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

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: $2,000
  • 貴賓價: 9.5 折 $1,900
  • 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 強大的資料視覺化函式庫創建清晰且具影響力的互動式資料視覺化

主要特點


  • 學習並使用 Python 互動式函式庫，如 Bokeh 和 Plotly

  • 探索不同的視覺化原則，了解何時使用哪一種

  • 使用真實世界的資料創建互動式資料視覺化

書籍描述

隨著大量資料不斷生成，能夠將資料呈現為具影響力且有趣的視覺化的開發者始終受到需求。《使用 Python 進行互動式資料視覺化》提升了您的資料探索技能，告訴您有關 Python 互動式資料視覺化的所有知識。

您將首先學習如何使用 Matplotlib 和 Seaborn 這些非互動式資料視覺化函式庫繪製各種圖表。您將研究不同類型的視覺化，進行比較，並找出如何選擇特定類型的視覺化以符合您的需求。在掌握各種非互動式視覺化函式庫後，您將學習直觀且具說服力的資料視覺化原則，並使用 Bokeh 和 Plotly 將您的視覺效果轉化為強有力的故事。您還將深入了解如何通過互動式資料和模型視覺化來優化回歸模型的性能。

在課程結束時，您將擁有一套新的技能，使您成為將資料視覺化轉化為引人入勝且有趣故事的首選人選。

您將學到什麼


  • 探索並應用不同的互動式資料視覺化技術

  • 操作繪圖參數和樣式以創建吸引人的圖表

  • 為不同的受眾自訂資料視覺化

  • 使用互動式函式庫設計資料視覺化

  • 使用 Matplotlib、Seaborn、Altair 和 Bokeh 繪製吸引人的圖表

  • 為不同情境自訂資料視覺化

本書適合誰

本書旨在為 Python 開發者、資料分析師和資料科學家提供堅實的訓練基礎，使他們能夠以最佳方式呈現關鍵資料洞察，吸引使用者的注意和想像。它作為一個簡單的逐步指南，展示不同類型和組件的視覺化、有效互動的原則和技術，以及在創建互動式資料視覺化時應避免的常見陷阱。學生應具備中級的 Python 程式碼撰寫能力，並對使用如 pandas 等函式庫有一定的熟悉度。

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者和研究者。她在印度比爾拉科技與科學學院（Birla Institute of Technology and Science Pilani）獲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開發基於統計學、機器學習和數據可視化技術的方法，以從異質基因組數據中提取有關神經退行性疾病的見解。

Sharath Chandra Guntuku 是一位自然語言處理和多媒體計算的研究者。他在印度比爾拉科技與科學學院獲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的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的健康與心理問題。

Shubhangi Hora 是一位 Python 開發者、人工智慧愛好者、數據科學家和作家。她擁有計算機科學和心理學的背景，對與心理健康相關的人工智慧特別感興趣。除此之外，她對表演藝術也有興趣，並且是一位受過訓練的音樂家。

Anshu Kumar 是一位數據科學家，擁有超過 5 年的經驗，專注於解決自然語言處理和推薦系統中的複雜問題。他在印度理工學院馬德拉斯分校（IIT Madras）獲得計算機科學的碩士學位。他也是 SpringBoard 的導師。他目前的興趣包括為大規模多語言數據集構建語義搜索、文本摘要和內容推薦。

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. 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

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