Getting started with Python LanguageList comprehensionsFilterListFunctionsDecoratorsMath ModuleLoopsRandom moduleComparisonsImporting modulesSorting, Minimum and MaximumOperator moduleVariable Scope and BindingBasic Input and OutputFiles & Folders I/OJSON ModuleString MethodsMetaclassesIndexing and SlicingGeneratorsSimple Mathematical OperatorsReduceMap FunctionExponentiationSearchingDictionaryClassesCountingManipulating XMLDate and TimeSetCollections moduleParallel computationMultithreadingWriting extensionsUnit TestingRegular Expressions (Regex)Bitwise OperatorsIncompatibilities moving from Python 2 to Python 3Virtual environmentsCopying dataTupleContext Managers (“with” Statement)Hidden FeaturesEnumString FormattingConditionalsComplex mathUnicode and bytesThe __name__ special variableChecking Path Existence and PermissionsPython NetworkingAsyncio ModuleThe Print Functionos.pathCreating Python packagesParsing Command Line argumentsHTML ParsingSubprocess Librarysetup.pyList slicing (selecting parts of lists)SocketsItertools ModuleRecursionBoolean OperatorsThe dis moduleType Hintspip: PyPI Package ManagerThe locale ModuleExceptionsWeb scraping with PythonDeque ModuleDistributionProperty ObjectsOverloadingDebuggingReading and Writing CSVDynamic code execution with `exec` and `eval`PyInstaller - Distributing Python CodeIterables and IteratorsData Visualization with PythonThe Interpreter (Command Line Console)*args and **kwargsFunctools ModuleGarbage CollectionIndentationSecurity and CryptographyPickle data serialisationurllibBinary DataPython and ExcelIdiomsMethod OverridingDifference between Module and PackageData SerializationPython concurrencyIntroduction to RabbitMQ using AMQPStormPostgreSQLDescriptorCommon PitfallsMultiprocessingtempfile NamedTemporaryFileWorking with ZIP archivesStackProfilingUser-Defined MethodsWorking around the Global Interpreter Lock (GIL)DeploymentLoggingProcesses and ThreadsThe os ModuleComments and DocumentationDatabase AccessPython HTTP ServerAlternatives to switch statement from other languagesList destructuring (aka packing and unpacking)Accessing Python source code and bytecodeMixinsAttribute AccessArcPyPython Anti-PatternsPlugin and Extension ClassesWebsocketsImmutable datatypes(int, float, str, tuple and frozensets)String representations of class instances: __str__ and __repr__ methodsArraysOperator PrecedencePolymorphismNon-official Python implementationsList ComprehensionsWeb Server Gateway Interface (WSGI)2to3 toolAbstract syntax treeAbstract Base Classes (abc)UnicodeSecure Shell Connection in PythonPython Serial Communication (pyserial)Neo4j and Cypher using Py2NeoBasic Curses with PythonPerformance optimizationTemplates in pythonPillowThe pass statementLinked List Nodepy.testDate FormattingHeapqtkinterCLI subcommands with precise help outputDefining functions with list argumentsSqlite3 ModulePython PersistenceTurtle GraphicsConnecting Python to SQL ServerDesign PatternsMultidimensional arraysAudioPygletQueue ModuleijsonWebbrowser ModuleThe base64 ModuleFlaskgroupby()Sockets And Message Encryption/Decryption Between Client and ServerpygameInput, Subset and Output External Data Files using Pandashashlibgetting start with GZipDjangoctypesCreating a Windows service using PythonPython Server Sent EventsMutable vs Immutable (and Hashable) in PythonPython speed of programconfigparserLinked listsCommonwealth ExceptionsOptical Character RecognitionPython Data TypesPartial functionspyautogui modulegraph-toolUnzipping FilesFunctional Programming in PythonPython Virtual Environment - virtualenvsysvirtual environment with virtualenvwrapperCreate virtual environment with virtualenvwrapper in windowsPython Requests PostPlotting with MatplotlibPython Lex-YaccChemPy - python packagepyaudioshelveUsage of "pip" module: PyPI Package ManagerIoT Programming with Python and Raspberry PICode blocks, execution frames, and namespaceskivy - Cross-platform Python Framework for NUI DevelopmentCall Python from C#Similarities in syntax, Differences in meaning: Python vs. JavaScriptWriting to CSV from String or ListRaise Custom Errors / ExceptionsUsing loops within functionsPandas Transform: Preform operations on groups and concatenate the results

Multidimensional arrays

Other topics

Lists in lists

A good way to visualize a 2d array is as a list of lists. Something like this:

lst=[[1,2,3],[4,5,6],[7,8,9]]

here the outer list lst has three things in it. each of those things is another list: The first one is: [1,2,3], the second one is: [4,5,6] and the third one is: [7,8,9]. You can access these lists the same way you would access another other element of a list, like this:

print (lst[0])
#output: [1, 2, 3]

print (lst[1])
#output: [4, 5, 6]

print (lst[2])
#output: [7, 8, 9]

You can then access the different elements in each of those lists the same way:

print (lst[0][0])
#output: 1 

print (lst[0][1])
#output: 2 

Here the first number inside the [] brackets means get the list in that position. In the above example we used the number 0 to mean get the list in the 0th position which is [1,2,3]. The second set of [] brackets means get the item in that position from the inner list. In this case we used both 0 and 1 the 0th position in the list we got is the number 1 and in the 1st position it is 2

You can also set values inside these lists the same way:

lst[0]=[10,11,12]

Now the list is [[10,11,12],[4,5,6],[7,8,9]]. In this example we changed the whole first list to be a completely new list.

lst[1][2]=15

Now the list is [[10,11,12],[4,5,15],[7,8,9]]. In this example we changed a single element inside of one of the inner lists. First we went into the list at position 1 and changed the element within it at position 2, which was 6 now it's 15.

Lists in lists in lists in...

This behaviour can be extended. Here is a 3-dimensional array:

[[[111,112,113],[121,122,123],[131,132,133]],[[211,212,213],[221,222,223],[231,232,233]],[[311,312,313],[321,322,323],[331,332,333]]]

As is probably obvious, this gets a bit hard to read. Use backslashes to break up the different dimensions:

[[[111,112,113],[121,122,123],[131,132,133]],\
 [[211,212,213],[221,222,223],[231,232,233]],\
 [[311,312,313],[321,322,323],[331,332,333]]]

By nesting the lists like this, you can extend to arbitrarily high dimensions.

Accessing is similar to 2D arrays:

print(myarray)
print(myarray[1])
print(myarray[2][1])
print(myarray[1][0][2])
etc.

And editing is also similar:

myarray[1]=new_n-1_d_list
myarray[2][1]=new_n-2_d_list
myarray[1][0][2]=new_n-3_d_list #or a single number if you're dealing with 3D arrays
etc.

Contributors

Topic Id: 8186

Example Ids: 26312,28513

This site is not affiliated with any of the contributors.