mat([[3,4], [5,6]])

Also see NumPy

Basic Matrix Routines

Inverse

>>> A.I Inverse

>>> linalg.inv(A) Inverse

>>> A.T Tranpose matrix

>>> A.H Conjugate transposition

>>> np.trace(A) Trace

Norm

>>> linalg.norm(A) Frobenius norm

>>> linalg.norm(A,1) L1 norm (max column sum)

>>> linalg.norm(A,np.inf) L inf norm (max row sum)

Rank

>>> np.linalg.matrix_rank(C) Matrix rank

Determinant

>>> linalg.det(A) Determinant

Solving linear problems

>>> linalg.solve(A,b) Solver for dense matrices

>>> E = np.mat(a).T Solver for dense matrices

>>> linalg.lstsq(D,E) Least-squares solution to linear matrix

equation

Generalized inverse

>>> linalg.pinv(C) Compute the pseudo-inverse of a matrix

(least-squares solver)

>>> linalg.pinv2(C) Compute the pseudo-inverse of a matrix

(SVD)

Addition

>>> np.add(A,D) Addition

Subtraction

>>> np.subtract(A,D) Subtraction

Division

>>> np.divide(A,D) Division

Multiplication

>>> np.multiply(D,A) Multiplication

>>> np.dot(A,D) Dot product

>>> np.vdot(A,D) Vector dot product

>>> np.inner(A,D) Inner product

>>> np.outer(A,D) Outer product

>>> np.tensordot(A,D) Tensor dot product

>>> np.kron(A,D) Kronecker product

Exponential Functions

>>> linalg.expm(A) Matrix exponential

>>> linalg.expm2(A) Matrix exponential (Taylor Series)

>>> linalg.expm3(D) Matrix exponential (eigenvalue

decomposition)

Logarithm Function

>>> linalg.logm(A) Matrix logarithm

Trigonometric Tunctions

>>> linalg.sinm(D) Matrix sine

>>> linalg.cosm(D) Matrix cosine

>>> linalg.tanm(A) Matrix tangent

Hyperbolic Trigonometric Functions

>>> linalg.sinhm(D) Hypberbolic matrix sine

>>> linalg.coshm(D) Hyperbolic matrix cosine

>>> linalg.tanhm(A) Hyperbolic matrix tangent

Matrix Sign Function

>>> np.sigm(A) Matrix sign function

Matrix Square Root

>>> linalg.sqrtm(A) Matrix square root

Arbitrary Functions

>>> linalg.funm(A, lambda x: x*x) Evaluate matrix function

Matrix Functions

Asking For Help

Decompositions

Eigenvalues and Eigenvectors

>>> la, v = linalg.eig(A) Solve ordinary or generalized

eigenvalue problem for square matrix

>>> l1, l2 = la Unpack eigenvalues

>>> v[:,0] First eigenvector

>>> v[:,1] Second eigenvector

>>> linalg.eigvals(A) Unpack eigenvalues

Singular Value Decomposition

>>> U,s,Vh = linalg.svd(B) Singular Value Decomposition (SVD)

>>> M,N = B.shape

>>> Sig = linalg.diagsvd(s,M,N) Construct sigma matrix in SVD

LU Decomposition

>>> P,L,U = linalg.lu(C) LU Decomposition

>>> F = np.eye(3, k=1) Create a 2X2 identity matrix

>>> G = np.mat(np.identity(2)) Create a 2x2 identity matrix

>>> C[C > 0.5] = 0

>>> H = sparse.csr_matrix(C) Compressed Sparse Row matrix

>>> I = sparse.csc_matrix(D) Compressed Sparse Column matrix

>>> J = sparse.dok_matrix(A) Dictionary Of Keys matrix

>>> E.todense() Sparse matrix to full matrix

>>> sparse.isspmatrix_csc(A) Identify sparse matrix

Creating Sparse Matrices

Inverse

>>> sparse.linalg.inv(I)

Inverse

Norm

>>> sparse.linalg.norm(I)

Norm

Solving linear problems

>>> sparse.linalg.spsolve(H,I)

Solver for sparse matrices

Sparse Matrix Routines

Sparse Matrix Functions

>>> sparse.linalg.expm(I) Sparse matrix exponential

Sparse Matrix Decompositions

>>> la, v = sparse.linalg.eigs(F,1) Eigenvalues and eigenvectors

>>> sparse.linalg.svds(H, 2) SVD

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余0页未读，立即下载

xuwx66

粉丝: 1
资源: 26

会员权益专享

各类速查表汇总-Python For Data Science Cheat Sheet SciPy - Linear Algeb...

评论0

会员权益专享

最新资源

各类速查表汇总-Python For Data Science Cheat Sheet SciPy - Linear Algeb...

评论0

python_cheatsheet_完美总结.pdf

cs230讲义-super-cheatsheet-deep-learning

Magento Events CheatSheet (Magento事件速查表)

ROS cheatsheet ROS命令大全

probability and statistics cheatsheet 概率论与统计 总结

pandas全部操作指令表

数据结构 cheatsheet

Java cheat sheet

bash script cheatsheet

Emmet Cheat Sheet PDF版

TensorFlow Cheat Sheet

conda CheatSheet

cheatengine70教程

cheat engine 过np

我要学习Cheat Engine,你有什么好的建议

如何用Cheat Engine去修改游戏数据？

cheatengine教

coding 面试注意事项

一张表了解 CoAP 协议

会员权益专享

最新资源

probability and statistics cheatsheet 概率论与统计总结