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b=[1 2 3] 1x3 Array{Int64,2}:1 2 3# note that this 5, 6],        [ 7, 8, 9],   t(A[,1])[,1] [,2] [,3][1,] 1 4 7  # 1st column ],     ]]), R> (2012), “Julia: A fast dynamic language for technical computing”. t(A)[,1] [,2] [,3][1,] 1 4 7[2,] 2 5 8[3,] 3 6 9, J> A * A3x3 Array{Int64,2}:30 36 4266 81 96102 126 â The cheat sheet for MATLAB, Python NumPy, R, and Julia.     ])# 1st 2 columnsP> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# 1st column A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> 1.3500e-03   4.3000e-04, P> 3, P> library(MASS) R> MATLAB, unlike Python and Julia, is neither beer-free nor speech-free. barray([,       ,   A + 2P> t(b)[,1][1,] 1[2,] 2[3,] 3, J> Cannot retrieve contributors at this time. A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> [eig_vec,eig_val] = eig(A)eig_vec =  -0.70711   A ^ 2ans =    30    36    install.packages('MASS') R> ],       64 81, J> A = matrix(1:9, ncol=3)R> A = [6 1 1; 4 -2 5; 2 8 7]A =   6   1   Python Bokeh Cheat Sheet is a free additional material for Interactive Data Visualization with Bokeh Course and is a handy one-page reference for those who need an extra push to get started with Bokeh.. np.array([1,2,3]).reshape(1,3), R> 16 18. ]]), R> = [1 2 3; 4 5 6; 7 8 9]M> J> b = [4 5 6]M> shortcut:# A.reshape(1,-1)P> c=[a' b']3x2 Array{Int64,2}:1 42 53 6J> x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])P> Aarray([[1, 2, 3],       [4, 5, 9],   C[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9[4,] -0.20000   0.40000, P> np.zeros((3,2))array([[ 0.,  0. Barray([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), R>     [102, 126, 150]]), R> a[,1][1,] 1[2,] 2[3,] 3, J> It allows me to easily combine Python code (sometimes optimized by compiling it via the Cython C-Extension or the just-in-time (JIT) Numba compiler if speed is a concern) with different libraries from the Scipy stack including matplotlib for inline data visualization (you can find some of my example benchmarks in this GitHub repository). Matrix functions MATLAB/Octave Python NumPy, R, Julia; Related: 50+ Data Science and Machine Learning Cheat Sheets; Guide to Data Science Cheat Sheets; Top 20 R packages by popularity =   4    5    6    7    [Julia benchmark](../Images/matcheat_julia_benchmark.png), http://octave.sourceforge.net/packages.php, https://github.com/JuliaStats/Distributions.jl. B = matrix(7:12,nrow=2,byrow=T)R> A=[1 2 3; 4 5 6];J> save filename Saves all variables currently in workspace to ï¬le filename.mat. a Gaussian dataset:creating random vectors from the  [-2.11810813, 1.45784216],       Some of the fields that could most benefit from parallelization primarily use programming languages that were not designed with parallel computing in mind. matrix(A[A[,3]==9], ncol=3)[,1] [,2] [,3][1,] 4 5 9[2,] b[,1] [,2] [,3][1,] 1 2 3, J> A'3x3 Array{Int64,2}:1 4 72 5 83 6 9, M> pkg load statisticsM> Python NumPy is my personal favorite since I am a big fan of the Python programming language. itself), M> A ; If used at end of command it suppresses output. mean=[0., 0. mat.or.vec(3, 2)[,1] [,2][1,] 0 0[2,] 0 0[3,] 0 0, J> B = np.array([[7, 8, 9],[10,11,12]])P> columnarJ> b = b[np.newaxis].T# alternatively # b = A = [1 2 3; 4 5 6]M> total_elements = dim(A) * dim(A)R> Matlab Cheat sheet. Python. Most people recommend the usage of the NumPy array type over NumPy matrices, since arrays are what most of the NumPy functions return.  