最新章節
- Summary
- Survival data analysis on cancer
- Combining geographical and population health data
- Map-based visualization for geographical data
- Visualizing population health information
- Exploratory Data Analytics and Infographics
品牌：中圖公司
上架時間：2021-07-02 18:23:23
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：2021-07-02 19:35:17
- Survival data analysis on cancer
- Combining geographical and population health data
- Map-based visualization for geographical data
- Visualizing population health information
- Exploratory Data Analytics and Infographics
- Summary
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Principal Component Analysis (PCA)
- Methods for dimensions reduction
- Indicating statistical significance
- Replacing bar charts with mean-and-error plots
- Stacked bar chart and layered histogram
- Visualizing statistical data more intuitively
- Summary of styling plots for slideshows posters and journal articles
- Adaptations
- Distance from the audience
- Space allowed
- Display time
- Styling plots for slideshows posters and journal articles
- Use minimal marker shapes
- Typography
- Spacing
- Size and weight
- Color and hue
- Giving emphasis and avoiding clutter
- Grouping
- Ordering plots and data series logically
- Getting organized
- The Gestalt principles of visual perception
- The science of visual perception
- Crafting your graph
- Targeting your audience
- Choosing the right plot
- Do we need the plot?
- Planning your figure
- General rules of effective visualization
- A Practical Guide to Scientific Plotting
- Summary
- Creating animations
- Installation of FFmpeg
- Creating animated plots
- Plot.ly-based backend
- Interactive backend for Jupyter Notebook
- Tkinter-based backend
- Interactive backends
- Non-interactive backends
- Scraping information from websites
- Adding Interactivity and Animating Plots
- Summary
- Caveats of Matplotlib 3D
- 3D bar chart
- 3D scatter plot
- Three-dimensional (3D) plots
- Building a comprehensive stock chart
- Visualizing various stock market indicators
- Candlestick plot in matplotlib.finance
- Heatmap in Seaborn
- Other two-dimensional multivariate plots
- Pair plot in Seaborn
- Faceted grid in Seaborn
- Factor plot in Seaborn
- Two-dimensional faceted plots
- Getting the percentage change of the closing price
- Converting the date to a supported format
- Grouping the companies by industry
- Getting End-of-Day (EOD) stock data from Quandl
- Visualizing Multivariate Data
- Summary
- Color scheme and color palettes
- More about colors
- Fine-tuning the style of the figure
- Changing the size of the figure
- Removing spines from the figure
- Preset themes
- Controlling Seaborn figure aesthetics
- Box plot and violin plot
- Strip plot and swarm plot
- Categorical scatter plot
- Visualizing categorical data
- Scatter plot in Seaborn
- Visualizing a bivariate distribution
- Histogram and distribution fitting in Seaborn
- Bar chart in Seaborn
- Visualizing univariate distribution
- Introducing Seaborn
- Area chart and stacked area chart
- Visualizing the trend of data
- Importing online financial data in the JSON format
- Importing online population data in the CSV format
- Introducing pandas
- XML
- JSON
- CSV
- Typical API data formats
- Visualizing Online Data
- Summary
- Adding image annotations
- Adding shapes
- Adding graphical annotations
- Labeling data values on a bar chart
- Adding arrows
- Adding a textbox with axis.text
- Adding text and arrows with axis.annotate
- Adding text annotations
- Annotations
- Using inset_axes
- Drawing a basic inset plot
- Drawing inset plots
- Stacking subplots of different dimensions with subplot2grid
- Auto-aligning figure elements with pyplot.tight_layout
- Aligning subplots with pyplot.tight_layout
- Modifying subplot axes dimensions via pyplot.subplots_adjust
- Setting dimensions when adding subplot axes with figure.add_axes
- Adjusting margins
- Sharing axes between subplots
- Using pyplot.subplots() to specify handles
- Adding subplots using pyplot.subplot
- Adding subplots
- Adjusting spines
- Adjusting the size of the figure
- Adjusting the layout
- Figure Layout and Annotations
- Summary
- Test your skills
- Adding a legend
- Adding a title to your figure
- Title and legend
- Customizing a style sheet
- Resetting to default styles
- Applying a style sheet
