舉報 
會員
Matplotlib for Python Developers
最新章節：
Summary
ThisbookisessentiallyforanyonewhowantstocreateintuitivedatavisualizationsusingtheMatplotliblibrary.Ifyou’readatascientistoranalystandwishtocreateattractivevisualizationsusingPython,you’llfindthisbookuseful.SomeknowledgeofPythonprogrammingisallyouneedtogetstarted.
目錄(234章)
倒序
- 封面
- Title Page
- Copyright and Credits
- Matplotlib for Python Developers Second Edition
- Dedication
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the authors
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introduction to Matplotlib
- What is Matplotlib?
- Merits of Matplotlib
- Easy to use
- Diverse plot types
- Hackable to the core (only when you want)
- Open source and community support
- What's new in Matplotlib 2.x?
- Improved functionality and performance
- Improved color conversion API and RGBA support
- Improved image support
- Faster text rendering
- Change in the default animation codec
- Changes in default styles
- Matplotlib website and online documentation
- Output formats and backends
- Static output formats
- Raster images
- Vector images
- Setting up Matplotlib
- Installing Python
- Python installation for Windows
- Python installation for macOS
- Python installation for Linux
- Installing Matplotlib
- About the dependencies
- Installing the pip Python package manager
- Installing Matplotlib with pip
- Setting up Jupyter Notebook
- Starting a Jupyter Notebook session
- Running Jupyter Notebook on a remote server
- Editing and running code
- Manipulating notebook kernel and cells
- Embed your Matplotlib plots
- Documenting in Markdown
- Save your hard work!
- Summary
- Getting Started with Matplotlib
- Loading data
- List
- NumPy array
- pandas DataFrame
- Our first plots with Matplotlib
- Importing the pyplot
- Line plot
- Scatter plot
- Overlaying multiple data series in a plot
- Multiline plots
- Scatter plot to show clusters
- Adding a trendline over a scatter plot
- Adjusting axes grids labels titles and legends
- Adjusting axis limits
- Adding axis labels
- Adding a grid
- Titles and legends
- Adding a title
- Adding a legend
- A complete example
- Saving plots to a file
- Setting the output format
- Setting the figure resolution
- Jupyter support
- Interactive navigation toolbar
- Configuring Matplotlib
- Configuring within Python code
- Reverting to default settings
- Global setting via configuration rc file
- Finding the rc configuration file
- Editing the rc configuration file
- Summary
- Decorating Graphs with Plot Styles and Types
- Controlling the colors
- Default color cycle
- Single-lettered abbreviations for basic colors
- Standard HTML color names
- RGB or RGBA color code
- Hexadecimal color code
- Depth of grayscale
- Colormaps
- Creating custom colormaps
- Line and marker styles
- Marker styles
- Choosing the shape of markers
- Using custom characters as markers
- Adjusting marker sizes and colors
- Fine-tuning marker styles with keyword arguments
- Line styles
- Color
- Line thickness
- Dash patterns
- Designing a custom dash style
- Cap styles
- Spines
- More native Matplotlib plot types
- Choosing the right plot
- Histogram
- Bar plot
- Setting bar plot properties
- Drawing bar plots with error bars using multivariate data
- Mean-and-error plots
- Pie chart
- Polar chart
- Controlling radial and angular grids
- Text and annotations
- Adding text annotations
- Font
- Mathematical notations
- Mathtext
- LaTeX support
- External text renderer
- Arrows
- Using style sheets
- Applying a style sheet
- Creating own style sheet
- Resetting to default styles
- Aesthetics and readability considerations in styling
- Suitable font styles
- Effective use of colors
- Keeping it simple
- Summary
- Advanced Matplotlib
- Drawing Subplots
- Initiating a figure with plt.figure()
- Initiating subplots as axes with plt.subplot()
