Python Data Science Handbook: Essential Tools for Working with Data, 2/e (Paperback)
暫譯: Python 數據科學手冊：數據處理的基本工具，第二版 (平裝本)

Vanderplas, Jake

Python Data Science Handbook: Essential Tools for Working with Data, 2/e (Paperback)

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商品描述

Python 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.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how:

- IPython and Jupyter provide computational environments for scientists using Python
- NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
- Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
- Matplotlib includes capabilities for a flexible range of data visualizations
- Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

商品描述(中文翻譯)

Python 是許多研究人員的首選工具，主要是因為它擁有用於儲存、操作和從數據中獲取洞見的庫。雖然有多個資源針對這個數據科學堆疊中的各個部分，但只有在新版的《Python 數據科學手冊》中，您才能獲得所有資源——IPython、NumPy、pandas、Matplotlib、scikit-learn 以及其他相關工具。

熟悉閱讀和編寫 Python 代碼的科學家和數據分析師會發現這本全面的參考書的第二版非常適合解決日常問題：操作、轉換和清理數據；可視化不同類型的數據；以及使用數據來構建統計或機器學習模型。簡而言之，這是 Python 科學計算的必備參考書。

通過這本手冊，您將學到如何：

- IPython 和 Jupyter 為使用 Python 的科學家提供計算環境
- NumPy 包含 ndarray，用於高效儲存和操作密集數據陣列
- Pandas 包含 DataFrame，用於高效儲存和操作標籤/列式數據
- Matplotlib 包含靈活範圍的數據可視化功能
- Scikit-learn 幫助您構建高效且乾淨的 Python 實現，涵蓋最重要和最成熟的機器學習算法

作者簡介

Jake VanderPlas is a software engineer at Google Research, working on tools that support data-intensive research. He maintains a technical blog, Pythonic Perambulations,

to share tutorials and opinions related to statistics, open software, and scientific computing in Python. He creates and develops Python tools for use in data-intensive science, including packages like Scikit-Learn, SciPy, AstroPy, Altair, JAX, and many others. He participates in the broader data science community, developing and presenting talks and tutorials on scientific computing topics at various conferences in the data science world.

作者簡介(中文翻譯)

Jake VanderPlas 是 Google Research 的軟體工程師，專注於支援數據密集型研究的工具。他維護一個技術部落格，名為 Pythonic Perambulations，分享與統計、開放軟體和科學計算相關的教程和觀點。

他創建並開發用於數據密集型科學的 Python 工具，包括 Scikit-Learn、SciPy、AstroPy、Altair、JAX 等多個套件。他參與更廣泛的數據科學社群，在數據科學界的各種會議上開發並展示有關科學計算主題的演講和教程。

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