Python Data Cleaning And Preparation Best Practices
DOWNLOAD
Download Python Data Cleaning And Preparation Best Practices PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Python Data Cleaning And Preparation Best Practices book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Python Data Cleaning And Preparation Best Practices
DOWNLOAD
Author : Maria Zervou
language : en
Publisher:
Release Date : 2024-09-27
Python Data Cleaning And Preparation Best Practices written by Maria Zervou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with categories.
Hands On Data Preprocessing In Python
DOWNLOAD
Author : Roy Jafari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-21
Hands On Data Preprocessing In Python written by Roy Jafari and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-21 with Computers categories.
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 Description Hands-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 for This 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.
Python Data Science Mastery
DOWNLOAD
Author : Ethan Cole
language : en
Publisher: Independently Published
Release Date : 2025-11-22
Python Data Science Mastery written by Ethan Cole and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-22 with Computers categories.
Master the complete data science workflow with Python-no guesswork, no confusion, just clear, practical results. Python has become the backbone of modern data science, powering everything from predictive analytics to machine learning systems used across industries. Python Data Science Mastery gives you the knowledge, confidence, and hands-on skills to work like a real data scientist-using Python's most powerful libraries to clean data, uncover insights, build accurate models, and avoid common pitfalls like overfitting. Whether you're just starting your data science journey or looking to strengthen your professional skill set, this book delivers a step-by-step roadmap for mastering the essentials and applying them to real-world problems. What You'll Learn ✓ Clean and prepare messy real-world datasets using Pandas and NumPy ✓ Explore data in depth with visualizations that reveal patterns, trends, and correlations ✓ Build and evaluate machine learning models using Scikit-learn ✓ Understand and prevent overfitting to ensure your models generalize reliably ✓ Apply best practices used by professional data scientists in production environments ✓ Deploy simple predictive models using modern Python tools Each chapter includes practical exercises, examples, and clear explanations that help you build intuition-not just memorize code. By the end, you'll be able to confidently move from raw data to actionable insights and deployable models. Who This Book Is For This book is perfect for: Aspiring and practicing data scientists, analysts, AI enthusiasts, and engineers Students preparing for data science careers Professionals who want to add Python-based analytics to their skillset Anyone who wants to understand how real machine learning models are built and optimized No advanced math or prior experience is required just curiosity and a willingness to learn. Why This Book Stands Out Unlike many theory-heavy resources, Python Data Science Mastery focuses on practical application, giving you the tools and mindset needed to solve real problems. You'll learn the same workflow used in industry from data preprocessing and exploratory analysis to model evaluation, optimization, and deployment. This is the guide you wish you had when you started learning data science: clear, concise, and packed with hands-on value.
Python Data Cleaning Cookbook
DOWNLOAD
Author : Michael Walker
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Python Data Cleaning Cookbook written by Michael Walker and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Learn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips. Key Features Get to grips with new techniques for data preprocessing and cleaning for machine learning and NLP models Use new and updated AI tools and techniques for data cleaning tasks Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AI Book DescriptionJumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse without modification when you have new data. By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.What you will learn Using OpenAI tools for various data cleaning tasks Producing summaries of the attributes of datasets, columns, and rows Anticipating data-cleaning issues when importing tabular data into pandas Applying validation techniques for imported tabular data Improving your productivity in pandas by using method chaining Recognizing and resolving common issues like dates and IDs Setting up indexes to streamline data issue identification Using data cleaning to prepare your data for ML and AI models Who this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data with practical examples. Working knowledge of Python programming is all you need to get the most out of the book.
Data Preparation For Machine Learning
DOWNLOAD
Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2020-06-30
Data Preparation For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-30 with Computers categories.
Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.
Data Manipulation With Python Step By Step A Practical Guide With Examples
DOWNLOAD
Author : William E. Clark
language : en
Publisher: Walzone Press
Release Date : 2025-04-17
Data Manipulation With Python Step By Step A Practical Guide With Examples written by William E. Clark and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-17 with Computers categories.
Data Manipulation with Python Step by Step: A Practical Guide with Examples offers a clear and systematic approach to mastering data handling tasks in Python. The book begins with essential programming fundamentals, ensuring that readers, regardless of background, acquire a thorough grounding in variables, data types, control flow, and function definition. This foundation is progressively expanded to encompass the use of built-in data structures and the effective management of input and output across various file formats. As the book advances, it introduces the pandas library, providing detailed guidance on leveraging DataFrames and Series for efficient data organization, transformation, and analysis. Readers learn practical solutions for common challenges such as importing data, cleaning and standardizing datasets, handling missing or inconsistent values, and working with date and time information. Each concept is presented with clear explanations and relevant examples that facilitate immediate application to real-world data scenarios. Designed for students, analysts, and professionals, this book balances accessibility with technical rigor. By integrating practical tutorials and a complete project, it enables readers to translate foundational concepts into robust workflows for data preparation, exploration, and reporting. Upon completion, readers will be prepared to manage diverse data tasks with confidence, optimizing Python’s capabilities for effective data manipulation and analysis.
