Related skills
- Data Science
Associated roles
- Business analyst
- Data architect
- Data scientist
- Python developer
Contents
- New for the Second EditionConventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgmentsIn Memoriam: John D. Hunter (1968–2012)Acknowledgments for the Second Edition (2017)Acknowledgments for the First Edition (2012)
- 1.1 What Is This Book About?What Kinds of Data?1.2 Why Python for Data Analysis?Python as GlueSolving the “Two-Language” ProblemWhy Not Python?1.3 Essential Python LibrariesNumPypandasmatplotlibIPython and JupyterSciPyscikit-learnstatsmodels1.4 Installation and SetupWindowsApple (OS X, macOS)GNU/LinuxInstalling or Updating Python PackagesPython 2 and Python 3Integrated Development Environments (IDEs) and Text Editors1.5 Community and Conferences1.6 Navigating This BookCode ExamplesData for ExamplesImport ConventionsJargon
- 2.1 The Python Interpreter2.2 IPython BasicsRunning the IPython ShellRunning the Jupyter NotebookTab CompletionIntrospectionThe %run CommandExecuting Code from the ClipboardTerminal Keyboard ShortcutsAbout Magic CommandsMatplotlib Integration2.3 Python Language BasicsLanguage SemanticsScalar TypesControl Flow
- 3.1 Data Structures and SequencesTupleListBuilt-in Sequence FunctionsdictsetList, Set, and Dict Comprehensions3.2 FunctionsNamespaces, Scope, and Local FunctionsReturning Multiple ValuesFunctions Are ObjectsAnonymous (Lambda) FunctionsCurrying: Partial Argument ApplicationGeneratorsErrors and Exception Handling3.3 Files and the Operating SystemBytes and Unicode with Files3.4 Conclusion
- 4.1 The NumPy ndarray: A Multidimensional Array ObjectCreating ndarraysData Types for ndarraysArithmetic with NumPy ArraysBasic Indexing and SlicingBoolean IndexingFancy IndexingTransposing Arrays and Swapping Axes4.2 Universal Functions: Fast Element-Wise Array Functions4.3 Array-Oriented Programming with ArraysExpressing Conditional Logic as Array OperationsMathematical and Statistical MethodsMethods for Boolean ArraysSortingUnique and Other Set Logic4.4 File Input and Output with Arrays4.5 Linear Algebra4.6 Pseudorandom Number Generation4.7 Example: Random WalksSimulating Many Random Walks at Once4.8 Conclusion
- 5.1 Introduction to pandas Data StructuresSeriesDataFrameIndex Objects5.2 Essential FunctionalityReindexingDropping Entries from an AxisIndexing, Selection, and FilteringInteger IndexesArithmetic and Data AlignmentFunction Application and MappingSorting and RankingAxis Indexes with Duplicate Labels5.3 Summarizing and Computing Descriptive StatisticsCorrelation and CovarianceUnique Values, Value Counts, and Membership5.4 Conclusion
- 6.1 Reading and Writing Data in Text FormatReading Text Files in PiecesWriting Data to Text FormatWorking with Delimited FormatsJSON DataXML and HTML: Web Scraping6.2 Binary Data FormatsUsing HDF5 FormatReading Microsoft Excel Files6.3 Interacting with Web APIs6.4 Interacting with Databases6.5 Conclusion
- 7.1 Handling Missing DataFiltering Out Missing DataFilling In Missing Data7.2 Data TransformationRemoving DuplicatesTransforming Data Using a Function or MappingReplacing ValuesRenaming Axis IndexesDiscretization and BinningDetecting and Filtering OutliersPermutation and Random SamplingComputing Indicator/Dummy Variables7.3 String ManipulationString Object MethodsRegular ExpressionsVectorized String Functions in pandas7.4 Conclusion
- 8.1 Hierarchical IndexingReordering and Sorting LevelsSummary Statistics by LevelIndexing with a DataFrame’s columns8.2 Combining and Merging DatasetsDatabase-Style DataFrame JoinsMerging on IndexConcatenating Along an AxisCombining Data with Overlap8.3 Reshaping and PivotingReshaping with Hierarchical IndexingPivoting “Long” to “Wide” FormatPivoting “Wide” to “Long” Format8.4 Conclusion
- 9.1 A Brief matplotlib API PrimerFigures and SubplotsColors, Markers, and Line StylesTicks, Labels, and LegendsAnnotations and Drawing on a SubplotSaving Plots to Filematplotlib Configuration9.2 Plotting with pandas and seabornLine PlotsBar PlotsHistograms and Density PlotsScatter or Point PlotsFacet Grids and Categorical Data9.3 Other Python Visualization Tools9.4 Conclusion
- 10.1 GroupBy MechanicsIterating Over GroupsSelecting a Column or Subset of ColumnsGrouping with Dicts and SeriesGrouping with FunctionsGrouping by Index Levels10.2 Data AggregationColumn-Wise and Multiple Function ApplicationReturning Aggregated Data Without Row Indexes10.3 Apply: General split-apply-combineSuppressing the Group KeysQuantile and Bucket AnalysisExample: Filling Missing Values with Group-Specific ValuesExample: Random Sampling and PermutationExample: Group Weighted Average and CorrelationExample: Group-Wise Linear Regression10.4 Pivot Tables and Cross-TabulationCross-Tabulations: Crosstab10.5 Conclusion
