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Sản phẩm #:
35028587
Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights
88% of respondents would recommend this to a friend
Sản phẩm #:
35028587
VND 1299100
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This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques.
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What Stands Out
Modern Techniques
Incorporates cutting-edge methods for data cleaning, ensuring users are equipped with the latest strategies to tackle dirty data challenges effectively.
Comprehensive Tools
Offers an extensive selection of Python tools tailored for data cleaning, enabling users to efficiently extract valuable insights and enhance data quality.
User-Centric Approach
Designed for both beginners and seasoned analysts, providing practical examples and easy-to-follow instructions that simplify complex data cleaning processes.
Thông tin chi tiết sản phẩm
- 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 tricksKey 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 DescriptionGetting 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 forThis 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.Table of ContentsAnticipating Data Cleaning Issues when Importing Tabular Data into pandasAnticipating Data Cleaning Issues when Importing HTML and JSON into PandasTaking the Measure of Your DataIdentifying Issues in Subsets of DataUsing Visualizations for Exploratory Data AnalysisCleaning and Wrangling Data with Pandas Data Series OperationsFixing Messy Data When AggregatingAddressing Data Issues When Combining Data FramesTidying and Reshaping DataUser Defined Functions and Classes to Automate Data Cleaning
| Publisher | Packt Publishing |
| Publication date | December 11, 2020 |
| Language | English |
| Print length | 436 pages |
| ISBN-10 | 1800565666 |
| ISBN-13 | 978-1800565661 |
| Item Weight | 1.64 pounds (740 grams) |
| Dimensions | 7.5 x 0.99 x 9.25 inches (19.1 x 2.5 x 23.5 cm) |
Who Should Buy?
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Data Analysts
Data analysts looking to enhance their skills in data cleaning using modern Python techniques will find this cookbook invaluable.
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Data Scientists
Data scientists needing effective methods to preprocess datasets for analysis and model training will benefit greatly from this resource.
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Python Beginners
Beginners in Python who seek practical applications of data cleaning will find clear examples and guidance in this cookbook.
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Advanced Users
Advanced data professionals might find the cookbook's content too basic and not suitable for their complex data needs.
MÔ TẢ SẢN PHẨM
Dietary Supplement Disclaimer
Statements regarding dietary supplements have not been evaluated by the Food and Drug Administration and are not intended to diagnose, treat, cure, or prevent any disease or health condition.
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Hỏi Đáp dành cho Khách hàng
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câu hỏi:
What is the primary focus of the Python Data Cleaning Cookbook?
trả lời: The Python Data Cleaning Cookbook is designed to help data professionals learn modern techniques and practical Python tools that can effectively detect and eliminate dirty data. It emphasizes step-by-step recipes that simplify complex processes, making it easier for users to clean their datasets efficiently. By focusing on key principles and methodologies, the cookbook not only aids in improving data quality but also enhances the overall data analysis process, making it invaluable for professionals who aim to extract meaningful insights from their data. -
câu hỏi:
Who is the target audience for the Python Data Cleaning Cookbook?
trả lời: The cookbook targets data scientists, analysts, and anyone involved in data preparation and cleaning tasks, from beginners to experienced professionals. It is particularly useful for those who seek to enhance their skill set in Python and data analysis techniques. With practical recipes designed for various skill levels, readers can benefit from the insights whether they are just beginning their data journey or looking to refine advanced data cleaning strategies. -
câu hỏi:
What specific techniques does the Python Data Cleaning Cookbook cover?
trả lời: The Python Data Cleaning Cookbook covers a wide range of techniques including data validation, normalization, outlier detection, and handling missing values. Each section provides actionable recipes that are easy to follow. These techniques are crucial in ensuring that datasets are accurate, consistent, and ready for analysis, ultimately accelerating insights extraction. Users can apply these techniques in numerous domains, from business analytics to research, maximizing the impact of their data. -
câu hỏi:
How does the cookbook benefit those using Python for data projects?
