WIXLIB

Python For Data Science Cheat SheetSciPy - Linear AlgebraLearn More Python for Data Science Interactively at www.datacamp.comSciPyThe SciPy library is one of the core packages forscientific computing that provides mathematicalalgorithms and convenience functions built on theNumPy extension of Python.Interacting With NumPy Also see NumPyimport numpy as npa 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)]])Index Tricks np.mgrid[0:5,0:5]np.ogrid[0:2,0:2]np.r [[3,[0]*5,-1:1:10j]np.c [b,c]Create a dense meshgridCreate an open meshgridStack arrays vertically (row-wise)Create stacked column-wise arraysShape Manipulation k((a,b))np.hsplit(c,2)np.vpslit(d,2)Permute array dimensionsFlatten the arrayStack arrays horizontally (column-wise)Stack arrays vertically (row-wise)Split the array horizontally at the 2nd indexSplit the array vertically at the 2nd indexPolynomials from numpy import poly1d p poly1d([3,4,5])Create a polynomial objectVectorizing Functions np.vectorize(myfunc)Vectorize functionsType Handling np.real(c)np.imag(c)np.real if close(c,tol 1000)np.cast['f'](np.pi)Return the real part of the array elementsReturn the imaginary part of the array elementsReturn a real array if complex parts close to 0Cast object to a data typeOther Useful Functions(number of samples) g [3:] np.pinp.unwrap(g)Unwrapnp.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 misc.factorial(a)misc.comb(10,3,exact True)misc.central diff weights(3)misc.derivative(myfunc,1.0) from scipy import linalg, sparseCreating Matrices ABCD Basic Matrix RoutinesInverse A.Ilinalg.inv(A)A.TA.Hnp.trace(A)Norm linalg.norm(A) linalg.norm(A,1) e N things taken at k timeWeights for Np-point central derivativeFind the n-th derivative of a function at a pointInverseInverseTranpose matrixConjugate transpositionTraceFrobenius normL1 norm (max column sum)L inf norm (max row sum)Matrix rank linalg.det(A)Determinant linalg.solve(A,b) E np.mat(a).T linalg.lstsq(D,E)Solver for dense matricesSolver for dense matricesLeast-squares solution to linear matrixequationSolving linear problemsGeneralized inverse linalg.pinv(C)Compute the pseudo-inverse of a matrix(least-squares solver)Compute the pseudo-inverse of a matrix(SVD)F np.eye(3, k 1)G np.mat(np.identity(2))C[C 0.5] 0H sparse.csr matrix(C)I sparse.csc matrix(D)J sparse.dok matrix(A)E.todense()sparse.isspmatrix csc(A)Create a 2X2 identity matrixCreate a 2x2 identity matrixCompressed Sparse Row matrixCompressed Sparse Column matrixDictionary Of Keys matrixSparse matrix to full matrixIdentify sparse matrixSparse Matrix RoutinesInverseInverse sparse.linalg.norm(I)Norm sparse.linalg.spsolve(H,I)Solver for sparse matricesSparse Matrix Functions sparse.linalg.expm(I)Asking For Help help(scipy.linalg.diagsvd) np.info(np.matrix) np.subtract(A,D)Subtraction np.divide(A,D)Division MultiplicationDot productVector dot productInner productOuter productTensor dot productKronecker kron(A,D)Exponential Functions linalg.expm(A) linalg.expm2(A) linalg.expm3(D)Matrix exponentialMatrix exponential (Taylor Series)Matrix exponential (eigenvaluedecomposition)Logarithm Function linalg.logm(A)Matrix logarithm linalg.sinm(D) linalg.cosm(D) linalg.tanm(A)Matrix sineMatrix cosineMatrix tangent linalg.sinhm(D) linalg.coshm(D) linalg.tanhm(A)Hypberbolic matrix sineHyperbolic matrix cosineHyperbolic matrix tangent np.sigm(A)Matrix sign function linalg.sqrtm(A)Matrix square root linalg.funm(A, lambda x: x*x)Evaluate matrix functionTrigonometric TunctionsHyperbolic Trigonometric FunctionsMatrix Sign FunctionMatrix Square RootDecompositionsEigenvalues and Eigenvectors la, v linalg.eig(A) l1, l2 lav[:,0]v[:,1]linalg.eigvals(A)Singular Value Decomposition sparse.linalg.inv(I)Solving linear problemsAdditionSubtractionArbitrary FunctionsCreating Sparse Matrices np.add(A,D)Multiplication np.linalg.matrix rank(C)DeterminantMatrix mat([[3,4], [5,6]])Norm 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 valuesAlso see NumPyYou’ll use the linalg and sparse