Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3/e (Paperback)
McKinney, Wes
- 出版商: O'Reilly
- 出版日期: 2022-09-20
- 定價: $2,800
- 售價: 9.0 折 $2,520
- 語言: 英文
- 頁數: 579
- 裝訂: Quality Paper - also called trade paper
- ISBN: 109810403X
- ISBN-13: 9781098104030
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相關分類:
Python、程式語言、Data Science
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相關翻譯:
Python 資料分析, 3/e (Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3/e) (繁中版)
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商品描述
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
- Use the Jupyter notebook and IPython shell for exploratory computing
- Learn basic and advanced features in NumPy
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples
商品描述(中文翻譯)
獲取在Python中操作、處理、清理和分析數據集的權威手冊。第三版的這本實用指南已更新至Python 3.9和pandas 1.2，內容豐富，包含了實際案例研究，展示了如何有效解決各種數據分析問題。在過程中，您將學習到最新版本的pandas、NumPy和Jupyter。
本書由Python pandas項目的創始人Wes McKinney撰寫，是一本實用的、現代的Python數據科學工具入門書籍。對於新手分析師和新手Python程序員來說，這本書非常理想，可以幫助他們進入數據科學和科學計算領域。相關的數據文件和材料可以在GitHub上找到。
本書涵蓋以下內容：
- 使用Jupyter筆記本和IPython shell進行探索性計算
- 學習NumPy的基本和高級功能
- 開始使用pandas庫中的數據分析工具
- 使用靈活的工具加載、清理、轉換、合併和重塑數據
- 使用matplotlib創建有信息量的可視化圖表
- 應用pandas的groupby功能對數據集進行切片、切塊和總結
- 分析和操作常規和非常規時間序列數據
- 通過詳細的實例學習如何解決現實世界的數據分析問題
