book

Practical Data Analysis Using Jupyter Notebook

by Marc Wintjen

June 2020

Intermediate to advanced

322 pages

7h 25m

English

Packt Publishing

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Practical Data Analysis using Jupyter Notebook
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Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
The evolution of data analysis and why it is importantWhat makes a good data analyst?Know Your Data (KYD)Voice of the Customer (VOC)Always Be Agile (ABA)Understanding data types and their significanceUnstructured dataSemi-structured dataStructured dataCommon data typesData classifications and data attributes explainedData attributesUnderstanding data literacyReading dataWorking with dataAnalyzing dataArguing about the dataSummaryFurther reading
Technical requirementsInstalling Python and using Jupyter NotebookInstalling AnacondaRunning Jupyter and installing Python packages for data analysisStoring and retrieving data filesHello World! – running your first Python codeCreating a project folder hierarchyUploading a fileExploring Python packagesChecking for pandasChecking for NumPyChecking for sklearnChecking for MatplotlibChecking for SciPySummaryFuture reading
Technical requirementsUnderstanding a Python NumPy array and its importanceDifferences between single and multiple dimensional arraysMaking your first NumPy arrayUseful array functionsPractical use cases of NumPy and arraysAssigning values to arrays manuallyAssigning values to arrays directlyAssigning values to an array using a loopSummaryFurther reading

Technical requirementsTechniques for manipulating tabular dataUnderstanding pandas and DataFramesHandling essential data formatsCSVXMLData hierarchyDefined schemaJSONData dictionaries and data typesCreating our first DataFrame SummaryFurther reading
Technical requirementsIntroduction to SQL and relational databasesFrom SQL to pandas DataFramesData about your data explainedFundamental statisticsMetadata explainedThe importance of data lineageData flowThe input stageThe data ingestion stageThe data source stageThe data target stageBusiness rulesSummaryFurther reading
Technical requirementsData modeling for results Introducing dimensions and measuresAnatomy of a chart and data viz best practicesAnalyzing your dataWhy pie charts have lost groundArt versus scienceWhat makes great data visualizations? Comparative analysisDate and time trends explainedThe shape of the curveCreating your first time series chart SummaryFurther reading
Technical requirementsRetrieving, viewing, and storing tabular dataRetrievingViewing StoringLearning how to restrict, sort, and sift through dataRestrictingSortingSiftingCleaning, refining, and purifying data using PythonCombining and binning dataBinningSummaryFurther reading
Technical requirementsFoundations of join relationshipsOne-to-one relationshipsMany-to-one relationshipsMany-to-many relationshipLeft joinRight joinInner joinOuter joinJoin types in actionExplaining data aggregationUnderstanding the granularity of dataData aggregation in actionSummary statistics and outliersSummaryFurther reading<a href="https://matplotlib.org/index.html">
Technical requirementsExplaining distribution analysisKYDShape of the curveUnderstanding outliers and trendsGeoanalytical techniques and tipsFinding patterns in dataSummaryFurther reading
Technical requirementsPreparing to work with unstructured dataCorpus in actionTokenization explainedTokenize in actionCounting words and exploring resultsCounting wordsNormalizing text techniquesStemming and lemmatization in actionExcluding words from analysisSummaryFurther reading
Technical requirementsWhy sentiment analysis is importantElements of an NLP modelCreating a prediction outputSentiment analysis packagesSentiment analysis in actionManual inputSocial media file inputSummaryFurther reading
Technical requirementsDiscovering real-world datasetsData.govThe Humanitarian Data ExchangeThe World BankOur World in DataReporting resultsStorytellingThe Capstone projectKYD sourcesExerciseSummaryFurther reading
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Content preview from Practical Data Analysis Using Jupyter Notebook

Section 2: Solutions for Data Discovery

In this section, we'll learn how to visualize data for analysis by working with time series data. Then, we'll learn how to clean and join multiple datasets together using both SQL and DataFrames with Python. After that, we'll go back to data visualization and learn about the best practices when it comes to data storytelling.By the end of this section, you will understand the foundations of descriptive analytics.

This section includes the following chapters:

Chapter 6, Visualizing and Working with Time Series Data
Chapter 7, Exploring,Cleaning, Refining, and Blending Datasets
Chapter 8, Understanding Joins, Relationships, and Aggregates
Chapter 9, Plotting, Visualization, and Storytelling

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Publisher Resources

ISBN: 9781838826031Supplemental Content