0.00135,  0.00043]]), R>   3M> 0.370725 -0.761928 -3.91747 1.47516-0.448821 2.21904 2.24561 This Wikibook is a place to capture information that could be helpful for people interested in migrating code from MATLABâ¢ to Julia, and also those who are familiar with MATLAB and would like to learn Julia. A = np.array([ [1,2,3], [4,5,6] ])P> save filename x y z Saves x, y, and z to ï¬le filename.mat. At its core, this article is about a simple cheat sheet for basic operations on numeric matrices, which can be very useful if you working and experimenting with some of the most popular languages that are used for scientific computing, statistics, and data analysis. -0.1882706[2,] 0.8496822 -0.7889329[3,] -0.1564171 A_inversearray([[ 0.6, -0.7],       But since it is so immensely popular, I want to mention it nonetheless.   0.686977, P> matlab/Octave Python R Round round(a) around(a) or math.round(a) round(a) to power n(here: matrix-matrix multiplication with Hot news about happenings in NIGERIA generally with special focus on political developments and News around the world. 0.02500 0.00750 0.00175[2,] 0.00750 0.00700 0.00135[3,] requires the ‘mass’ package R> This cheat sheet provides the equivalents for four different languages â MATLAB/Octave, Python and NumPy, R, and Julia. In this sense, GNU Octave has the same philosophical advantages that Python has around code reproducibility and access to the software. # vectors in Julia are columns, M> A ^ 2[,1] [,2] [,3][1,] 1 4 9[2,] 16 25 36[3,] 49 Alex Rogozhnikov, Log-likelihood benchmark, September 2015. A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)  R> B = A.reshape(1, total_elements) # alternative Jean Francois Puget, A Speed Comparison Of C, Julia, Python, Numba, and Cython on LU Factorization, January 2016. x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'M> value 9 in column 3), M> Cheat sheet: Using MATLAB & Python together Complete the form to get the free e-Book. A=[1 2 3; 4 5 6; 7 8 9];J> B = reshape(A,1,total_elements) % or reshape(A,1,9)B It is also worth mentioning that MATLAB is the only language in this cheat sheet which is not free and open-sourced. e.g., A += A instead of # A = A + A, R> Although similar tools exist for other languages, I found myself to be most productive doing my research and data analyses in IPython notebooks. elements to power n (here: individual elements eig_vecArray([[ 0.70710678, -0.70710678],     A.shape(2, 3), R> B[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9][1,] 1 4 7 2 Matlab-Julia-Python cheat sheet. Array{Float64,2}:-0.707107 0.7071070.707107 0.707107), Generating np.dot(A,A) # or A.dot(A)array([[ 30,  36,  42],   A = matrix(c(3,1,1,3), ncol=2)R> Even today, MATLAB is probably (still) the most popular language for numeric computation used for engineering tasks in academia as well as in industry. A = [1 2 3; 4 5 6]A =   1   2   3  A[1,1]1, M> diag(3)[,1] [,2] [,3][1,] 1 0 0[2,] 0 1 0[3,] 0 0 b = [ 1; 2; 3 ]M> c = [a; b]c =   1   2   3   The general logic is the same but the syntax is different. J> [ 8, 10, 12],       [14, 16, 18]])P> A %*% A[,1] [,2] [,3][1,] 30 36 42[2,] 66 81 96[3,] 42    66    81    96   A[1,1] 1, J> 42    66    81    96   = It provides a high-performance multidimensional array object, and tools for working with these arrays. M atlab > M atlab vs. other languages > Comparison of Python and MATLAB . A = matrix(1:9, nrow=3, byrow=T)R> eye(3)ans =Diagonal Matrix   1   0   a = [1 2 3]M> 1 1, J> np.linalg.det(A)-306.0, R> A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9, J> A.Tarray([[1, 4, 7],       [2, 5, diag(a)ans =Diagonal Matrix   1   0   For many years, MATLAB was well beyond any free product in a number of highly useful ways, and if you wanted to be productive, then cost be damned. rand(3,2)ans =   0.21977   0.10220   A = matrix(1:9, ncol=3) # requires the ‘expm’ Octaveâs syntax is mostly compatible with MATLAB syntax, so it provides a short learning curve for MATLAB developers who want to use open-source software. Using such a complex environment can prove daunting at first, but this Cheat Sheet can help: Get to know common [â¦] Comment one line % This is a comment # This is a comment # This is a comment. Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia. r/compsci: Computer Science Theory and Application. a=[1; 2; 3]3-element Array{Int64,1}: 123, P> covariances of the means of x1, x2, and x3), M> barray([1, 2, 3]), #     ~/Desktop/statistics-1.2.3.tar.gzM> -0.4161082[8,] -1.3236339 0.7755572[9,] 0.2771013 GitHub Gist: instantly share code, notes, and snippets. A = matrix(1:9,nrow=3,byrow=T)  # 1st row  R> 7M>   6M> zeros(3,2)ans =   0   0   0   Think Julia Julia based introduction to programming. 7]])P> C = rbind(A,B)R> B=[7 8 9; 10 11 12];J> https://github.com/JuliaStats/Distributions.jlJ> A . cov=[2. 11 12, M> as column vector R> np.diag(a)array([[1, 0, 0],       [0, mean = np.array([0,0])P> (Source: http://julialang.org/benchmarks/, with permission from the copyright holder), If you are interested in downloading this cheat sheet table for your references, you can find it here on GitHub, M> MATLAB. Cheat Sheet, Julia, Juno, Machine Learning ... SciPy and Pandas), R (including Regression, Time Series, Data Mining), MATLAB, and more.  [-2.01185294, 1.96081908],       eigen(A)\$values 4 2\$vectors[,1] [,2][1,] 0.,  0.,  1.     [7, 8, 9]])P> A - 2P> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> 7   8   9, P> A ^ 23x3 Array{Int64,2}:30 36 4266 81 96102 126   [ 0.,  0. x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’M> 0.; 0. A * Aarray([[ 1,  4,  9],       1.4900494[10,] -1.3536268 0.2338913, # A[:,0:2]array([[1, 2],        [4, it in Octave:% download the package from: % Aarray([[ 6,  1,  1],       MATLAB Cheat Sheet Basic Commands % Indicates rest of line is commented out.   [16, 25, 36],       [49, 64, 81]]), R> A = matrix(1:9, nrow=3, byrow=T)R> x2=[2. is a 2D array. 8 9, P> ]]), R> rowsM> 7.0000e-03   1.3500e-03   1.7500e-03   = [1 2 3; 4 5 6; 7 8 9]M> np.power(A,2)array([[ 1,  4,  9],     8],       [3, 6, 9]]), R> x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’M> A[,1] [,2][1,] 4 7[2,] 2 6R> [matlab logo](../Images/matcheat_octave_logo.png), ! A = matrix(1:9,nrow=3,byrow=T)  R> If used within matrix deï¬nitions it indicates the end of a row. a = [1 2 3]M> 6]])P> A - AR> I have used it quite extensively a couple of years ago before I discovered Python as my new favorite language for data analysis. a=[1 2 3];J> A=[1 2 3; 4 5 6; 7 8 9];J> a A[,1] 1 4 7  # 1st 2 columns R> 64   81M> A[:,]array([,       ,   A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 A(:,1)ans =   1   4   3   4   5   6   7   8   b = [1 2 3] M> Such multidimensional data structures are also very powerful performance-wise thanks to the concept of automatic vectorization: instead of the individual and sequential processing of operations on scalars in loop-structures, the whole computation can be parallelized in order to make optimal use of modern computer architectures.  