trả lời: The cookbook's structured approach offers a wealth of practical examples and code snippets that can be readily applied to real data projects. By following these recipes, users gain hands-on experience and improve their Python proficiency, particularly in data manipulation using libraries like Pandas and NumPy. This practical knowledge is essential for tackling data cleaning challenges in any project, allowing users to become more effective and efficient in their work. -
câu hỏi:
Are there any prerequisites for using the Python Data Cleaning Cookbook?
trả lời: While there are no strict prerequisites, a basic understanding of Python programming and familiarity with data manipulation concepts will enhance the reading experience. The cookbook assumes that users have some foundational knowledge of Python syntax and libraries. Readers new to Python may benefit from introductory resources before diving into the specific data cleaning techniques discussed in the cookbook. -
câu hỏi:
Can the techniques in the Python Data Cleaning Cookbook be applied to large datasets?
trả lời: Yes, the techniques presented in the Python Data Cleaning Cookbook are designed to handle datasets of various sizes, including large data volumes. The use of efficient coding practices and optimized libraries ensures that users can process large datasets without significant performance issues. This capability is essential in today’s data-driven world, as many organizations regularly deal with extensive data sets that require thorough cleaning for accurate analysis. -
câu hỏi:
What types of data sources does the Python Data Cleaning Cookbook focus on?
trả lời: The cookbook focuses on a range of data sources including CSV files, Excel spreadsheets, SQL databases, and JSON formats. It provides guidance on how to clean and prepare data from these sources effectively. This versatility ensures that users can work with different kinds of data seamlessly, making it easier to integrate new datasets into their analysis workflows, regardless of the format they originate from. -
câu hỏi:
Will I find examples and case studies in the Python Data Cleaning Cookbook?
trả lời: Yes, the cookbook includes numerous examples and real-world case studies that illustrate how the various data cleaning techniques can be applied in practice. These examples help users visualize the outcomes of the methods presented, enhancing the learning experience. By contextualizing the recipes within real scenarios, users can better understand their applications and relevance in different industries, making the cookbook a practical tool for learning. -
câu hỏi:
Is the Python Data Cleaning Cookbook suitable for self-study?
trả lời: Absolutely! The structured format of the cookbook, complete with step-by-step instructions, makes it perfect for self-study. Each recipe focuses on a specific cleaning task, allowing readers to easily follow along and apply the concepts independently. This is particularly beneficial for those who prefer to learn at their own pace or who are managing projects outside of a formal classroom setting, making it an ideal resource for personal development. -
câu hỏi:
Where can I buy the Python Data Cleaning Cookbook in Vietnam?
trả lời: You can purchase the Python Data Cleaning Cookbook through Ubuy in Vietnam. Ubuy is a reliable platform that offers a wide selection of books and educational resources, ensuring you can get this essential cookbook conveniently delivered to your doorstep. Simply visit the Ubuy website, search for the cookbook, and experience a seamless shopping experience.
Data Mining Editorial Review
Python Data Cleaning Cookbook provides a comprehensive guide for software developers who need to process, clean and refine their datasets. The cookbook format, where each recipe provides a coding solution to specific problems, is effective in providing a range of techniques to help users extract meaningful insights. The book covers topics like detecting anomalies, visualizing data, and processing it at a macroscopic level. One of the standout features of the book is the author's ability to provide a 'WHY' behind data processing tasks, giving readers a deeper understanding of the concepts. The book is approachable for those new to Python and data processing and provides hands-on examples to help Consolidate information.
Customer Reviews & Ratings
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- Comprehensive guide for processing, cleaning and refining datasets
- Effective cookbook format with each recipe addressing specific problems
- Covers detecting anomalies, visualizing data and processing data at a macroscopic level
- 'WHY' behind data processing tasks provided
- Approachable for beginners
- Provides hands-on examples
Nhược điểm
- Some beginners may find it challenging to follow along
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VND 1299100
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Các tính năng và lợi ích
- Discover various data cleaning techniques to reveal key insights
- Manipulate data of different complexities to shape them into the right form for business needs
- Clean, monitor, and validate large data volumes to diagnose problems before analyzing
- Create visualizations to gain insights and identify data issues
- Build functions and classes for automating data cleaning
- Requires only working knowledge of Python programming
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