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Big Data Analysis with Python
Processingbigdatainrealtimeischallengingduetoscalability,informationinconsistency,andfaulttolerance.BigDataAnalysiswithPythonteachesyouhowtousetoolsthatcancontrolthisdataavalancheforyou.Withthisbook,you'lllearnpracticaltechniquestoaggregatedataintousefuldimensionsforposterioranalysis,extractstatisticalmeasurements,andtransformdatasetsintofeaturesforothersystems.ThebookbeginswithanintroductiontodatamanipulationinPythonusingpandas.You'llthengetfamiliarwithstatisticalanalysisandplottingtechniques.Withmultiplehands-onactivitiesinstore,you'llbeabletoanalyzedatathatisdistributedonseveralcomputersbyusingDask.Asyouprogress,you'llstudyhowtoaggregatedataforplotswhentheentiredatacannotbeaccommodatedinmemory.You'llalsoexploreHadoop(HDFSandYARN),whichwillhelpyoutacklelargerdatasets.ThebookalsocoversSparkandexplainshowitinteractswithothertools.Bytheendofthisbook,you'llbeabletobootstrapyourownPythonenvironment,processlargefiles,andmanipulatedatatogeneratestatistics,metrics,andgraphs.
目錄(73章)
倒序
- 封面
- 版權頁
- Preface
- Chapter 1 The Python Data Science Stack
- Introduction
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Summary
- Chapter 2 Statistical Visualizations
- Introduction
- Types of Graphs and When to Use Them
- Components of a Graph
- Seaborn
- Which Tool Should Be Used?
- Types of Graphs
- Pandas DataFrames and Grouped Data
- Changing Plot Design: Modifying Graph Components
- Exporting Graphs
- Summary
- Chapter 3 Working with Big Data Frameworks
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
- Chapter 4 Diving Deeper with Spark
- Introduction
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Summary
- Chapter 5 Handling Missing Values and Correlation Analysis
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
- Chapter 6 Exploratory Data Analysis
- Introduction
- Defining a Business Problem
- Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
- Structured Approach to the Data Science Project Life Cycle
- Summary
- Chapter 7 Reproducibility in Big Data Analysis
- Introduction
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Summary
- Chapter 8 Creating a Full Analysis Report
- Introduction
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
- Summary
- Appendix
- Chapter 01: The Python Data Science Stack
- Chapter 02: Statistical Visualizations Using Matplotlib and Seaborn
- Chapter 03: Working with Big Data Frameworks
- Chapter 04: Diving Deeper with Spark
- Chapter 05: Missing Value Handling and Correlation Analysis in Spark
- Chapter 6: Business Process Definition and Exploratory Data Analysis
- Chapter 07: Reproducibility in Big Data Analysis
- Chapter 08: Creating a Full Analysis Report 更新時間：2021-06-11 13:46:55
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