Python 數(shù)據(jù)科學(xué)備忘單Python 基礎(chǔ)知識

PythonForDataScienceCheatSheet

PythonBasics

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VariablesandDataTypes

NumpyArrays

AlsoseeLists

SelectingNumpyArrayElements

Indexstartsat0

NumpyArrayOperations

NumpyArrayFunctions

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my_2darray[rows,columns]

Selectitemsatindex0and1

Selectitematindex1

Subset

>>>my_array[1]

2

Slice

>>>my_array[0:2]

array([1,2])

Subset2DNumpyarrays

>>>my_2darray[:,0]

array([1,4])

>>>my_array>3

array([False,False,False,True],dtype=bool)

>>>my_array*2

array([2,4,6,8])

>>>my_array+np.array([5,6,7,8])

array([6,8,10,12])

>>>my_list=[1,2,3,4]

>>>my_array=np.array(my_list)

>>>my_2darray=np.array([[1,2,3],[4,5,6]])

VariableAssignment

Lists

Selectitematindex1Select3rdlastitem

Selectitemsatindex1and2Selectitemsafterindex0Selectitemsbeforeindex3Copymy_list

my_list[list][itemOfList]

Subset

>>>my_list[1]

>>>my_list[-3]

Slice

>>>my_list[1:3]

>>>my_list[1:]

>>>my_list[:3]

>>>my_list[:]

SubsetListsofLists

>>>my_list2[1][0]

>>>my_list2[1][:2]

>>>a='is'

>>>b='nice'

>>>my_list=['my','list',a,b]

>>>my_list2=[[4,5,6,7],[3,4,5,6]]

SelectingListElements

AlsoseeNumPyArrays

Libraries

Importlibraries

>>>importnumpy

>>>importnumpyasnp

Selectiveimport

>>>frommathimportpi

InstallPython

Dataanalysis

Machinelearning

Scientificcomputing

2Dplotting

Leadingopendatascienceplatform FreeIDEthatisincluded CreateandsharepoweredbyPython withAnaconda documentswithlivecode,

visualizations,text,...

Indexstartsat0

>>>x=5

>>>x5

CalculationsWithVariables

>>>x+2

Sumoftwovariables

7

>>>x-2

Subtractionoftwovariables

3

>>>x*2

Multiplicationoftwovariables

10

>>>x**2

Exponentiationofavariable

25

>>>x%2

Remainderofavariable

1

>>>x/float(2)

Divisionofavariable

2.5

GettheindexofanitemCountanitem

AppendanitematatimeRemoveanitem

RemoveanitemReversethelistAppendanitemRemoveanitemInsertanitemSortthelist

>>>my_list.index(a)

>>>my_list.count(a)

>>>my_list.append('!')

>>>my_list.remove('!')

>>>del(my_list[0:1])

>>>my_list.reverse()

>>>my_list.extend('!')

>>>my_list.pop(-1)

>>>my_list.insert(0,'!')

>>>my_list.sort()

str()

'5','3.45','True'

Variablestostrings

int()

5,3,1

Variablestointegers

float()

5.0,1.0

Variablestofloats

bool()

True,True,True

Variablestobooleans

TypesandTypeConversion

ListOperations

>>>my_list+my_list

['my','list','is','nice','my','list','is','nice']

>>>my_list*2

['my','list','is','nice','my','list','is','nice']

>>>my_list2>4

True

ListMethods

AskingForHelp

>>>help(str)

>>>my_string='thisStringIsAwesome'

>>>my_string

'thisStringIsAwesome'

Strings

>>>my_string*2

'thisStringIsAwesomethisStringIsAwesome'

>>>my_string+'Innit'

'thisStringIsAwesomeInnit'

>>>'m'inmy_string

True

StringOperations

StringOperations

>>>my_string[3]

>>>my_string[4:9]

StringMethods

Indexstartsat0

>>>my_array.shape

>>>np.append(other_array)

GetthedimensionsofthearrayAppenditemstoanarray

>>>np.insert(my_array,1,5)

Insertitemsinanarray

>>>np.delete(my_array,[1])

Deleteitemsinanarray

>>>np.mean(my_array)

Meanofthearray

>>>np.median(my_array)

Medianofthearray

>>>my_array.corrcoef()

