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Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics
Get your raw data cleaned up and ready for processing to design better data analytic solutions
Key FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects.
With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data.
You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment.
The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data.
By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.
What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don’t need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Table of ContentsReview of the Core Modules of NumPy and PandasReview of Another Core Module - MatplotlibData – What Is It Really?DatabasesData VisualizationPredictionClassificationClustering AnalysisData Cleaning Level I - Cleaning Up the TableData Cleaning Level II - Unpacking, Restructuring, and Reformulating the TableData Cleaning Level III- Missing Values, Outliers, and ErrorsData Fusion and Data IntegrationData ReductionData Transformation and MassagingCase Study 1 - Mental Health in TechCase St
Key FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects.
With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data.
You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment.
The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data.
By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.
What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don’t need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Table of ContentsReview of the Core Modules of NumPy and PandasReview of Another Core Module - MatplotlibData – What Is It Really?DatabasesData VisualizationPredictionClassificationClustering AnalysisData Cleaning Level I - Cleaning Up the TableData Cleaning Level II - Unpacking, Restructuring, and Reformulating the TableData Cleaning Level III- Missing Values, Outliers, and ErrorsData Fusion and Data IntegrationData ReductionData Transformation and MassagingCase Study 1 - Mental Health in TechCase St
602 pages, Kindle Edition
Published January 21, 2022
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