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# Pandas 用户指南目录 “用户指南” 按主题划分区域涵盖了几乎所有Pandas的功能。每个小节都介绍了一个主题（例如“处理缺失的数据”），并讨论了Pandas如何解决问题，其中包含许多示例。 刚开始接触Pandas的同学应该从十分钟入门Pandas开始看起。 有关任何特定方法的更多信息，请参阅API参考。 IO工具（文本，CSV，HDF5，…）CSV & text filesJSONHTMLExcel filesOpenDocument SpreadsheetsClipboardPicklingmsgpackHDF5 (PyTables)FeatherParquetSQL queriesGoogle BigQueryStata formatSAS formatsOther file formatsPerformance considerations索引和数据选择器Different choices for indexingBasicsAttribute accessSlicing rangesSelection by labelSelection by positionSelection by callableIX indexer is deprecatedIndexing with list with missing labels is deprecatedSelecting random samplesSetting with enlargementFast scalar value getting and settingBoolean indexingIndexing with isinThe where() Method and MaskingThe query() MethodDuplicate dataDictionary-like get() methodThe lookup() methodIndex objectsSet / reset indexReturning a view versus a copy多索引/高级索引Hierarchical indexing (MultiIndex)Advanced indexing with hierarchical indexSorting a MultiIndexTake methodsIndex typesMiscellaneous indexing FAQ合并、联接和连接Concatenating objectsDatabase-style DataFrame or named Series joining/mergingTimeseries friendly merging重塑和数据透视表Reshaping by pivoting DataFrame objectsReshaping by stacking and unstackingReshaping by MeltCombining with stats and GroupByPivot tablesCross tabulationsTilingComputing indicator / dummy variablesFactorizing valuesExamplesExploding a list-like column处理文本字符串Splitting and replacing stringsConcatenationIndexing with .strExtracting substringsTesting for Strings that match or contain a patternCreating indicator variablesMethod summary处理丢失的数据Values considered “missing”Sum/prod of empties/nansNA values in GroupByFilling missing values: fillnaFilling with a PandasObjectDropping axis labels with missing data: dropnaInterpolationReplacing generic valuesString/regular expression replacementNumeric replacement分类数据Object creationCategoricalDtypeDescriptionWorking with categoriesSorting and orderComparisonsOperationsData mungingGetting data in/outMissing dataDifferences to R’s factorGotchasNullable整型数据类型可视化Basic plotting: plotOther plotsPlotting with missing dataPlotting ToolsPlot FormattingPlotting directly with matplotlibTrellis plotting interface计算工具Statistical functionsWindow FunctionsAggregationExpanding windowsExponentially weighted windows组操作: 拆分-应用-组合Splitting an object into groupsIterating through groupsSelecting a groupAggregationTransformationFiltrationDispatching to instance methodsFlexible applyOther useful featuresExamples时间序列/日期方法OverviewTimestamps vs. Time SpansConverting to timestampsGenerating ranges of timestampsTimestamp limitationsIndexingTime/date componentsDateOffset objectsTime Series-Related Instance MethodsResamplingTime span representationConverting between representationsRepresenting out-of-bounds spansTime zone handling时间增量ParsingOperationsReductionsFrequency conversionAttributesTimedeltaIndexResampling样式Building stylesFiner control: slicingFiner Control: Display ValuesBuiltin stylesSharing stylesOther OptionsFun stuffExport to ExcelExtensibility选项和设置OverviewGetting and setting optionsSetting startup options in Python/IPython environmentFrequently Used OptionsAvailable optionsNumber formattingUnicode formattingTable schema display提高性能Cython (writing C extensions for pandas)Using NumbaExpression evaluation via >eval()稀疏数据结构SparseArraySparseDtypeSparse accessorSparse calculationMigratingInteraction with scipy.sparseSparse subclasses常见问题(FAQ)DataFrame memory usageUsing if/truth statements with pandasNaN, Integer NA values and NA type promotionsDifferences with NumPyThread-safetyByte-Ordering issues烹饪指南IdiomsSelectionMultiIndexingMissing dataGroupingTimeseriesMergePlottingData In/OutComputationTimedeltasAliasing axis namesCreating example data
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