Correlationcoefficient

>>>np.std(my_array)

Standarddeviation

StringtouppercaseStringtolowercaseCountStringelementsReplaceStringelementsStripwhitespaces

>>>my_string.upper()

>>>my_string.lower()

>>>my_string.count('w')

>>>my_string.replace('e','i')

>>>my_string.strip()

PythonForDataScienceCheatSheet

JupyterNotebook

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WorkingwithDifferentProgrammingLanguages

Kernelsprovidecomputationandcommunicationwithfront-endinterfaceslikethenotebooks.Therearethreemainkernels:

IRkernel IJulia

InstallingJupyterNotebookwillautomaticallyinstalltheIPythonkernel.

Widgets

Notebookwidgetsprovidetheabilitytovisualizeandcontrolchangesinyourdata,oftenasacontrollikeaslider,textbox,etc.

YoucanusethemtobuildinteractiveGUIsforyournotebooksortosynchronizestatefulandstatelessinformationbetweenPythonandJavaScript.

Saving/LoadingNotebooks

Createnewnotebook

Makeacopyofthecurrentnotebook

Savecurrentnotebookandrecordcheckpoint

Previewoftheprintednotebook

Closenotebook&stoprunninganyscripts

Openanexistingnotebook

Renamenotebook

Revertnotebooktoapreviouscheckpoint

Downloadnotebookas

IPythonnotebook

Python

HTML

Markdown

reST

Restartkernel

Restartkernel&runallcells

Restartkernel&runallcells

CommandMode:

Interruptkernel

Interruptkernel&clearalloutput

Connectbacktoaremotenotebook

Runotherinstalledkernels

Downloadserializedstateofallwidgetmodelsinuse

Savenotebookwithinteractivewidgets

Embedcurrentwidgets

15

13 14

WritingCodeAndText

LaTeX

PDF

1 2 3 4 5 67 8910 11 12

Codeandtextareencapsulatedby3basiccelltypes:markdowncells,codecells,andrawNBConvertcells.

EditCells

EditMode:

Saveandcheckpoint

Insertcellbelow

Interruptkernel

Restartkernel

Cutcurrentlyselectedcellstoclipboard

Pastecellsfromclipboardabovecurrentcell

Pastecellsfrom

Copycellsfromclipboardtocurrentcursorposition

Pastecellsfromclipboardbelowcurrentcell

ExecutingCells

Runselectedcell(s) Runcurrentcellsdownandcreateanewone

below

Cutcell

Copycell(s)

Pastecell(s)below

Movecellup

Movecelldown

Runcurrentcell

AskingForHelp

Displaycharacteristics

Opencommandpalette

Currentkernel

Kernelstatus

Logoutfromnotebookserver

clipboardontopofcurrentcel

Revert“DeleteCells”

invocation

Mergecurrentcellwiththeoneabove

Movecurrentcellup

Adjustmetadataunderlyingthecurrentnotebook

Removecellattachments

Pasteattachmentsofcurrentcell

InsertCells

Addnewcellabovethecurrentone

Deletecurrentcells

Splitupacellfromcurrentcursorposition

Mergecurrentcellwiththeonebelow

Movecurrentcelldown

Findandreplaceinselectedcells

Copyattachmentsofcurrentcell

Insertimageinselectedcells

Addnewcellbelowthecurrentone

Runcurrentcellsdownandcreateanewoneabove

Runallcellsabovethecurrentcell

Changethecelltypeofcurrentcell

toggle,togglescrollingandclearalloutput

ViewCells

ToggledisplayofJupyterlogoandfilename

Togglelinenumbersincells

Runallcells

Runallcellsbelowthecurrentcell

toggle,togglescrollingandclearcurrentoutputs

Toggledisplayoftoolbar

Toggledisplayofcellactionicons:

None

Editmetadata

Rawcellformat

Slideshow

Attachments

Tags

WalkthroughaUItour

Editthebuilt-inkeyboardshortcuts

Descriptionofmarkdownavailableinnotebook

PythonhelptopicsNumPyhelptopicsMatplotlibhelptopics

Pandashelptopics

DataCamp

Listofbuilt-inkeyboardshortcuts

Notebookhelptopics

InformationonunofficialJupyterNotebookextensions

IPythonhelptopicsSciPyhelptopicsSymPyhelptopics

AboutJupyterNotebook

LearnPythonforDataScienceInteractively

NumPy

TheNumPylibraryisthecorelibraryforscientificcomputinginPython.Itprovidesahigh-performancemultidimensionalarrayobject,andtoolsforworkingwiththesearrays.

