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

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

Python是許多研究人員的一流工具，主要是因為它的庫可以存儲、操作和從數據中獲取洞察力。有一些資源可以用於這個數據科學堆棧的各個組件，但只有在《Python數據科學手冊》的新版本中，您才能獲得它們全部——IPython、NumPy、pandas、Matplotlib、scikit-learn和其他相關工具。

熟悉閱讀和編寫Python代碼的工作科學家和數據分析師將會發現這本全面的桌面參考書第二版非常適合應對日常問題：操作、轉換和清理數據；可視化不同類型的數據；以及使用數據構建統計或機器學習模型。簡單地說，這是Python科學計算的必備參考書。

通過這本手冊，您將學到以下內容：
- IPython和Jupyter為使用Python的科學家提供計算環境
- NumPy包括ndarray，用於高效存儲和操作密集數據數組
- Pandas包含DataFrame，用於高效存儲和操作帶有標籤/列的數據
- Matplotlib具有靈活的數據可視化功能
- Scikit-learn幫助您構建最重要和成熟的機器學習算法的高效且乾淨的Python實現

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.

Jake VanderPlas是Google Research的軟體工程師，致力於開發支援大數據研究的工具。他維護一個技術部落格，名為Pythonic Perambulations，用來分享與統計、開源軟體和科學計算相關的教學和意見。他創建和開發Python工具，用於大數據科學，包括Scikit-Learn、SciPy、AstroPy、Altair、JAX等套件。他也參與更廣泛的數據科學社群，開發並在各種數據科學會議上發表關於科學計算主題的演講和教學。