modules. Note that scipy.linalg contains and expands on numpy.linalg. linalg.pinv2(C) def myfunc(a):if a 0:return a*2else:return a/2Linear AlgebraSparse matrix exponentialSolve ordinary or generalizedeigenvalue problem for square matrixUnpack eigenvaluesFirst eigenvectorSecond eigenvectorUnpack eigenvalues U,s,Vh linalg.svd(B)Singular Value Decomposition (SVD) M,N B.shape Sig linalg.diagsvd(s,M,N) Construct sigma matrix in SVDLU Decomposition P,L,U linalg.lu(C)LU DecompositionSparse Matrix Decompositions la, v sparse.linalg.eigs(F,1) sparse.linalg.svds(H, 2)DataCampEigenvalues and eigenvectorsSVDLearn Python for Data Science Interactively

Python For Data Science Cheat SheetPandas BasicsLearn Python for Data Science Interactively at www.DataCamp.comAsking For HelpSelectionAlso see NumPy ArraysGetting s['b']Get one element df[1:]Get subset of a DataFrame-5PandasThe Pandas library is built on NumPy and provides easy-to-usedata structures and data analysis tools for the Pythonprogramming language.Dropping help(pd.Series.loc)12CountryIndiaBrazilCapitalNew DelhiBrasíliaPopulation1303171035207847528By Position import pandas as pd df.iloc([0],[0])'Belgium'Pandas Data StructuresIndexa3b-5c7d4 s pd.Series([3, -5, 7, 4], index ['a', 'b', 'c', 'd'])DataFrameColumnsIndexSelect single value by row &column'Belgium'A one-dimensional labeled arraycapable of holding any data ulation11190846New Delhi 1303171035BrasíliaA two-dimensional labeleddata structure with columnsof potentially different types207847528 data {'Country': ['Belgium', 'India', 'Brazil'],'Capital': ['Brussels', 'New Delhi', 'Brasília'],'Population': [11190846, 1303171035, 207847528]} df pd.DataFrame(data,columns ['Country', 'Capital', 'Population'])'Belgium'Select single value by row &column labels df.at([0], ['Country'])'Belgium'By Label/Position df.ix[2]Select single row ofsubset of rows df.ix[:,'Capital']Select a single column ofsubset of columns df.ix[1,'Capital']Select rows and columnsCountryBrazilCapitalBrasíliaPopulation 207847528012BrusselsNew DelhiBrasíliaBoolean IndexingSettingSet index a of Series s to 6Read and Write to Excel pd.read excel('file.xlsx') pd.to excel('dir/myDataFrame.xlsx', sheet name 'Sheet1')Read multiple sheets from the same file xlsx pd.ExcelFile('file.xls') df pd.read excel(xlsx, 'Sheet1')(rows,columns)Describe indexDescribe DataFrame columnsInfo on DataFrameNumber of non-NA values .idxmax()df.describe()df.mean()df.median()Sum of valuesCummulative sum of valuesMinimum/maximum valuesMinimum/Maximum index valueSummary statisticsMean of valuesMedian of valuesApplying Functions f lambda x: x*2 df.apply(f) df.applymap(f)Apply functionApply function element-wiseInternal Data Alignment s3 pd.Series([7, -2, 3], index ['a', 'c', 'd']) s s3a10.0c5.0bdNaN7.0Arithmetic Operations with Fill MethodsI/O pd.read csv('file.csv', header None, nrows 5) df.to .info()df.count()NA values are introduced in the indices that don’t overlap: s[ (s 1)]Series s where value is not 1 s[(s -1) (s 2)]s where value is -1 or 2 df[df['Population'] 1200000000] Use filter to adjust DataFrameRead and Write to CSV Data Alignment'New Delhi' s['a'] 6 df.sort index()Sort by labels along an axis df.sort values(by 'Country') Sort by the values along an axis df.rank()Assign ranks to entriesSummaryBy Label df.loc([0], ['Country'])Sort & RankBasic Information df.iat([0],[0])SeriesDrop values from rows (axis 0) df.drop('Country', axis 1) Drop values from columns(axis 1)Retrieving Series/DataFrame InformationSelecting, Boolean Indexing & SettingUse the following import convention: s.drop(['a', 'c'])Read and Write to SQL Query or Database Table from sqlalchemy import create engineengine create engine('sqlite:///:memory:')pd.read sql("SELECT * FROM my table;", engine)pd.read sql table('my table', engine)pd.read sql query("SELECT * FROM my table;", engine)read sql()is a convenience wrapper around read sql table() andread sql query() pd.to sql('myDf', engine)You can also do the internal data alignment yourself withthe help of the fill methods: s.add(s3, fill value 0)abcd10.0-5.05.07.0 s.sub(s3, fill value 2) s.div(s3, fill value 4) s.mul(s3, fill value 3)DataCampLearn Python for Data Science Interactively

