はじめに — マンガと学ぶデータビジュアライゼーション
はじめに# データビジュアライゼーションとは，数値や文章などのデータに基づいた情報を，人間が理解しやすい形に視覚化する技術を指します． このサイトは，文化庁のメディア芸術データベース・ラボ（MADB Lab）で公開されている四大少年誌（ 週刊少年サンデー， 週刊少年ジャンプ， 週刊少年チャンピオン， 週刊少年マガジン ）のデータを用いて，データビジュアライゼーションの学習を手助けすることを目指しています． データビジュアライゼーション（に限らずデータ分析全般）の学習において重要なのは，分析対象のデータに興味を持てるかどうかです． 本書では約47年の四大少年誌のマンガ作品データを採用しているため，モチベーションを保ちつつ学習を進めることが可能です．
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data. TensorWatch is designed to be flexible and extensible so you can also build your own custo
TensorWatch: A debugging and visualization system for machine learning
Microsoft makes AI debugging and visualization tool TensorWatch open source Published June 25, 2019 By Shital Shah , Principal Research Engineer Roland Fernandez , Principal Researcher Steven Drucker , Partner Research Manager Maxim Lukiyanov , Principal Program Manager Susan Dumais , Technical Fellow & Managing Director The rise of deep learning is accompanied by ever-increasing model complexity,
Welcome to Bokeh — Bokeh documentation
Bokeh documentation# Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. Finding the right documentation resources# Bokeh’s documentation consists
かっこいいグラフを作るPythonモジュールPyCoolPlotを作った - MyEnigma
外資系コンサルが実践する 資料作成の基本 パワーポイント、ワード、エクセルを使い分けて「伝える」→「動かす」王道70posted with カエレバ吉澤 準特 日本能率協会マネジメントセンター 2014-08-20 Amazonで最安値を探す楽天市場で最安値を探すYahooショッピングで最安値を探す 目次 目次 はじめに PyCoolPlot 必要なもの ダウンロードの仕方 使い方 Horizontal bar plot Line graph Time bar chart 参考資料 MyEnigma Supporters はじめに いつもグラフを書く時にデザインに迷うのですが、 下記の記事を見て、すぐにmaplotlibで実装したくなりました。 note.mu しかし、 もたもたしているうちに先を越されてしまったのですが、 「データ視覚化のデザイン #1」をmatplotlibで実装する
Python Plotting for Exploratory Analysis
Python Plotting for Exploratory Data Analysis The simple graph has brought more information to the data analyst's mind than any other device. Contents Introduction Examples Bar Chart Histogram Scatter Plot Time Series Scatter Plot with Faceted with Color Scatter Plot with Points Sized by Continuous Value Scatter Plot Faceted on One Variable Scatter Plot Faceted on Two Variables Scatter Plot and Re
blockdiag - simple diagram images generator — blockdiag 1.0 documentation
blockdiag - simple diagram images generator¶ blockdiag and its family generate diagram images from simple text files: Features: Supports many types of diagrams block diagram (w/ blockdiag) sequence diagram (w/ seqdiag) activity diagram (w/ actdiag) logical network diagram (w/ nwdiag) Generates beautiful diagram images from simple text format (similar to graphviz’s DOT format) Layouts diagram eleme
The PML Visualization Project
Introduction In this project we study the space PML(S) of projective measured laminations on a surface S of negative Euler characteristic. Our goal is to make meaningful pictures of this space and its hierarchical structure using William Thurston's embedding of PML(S) into a cotangent space of Teichmüller space. In these visualizations, the points in PML(S) representing simple closed hyperbolic ge
Project Jupyter
Free software, open standards, and web services for interactive computing across all programming languages JupyterLab: A Next-Generation Notebook Interface JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and ma
Welcome to Bokeh — Bokeh documentation
Bokeh documentation# Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. Finding the right documentation resources# Bokeh’s documentation consists
seaborn: statistical data visualization — seaborn 0.13.2 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
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NXT User Docs
GitHub - nxt-dev/nxt_editor: The visual node editor for nxt, a python layering and sequential execution tool for animation and VFX
GitHub - bokeh/bokeh: Interactive Data Visualization in the browser, from Python
GitHub - CodecrumbsIO/codecrumbs: Learn, design or document codebase by putting breadcrumbs in source code. Live updates, multi-language support and more.
Python's Visualization Landscape (PyCon 2017)
ETE Toolkit - Analysis and Visualization of (phylogenetic) trees
GitHub - ContextLab/hypertools: A Python toolbox for gaining geometric insights into high-dimensional data
HyperTools: A python toolbox for gaining geometric insights into high-dimensional data — hypertools 0.8.1 documentation
Advance AI with Open Source | Anaconda