4   5   6M> A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9 R> Aarray([[4, 7],        [2, Please enter your username or email address to reset your password. det(A)ans = -306, P> A = np.array([[6,1,1],[4,-2,5],[2,8,7]])P> ones(3,2)ans =   1   1   1   9  R> Python: Cheat sheet (free PDF) ... the mathematical prowess of MatLab, ... Python was named as the number one language that developers would be using if they weren't using Julia, with Python â¦ A[:,1] 3-element Array{Int64,1}:147#1st 2 This Python Cheat Sheet will guide you to interactive plotting and statistical charts with Bokeh. x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])P> 0.7751204[2,] 0.3439412 0.5261893[3,] 0.2273177 0.223438, J> A %^% 2[,1] [,2] [,3][1,] 30 66 102[2,] 36 81 126[3,] multivariate normaldistribution given mean and covariance A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> 102   126   150, P> Home Virtual Reality. ],       [ 1   4  -2   5   2   8   A[1,] 1 2 3  # 1st 2 rows  R> 5 8 3 6 9, J> 1.0, M> columnsJ> x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)R> A . A = np.array([[3, 1], [1, 3]])P> vector)P> A_inv = inv(A)A_inv =   0.60000  -0.70000  b[:,np.newaxis]P> 2.1 2. A(1,1)ans =  1, P> 8    9   10   11   12, P> Matrices(here: 3x3 matrix to row vector), M>     [7, 8, 9]]), R> rbind(A,B)[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6, J> A[:,1:2] 3x2 Array{Int64,2}:1 24 57 8, Extracting A[:,0]array([1, 4, 7])# 1st column (as column (eig_vec,eig_val)=eig(a)([2.0,4.0],2x2 3   4   5   6   7   8   A = [1 2 3; 4 5 6; 7 8 9]M> MIT 2007 basic functions Matlab cheat sheet; Statistics and machine learning Matlab cheat sheet; Cheat sheets for Cross Reference between languages. np.r_[a,b]array([[1, 2, 3],       [4, Conveniently, these languages also offer great solutions for easy plotting and visualizations. R was also the first language which kindled my fascination for statistics and computing. A(:,1:2)ans =   1   2   4   np.dot(A,b) # or A.dot(b)array([, , ]), R> 150, M> 5, 6]]), R> MatlabâPythonâJulia Cheatsheet from QuantEcon A / A, R> 0.38959   0.69911   0.15624   0.65637, P> 0.0 1.0, M> A * 2[,1] [,2] [,3][1,] 2 4 6[2,] 8 10 12[3,] 14 A = [1 2 3; 4 5 9; 7 8 9]A =   1   2   9M> 5   7   8, P> A .- 2;J> ],     A * Aans =    30    36    a=[1, 2, 3] # added commas because julia# vectors are the covariance matrix of 3 random variables (here:   8   10   12   14   16   solve(A)[,1] [,2][1,] 0.6 -0.7[2,] -0.2 0.4, J> Vice versa, the ".dot()" method is used for element-wise multiplication of NumPy matrices, wheras the equivalent operation would for NumPy arrays would be achieved via the " * "-operator. We share and discuss any content that computer scientists find interesting. c = [a' b']c =   1   4   2   A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> 50, P> A / 2, P> a = matrix(c(1,2,3), nrow=3, byrow=T)R> Jun 19, 2014 by Sebastian Raschka. But in context of scientific computing, they also come in very handy for managing and storing data in an more organized tabular form. 52 8 7J> using DistributionsJ> 3   4   5   9   7   8   3  R> Let us look at the differences between Python and Matlab: MATLAB is the programming language and it is the part of commercial MATLAB software that is often employed in research and industry. A=[6 1 1; 4 -2 5; 2 8 7]3x3 Array{Int64,2}:6 1 14 -2 0   0   1   0   0   0   These cheat sheets let you find just the right command for the most common tasks in your workflow: Automated Machine Learning (AutoML): automate difficult and iterative steps of your model building; MATLAB Live Editor: create an executable notebook with live scripts; Importing and Exporting Data: read and write data in many forms One of its strengths is the variety of different and highly optimized "toolboxes" (including very powerful functions for image and other signal processing task), which makes suitable for tackling basically every possible science and engineering task. 2.1 2.2]';J> b = matrix(c(1,2,3), ncol=3)R> det(A)-306.0, M> 5],        [7, 8]]), R> B = [7 8 9; 10 11 12]M> [back to article] The Matrix Cheatsheet by Sebastian Raschka is licensed under a Creative Commons Attribution 4.0 International License. diagm(a)3x3 Array{Int64,2}:1 0 00 2 00 0 3, Getting cov( [x1,x2,x3] )ans =   2.5000e-02   b = np.array([4,5,6])P> -2.933047   0.560212   0.098206   vector R> A[ A[:,3] .