Usethefollowingimportconvention:

>>>importnumpyasnp

NumPyArrays

2

1Darray

2Darray

axis1

axis0

3Darray

axis2

axis1

axis0

CreatingArrays

InitialPlaceholders

I/O

Saving&LoadingOnDisk

Saving&LoadingTextFiles

DataTypes

CreateanarrayofzerosCreateanarrayofonesCreateanarrayofevenlyspacedvalues(stepvalue)

Createanarrayofevenly

spacedvalues(numberofsamples)

CreateaconstantarrayCreatea2X2identitymatrix

CreateanarraywithrandomvaluesCreateanemptyarray

>>>np.zeros((3,4))

>>>np.ones((2,3,4),dtype=16)

>>>d=np.arange(10,25,5)

>>>np.linspace(0,2,9)

>>>e=np.full((2,2),7)

>>>f=np.eye(2)

>>>np.random.random((2,2))

>>>np.empty((3,2))

>>>np.loadtxt("myfile.txt")

>>>np.genfromtxt("my_file.csv",delimiter=',')

>>>np.savetxt("myarray.txt",a,delimiter="")

>>>np.save('my_array',a)

>>>np.savez('array.npz',a,b)

>>>np.load('my_array.npy')

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

>>>b=np.array([(1.5,2,3),(4,5,6)],dtype=float)

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

dtype=float)

1 2

3

Subsetting,Slicing,Indexing

AlsoseeLists

Subsetting

>>>a[2]

3

>>>b[1,2]

6.0

Slicing

>>>a[0:2]

array([1,2])

>>>b[0:2,1]

array([2.,5.])

>>>b[:1]

array([[1.5,2.,3.]])

>>>c[1,...]

array([[[3.,2.,1.],

[4.,5.,6.]]])

>>>a[::-1]

array([3,2,1])

BooleanIndexing

>>>a[a<2]

array([1])

12

3

1.52

4

5

3

6

Selecttheelementatthe2ndindex

Selecttheelementatrow0column2(equivalenttob[1][2])

123 Selectitemsatindex0and1

Selectitemsatrows0and1incolumn1

Selectallitemsatrow0(equivalenttob[0:1,:])

Sameas[1,:,:]

Reversedarraya

123 Selectelementsfromalessthan2

FancyIndexing

>>>b[[1,0,1,0],[0,1,2,0]] Selectelements(1,0),(0,1),(1,2)and(0,0)

array([4.,2.,6.,1.5])

>>>b[[1,0,1,0]][:,[0,1,2,0]] Selectasubsetofthematrix’srows

array([[4.,5.,6.,4.], andcolumns

[1.5,2.,3.,1.5],

[4.,5.,6.,4.],

[1.5,2.,3.,1.5]])

ArrayManipulation

CopyingArrays

SortingArrays

Sortanarray

Sorttheelementsofanarray'saxis

>>>a.sort()

>>>c.sort(axis=0)

InspectingYourArray

>>>a.shape

Arraydimensions

>>>len(a)

Lengthofarray

>>>b.ndim

Numberofarraydimensions

>>>e.size

Numberofarrayelements

>>>b.dtype

Datatypeofarrayelements

>>>

Nameofdatatype

>>>b.astype(int)

Convertanarraytoadifferenttype

>>>a.sum()

Array-wisesum

>>>a.min()

Array-wiseminimumvalue

>>>b.max(axis=0)

Maximumvalueofanarrayrow

>>>b.cumsum(axis=1)

Cumulativesumoftheelements

>>>a.mean()

Mean

>>>b.median()

Median

>>>a.corrcoef()

Correlationcoefficient

>>>np.std(b)

Standarddeviation

TransposingArray

>>>i=np.transpose(b)

>>>i.T

PermutearraydimensionsPermutearraydimensions

ChangingArrayShape

>>>b.ravel()

>>>g.reshape(3,-2)

Flattenthearray

Reshape,butdon’tchangedata

Adding/RemovingElements

>>>h.resize((2,6))

Returnanewarraywithshape(2,6)

>>>np.append(h,g)

Appenditemstoanarray

>>>np.insert(a,1,5)

Insertitemsinanarray

>>>np.delete(a,[1])

Deleteitemsfromanarray

CombiningArrays

>>>np.concatenate((a,d),axis=0)

Concatenatearrays

array([1,2,3,10,15,20])

>>>np.vstack((a,b))

Stackarraysvertically(row-wise)

array([[1.,2.,3.],

[1.5,2.,3.],

[4.,5.,6.]])

