Interactive Data Visualization with Python : Present your data as an effective and compelling story, 2/e (Paperback) (使用 Python 進行互動式數據視覺化：將數據呈現為有效且引人入勝的故事，第二版)

Belorkar, Abha, Guntuku, Sharath Chandra, Hora, Shubhangi

Interactive Data Visualization with Python : Present your data as an effective and compelling story, 2/e (Paperback)
  • 出版商: Packt Publishing
  • 出版日期: 2020-04-13
  • 售價: $1,980
  • 貴賓價: 9.5 折 $1,881
  • 語言: 英文
  • 頁數: 362
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800200943
  • ISBN-13: 9781800200944
  • 相關分類: Python、程式語言、Data-visualization

    • 海外代購書籍(需單獨結帳)

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商品描述

Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python

Key Features

  • Study and use Python interactive libraries, such as Bokeh and Plotly
  • Explore different visualization principles and understand when to use which one
  • Create interactive data visualizations with real-world data

Book Description

With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.

You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.

By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.

What you will learn

  • Explore and apply different interactive data visualization techniques
  • Manipulate plotting parameters and styles to create appealing plots
  • Customize data visualization for different audiences
  • Design data visualizations using interactive libraries
  • Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots
  • Customize data visualization for different scenarios

Who this book is for

This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

商品描述(中文翻譯)

使用Python強大的數據可視化庫，創建自己清晰而有影響力的互動數據可視化。

主要特點：

- 學習和使用Python的互動庫，如Bokeh和Plotly。
- 探索不同的可視化原則，了解何時使用哪種可視化方法。
- 使用真實世界的數據創建互動數據可視化。

書籍描述：

隨著不斷生成大量數據，能夠以有影響力和有趣的可視化方式呈現數據的開發人員一直受到需求。《使用Python進行互動數據可視化》將提高您的數據探索技能，告訴您有關Python中互動數據可視化的一切。

您將首先學習如何使用Matplotlib和Seaborn這些非互動數據可視化庫繪製各種圖表。您將研究不同類型的可視化，進行比較，並找出如何選擇特定類型的可視化以滿足您的需求。在熟悉各種非互動可視化庫之後，您將學習直觀和有說服力的數據可視化原則，並使用Bokeh和Plotly將您的可視化轉化為有力的故事。您還將瞭解互動數據和模型可視化如何優化回歸模型的性能。

通過本書的學習，您將獲得一套新的技能，使您成為將數據可視化轉化為引人入勝和有趣故事的專家。

您將學到什麼：

- 探索並應用不同的互動數據可視化技術。
- 操縱繪圖參數和樣式，創建吸引人的圖表。
- 為不同的受眾定制數據可視化。
- 使用互動庫設計數據可視化。
- 使用Matplotlib、Seaborn、Altair和Bokeh繪製吸引人的圖表。
- 為不同情境定制數據可視化。

本書適合對象：

本書旨在為Python開發人員、數據分析師和數據科學家提供堅實的培訓基礎，使他們能夠以最能吸引用戶注意力和想像力的方式呈現關鍵數據洞察。它作為一個簡單的逐步指南，演示了可視化的不同類型和組件、有效互動性的原則和技術，以及在創建互動數據可視化時應避免的常見問題。學生應具備中級水平的Python編程能力，以及對使用pandas等庫的一些熟悉。

作者簡介

Abha Belorkar is an educator and researcher in computer science. She received her bachelor's degree in computer science from Birla Institute of Technology and Science Pilani, India and her Ph.D. from the National University of Singapore. Her current research work involves the development of methods powered by statistics, machine learning, and data visualization techniques to derive insights from heterogeneous genomics data on neurodegenerative diseases.

Sharath Chandra Guntuku is a researcher in natural language processing and multimedia computing. He received his bachelor's degree in computer science from Birla Institute of Technology and Science, Pilani, India and his Ph.D. from Nanyang Technological University, Singapore. His research aims to leverage large-scale social media image and text data to model social health outcomes and psychological traits. He uses machine learning, statistical analysis, natural language processing, and computer vision to answer questions pertaining to health and psychology in individuals and communities.

Shubhangi Hora is a Python developer, artificial intelligence enthusiast, data scientist, and writer. With a background in computer science and psychology, she is particularly passionate about mental health-related AI. Apart from this, she is interested in the performing arts and is a trained musician.

Anshu Kumar is a data scientist with over 5 years of experience in solving complex problems in natural language processing and recommendation systems. He has an M.Tech. from IIT Madras in computer science. He is also a mentor at SpringBoard. His current interests are building semantic search, text summarization, and content recommendations for large-scale multilingual datasets.

作者簡介(中文翻譯)

Abha Belorkar 是一位計算機科學的教育家和研究人員。她在印度比爾拉理工學院取得計算機科學學士學位，並在新加坡國立大學獲得博士學位。她目前的研究工作涉及利用統計、機器學習和數據可視化技術來從異質基因組學數據中獲取神經退行性疾病的洞察。

Sharath Chandra Guntuku 是自然語言處理和多媒體計算的研究人員。他在印度比爾拉理工學院取得計算機科學學士學位，並在新加坡南洋理工大學獲得博士學位。他的研究旨在利用大規模社交媒體圖像和文本數據來建模社會健康結果和心理特徵。他使用機器學習、統計分析、自然語言處理和計算機視覺來回答有關個人和社區健康和心理的問題。

Shubhangi Hora 是一位Python開發人員、人工智能愛好者、數據科學家和作家。她在計算機科學和心理學方面有背景，對與心理健康相關的人工智能特別熱衷。除此之外，她對表演藝術也很感興趣，並且是一位受過訓練的音樂家。

Anshu Kumar 是一位資料科學家，擁有超過5年的自然語言處理和推薦系統解決複雜問題的經驗。他在印度馬德拉斯理工學院獲得計算機科學碩士學位。他還是SpringBoard的導師。他目前的興趣是為大規模多語言數據集建立語義搜索、文本摘要和內容推薦系統。

目錄大綱

  1. Introduction to Visualization with Python-Basic and Customized Plotting
  2. Static Visualization - Global Patterns and Summary Statistics
  3. From Static to Dynamic Visualization
  4. Interactive Visualization of Data across Strata
  5. Interactive Visualization of Data across Time
  6. Interactive Visualization of Data across Geographical Regions
  7. Avoiding Common Pitfalls to Create Interactive Visualization

目錄大綱(中文翻譯)


  1. 使用Python進行視覺化介紹-基本和自定義繪圖

  2. 靜態視覺化-全球模式和摘要統計

  3. 從靜態到動態視覺化

  4. 跨層次交互式數據視覺化

  5. 跨時間交互式數據視覺化

  6. 跨地理區域交互式數據視覺化

  7. 避免常見陷阱以創建交互式視覺化

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