Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2/e (Paperback)

Wes McKinney

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2/e (Paperback)

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商品描述

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second 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, IPython, 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 IPython shell and Jupyter notebook for exploratory computing
  • Learn basic and advanced features in NumPy (Numerical Python)
  • 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完整指南。第二版更新至Python 3.6，這本實踐指南充滿了實際案例研究，展示了如何有效解決各種數據分析問題。在過程中，您將學習最新版本的pandas、NumPy、IPython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，這是理想的選擇。數據文件和相關資料可在GitHub上獲得。

- 使用IPython shell和Jupyter notebook進行探索性計算
- 學習NumPy（數值Python）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
- 分析和操作常規和不規則時間序列數據
- 通過詳細的實例學習如何解決現實世界的數據分析問題

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