>>>np.r_[e,f]

Stackarraysvertically(row-wise)

>>>np.hstack((e,f))

array([[7.,7.,1.,0.],

Stackarrayshorizontally(column-wise)

[7.,7.,0.,1.]])

>>>np.column_stack((a,d))

Createstackedcolumn-wisearrays

array([[1,10],

[2,15],

[3,20]])

>>>np.c_[a,d]

Createstackedcolumn-wisearrays

SplittingArrays

>>>np.hsplit(a,3)

[array([1]),array([2]),array([3])]

>>>np.vsplit(c,2)

[array([[[1.5,2.,1.],

[4.,5.,6.]]]),

array([[[3.,2.,3.],

[4.,5.,6.]]])]

Splitthearrayhorizontallyatthe3rdindex

Splitthearrayverticallyatthe2ndindex

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>>>h=a.view()

>>>np.copy(a)

>>>h=a.copy()

CreateaviewofthearraywiththesamedataCreateacopyofthearray

Createadeepcopyofthearray

>>>64

Signed64-bitintegertypes

>>>np.float32

Standarddouble-precisionfloatingpoint

>>>plex

Complexnumbersrepresentedby128floats

>>>np.bool

BooleantypestoringTRUEandFALSEvalues

>>>np.object

Pythonobjecttype

>>>np.string_

Fixed-lengthstringtype

>>>np.unicode_

Fixed-lengthunicodetype

PythonForDataScienceCheatSheet

NumPyBasics

ArrayMathematics

ArithmeticOperations

Comparison

AggregateFunctions

Element-wisecomparison

Element-wisecomparisonArray-wisecomparison

>>>a==b

array([[False,True,True],

[False,False,False]],dtype=bool)

>>>a<2

array([True,False,False],dtype=bool)

>>>np.array_equal(a,b)

Subtraction

SubtractionAddition

AdditionDivision

DivisionMultiplication

MultiplicationExponentiationSquareroot

PrintsinesofanarrayElement-wisecosine

Element-wisenaturallogarithmDotproduct

>>>np.divide(a,b)

>>>a*b

array([[1.5, 4., 9.],

[4.,10.,18.]])

>>>np.multiply(a,b)

>>>np.exp(b)

>>>np.sqrt(b)

>>>np.sin(a)

>>>np.cos(b)

>>>np.log(a)

>>>e.dot(f)

array([[7.,7.],

[7.,7.]])

],

]])

>>>g=a-b

array([[-0.5,0.,0.],

[-3.,-3.,-3.]])

>>>np.subtract(a,b)

>>>b+a

array([[2.5,4.,6.],

[5.,7.,9.]])

>>>np.add(b,a)

>>>a/b

array([[0.66666667,1. ,1.

[0.25 ,0.4 ,0.5

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AskingForHelp

>>>(np.ndarray.dtype)

1.5

4

2

3

5

6

1.523

4

5

6

1.5

2

3

4

5

6

PythonForDataScienceCheatSheet

SciPy-LinearAlgebra

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LinearAlgebra

You’llusethelinalgandsparsemodules.Notethatscipy.linalgcontainsandexpandsonnumpy.linalg.

>>>fromscipyimportlinalg,sparse

MatrixFunctions

SciPy

TheSciPylibraryisoneofthecorepackagesforscientificcomputingthatprovidesmathematicalalgorithmsandconveniencefunctionsbuiltontheNumPyextensionofPython.

