book

Python for Data Science

by Yuli Vasiliev

August 2022

Beginner to intermediate

248 pages

6h 3m

English

No Starch Press

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Using Python for Data ScienceWho Should Read This Book? What’s in the Book?
Categories of DataUnstructured DataStructured DataSemistructured DataTime Series DataSources of DataAPIsWeb PagesDatabasesFilesThe Data Processing PipelineAcquisitionCleansingTransformationAnalysisStorageThe Pythonic WaySummary
ListsCreating a ListUsing Common List Object MethodsUsing Slice NotationUsing a List as a QueueUsing a List as a StackUsing Lists and Stacks for Natural Language ProcessingMaking Improvements with List ComprehensionsTuplesA List of TuplesImmutabilityDictionariesA List of DictionariesAdding to a Dictionary with setdefault()Loading JSON into a DictionarySetsRemoving Duplicates from SequencesPerforming Common Set OperationsExercise #1: Improved Photo Tag AnalysisSummary
NumPyInstalling NumPyCreating a NumPy ArrayPerforming Element-Wise OperationsUsing NumPy Statistical FunctionsExercise #2: Using NumPy Statistical Functionspandaspandas Installationpandas SeriesExercise #3: Combining Three Seriespandas DataFramesExercise #4: Using Different Joinsscikit-learnInstalling scikit-learnObtaining a Sample DatasetLoading the Sample Dataset into a pandas DataFrameSplitting the Sample Dataset into a Training Set and a Test SetTransforming Text into Numerical Feature VectorsTraining and Evaluating the ModelMaking Predictions on New DataSummary
Importing Data Using Python’s open() FunctionText FilesTabular Data FilesExercise #5: Opening JSON FilesBinary FilesExporting Data to FilesAccessing Remote Files and APIsHow HTTP Requests WorkThe urllib3 LibraryThe Requests LibraryExercise #6: Accessing an API with RequestsMoving Data to and from a DataFrameImporting Nested JSON StructuresConverting a DataFrame to JSONExercise #7: Manipulating Complex JSON StructuresLoading Online Data into a DataFrame with pandas-datareaderSummary
Relational DatabasesUnderstanding SQL StatementsGetting Started with MySQLDefining the Database StructureInserting Data into the DatabaseQuerying Database DataExercise #8: Performing a One-to-Many JoinUsing Database Analytics ToolsNoSQL DatabasesKey-Value StoresDocument-Oriented DatabasesExercise #9: Inserting and Querying Multiple DocumentsSummary
Data to AggregateCombining DataFramesGrouping and Aggregating the DataViewing Specific Aggregations by MultiIndexSlicing a Range of Aggregated ValuesSlicing Within Aggregation LevelsAdding a Grand TotalAdding SubtotalsExercise #10: Excluding Total Rows from the DataFrameSelecting All Rows in a GroupSummary

Combining Built-in Data StructuresCombining Lists and Tuples with +Combining Dictionaries with **Combining Corresponding Rows from Two StructuresImplementing Different Types of Joins for ListsConcatenating NumPy ArraysExercise #11: Adding New Rows/Columns to a NumPy ArrayCombining pandas Data StructuresConcatenating DataFramesJoining Two DataFramesSummary
Common VisualizationsLine GraphsBar GraphsPie ChartsHistogramsPlotting with MatplotlibInstalling MatplotlibUsing matplotlib.pyplotWorking with Figure and Axes ObjectsExercise #12: Combining Bins into an “Other” SliceUsing Other Libraries with MatplotlibPlotting pandas DataPlotting Geospatial Data with CartopyExercise #13: Drawing a Map with Cartopy and MatplotlibSummary
Obtaining Location DataTurning a Human-Readable Address into Geo CoordinatesGetting the Geo Coordinates of a Moving ObjectSpatial Data Analysis with geopy and ShapelyFinding the Closest ObjectFinding Objects in a Certain AreaExercise #14: Defining Two or More PolygonsCombining Both ApproachesExercise #15: Further Improving the Pick-Up AlgorithmCombining Spatial and Nonspatial DataDeriving Nonspatial AttributesExercise #16: Filtering Data with a List ComprehensionJoining Spatial and Nonspatial DatasetsSummary
Regular vs. Irregular Time SeriesCommon Time Series Analysis TechniquesCalculating Percentage ChangesRolling Window CalculationsCalculating the Percentage Change of a Rolling AverageMultivariate Time SeriesProcessing Multivariate Time SeriesAnalyzing Dependencies Between VariablesExercise #17: Adding More Metrics to Analyze DependenciesSummary
Association RulesSupportConfidenceLiftThe Apriori AlgorithmCreating a Transaction DatasetIdentifying Frequent ItemsetsGenerating Association RulesVisualizing Association RulesGaining Actionable Insights from Association RulesGenerating RecommendationsPlanning Discounts Based on Association RulesExercise #18: Mining Real Transaction DataSummary
Why Machine Learning?Types of Machine LearningSupervised LearningUnsupervised LearningHow Machine Learning WorksData to Learn FromA Statistical ModelPreviously Unseen DataA Sentiment Analysis Example: Classifying Product ReviewsObtaining Product ReviewsCleansing the DataSplitting and Transforming the DataTraining the ModelEvaluating the ModelExercise #19: Expanding the Example SetPredicting Stock TrendsGetting DataDeriving Features from Continuous DataGenerating the Output VariableTraining and Evaluating the ModelExercise #20: Experimenting with Different Stocks and New MetricsSummary