- 11.1 Date and Time Data Types and ToolsConverting Between String and Datetime11.2 Time Series BasicsIndexing, Selection, SubsettingTime Series with Duplicate Indices11.3 Date Ranges, Frequencies, and ShiftingGenerating Date RangesFrequencies and Date OffsetsShifting (Leading and Lagging) Data11.4 Time Zone HandlingTime Zone Localization and ConversionOperations with Time Zone−Aware Timestamp ObjectsOperations Between Different Time Zones11.5 Periods and Period ArithmeticPeriod Frequency ConversionQuarterly Period FrequenciesConverting Timestamps to Periods (and Back)Creating a PeriodIndex from Arrays11.6 Resampling and Frequency ConversionDownsamplingUpsampling and InterpolationResampling with Periods11.7 Moving Window FunctionsExponentially Weighted FunctionsBinary Moving Window FunctionsUser-Defined Moving Window Functions11.8 Conclusion
- 12.1 Categorical DataBackground and MotivationCategorical Type in pandasComputations with CategoricalsCategorical Methods12.2 Advanced GroupBy UseGroup Transforms and “Unwrapped” GroupBysGrouped Time Resampling12.3 Techniques for Method ChainingThe pipe Method12.4 Conclusion
- 13.1 Interfacing Between pandas and Model Code13.2 Creating Model Descriptions with PatsyData Transformations in Patsy FormulasCategorical Data and Patsy13.3 Introduction to statsmodelsEstimating Linear ModelsEstimating Time Series Processes13.4 Introduction to scikit-learn13.5 Continuing Your Education
- 14.1 1.USA.gov Data from BitlyCounting Time Zones in Pure PythonCounting Time Zones with pandas14.2 MovieLens 1M DatasetMeasuring Rating Disagreement14.3 US Baby Names 1880–2010Analyzing Naming Trends14.4 USDA Food Database14.5 2012 Federal Election Commission DatabaseDonation Statistics by Occupation and EmployerBucketing Donation AmountsDonation Statistics by State14.6 Conclusion
- A.1 ndarray Object InternalsNumPy dtype HierarchyA.2 Advanced Array ManipulationReshaping ArraysC Versus Fortran OrderConcatenating and Splitting ArraysRepeating Elements: tile and repeatFancy Indexing Equivalents: take and putA.3 BroadcastingBroadcasting Over Other AxesSetting Array Values by BroadcastingA.4 Advanced ufunc Usageufunc Instance MethodsWriting New ufuncs in PythonA.5 Structured and Record ArraysNested dtypes and Multidimensional FieldsWhy Use Structured Arrays?A.6 More About SortingIndirect Sorts: argsort and lexsortAlternative Sort AlgorithmsPartially Sorting Arraysnumpy.searchsorted: Finding Elements in a Sorted ArrayA.7 Writing Fast NumPy Functions with NumbaCreating Custom numpy.ufunc Objects with NumbaA.8 Advanced Array Input and OutputMemory-Mapped FilesHDF5 and Other Array Storage OptionsA.9 Performance TipsThe Importance of Contiguous Memory
- B.1 Using the Command HistorySearching and Reusing the Command HistoryInput and Output VariablesB.2 Interacting with the Operating SystemShell Commands and AliasesDirectory Bookmark SystemB.3 Software Development ToolsInteractive DebuggerTiming Code: %time and %timeitBasic Profiling: %prun and %run -pProfiling a Function Line by LineB.4 Tips for Productive Code Development Using IPythonReloading Module DependenciesCode Design TipsB.5 Advanced IPython FeaturesMaking Your Own Classes IPython-FriendlyProfiles and ConfigurationB.6 Conclusion
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Chapter 1.Preliminaries
1.1 What Is This Book About?
This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While “data analysis” is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you needfor data analysis.
What Kinds of Data?
When I say “data,” what am I referring to exactly? The primary focus is onstructured data, a deliberately vague term that encompasses many different common forms of data, such as:
Tabular or spreadsheet-like data in which each column may be a different type (string, numeric, date, or otherwise). This includes most kinds of data commonly stored in relational databases or tab- or comma-delimited text files.
Multidimensional arrays (matrices).
Multiple tables of data interrelated by key columns (what would be primary or foreign keys for a SQL user).
Evenly or unevenly spaced time series.
This is by no means a complete list. Even though it may not always be obvious, a large percentage of datasets can be transformed into a structured form that is more suitable for analysis and modeling. If not, it may be possible to extract features from a dataset into a structured form. As an example, a collection of news articles ...