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Matplotlib for Python Developers
最新章節：
Summary
ThisbookisessentiallyforanyonewhowantstocreateintuitivedatavisualizationsusingtheMatplotliblibrary.Ifyou’readatascientistoranalystandwishtocreateattractivevisualizationsusingPython,you’llfindthisbookuseful.SomeknowledgeofPythonprogrammingisallyouneedtogetstarted.
最新章節
- Summary
- Extracting falsely predicted images
- Examining the prediction performance for each digit
- Evaluating prediction results with visualizations
- Creating a CNN to recognize digits
- Drawing a t-SNE plot for our data
品牌：中圖公司
上架時間：2021-08-27 18:05:40
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Summary 更新時間：2021-08-27 18:48:52
- Extracting falsely predicted images
- Examining the prediction performance for each digit
- Evaluating prediction results with visualizations
- Creating a CNN to recognize digits
- Drawing a t-SNE plot for our data
- Importing the t-SNE method from scikit-learn
- Understanding t-Distributed stochastic neighbor embedding
- Exploring the data nature by the t-SNE method
- Plotting the 10 digits in subplots
- Examining the randomness of the dataset
- Extracting one sample each of digits 0-9
- Plotting sample images
- Importing the UCI ML handwritten digits dataset
- Visualizing sample images from the dataset
- Getting started
- Integrating Data Visualization into the Workflow
- Summary
- Creating animated plot
- Interactive backends
- Non-interactive backends
- Matplotlib graphical backends
- Scraping information from websites
- Using Seaborn to simplify visualization tasks
- Importing and visualizing data from a JSON API
- JSON
- CSV
- Typical API data formats
- Matplotlib in the Real World
- Summary
- Integrating the image into a Django template
- Integrating more pricing indicators
- Creating a Bitcoin candlestick view
- Creating a simple Django view
- Creating a Django app
- Showing Bitcoin prices using Django and Matplotlib
- Running the development server
- Django environment setup
- Installation of Django dependencies
- Starting a new Django site
- Django development in Docker containers
- More about Django
- Docker for Mac users
- Docker for Windows users
- Installing Docker
- Integrating Matplotlib with Web Applications
- Summary
- Embedding Matplotlib in a GUI from wxGlade
- A brief introduction to wxWidgets and wxPython
- Embedding Matplotlib in wxWidgets Using wxPython
- Summary
- Introducing QT Creator / QT Designer
- Differences between Qt 4 and PyQt 4
- A brief introduction to Qt 5 and PyQt 5
- Embedding Matplotlib in Qt 5
- Summary
- Designing the GUI using Glade
- Installing Glade
- Introduction to the GTK+3 signal system
- A brief introduction to GTK+3
- Installing and setting up GTK+3
- Embedding Matplotlib in GTK+3
- Summary
- GeoPandas
- Basemap
- Geographical plotting
- 3D plots with Axes3D
- Financial plotting
- Image plotting
- Showing hierarchy in multivariate data with clustermap
- Visualizing multivariate data with a heatmap
- Expanding plot types with Seaborn
- Showing the density of bivariate data with hexbin plots
- Showing distribution with the KDE plot
- More on Pandas-Matplotlib integration
- Using a non-linear axis scale
- Customizing tick formats with formatters
- Locators to display date and time
- Locating ticks in multiples with MultipleLocator
- Removing ticks with NullLocator
- Customizing tick spacing with locators
- Adjusting axes and ticks
- Adjusting subplot dimensions post hoc with plt.subplots_adjust
- Drawing inset plots with fig.add_axes()
- Aligning subplots of different dimensions with plt.subplot2grid()
- Setting the margin with plt.tight_layout()
- Shared axes
- Initiating an array of subplots with plt.subplots()
- Adding subplots with plt.figure.add_subplot()
- Initiating subplots as axes with plt.subplot()
- Initiating a figure with plt.figure()
- Drawing Subplots
- Advanced Matplotlib
- Summary
- Keeping it simple
- Effective use of colors
- Suitable font styles
- Aesthetics and readability considerations in styling
- Resetting to default styles
- Creating own style sheet
- Applying a style sheet
- Using style sheets
- Arrows
- External text renderer
- LaTeX support
- Mathtext
- Mathematical notations
- Font
- Adding text annotations
- Text and annotations
- Controlling radial and angular grids
- Polar chart
- Pie chart
- Mean-and-error plots
- Drawing bar plots with error bars using multivariate data
- Setting bar plot properties
- Bar plot
- Histogram
- Choosing the right plot
- More native Matplotlib plot types
- Spines
- Cap styles
- Designing a custom dash style
- Dash patterns
- Line thickness
- Color
- Line styles
- Fine-tuning marker styles with keyword arguments
- Adjusting marker sizes and colors
- Using custom characters as markers
- Choosing the shape of markers
- Marker styles
- Line and marker styles
- Creating custom colormaps
- Colormaps
- Depth of grayscale
- Hexadecimal color code
- RGB or RGBA color code
- Standard HTML color names
- Single-lettered abbreviations for basic colors
- Default color cycle
- Controlling the colors
- Decorating Graphs with Plot Styles and Types
- Summary
- Editing the rc configuration file
- Finding the rc configuration file
- Global setting via configuration rc file
- Reverting to default settings
- Configuring within Python code
- Configuring Matplotlib
- Interactive navigation toolbar
- Jupyter support
- Setting the figure resolution
- Setting the output format
- Saving plots to a file
- A complete example
- Adding a legend
- Adding a title
- Titles and legends
- Adding a grid
- Adding axis labels
- Adjusting axis limits
- Adjusting axes grids labels titles and legends
- Adding a trendline over a scatter plot
- Scatter plot to show clusters
- Multiline plots
- Overlaying multiple data series in a plot
- Scatter plot
- Line plot
- Importing the pyplot
- Our first plots with Matplotlib
- pandas DataFrame
- NumPy array
- List
- Loading data
- Getting Started with Matplotlib
- Summary
- Save your hard work!
- Documenting in Markdown
- Embed your Matplotlib plots
- Manipulating notebook kernel and cells
- Editing and running code
- Running Jupyter Notebook on a remote server
- Starting a Jupyter Notebook session
- Setting up Jupyter Notebook
- Installing Matplotlib with pip
- Installing the pip Python package manager
- About the dependencies
- Installing Matplotlib
- Python installation for Linux
- Python installation for macOS
- Python installation for Windows
- Installing Python
- Setting up Matplotlib
- Vector images
- Raster images
- Static output formats
- Output formats and backends
- Matplotlib website and online documentation
- Changes in default styles
- Change in the default animation codec
- Faster text rendering
- Improved image support
- Improved color conversion API and RGBA support
- Improved functionality and performance
- What's new in Matplotlib 2.x?
- Open source and community support
- Hackable to the core (only when you want)
- Diverse plot types
- Easy to use
- Merits of Matplotlib
- What is Matplotlib?
- Introduction to Matplotlib
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the authors
- Contributors
- PacktPub.com
- Why subscribe?
- Packt Upsell
- Dedication
- Matplotlib for Python Developers Second Edition
- Copyright and Credits
- Title Page
- 封面
- 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()
- Initiating an array of subplots with plt.subplots()
- 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