Addition

>>>np.add(A,D)

Subtraction

>>>np.subtract(A,D)

Division

>>>np.divide(A,D)

Multiplication

>>>np.multiply(D,A)

>>>np.dot(A,D)

>>>np.vdot(A,D)

>>>np.inner(A,D)

>>>np.outer(A,D)

>>>np.tensordot(A,D)

>>>np.kron(A,D)

ExponentialFunctions

>>>linalg.expm(A)

>>>linalg.expm2(A)

>>>linalg.expm3(D)

AdditionSubtractionDivision

MultiplicationDotproduct

Vectordotproduct

InnerproductOuterproductTensordotproductKroneckerproduct

Matrixexponential

Matrixexponential(TaylorSeries)

Matrixexponential(eigenvalue

decomposition)

LogarithmFunction

>>>linalg.logm(A)

TrigonometricTunctions

>>>linalg.sinm(D)

>>>linalg.cosm(D)

>>>linalg.tanm(A)

HyperbolicTrigonometricFunctions

>>>linalg.sinhm(D)

>>>linalg.coshm(D)

>>>linalg.tanhm(A)

MatrixSignFunction

>>>np.sigm(A)

MatrixSquareRoot

>>>linalg.sqrtm(A)

ArbitraryFunctions

>>>linalg.funm(A,lambdax:x*x)

Matrixlogarithm

MatrixsineMatrixcosineMatrixtangent

HypberbolicmatrixsineHyperbolicmatrixcosineHyperbolicmatrixtangent

MatrixsignfunctionMatrixsquarerootEvaluatematrixfunction

CreatingMatrices

AlsoseeNumPy

>>>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]])

InverseInverse

Tranposematrix

ConjugatetranspositionTrace

Frobeniusnorm

L1norm(maxcolumnsum)Linfnorm(maxrowsum)

MatrixrankDeterminant

SolverfordensematricesSolverfordensematrices

Least-squaressolutiontolinearmatrixequation

Computethepseudo-inverseofamatrix(least-squaressolver)

Computethepseudo-inverseofamatrix(SVD)

Inverse

>>>A.I

>>>linalg.inv(A)

>>>A.T

>>>A.H

>>>np.trace(A)

Norm

>>>linalg.norm(A)

>>>linalg.norm(A,1)

>>>linalg.norm(A,np.inf)

Rank

>>>np.linalg.matrix_rank(C)

Determinant

>>>linalg.det(A)

Solvinglinearproblems

>>>linalg.solve(A,b)

>>>E=np.mat(a).T

>>>linalg.lstsq(D,E)

Generalizedinverse

>>>linalg.pinv(C)

>>>linalg.pinv2(C)

>>>np.mgrid[0:5,0:5]

Createadensemeshgrid

>>>np.ogrid[0:2,0:2]

Createanopenmeshgrid

>>>np.r_[[3,[0]*5,-1:1:10j]

Stackarraysvertically(row-wise)

>>>np.c_[b,c]

Createstackedcolumn-wisearrays

BasicMatrixRoutines

InteractingWithNumPy

AlsoseeNumPy

IndexTricks

ShapeManipulation

Polynomials

VectorizingFunctions

TypeHandling

OtherUsefulFunctions

ReturntheangleofthecomplexargumentCreateanarrayofevenlyspacedvalues

(numberofsamples)

Unwrap

Createanarrayofevenlyspacedvalues(logscale)Returnvaluesfromalistofarraysdependingonconditions

Factorial

CombineNthingstakenatktimeWeightsforNp-pointcentralderivative

Findthen-thderivativeofafunctionatapoint

>>>np.angle(b,deg=True)

>>>g=np.linspace(0,np.pi,num=5)

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

>>>np.unwrap(g)

>>>np.logspace(0,10,3)

>>>np.select([c<4],[c*2])

>>>misc.factorial(a)

>>>b(10,3,exact=True)

>>>misc.central_diff_weights(3)

>>>misc.derivative(myfunc,1.0)

ReturntherealpartofthearrayelementsReturntheimaginarypartofthearrayelementsReturnarealarrayifcomplexpartscloseto0Castobjecttoadatatype

>>>np.real(c)

>>>np.imag(c)

>>>np.real_if_close(c,tol=1000)

>>>np.cast['f'](np.pi)

Vectorizefunctions

>>>defmyfunc(a):

ifa<0:returna*2

else:

returna/2

>>>np.vectorize(myfunc)