- Using style sheets
- Logit scale
- Symmetrical logarithmic scale
- Advanced example
- Changing the base of the log scale
- Logarithmic scale
- Nonlinear axis
- Axes
- Rotating tick labels
- Trying out the ticker locator and formatter
- Setting label sizes
- Formatting axes by numerical values
- Setting labels with user-defined functions
- Setting labels with strings
- Fixing labels
- Removing tick labels
- Customizing tick formats
- Locating ticks by datetime
- Set scaling of ticks by mathematical functions
- Setting ticks by the number of data points
- Automatic tick settings
- Drawing ticks in multiples
- Removing ticks
- Adjusting tick spacing
- Ticks
- Adding grids
- Grids
- Customizing grids ticks and axes
- Adjusting marker sizes
- Choosing markers
- Markers
- Advanced example
- Setting capstyle of dashes
- Choosing dash patterns
- Lines
- Customizing lines and markers
- LaTeX support
- Checking available fonts in system
- Font family
- Font weight
- Font style
- Font size
- Font appearance
- Font
- Adjusting text formats
- Aesthetic and readability considerations
- Using specific colors in the color cycle
- Depth of grayscale
- Hexadecimal color code
- RGB or RGBA color code
- Names of standard HTML colors
- Single letters for basic built-in colors
- Setting colors in Matplotlib
- Glossary of objects in a Matplotlib figure
- Basic structure of a Matplotlib figure
- Figure Aesthetics
- Summary
- Adjusting the resolution
- Post (Postscript)
- SVG (Scalable Vector Graphics)
- PDF (Portable Document Format)
- PNG (Portable Network Graphics)
- Setting the output format
- Saving the figure
- Viewing the figure
- Plotting a curve
- Importing the Matplotlib pyplot module
- The pandas way
- The Numpy way
- The basic Python way
- Loading data from files
- pandas dataframe
- Numpy array
- List
- Data structures
- Loading data for plotting
- Plotting our first graph
- All set to go!
- Saving the notebook project
- Viewing Matplotlib plots
- Jotting down notes in Markdown mode
- Editing and running code
- Starting a Jupyter notebook session
- Using Jupyter notebook
- Installing Jupyter notebook
- Why Jupyter notebook?
- Setting up Jupyter notebook
- Installing Matplotlib with pip
- Installing the pip Python package manager
- Installing the Matplotlib dependencies
- Linux
- macOS
- Using Python
- Windows
- Setting up Python
- Setting up the plotting environment
- Change in Axes property keywords
- Style parameter blacklist
- New configuration parameters (rcParams)
- Changes in settings
- Change in the default animation codec
- Faster text rendering
- Improved image support
- Improved color conversion API and RGBA support
- Improved functionality or performance
- Fonts
- Patch edges and color
- Line style
- Legend
- Scatter plot
- Colormap
- Color cycle
- Changes to the default style
- What's new in Matplotlib 2.0?
- What is Matplotlib?
- Hello Matplotlib!
- Hello Plotting World!
- Questions
- Piracy
- Errata
- Downloading the color images of this book
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- Why subscribe?
- www.PacktPub.com
- About the Reviewer
- About the Authors
- Credits
- Matplotlib 2.x By Example
- Copyright
- Title Page
- cover
- cover
- Title Page
- Copyright
- Matplotlib 2.x By Example
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- Hello Plotting World!
- Hello Matplotlib!
- What is Matplotlib?
- What's new in Matplotlib 2.0?
- Changes to the default style
- Color cycle
- Colormap
- Scatter plot
- Legend
- Line style
- Patch edges and color
- Fonts
- Improved functionality or performance
- Improved color conversion API and RGBA support
- Improved image support
- Faster text rendering
- Change in the default animation codec
- Changes in settings
- New configuration parameters (rcParams)
- Style parameter blacklist
- Change in Axes property keywords
- Setting up the plotting environment
- Setting up Python
- Windows
- Using Python
- macOS
- Linux
- Installing the Matplotlib dependencies
- Installing the pip Python package manager
- Installing Matplotlib with pip
- Setting up Jupyter notebook
- Why Jupyter notebook?
- Installing Jupyter notebook
- Using Jupyter notebook
- Starting a Jupyter notebook session
- Editing and running code
- Jotting down notes in Markdown mode
- Viewing Matplotlib plots
- Saving the notebook project
- All set to go!