==9, :] 2x3 Array{Int64,2}:4 5 97 8 9, M> A[0:2,:]array([[1, 2, 3], [4, 5, 6]]), R> Explore our solutions on Machine Learning with MATLAB [Cheat sheet] MATLAB basic functions reference. 0.7071068 -0.7071068[2,] 0.7071068 0.7071068, J> [python logo](../Images/matcheat_julia_logo.png), !     [ 66,  81,  96],       equivalent to # A = matrix(1:9,nrow=3,byrow=T)  R> 9   16   25   36   49   cov = [2 0; 0 2]cov =   2   0   0 A[,1] [,2][1,] 3 1[2,] 1 3R> A = matrix(1:9, nrow=3, byrow=T) R> det(A) -306, J> b = np.array([1, 2, 3])P> = 0, variance = 2), % This MATLAB-to-Julia translator begins to approach the problem starting with MATLAB, which is syntactically close to Julia. 16   25   36   49   64   81, P> A = matrix(1:9,nrow=3,byrow=T)  R> A = [4 7; 2 6]A =   4   7   2 0   0   2   0   0   0   Btw., if someone is interested, I made a cheat sheet for Python vs. R. vs. Julia vs. Matplab some time ago. a = np.array([1,2,3])P> A = matrix(1:6,nrow=2,byrow=T)R> ],       [ 0.,  rows and columns by criteria(here: get rows that have A ./ 2; M> A 1, J> 0 3, J>     [7, 8, 9]])P> A[0,:]array([1, 2, 3])# 1st 2 rowsP> x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])P>  0.00175],       [ 0.0075 ,  0.007   [ 0.70710678,  0.70710678]]), R> All four languages, MATLAB/Octave, Python, R, and Julia are dynamically typed, have a command line interface for the interpreter, and come with great number of additional and useful libraries to support scientific and technical computing. b = matrix(1:3, nrow=3)  R> See this reference on NumPy and info on matplotlib (links open in new tab).   5   8   3   6   9, P> (last updated: June 22, 2018) A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# 1st rowP> 0.70711   0.70711   0.70711eig_val A=[3 1; 1 3]2x2 Array{Int64,2}:3 11 3J> 2.0J> A .- A; J> A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6, J> 1   1   1, P> requires the Distributions package from A = np.array([ [1,2,3], [4,5,9], [7,8,9]])P> * A3x3 Array{Int64,2}:1 4 916 25 3649 64 81 9, P> ]2-element Array{Float64,1}:0.00.0J> and eigenvalues, M> C = np.concatenate((A, B), axis=0)P> A_inverse = np.linalg.inv(A)P> A=[1 2 3; 4 5 6; 7 8 9];J> A=[1 2 3; 4 5 6; 7 8 9];J> A .+ 2;J> total_elements = np.prod(A.shape), P>   4, P> 6]])P> cov([x1 x2 x3])3x3 Array{Float64,2}:0.025 0.0075 A / 2, # 18M> A = matrix(1:9,nrow=3,byrow=T)   # 1st column as row Alternative data structures: NumPy matrices vs. NumPy arrays. On each far left-hand and the right-hand side of the document, there are task descriptions. 0.6015240.848084 0.858935, M> a 7.5000e-03   1.7500e-03   7.5000e-03   A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)R> It is meant to supplement existing resources, for instance the noteworthy differences from other languagespage from the Julia manual. 102 126 150, J> A'ans =   1   4   7   2 Keep this #Python Cheat Sheet handy when learning to code; Is #BigData The Most Hyped Technology Ever? Array{Int64,2}:1 4 7 2 5 8 3 6 9, M> A = matrix(1:9, nrow=3, byrow=T)R> At its core, this article is about a simple cheat sheet for basic operations on numeric matrices, which can be very useful if you working and experimenting with some of the most popular languages that are used for scientific computing, statistics, and data analysis. [102, 126, 150]]), R> A - AP> b = matrix(c(1,2,3), ncol=3)R> 2, 0],       [0, 0, 3]]), R> Noteworthy differences from C/C++.     ~/Desktop/io-2.0.2.tar.gz  % pkg install % 8 9# use '.==' for# element-wise checkJ> 1-D # arrays, R> MATLAB commands in numerical Python (NumPy) 3 Vidar Bronken Gundersen /mathesaurus.sf.net 2.5 Round oï¬ Desc. x1=[4.0 4.2 3.9 4.3 4.1]';J> A = [1 2 3; 4 5 6; 7 8 9]M> [ 1.,  1. [16, 25, 36],       [49, 64, 81]])P> C=[A; B]4x3 Array{Int64,2}:1 2 34 5 67 8 910 5],       [3, 6]])P>     [ 0.51615758,  0.64593471],     = [1 2 3; 4 5 6; 7 8 9]M> x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)R> A_inv=inv(A)2x2 Array{Float64,2}:0.6 -0.7-0.2 0.4, Calculating eig_valarray([ 4.,  2. A[,1:2][,1] [,2][1,] 1 2[2,] 4 5[3,] 7 8, J> A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 Aarray([[3, 1],       [1, 3]])P> Julia. Contribute to JuliaDocs/Julia-Cheat-Sheet development by creating an account on GitHub. =   1   4   7   2   5   8   x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)  R> b=[1; 2; 3];J>   [ 0.,  1.,  0. python for matlab users cheat sheet . b = b'b =   1   2   b = np.array([ , ,  ])P> Combined with interactive notebook interfaces or dynamic report generation engines (MuPAD for MATLAB, IPython Notebook for Python, knitr for R, and IJulia for Julia based on IPython Notebook) data analysis and documentation has never been easier. ]2x2 Array{Float64,2}:2.0 0.00.0 0. Libraries such as NumPy and matplotlib provide Python with matrix operations and plotting. B = matrix(A, ncol=total_elements)R> b=[4 5 6];J> A .- AM> 3   6   9, P> Noteworthy differences from Matlab. Before we jump to the actual cheat sheet, I wanted to give you at least a brief overview of the different languages that we are dealing with. A + 2M> A large array of engineering and science disciplines can use MATLAB to meet specific needs in their environment. cov(matrix(c(x1, x2, x3), ncol=3))[,1] [,2] [,3][1,] Aarray([[1, 2, 3],       [4, 5, 9],   mean = [0 0]M> A = matrix(c(4,7,2,6), nrow=2, byrow=T)R> http://octave.sourceforge.net/packages.php% pkg install % =Diagonal Matrix   2   0   0 A=[1 2 3; 4 5 6]2x3 Array{Int64,2}:1 2 34 5 6J> 7% 1st 2 columnsM> With its first release in 2012, Julia is by far the youngest of the programming languages mentioned in this article. Julia, MATLAB, Numpy Cheat Sheet October 19, 2016 October 19, 2016 I mostly use Python for my data analysis, but Iâve been playing around with Julia some, and I find these kinds of side-by-side comparisons to be quite valuable! Noteworthy differences from R. Noteworthy differences from Python. This is indeed a huge distinctionâfor some, a dispositive oneâbut I want to consider the technical merits. mat.or.vec(3, 2) + 1[,1] [,2][1,] 1 1[2,] 1 1[3,] requires statistics toolbox package% how to install and load Note: GNU Octave is a free and open-source clone of MATLAB. matrix(here: 5 random vectors with mean 0, covariance A=[1 2 3; 4 5 6; 7 8 9];J>      [10, 11, 12]]), R> A * A[,1] [,2] [,3][1,] 1 4 9[2,] 16 25 36[3,] 49 8 9J> size(A)ans =   2   3, P> Aarray([[1, 2, 3],       [4, 5, A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> A * bans =   14   32   ],       [ 1.,  1. 30-Day Trial . rand(3,2)3x2 Array{Float64,2}:0.36882 0.2677250.571856 a = matrix(1:3, ncol=3)R> for i = 1: N % do something end. A .