Createapolynomialobject

>>>fromnumpyimportpoly1d

>>>p=poly1d([3,4,5])

>>>importnumpyasnp

>>>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)]])

Createa2X2identitymatrixCreatea2x2identitymatrix

CompressedSparseRowmatrixCompressedSparseColumnmatrixDictionaryOfKeysmatrix

Sparsematrixtofullmatrix

Identifysparsematrix

>>>F=np.eye(3,k=1)

>>>G=np.mat(np.identity(2))

>>>C[C>0.5]=0

>>>H=sparse.csr_matrix(C)

>>>I=sparse.csc_matrix(D)

>>>J=sparse.dok_matrix(A)

>>>E.todense()

>>>sparse.isspmatrix_csc(A)

>>>np.transpose(b)

Permutearraydimensions

>>>b.flatten()

Flattenthearray

>>>np.hstack((b,c))

Stackarrayshorizontally(column-wise)

>>>np.vstack((a,b))

Stackarraysvertically(row-wise)

>>>np.hsplit(c,2)

Splitthearrayhorizontallyatthe2ndindex

>>>np.vpslit(d,2)

Splitthearrayverticallyatthe2ndindex

CreatingSparseMatrices

Decompositions

InverseNorm

Solverforsparsematrices

Inverse

>>>sparse.linalg.inv(I)

Norm

>>>sparse.linalg.norm(I)

Solvinglinearproblems

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

SolveordinaryorgeneralizedeigenvalueproblemforsquarematrixUnpackeigenvalues

FirsteigenvectorSecondeigenvectorUnpackeigenvalues

SingularValueDecomposition(SVD)ConstructsigmamatrixinSVD

LUDecomposition

EigenvaluesandEigenvectors

>>>la,v=linalg.eig(A)

>>>l1,l2=la

>>>v[:,0]

>>>v[:,1]

>>>linalg.eigvals(A)

SingularValueDecomposition

>>>U,s,Vh=linalg.svd(B)

>>>M,N=B.shape

>>>Sig=linalg.diagsvd(s,M,N)

LUDecomposition

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

SparseMatrixRoutines

SparseMatrixFunctions

SparseMatrixDecompositions

Sparsematrixexponential

>>>sparse.linalg.expm(I)

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EigenvaluesandeigenvectorsSVD

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

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

AskingForHelp

>>>help(scipy.linalg.diagsvd)

>>>(np.matrix)

PythonForDataScienceCheatSheet

PandasBasics

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Pandas

ThePandaslibraryisbuiltonNumPyandprovideseasy-to-usedatastructuresanddataanalysistoolsforthePythonprogramminglanguage.

Usethefollowingimportconvention:

>>>importpandasaspd

PandasDataStructures

Series

a

3

b

-5

c

7

d

4

Aone-dimensionallabeledarraycapableofholdinganydatatype

AskingForHelp

>>>help(pd.Series.loc)

Selection

>>>s['b']

-5

>>>df[1:]

Country

India

Brazil

CapitalNewDelhiBrasília

Population1303171035

207847528

Getoneelement

GetsubsetofaDataFrame

Getting

ByPosition

>>>df.iloc([0],[0])

'Belgium'

>>>df.iat([0],[0])

'Belgium'

ByLabel

>>>df.loc([0],['Country'])

'Belgium'

>>>df.at([0],['Country'])

'Belgium'

ByLabel/Position

>>>df.ix[2]

Country Brazil

Capital BrasíliaPopulation207847528

>>>df.ix[:,'Capital']

Brussels

NewDelhi

Brasília

>>>df.ix[1,'Capital']

'NewDelhi'

BooleanIndexing

>>>s[~(s>1)]

>>>s[(s<-1)|(s>2)]

>>>df[df['Population']>1200000000]

Setting

>>>s['a']=6

Selecting,BooleanIndexing&Setting

AlsoseeNumPyArrays

Selectsinglevaluebyrow&column

Dropping

Dropvaluesfromrows(axis=0)Dropvaluesfromcolumns(axis=1)

>>>s.drop(['a','c'])

>>>df.drop('Country',axis=1)

Sort&Rank

SortbylabelsalonganaxisSortbythevaluesalonganaxisAssignrankstoentries

>>>df.sort_index()

