Download Python Data Science Handbook

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Kirby Carpinello

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Jan 20, 2024, 2:34:05 PM1/20/24

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_OC_InitNavbar("child_node":["title":"My library","url":" =114584440181414684107\u0026source=gbs_lp_bookshelf_list","id":"my_library","collapsed":true,"title":"My History","url":"","id":"my_history","collapsed":true],"highlighted_node_id":"");Python Data Science Handbook: Essential Tools for Working with DataJake VanderPlas, Jacob T. VanderplasO'Reilly Media, 2023 - Computers - 563 pagesPython is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.

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O'Reilly
Amazon.com
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_OC_InitNavbar("child_node":["title":"My library","url":" =114584440181414684107\u0026source=gbs_lp_bookshelf_list","id":"my_library","collapsed":true,"title":"My History","url":"","id":"my_history","collapsed":true],"highlighted_node_id":"");Python Data Science HandbookJake VanderPlas"O'Reilly Media, Inc.", Dec 6, 2022 - Computers - 590 pagesPython is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.

Extracting valuable business insights is no longer a 'nice-to-have', but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time.

This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn.

This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress.

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.