learning pandas

很适合入门的的一本pandas学习的书，书是英文文字版本的，清晰。Learning pandaswww.allitebooks.comladle oi uonllenilsLearning pandasCreditsAbout the authorAbout the reviewerswww.Packtpub.comSupport files, eBooks, discount offers, and moreWhy subscribe?Free access for packt account holdersPreefaceWhat this book coversWhat you need for this bookWho this book is forConventionsReader feedbackCustomer supportDownloading the example codeDownloading the color images of this bookErrataPiracyQuestions1. A Tour of pandaspandas and why it is importantpandas and IPython NotebooksReferencing pandas in the applicationPrimary pandas objectsThe pandas series obiectThe pandas Data Frame objectLoading data from files and the webLoading Csv data from fileswww.allitebooks.comSimplicity of visualization of pandas dataSummary2. Installing pandasGetting AnacondaInstalling AnacondaInstalling Anaconda on LinuxInstalling Anaconda on Mac os XInstalling Anaconda on WindowsEnsuring pandas is up to dateRunning a small pandas sample in IPythonStarting the IPython Notebook serverstalling and running IPython NotebooksUsing Wakari for pandasSummary3. NumPy for pandasstalling and importing NumPyBenefits and characteristics of NumPy arraysCreating NumPy arrays and performing basic array operationsSelecting array elementsLogical operations on arraysSlicing arraysReshaping arrayscombining arraysSplitting arraysUseful numerical methods of NumPy arraysSummary4. The pandas Series ObjectThe Series objectImporting pandasCreating serieswww.allitebooks.comPeeking at data with heads, tails, and takeLooking up values in SeriesAlignment via index labelsArithmetic operationsThe special case of Not-A-Number(naN)Boolean selectionReindexing a seriesModifying a Series in-placeSlicing a seriesSummar5. The pandas DataFrame objectCreating Data Frame from scratchExample dataS&P500Monthly stock historical pricesSelecting columns of a Data frameSelecting rows and values of a Data Frame using the indexSlicing using the ll operatorSelecting rows by index label and location:locl and . ilocllSelecting rows by index label and/or location: ixScalar lookup by label or location using. at and . iatLISelecting rows of a Data Frame by boolean selectionModifying the structure and content of DataFrameRenaming columnsAdding and inserting columnsReplacing the contents of a columnDeleting columns in a data frameAdding rows to a DataFrameAppending rows with, appendConcatenating data frame objects with pd concatwww.allitebooks.comRemoving rows from a data frameRemoving rows using. dropoRemoving rows using boolean selectionRemoving rows using a sliceChanging scalar values in a DataFramearithmetic on a data frameResetting and reindexinghierarchical indexingSummarized data and descriptive statisticsSummary6. Accessing DataSetting up the IPython notebookCSV and Text/Tabular formatThe sample Csv data setReading a Csv file into a data frameSpecifying the index column when reading a Csv fileData type inference and specificationSpecifying column namesSpecifying specific columns to loadSaving Data Frame to a Csv fileGeneral field-delimited dataHandling noise rows in field-delimited dataReading and writing data in an Excel formatReading and writing json filesReading hTML data from the WebReading and writing HDF5 format filesaccessing data on the web and in the cloudReading and writing from/to SQL databaseReading data from remote data servicesReading stock data from Yahoo and google Financewww.allitebooks.comReading economic data from the Federal Reserve Bank of St LouisAccessing Kenneth French's dataReading from the World BankSummary7. Tidying Up Your DataWhat is tidying your data?Setting up the IPython notebookWorking with missing dataDetermining Nan values in Series and Data Frame obiectsSelecting out or dropping missing dataHow pandas handles nan values in mathematical operationsFilling in missing dataForward and backward filling of missing valuesFilling using index labelsInterpolation of missing valuesHandling duplicate dataTransforming DataMappingReplacing valuesApplying functions to transform dataSummar8. Combining and Reshaping dataSetting up the ipython notebookConcatenating dataMerging and joining dataAn overview of mergesSpecifying the join semantics of a merge operationotingStacking and unstackingStacking using nonhierarchical indexeswww.allitebooks.comeltingPerformance benefits of stacked dataSummary9. Grouping and aggregating dataSetting up the ipython notebookThe split, apply, and combine(saC_patternSplitData for the examplesGrouping by a single column's valuesAccessing the results of groupingGrouping using index levelsApplyApplying aggregation functions to groupsThe transformation of group dataAn overview of transformationPractical examples of transformationFiltering groupsDiscretization and BinningSummary0. Time-series dataSetting up the Ipython notebookRepresentation of dates time, and intervalsThe datetime, day, and time objectsTimestamp objectsmedelaIntroducing time-series dataDatetimeIndexCreating time-series data with specific frequenciesCalculating new dates using offsetsDate offsetswww.allitebooks.comRepresenting durations of time using Period objectsThe period obiectPeriodIndexHandling holidays using calendarsNormalizing timestamps using time zonesManipulating time-series dataShifting and laggingFrequency conversionUp and down resamplingTime-series moving-window operationsSummary11. VisualizationSetting up the IPython notebookPlotting basics with pandasCreating time-series charts with plotAdorning and styling your time-series plotAdding a title and changing axes labelsSpecifying the legend content and positionSpecifying line colors, styles, thickness, and markersSpecifying tick mark locations and tick labelsFormatting axes tick date labels using formattersCommon plots used in statistical analysesBar plotsHistogramsBox and whisker chartsArea plotsScatter plotsDensity plotThe scatter plot matrixHeatmapswww.allitebooks.com