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Название: Mastering Data Analysis with Python: A Comprehensive Guide to NumPy, Pandas, and Matplotlib
Автор: Rajender Kumar
Издательство: Jamba Academy
Год: 2023
Страниц: 532
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
This book aimed at individuals looking to enhance their data analysis skills. The book provides a comprehensive guide to the Python libraries NumPy, Pandas, and Matplotlib, which are commonly used for data analysis tasks. The book is divided into several chapters, each of which covers a different topic in data analysis. The first chapter introduces the NumPy library, which is used for numerical computing tasks such as array manipulation, linear algebra, and Fourier analysis. The subsequent chapters cover the Pandas library, which is used for data manipulation and analysis tasks such as data cleaning, merging, and aggregation. To make it even more convenient for readers, we are offering all the code discussed in the book as Jupyter notebooks on the GitHub. Jupyter notebooks provide an interactive computing environment that enables users to write and run code, as well as create visualizations and documentation in a single document. This makes it a perfect tool for learning and experimenting with Machine Learning and Deep Learning concepts. The code provided on the Github repository can be downloaded and used freely by readers. The notebooks are organized according to the chapters in the book, making it easier for readers to find the relevant code for each concept.
Автор: Rajender Kumar
Издательство: Jamba Academy
Год: 2023
Страниц: 532
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
This book aimed at individuals looking to enhance their data analysis skills. The book provides a comprehensive guide to the Python libraries NumPy, Pandas, and Matplotlib, which are commonly used for data analysis tasks. The book is divided into several chapters, each of which covers a different topic in data analysis. The first chapter introduces the NumPy library, which is used for numerical computing tasks such as array manipulation, linear algebra, and Fourier analysis. The subsequent chapters cover the Pandas library, which is used for data manipulation and analysis tasks such as data cleaning, merging, and aggregation. To make it even more convenient for readers, we are offering all the code discussed in the book as Jupyter notebooks on the GitHub. Jupyter notebooks provide an interactive computing environment that enables users to write and run code, as well as create visualizations and documentation in a single document. This makes it a perfect tool for learning and experimenting with Machine Learning and Deep Learning concepts. The code provided on the Github repository can be downloaded and used freely by readers. The notebooks are organized according to the chapters in the book, making it easier for readers to find the relevant code for each concept.