>>>df.sort_values(by='Country')

>>>df.rank()

RetrievingSeries/DataFrameInformation

BasicInformation

>>>df.shape

(rows,columns)

>>>df.index

Describeindex

>>>df.columns

DescribeDataFramecolumns

>>>()

InfoonDataFrame

>>>df.count()

Numberofnon-NAvalues

>>>df.sum()

Sumofvalues

>>>df.cumsum()

Cummulativesumofvalues

>>>df.min()/df.max()

Minimum/maximumvalues

>>>df.idxmin()/df.idxmax()

Minimum/Maximumindexvalue

>>>df.describe()

Summarystatistics

>>>df.mean()

Meanofvalues

>>>df.median()

Medianofvalues

Summary

Selectsinglevaluebyrow&columnlabels

Index

>>>s=pd.Series([3,-5,7,4],index=['a','b','c','d'])

DataFrame

Country

Capital

Population

0

Belgium

Brussels

11190846

1

India

NewDelhi

1303171035

2

Brazil

Brasília

207847528

Columns

Applyfunction

Applyfunctionelement-wise

>>>f=lambdax:x*2

>>>df.apply(f)

>>>df.applymap(f)

ApplyingFunctions

Index

Atwo-dimensionallabeleddatastructurewithcolumnsofpotentiallydifferenttypes

Selectsinglerowofsubsetofrows

Selectasinglecolumnofsubsetofcolumns

DataAlignment

InternalDataAlignment

NAvaluesareintroducedintheindicesthatdon’toverlap:

Selectrowsandcolumns

>>>data={'Country':['Belgium','India','Brazil'],

'Capital':['Brussels','NewDelhi','Brasília'],'Population':[11190846,1303171035,207847528]}

>>>df=pd.DataFrame(data,

columns=['Country','Capital','Population'])

I/O

>>>pd.read_csv('file.csv',header=None,nrows=5)

>>>df.to_csv('myDataFrame.csv')

ReadandWritetoCSV

ReadandWritetoExcel

Seriesswherevalueisnot>1

swherevalueis<-1or>2

UsefiltertoadjustDataFrame

SetindexaofSeriessto6

ReadandWritetoSQLQueryorDatabaseTable

>>>fromsqlalchemyimportcreate_engine

>>>engine=create_engine('sqlite:///:memory:')

>>>pd.read_sql("SELECT*FROMmy_table;",engine)

>>>pd.read_sql_table('my_table',engine)

>>>pd.read_sql_query("SELECT*FROMmy_table;",engine)

>>>pd.read_excel('file.xlsx')

>>>pd.to_excel('dir/myDataFrame.xlsx',sheet_name='Sheet1')

Readmultiplesheetsfromthesamefile

>>>xlsx=pd.ExcelFile('file.xls')

>>>df=pd.read_excel(xlsx,'Sheet1')

read_sql()isaconveniencewrapperaroundread_sql_table()and

read_sql_query()

>>>s3=pd.Series([7,-2,3],index=['a','c','d'])

>>>s+s3

a 10.0

b NaN

c 5.0

d 7.0

ArithmeticOperationswithFillMethods

Youcanalsodotheinternaldataalignmentyourselfwiththehelpofthefillmethods:

>>>s.add(s3,fill_value=0)

a

10.0

b

-5.0

c

5.0

d

7.0

>>>s.sub(s3,fill_value=2)

>>>s.div(s3,fill_value=4)

>>>s.mul(s3,fill_value=3)

>>>pd.to_sql('myDf',engine)

DataCamp

LearnPythonforDataScienceInteractively

LoadingTheData

AlsoseeNumPy&Pandas

YourdataneedstobenumericandstoredasNumPyarraysorSciPysparsematrices.Othertypesthatareconvertibletonumericarrays,suchasPandasDataFrame,arealsoacceptable.