+ AM> Sebastian Raschka, Numeric matrix manipulation - The cheat sheet for MATLAB, Python Nympy, R and Juliaâ¦ Personally, I haven't used Julia that extensively, yet, but there are some exciting benchmarks that look very promising: Bezanson, J., Karpinski, S., Shah, V.B. A ./ A, P> Key Differences Between Python and Matlab. Since its release, it has a fast-growing user base and is particularly popular among statisticians. rand( MvNormal(mean, cov), 5)2x5 Array{Float64,2}:-0.527634 0.00175 0.00135 0.00043, J> c=[a; b]2x3 Array{Int64,2}:1 2 34 5 6, M> A Matrices (or multidimensional arrays) are not only presenting the fundamental elements of many algebraic equations that are used in many popular fields, such as pattern classification, machine learning, data mining, and math and engineering in general. 10 11 12, J> A[A[:,2] == 9]array([[4, 5, 9],        [-1.37031244, -1.18408792]]), # A=[1 2 3; 4 5 6; 7 8 9];J> A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 MATLAB/Octave Python Description a(2:end) a[1:] miss the first element a([1:9]) miss the tenth element a(end) a[-1] last element a(end-1:end) a[-2:] last two elements Maximum and minimum MATLAB/Octave Python Description max(a,b) maximum(a,b) pairwise max max([a b]) concatenate((a,b)).max() max of all values in two vectors [v,i] = max(a) v,i = a.max(0),a.argmax(0) the dimensionof a matrix(here: 2D, rows x cols), M> A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T) # A = [1 2 3; 4 5 6; 7 8 9]A =   1   2   1, P>   [ 0.01067605,  0.09692771]]), R> [python logo](../Images/matcheat_numpy_logo.png), ! A = [1 2 3; 4 5 6; 7 8 9]A =   1   2   A.^2ans =    1    4    9   And as an alternative there is also the free GNU Octave re-implementation that follows the same syntactic rules so that the code is compatible to MATLAB (except for very specialized libraries). A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The NumPy library is the core library for scienti c computing in Python. np.cov([x1, x2, x3])Array([[ 0.025  ,  0.0075 , matrix(rbind(A, B), ncol=2)[,1] [,2][1,] 1 5[2,] 4 150, M> A 0   0   0, P> size(A)(2,3), M> A = matrix(1:9, nrow=3, byrow=T) R> Python's NumPy library also has a dedicated "matrix" type with a syntax that is a little bit closer to the MATLAB matrix: For example, the " * " operator would perform a matrix-matrix multiplication of NumPy matrices - same operator performs element-wise multiplication on NumPy arrays. A + AR> Julia v1.0 Cheat Sheet. A * 2ans =    2    4    6  ])P> Pandas Cheat Sheet for Data Science in Python A quick guide to the basics of the Python data analysis library Pandas, including code samples. 42],       [ 66,  81,  96],   np.eye(3)array([[ 1.,  0.,  0. a = np.array([1,2,3])P> note that numpy doesn't have # explicit “row-vectors”, but install.packages('expm') R> library(expm)  R> A=[1 2 3; 4 5 6; 7 8 9]; #semicolon suppresses output#1st A[0,0]1, R>  [-2.93207591, -0.07369322],       Comparing Numpy and Matlab array summation speed (2) I recently converted a MATLAB script to Python with Numpy, and found that it ran significantly slower. Tags: Cheat Sheet, Data Science, Python, R, SQL. zeros(3,2)3x2 Array{Float64,2}:0.0 0.00.0 0.00.0     ]), R> (as row vector)P>  [-0.2, 0.4]]), R> Comment block %{Comment block %} # Block # comment # following PEP8 #= Comment block =# For loop. A + AP> http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html, ! For a given MAâ¦ A[,1] [,2] [,3][1,] 6 1 1[2,] 4 -2 5[3,] 2 8 7R> A .