- Adding subplots with plt.figure.add_subplot()
- Shared axes
- Setting the margin with plt.tight_layout()
- Aligning subplots of different dimensions with plt.subplot2grid()
- Drawing inset plots with fig.add_axes()
- Adjusting subplot dimensions post hoc with plt.subplots_adjust
- Adjusting axes and ticks
- Customizing tick spacing with locators
- Removing ticks with NullLocator
- Locating ticks in multiples with MultipleLocator
- Locators to display date and time
- Customizing tick formats with formatters
- Using a non-linear axis scale
- More on Pandas-Matplotlib integration
- Showing distribution with the KDE plot
- Showing the density of bivariate data with hexbin plots
- Expanding plot types with Seaborn
- Visualizing multivariate data with a heatmap
- Showing hierarchy in multivariate data with clustermap
- Image plotting
- Financial plotting
- 3D plots with Axes3D
- Geographical plotting
- Basemap
- GeoPandas
- Summary
- Embedding Matplotlib in GTK+3
- Installing and setting up GTK+3
- A brief introduction to GTK+3
- Introduction to the GTK+3 signal system
- Installing Glade
- Designing the GUI using Glade
- Summary
- Embedding Matplotlib in Qt 5
- A brief introduction to Qt 5 and PyQt 5
- Differences between Qt 4 and PyQt 4
- Introducing QT Creator / QT Designer
- Summary
- Embedding Matplotlib in wxWidgets Using wxPython
- A brief introduction to wxWidgets and wxPython
- Embedding Matplotlib in a GUI from wxGlade
- Summary
- Integrating Matplotlib with Web Applications
- Installing Docker
- Docker for Windows users
- Docker for Mac users
- More about Django
- Django development in Docker containers
- Starting a new Django site
- Installation of Django dependencies
- Django environment setup
- Running the development server
- Showing Bitcoin prices using Django and Matplotlib
- Creating a Django app
- Creating a simple Django view
- Creating a Bitcoin candlestick view
- Integrating more pricing indicators
- Integrating the image into a Django template
- Summary
- Matplotlib in the Real World
- Typical API data formats
- CSV
- JSON
- Importing and visualizing data from a JSON API
- Using Seaborn to simplify visualization tasks
- Scraping information from websites
- Matplotlib graphical backends
- Non-interactive backends
- Interactive backends
- Creating animated plot
- Summary
- Integrating Data Visualization into the Workflow
- Getting started
- Visualizing sample images from the dataset
- Importing the UCI ML handwritten digits dataset
- Plotting sample images
- Extracting one sample each of digits 0-9
- Examining the randomness of the dataset
- Plotting the 10 digits in subplots
- Exploring the data nature by the t-SNE method
- Understanding t-Distributed stochastic neighbor embedding
- Importing the t-SNE method from scikit-learn
- Drawing a t-SNE plot for our data
- Creating a CNN to recognize digits
- Evaluating prediction results with visualizations
- Examining the prediction performance for each digit
- Extracting falsely predicted images
- Summary 更新時間：2021-08-27 18:48:52
推薦閱讀
- Redis入門指南（第3版）
- Moodle Administration Essentials
- Learning Bayesian Models with R
- 編程珠璣（續）
- Blender 3D Incredible Machines
- TypeScript實戰指南
- 零基礎學Python數據分析（升級版）
- Redis Essentials
- 零基礎入門學習Python
- HTML5權威指南
- OpenCV 3 Blueprints
- Python 3 數據分析與機器學習實戰
- Building Business Websites with Squarespace 7（Second Edition）
- Software Development on the SAP HANA Platform
- JavaScript前端開發基礎教程
- Visual Basic語言程序設計上機指導與練習（第3版）
- Kotlin語言實例精解
- 現代C++語言核心特性解析
- INSTANT Lift Web Applications How-to
- 計算機信息技術實踐教程
- WCF 4.5 Multi-Layer Services Development with Entity Framework（Third Edition）
- 軟件測試（第2版）
- Java 9 Cookbook
- Kivy Blueprints
- 胸有成竹！數據分析的SPSS和SAS EG進階（第2版）
- 數字媒體交互設計（中級）：App產品交互設計方法與案例
- Unity Virtual Reality Projects
- Android模塊化開發項目式教程（Android Studio）
- Articulate Storyline Essentials
- Mastering Immutable.js
