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|a eng
|2 639-2
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1 |
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|a Walker, Michael.
|4 aut.
|e Auteur
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| 245 |
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|a Python Data Cleaning Cookbook :
|b Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
|c Michael Walker.
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| 264 |
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|a Birmingham :
|b Packt Publishing.
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| 264 |
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|a Paris :
|b Cyberlibris,
|c 2024.
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|2 rdacontent
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|2 rdamedia
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|a Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781803246291.jpg).
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|a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement
|e Cyberlibris
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| 520 |
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|a Learn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips.Key FeaturesGet to grips with new techniques for data preprocessing and cleaning for machine learning and NLP modelsUse new and updated AI tools and techniques for data cleaning tasksClean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AIBook 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 learnUsing OpenAI tools for various data cleaning tasksProducing summaries of the attributes of datasets, columns, and rowsAnticipating data-cleaning issues when importing tabular data into pandasApplying validation techniques for imported tabular dataImproving your productivity in pandas by using method chainingRecognizing and resolving common issues like dates and IDsSetting up indexes to streamline data issue identificationUsing data cleaning to prepare your data for ML and AI modelsWho 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 with practical examples. Working knowledge of Python programming is all you need to get the most out of the book
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