Data Cleaning For Effective Data Science
DOWNLOAD
Author : David Mertz
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-02
Data Cleaning For Effective Data Science written by David Mertz and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02 with categories.
Python Data Cleaning Cookbook
DOWNLOAD
Author : Michael Walker
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-12-11
Python Data Cleaning Cookbook written by Michael Walker and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-11 with Computers categories.
Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks Key FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook Description Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it. What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.
Data Science With Python
DOWNLOAD
Author : Julian James McKinnon
language : en
Publisher: Computer DM-Academy
Release Date : 2021-03-25
Data Science With Python written by Julian James McKinnon and has been published by Computer DM-Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with categories.
-- 55% OFF for Bookstores! -- Data analysis is just getting started. There's no limit to the amount of data available, and more companies are now interested in data analysis. For you, it's important to understand the concepts of data analysis and then, through practice, build a good command on working with different datasets. If you are feeling confident enough after finishing this book, you can move towards data science. It's much more complex, contains more abstract concepts, there's more mathematics involved, and it's easier to get lost. The more difficult the field, the higher the rewards. That's why data science is one of the most promising careers today. Data science is a role that is taking up a lot of space for many businesses. There is a wealth of information out there that they are able to use for their own advantage, but they just need to know where to gather it and how to analyze all of that data for their own needs. Sometimes, this is going to be a process that takes a lot of time and effort and can be hard to keep up with and ensure that we are doing it in the right manner. Data science is the process of gathering, organizing and cleaning, analyzing, and then visualizing data so that we can use that information to make smart business decisions. It is becoming more and more important to a lot of businesses, and it is likely that this will take over as one of the main forms of making big decisions in the future. With that in mind, let's take some time to look more in-depth at data science and how businesses are using it for their own needs. Many businesses, no matter what kind of industry they conduct business in, will find that working with data science is one of the best options for them. Data science can help them to really learn about their industry, and even gain a leg up on the competition. Many of the companies out there are going to already collect a lot of data and information about things like the competition, the industry, and their customers, and data science is going to help them to see what insights and information are inside of that data and use it for their advantage. There are many times when bringing out data science is going to be beneficial, and it will be able to propel your business forward more than anything else can do. When we can focus on the data and the process of analyzing it and seeing what good insights and predictions are inside, we will be able to make accurate decisions that will help us to make a big difference. Companies that have been able to implement a successful data science project from beginning to end are the ones who are doing the best overall in their respective industries. This book gives a comprehensive guide on the following: What Is Data Science? Basics of Python The Best Python Libraries for Data Science Data Science and Applications The Lifecycle of Data Science Probability, Statistics, and Data Types Most Common Data Science Problems Comparison of Python with Other Languages Data Cleaning and Preparation Data Visualization ...And more!!! Buy your copy of the book now and enjoy more content. What are you waiting for?
Python Data Wrangling For Business Analytics
DOWNLOAD
Author : George Snypes
language : en
Publisher:
Release Date : 2024-11-18
Python Data Wrangling For Business Analytics written by George Snypes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Business & Economics categories.
Master the essential skills of modern data analysis with this comprehensive guide to Python data wrangling, data cleaning, and business analytics. Whether you're a business analyst moving from Excel to Python, a data scientist optimizing workflows, or an analytics professional handling large datasets, this practical guide bridges the gap between basic Python programming and real-world data challenges. What Makes This Book Different: Unlike theoretical guides, this hands-on manual tackles actual business scenarios you'll encounter daily. Learn through practical exercises using real-world datasets from various industries. Master professional-grade data cleaning techniques used by leading companies for customer analysis, sales reporting, financial data processing, and marketing analytics. Essential Skills You'll Master: Data cleaning and preprocessing with pandas and numpy form the foundation of your learning journey. You'll advance to automated data validation and quality checks, ensuring your analyses are built on reliable data. Through hands-on practice, you'll develop expertise in advanced data transformation techniques and complex dataset merging. Time series data handling becomes second nature as you work through real examples. The book covers text data processing, standardization techniques, ETL pipeline development, and crucial performance optimization methods for large datasets. Real-World Applications: Your journey through data wrangling will focus on practical business scenarios. You'll learn to handle data challenges in customer analytics, transforming raw customer data into actionable segments. Sales performance tracking becomes straightforward as you master data integration techniques. Financial reporting transforms from a manual process into an automated workflow. Marketing campaign analysis, supply chain analytics, and operations management datasets become opportunities rather than obstacles. You'll work with multiple data sources, from Excel files and databases to APIs and cloud services. Technical Coverage: The comprehensive guide to pandas for data manipulation starts with fundamentals and progresses to advanced techniques. You'll master step-by-step data cleaning workflows that can be applied immediately in your daily work. Missing data handling strategies ensure no valuable information is lost. Data validation frameworks protect the integrity of your analysis. Automated reporting techniques save hours of manual work. Best practices for reproducible analysis ensure your work meets professional standards. Code optimization methods keep your solutions scalable and efficient.