Python For Data Science Cheat SheetScikit-LearnNaive Bayes from sklearn.metrics import classification report Precision, recall, f1-score print(classification report(y test, y pred)) and supportClassification ReportKNNUnsupervised Learning EstimatorsK MeansSupervised learning lr.fit(X, y) knn.fit(X train, y train) svc.fit(X train, y train)Unsupervised Learning k means.fit(X train) pca model pca.fit transform(X train)Standardizationfrom sklearn.preprocessing import StandardScalerscaler StandardScaler().fit(X train)standardized X scaler.transform(X train)standardized X test scaler.transform(X test)Normalization from sklearn.preprocessing import Normalizerscaler Normalizer().fit(X train)normalized X scaler.transform(X train)normalized X test scaler.transform(X test)Binarization from sklearn.preprocessing import Binarizer binarizer Binarizer(threshold 0.0).fit(X) binary X binarizer.transform(X)Mean Squared ErrorR² Score from sklearn.metrics import r2 score r2 score(y true, y pred)Clustering MetricsAdjusted Rand IndexFit the model to the data from sklearn.metrics import adjusted rand score adjusted rand score(y true, y pred)HomogeneityFit the model to the dataFit to data, then transform itPredictionSupervised Estimators y pred svc.predict(np.random.random((2,5))) Predict labels y pred lr.predict(X test)Predict labels y pred knn.predict proba(X test)Estimate probability of a labelUnsupervised Estimators y pred k means.predict(X test) from sklearn.metrics import homogeneity score homogeneity score(y true, y pred)V-measure from sklearn.metrics import v measure score metrics.v measure score(y true, y pred)Cross-ValidationPredict labels in clustering algosPreprocessing The Data Mean Absolute Error from sklearn.metrics import mean squared error mean squared error(y test, y pred)Model Fittingimport numpy as npX np.random.random((10,5))y 'F'])X[X 0.7] 0Regression MetricsPrincipal Component Analysis (PCA) from sklearn.cluster import KMeans k means KMeans(n clusters 3, random state 0)Your data needs to be numeric and stored as NumPy arrays or SciPy sparsematrices. Other types that are convertible to numeric arrays, such as PandasDataFrame, are also acceptable.Confusion Matrix from sklearn.metrics import confusion matrix print(confusion matrix(y test, y pred)) from sklearn.metrics import mean absolute error y true [3, -0.5, 2] mean absolute error(y true, y pred) from sklearn.decomposition import PCA pca PCA(n components 0.95)Also see NumPy & Pandas from sklearn.model selection import train test split X train, X test, y train, y test train test split(X,y,random state 0)Support Vector Machines (SVM) from sklearn import neighbors knn neighbors.KNeighborsClassifier(n neighbors 5)from sklearn import neighbors, datasets, preprocessingfrom sklearn.model selection import train test splitfrom sklearn.metrics import accuracy scoreiris datasets.load iris()X, y iris.data[:, :2], iris.targetX train, X test, y train, y test train test split(X, y, random state 33)scaler preprocessing.StandardScaler().fit(X train)X train scaler.transform(X train)X test scaler.transform(X test)knn neighbors.KNeighborsClassifier(n neighbors 5)knn.fit(X train, y train)y pred knn.predict(X test)accuracy score(y test, y pred)Accuracy ScoreEstimator score method knn.score(X test, y test) from sklearn.metrics import accuracy score Metric scoring functions accuracy score(y test, y pred) from sklearn.naive bayes import GaussianNB gnb GaussianNB()A Basic ExampleTraining And Test DataClassification Metrics from sklearn.svm import SVC svc SVC(kernel 'linear')Scikit-learn is an open source Python library thatimplements a range of machine learning,preprocessing, cross-validation and visualizationalgorithms using a unified interface. Supervised Learning Estimators from sklearn.linear model