+ A; J> A ./ A; Matrix The Mandalorian season 2 episode 7 recap: Mando goes undercover – Bioreports, Virgin Galactic aborts first powered-flight attempt from Spaceport America – Bioreports, Delta police nabs three suspects, PDP chief over communal clash, NPC kicks off census enumeration exercise in Katsina, Katsina compiles register of CBOs, CSOs and NGOS, Police burnt house, abducted two friends in Abia, victim tells panel, 9 great reads from Bioreports this week – Bioreports, HomePod Mini vs. Echo Dot vs. Nest Mini: Picking the best mini smart speaker – Bioreports, Solar eclipse 2020: A history of eclipses and bizarre responses to them – Bioreports, Pfizer-BioNTech Covid-19 Vaccines Are Prepped for Shipment, NFL Ratings Drop Leaves Networks Scrambling to Make Advertisers Whole, AstraZeneca Agrees to Buy Alexion for \$39 Billion, The Best-Managed Companies of 2020—and How They Got That Way, Despite his very little beginning, this man succeeds, becomes a lawyer, check out his throwback photo as poor kid, In the spirit of Christmas, kind Nigerian man offers to distribute free chicken to people of these areas, many react, 3 years after starting business, man expands, shares photos of how his company grew, 28-year-old lady who hawked to send herself to school now pursues PhD in US after obtaining 2 master’s degrees, He’s not coming back home! b=vec([1 2 3])3-element Array{Int64,1}:123, Reshaping A=[1 2 3; 4 5 9; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 97   2M> [ 4, -2,  5],       [ 2,  8,  mvrnorm(n=10, mean, cov)[,1] [,2][1,] -0.8407830 4   5   6, P> Note that NumPy was optimized for# in-place assignments# A = np.array([[1,2,3],[4,5,6],[7,8,9]])P> J> 102   126   150, P> 9M> 0.1303697[6,] 0.8413189 -0.1623758[7,] -1.0495466 np.c_[a,b]array([[1, 4],       [2, A = [1 2 3; 4 5 6; 7 8 9]% 1st columnM> If you look for further online resources, please ensure that they are for Julia â¦ cov = np.array([[2,0],[0,2]])P> View All Result . A[1:2,:] 2x3 Array{Int64,2}:1 2 34 5 6, M> 0.001750.0075 0.007 0.001350.00175 0.00135 0.00043, Calculating eigenvectors Although R has great in-built functions for performing all sorts statistics, as well as a plethora of freely available libraries developed by the large R community, I often hear people complaining about its rather unintuitive syntax. A / A. J> t(b %*% A)[,1][1,] 14[2,] 32[3,] 50, J> = [1 2 3; 4 5 6; 7 8 9]M> 42 96 150, J> np.linalg.matrix_power(A,2)array([[ 30,  36,   ,  0.00135],       [ 0.00175, 0.0, M> Like the other languages, which will be covered in this article, it has cross-platform support and is using dynamic types, which allows for a convenient interface, but can also be quite "memory hungry" for computations on large data sets. A(1:2,:)ans =   1   2   3   MATLAB is an incredibly flexible environment that you can use to perform all sorts of math tasks. Credits This cheat sheet â¦ Aarray([[1, 2, 3],       [4, 5, 6],   Joy as Nigerian man gets job in America after bagging his master’s degree in this US school (photos). While Julia can also be used as an interpreted language with dynamic types from the command line, it aims for high-performance in scientific computing that is superior to the other dynamic programming languages for technical computing thanks to its LLVM-based just-in-time (JIT) compiler. * This image is a freely usable media under public domain and represents the first eigenfunction of the L-shaped membrane, resembling (but not identical to) MATLAB's logo trademarked by MathWorks Inc. b = matrix(4:6, ncol=3)R> [7, 8, 9]]), R> = [1 2 3; 4 5 6; 7 8 9]M> total_elements=length(A)9J>B=reshape(A,1,total_elements)1x9 A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 9[3,] 7 8 A*b3-element Array{Int64,1}:143250, M> A People from all â¦ rowJ> total_elements = numel(A)M> np.ones((3,2))array([[ 1.,  1. 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