Learning pandas : High-performance data manipulation and analysis in Python, 2/e

Michael Heydt

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商品描述

Key Features

  • Get comfortable using pandas and Python as an effective data exploration and analysis tool
  • Explore pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process
  • A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas

Book Description

You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.

With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

What you will learn

  • Understand how data analysts and scientists think about of the processes of gathering and understanding data
  • Learn how pandas can be used to support the end-to-end process of data analysis
  • Use pandas Series and DataFrame objects to represent single and multivariate data
  • Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources
  • How to access data from external sources such as files, databases, and web services
  • Represent and manipulate time-series data and the many of the intricacies involved with this type of data
  • How to visualize statistical information
  • How to use pandas to solve several common data representation and analysis problems within finance

About the Author

Michael Heydt is a technologist, entrepreneur, and educator with decades of professional software development and financial and commodities trading experience. He has worked extensively on Wall Street specializing in the development of distributed, actor-based, highperformance, and high-availability trading systems. He is currently founder of Micro Trading Services, a company that focuses on creating cloud and micro service-based software solutions for finance and commodities trading. He holds a master's in science in mathematics and computer science from Drexel University, and an executive master's of technology management from the University of Pennsylvania School of Applied Science and the Wharton School of Business.

Table of Contents

  1. pandas and Data Science and Analysis
  2. Up and running with pandas
  3. Representing univariate data with the Series
  4. Representing tabular and multivariate data with the DataFrame
  5. Manipulation and indexing of DataFrame objects
  6. Indexing Data
  7. Categorical Data
  8. Numeric and Statistical Methods
  9. Grouping and Aggregating Data
  10. Tidying Up Your Data
  11. Combining, Relating and Reshaping Data
  12. Data Aggregation
  13. Time-Series Modelling
  14. Visualization
  15. Applications to Finance

商品描述(中文翻譯)

主要特點


  • 學習使用pandas和Python作為有效的數據探索和分析工具

  • 通過數據分析框架來探索pandas,並解釋pandas在數據分析過程中的適用性

  • 一本全面的pandas指南,提供許多清晰實用的示例,幫助您快速上手使用pandas

書籍描述

您將學習如何在Python中使用pandas進行數據分析。您將從數據建模開始,逐步進行數據訪問、數值和統計分析、索引和聚合分析,最後進行統計數據可視化和應用pandas於金融領域。

通過本書所學,您將迅速掌握pandas,並在數據操作、分析和科學的激動人心世界中發揮其優勢。

您將學到什麼


  • 了解數據分析師和科學家在收集和理解數據過程中的思維方式

  • 學習如何使用pandas支持數據分析的端到端過程

  • 使用pandas的Series和DataFrame對象表示單變量和多變量數據

  • 使用pandas進行數據切片、組合、分組和聚合

  • 如何從文件、數據庫和Web服務等外部源訪問數據

  • 表示和操作時間序列數據以及與此類數據相關的複雜性

  • 如何可視化統計信息

  • 如何使用pandas解決金融領域中的幾個常見數據表示和分析問題

關於作者

Michael Heydt 是一位技術專家、企業家和教育家,擁有數十年的專業軟件開發和金融商品交易經驗。他在華爾街工作多年,專注於開發分佈式、基於角色的高性能和高可用性交易系統。他目前是Micro Trading Services的創始人,該公司專注於為金融和商品交易創建基於雲和微服務的軟件解決方案。他擁有德雷塞爾大學的數學和計算機科學碩士學位,以及賓夕法尼亞大學應用科學學院和沃頓商學院的執行碩士技術管理學位。

目錄


  1. pandas和數據科學與分析

  2. 開始使用pandas

  3. 使用Series表示單變量數據

  4. 使用DataFrame表示表格和多變量數據

  5. 操作和索引DataFrame對象

  6. 數據索引

  7. 分類數據

  8. 數值和統計方法

  9. 分組和聚合數據

  10. 整理數據

  11. 組合、關聯和重塑數據

  12. 數據聚合

  13. 時間序列建模

  14. 可視化

  15. 應用於金融領域