>>>importnumpyasnp

>>>X=np.random.random((10,5))

>>>y=np.array(['M','M','F','F','M','F','M','M','F','F','F'])

>>>X[X<0.7]=0

CreateYourModel

SupervisedLearningEstimators

UnsupervisedLearningEstimators

PrincipalComponentAnalysis(PCA)

>>>fromsklearn.decompositionimportPCA

>>>pca=PCA(n_components=0.95)

KMeans

>>>fromsklearn.clusterimportKMeans

>>>k_means=KMeans(n_clusters=3,random_state=0)

LinearRegression

>>>fromsklearn.linear_modelimportLinearRegression

>>>lr=LinearRegression(normalize=True)

SupportVectorMachines(SVM)

>>>fromsklearn.svmimportSVC

>>>svc=SVC(kernel='linear')

NaiveBayes

>>>fromsklearn.naive_bayesimportGaussianNB

>>>gnb=GaussianNB()

KNN

>>>fromsklearnimportneighbors

>>>knn=neighbors.KNeighborsClassifier(n_neighbors=5)

ModelFitting

Fitthemodeltothedata

Fittodata,thentransformit

Fitthemodeltothedata

Supervisedlearning

>>>lr.fit(X,y)

>>>knn.fit(X_train,y_train)

>>>svc.fit(X_train,y_train)

UnsupervisedLearning

>>>k_means.fit(X_train)

>>>pca_model=pca.fit_transform(X_train)

TuneYourModel

Prediction

PredictlabelsPredictlabels

Estimateprobabilityofalabel

Predictlabelsinclusteringalgos

SupervisedEstimators

>>>y_pred=svc.predict(np.random.random((2,5)))

>>>y_pred=lr.predict(X_test)

>>>y_pred=knn.predict_proba(X_test)

UnsupervisedEstimators

>>>y_pred=k_means.predict(X_test)

EvaluateYourModel’sPerformance

ClassificationMetrics

RegressionMetrics

ClusteringMetrics

Cross-Validation

>>>fromsklearn.cross_validationimportcross_val_score

>>>print(cross_val_score(knn,X_train,y_train,cv=4))

>>>print(cross_val_score(lr,X,y,cv=2))

AdjustedRandIndex

>>>fromsklearn.metricsimportadjusted_rand_score

>>>adjusted_rand_score(y_true,y_pred)

Homogeneity

>>>fromsklearn.metricsimporthomogeneity_score

>>>homogeneity_score(y_true,y_pred)

V-measure

>>>fromsklearn.metricsimportv_measure_score

>>>metrics.v_measure_score(y_true,y_pred)

MeanAbsoluteError

>>>fromsklearn.metricsimportmean_absolute_error

>>>y_true=[3,-0.5,2]

>>>mean_absolute_error(y_true,y_pred)

MeanSquaredError

>>>fromsklearn.metricsimportmean_squared_error

>>>mean_squared_error(y_test,y_pred)

R2Score

>>>fromsklearn.metricsimportr2_score

>>>r2_score(y_true,y_pred)

Standardization

EncodingCategoricalFeatures

Normalization

ImputingMissingValues

Binarization

GeneratingPolynomialFeatures

PreprocessingTheData

>>>fromsklearn.preprocessingimportPolynomialFeatures

>>>poly=PolynomialFeatures(5)

>>>poly.fit_transform(X)

>>>fromsklearn.preprocessingimportBinarizer

>>>binarizer=Binarizer(threshold=0.0).fit(X)

>>>binary_X=binarizer.transform(X)

>>>fromsklearn.preprocessingimportImputer

>>>imp=Imputer(missing_values=0,strategy='mean',axis=0)

>>>imp.fit_transform(X_train)

>>>fromsklearn.preprocessingimportNormalizer

>>>scaler=Normalizer().fit(X_train)

>>>normalized_X=scaler.transform(X_train)

>>>normalized_X_test=scaler.transform(X_test)

>>>fromsklearn.preprocessingimportLabelEncoder

>>>enc=LabelEncoder()

>>>y=enc.fit_transform(y)

>>>fromsklearn.preprocessingimportStandardScaler

>>>scaler=StandardScaler().fit(X_train)

>>>standardized_X=scaler.transform(X_train)

>>>standardized_X_test=scaler.transform(X_test)

TrainingAndTestData

>>>fromsklearn.model_selectionimporttrain_test_split

>>>X_train,X_test,y_train,y_test=train_test_split(X,

y,random_state=0)

PythonForDataScienceCheatSheet

AccuracyScore

>>>knn.score(X_test,y_test)

Estimatorscoremethod

>>>fromsklearn.metricsimportaccuracy_score