import LinearRegression lr LinearRegression(normalize True)Scikit-learnLoading The DataEvaluate Your Model’s PerformanceLinear RegressionLearn Python for data science Interactively at www.DataCamp.com Create Your ModelEncoding Categorical Features from sklearn.preprocessing import LabelEncoder enc LabelEncoder() y enc.fit transform(y)Imputing Missing Values from sklearn.preprocessing import Imputer imp Imputer(missing values 0, strategy 'mean', axis 0) imp.fit transform(X train)Generating Polynomial Features from sklearn.preprocessing import PolynomialFeatures poly PolynomialFeatures(5) poly.fit transform(X) from sklearn.cross validation import cross val score print(cross val score(knn, X train, y train, cv 4)) print(cross val score(lr, X, y, cv 2))Tune Your ModelGrid Search from sklearn.grid search import GridSearchCV params {"n neighbors": np.arange(1,3),"metric": ["euclidean", "cityblock"]} grid GridSearchCV(estimator knn,param grid params) grid.fit(X train, y train) print(grid.best score ) print(grid.best estimator .n neighbors)Randomized Parameter Optimization from sklearn.grid search import RandomizedSearchCV params {"n neighbors": range(1,5),"weights": ["uniform", "distance"]} rsearch RandomizedSearchCV(estimator knn,param distributions params,cv 4,n iter 8,random state 5) rsearch.fit(X train, y train) print(rsearch.best score )DataCampLearn Python for Data Science Interactively

Python For Data Science Cheat SheetMatplotlibPlot Anatomy & WorkflowPlot AnatomyAxes/SubplotLearn Python Interactively at www.DataCamp.comMatplotlibY-axisMatplotlib is a Python 2D plotting library which producespublication-quality figures in a variety of hardcopy formatsand interactive environments acrossplatforms.1Prepare The DataAlso see Lists & NumPy1D Data import numpy as npx np.linspace(0, 10, 100)y np.cos(x)z np.sin(x)2D Data or Images 2data 2 * np.random.random((10, 10))data2 3 * np.random.random((10, 10))Y, X np.mgrid[-3:3:100j, -3:3:100j]U -1 - X**2 YV 1 X - Y**2from matplotlib.cbook import get sample dataimg np.load(get sample data('axes grid/bivariate normal.npy')) import matplotlib.pyplot as pltFigure fig plt.figure() fig2 plt.figure(figsize plt.figaspect(2.0))AxesAll plotting is done with respect to an Axes. In most cases, asubplot will fit your needs. A subplot is an axes on a grid system.3 import matplotlib.pyplot as pltx [1,2,3,4]Step 1y [10,20,25,30]fig plt.figure() Step 2ax fig.add subplot(111) Step 3ax.plot(x, y, color 'lightblue', linewidth 3) Step 3, 4ax.scatter([2,4,6],[5,15,25],color 'darkgreen',marker ' ') ax.set xlim(1, 6.5) plt.savefig('foo.png')Step 6 plt.show()FigureX-axis4Customize PlotColors, Color Bars & Color MapsMathtext plt.title(r' sigma i 15 ', fontsize 20)plt.plot(x, x, x, x**2, x, x**3)ax.plot(x, y, alpha 0.4)ax.plot(x, y, c 'k')fig.colorbar(im, orientation 'horizontal')im ax.imshow(img,cmap 'seismic')Limits, Legends & LayoutsLimits & Autoscaling Markers fig, ax plt.subplots() ax.scatter(x,y,marker ".") ax.plot(x,y,marker "o")fig.add axes()ax1 fig.add subplot(221) # row-col-numax3 fig.add subplot(212)fig3, axes plt.subplots(nrows 2,ncols 2)fig4, axes2 plt.subplots(ncols 3) ax.margins(x 0.0,y 0.1)ax.axis('equal')ax.set(xlim [0,10.5],ylim [-1.5,1.5])ax.set xlim(0,10.5) ax.set(title 'An Example Axes',ylabel 'Y-Axis',xlabel 'X-Axis') ax.legend(loc 'best')Set a title and x-and y-axis labels ax.xaxis.set(ticks range(1,5),ticklabels [3,100,-12,"foo"]) ax.tick params(axis 'y',direction 'inout',length 10)Manually set x-ticks fig3.subplots adjust(wspace 0.5,hspace 0.3,left 0.125,right 0.9,top 0.9,bottom 0.1) fig.tight layout()Adjust the spacing between subplotsText & Annotations ax.text(1,-2.1,'Example Graph',style 'italic') ax.annotate("Sine",xy (8, 0),xycoords 'data',xytext (10.5, 0),textcoords 'data',arrowprops dict(arrowstyle "- ",connectionstyle "arc3"),)Subplot Spacing axes[0,1].arrow(0,0,0.5,0.5) axes[1,1].quiver(y,z

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. It provides a high-performance multidimension