- Plotting our first graph
- Loading data for plotting
- Data structures
- List
- Numpy array
- pandas dataframe
- Loading data from files
- The basic Python way
- The Numpy way
- The pandas way
- Importing the Matplotlib pyplot module
- Plotting a curve
- Viewing the figure
- Saving the figure
- Setting the output format
- PNG (Portable Network Graphics)
- PDF (Portable Document Format)
- SVG (Scalable Vector Graphics)
- Post (Postscript)
- Adjusting the resolution
- Summary
- Figure Aesthetics
- Basic structure of a Matplotlib figure
- Glossary of objects in a Matplotlib figure
- Setting colors in Matplotlib
- Single letters for basic built-in colors
- Names of standard HTML colors
- RGB or RGBA color code
- Hexadecimal color code
- Depth of grayscale
- Using specific colors in the color cycle
- Aesthetic and readability considerations
- Adjusting text formats
- Font
- Font appearance
- Font size
- Font style
- Font weight
- Font family
- Checking available fonts in system
- LaTeX support
- Customizing lines and markers
- Lines
- Choosing dash patterns
- Setting capstyle of dashes
- Advanced example
- Markers
- Choosing markers
- Adjusting marker sizes
- Customizing grids ticks and axes
- Grids
- Adding grids
- Ticks
- Adjusting tick spacing
- Removing ticks
- Drawing ticks in multiples
- Automatic tick settings
- Setting ticks by the number of data points
- Set scaling of ticks by mathematical functions
- Locating ticks by datetime
- Customizing tick formats
- Removing tick labels
- Fixing labels
- Setting labels with strings
- Setting labels with user-defined functions
- Formatting axes by numerical values
- Setting label sizes
- Trying out the ticker locator and formatter
- Rotating tick labels
- Axes
- Nonlinear axis
- Logarithmic scale
- Changing the base of the log scale
- Advanced example
- Symmetrical logarithmic scale
- Logit scale
- Using style sheets
- Applying a style sheet
- Resetting to default styles
- Customizing a style sheet
- Title and legend
- Adding a title to your figure
- Adding a legend
- Test your skills
- Summary
- Figure Layout and Annotations
- Adjusting the layout
- Adjusting the size of the figure
- Adjusting spines
- Adding subplots
- Adding subplots using pyplot.subplot
- Using pyplot.subplots() to specify handles
- Sharing axes between subplots
- Adjusting margins
- Setting dimensions when adding subplot axes with figure.add_axes
- Modifying subplot axes dimensions via pyplot.subplots_adjust
- Aligning subplots with pyplot.tight_layout
- Auto-aligning figure elements with pyplot.tight_layout
- Stacking subplots of different dimensions with subplot2grid
- Drawing inset plots
- Drawing a basic inset plot
- Using inset_axes
- Annotations
- Adding text annotations
- Adding text and arrows with axis.annotate
- Adding a textbox with axis.text
- Adding arrows
- Labeling data values on a bar chart
- Adding graphical annotations
- Adding shapes
- Adding image annotations
- Summary
- Visualizing Online Data
- Typical API data formats
- CSV
- JSON
- XML
- Introducing pandas
- Importing online population data in the CSV format
- Importing online financial data in the JSON format
- Visualizing the trend of data
- Area chart and stacked area chart
- Introducing Seaborn
- Visualizing univariate distribution
- Bar chart in Seaborn
- Histogram and distribution fitting in Seaborn
- Visualizing a bivariate distribution
- Scatter plot in Seaborn
- Visualizing categorical data
- Categorical scatter plot
- Strip plot and swarm plot
- Box plot and violin plot
- Controlling Seaborn figure aesthetics
- Preset themes
- Removing spines from the figure
- Changing the size of the figure
- Fine-tuning the style of the figure
- More about colors
- Color scheme and color palettes
- Summary
- Visualizing Multivariate Data
- Getting End-of-Day (EOD) stock data from Quandl
- Grouping the companies by industry
- Converting the date to a supported format
- Getting the percentage change of the closing price
- Two-dimensional faceted plots
- Factor plot in Seaborn
- Faceted grid in Seaborn
- Pair plot in Seaborn
- Other two-dimensional multivariate plots
- Heatmap in Seaborn
- Candlestick plot in matplotlib.finance
- Visualizing various stock market indicators
- Building a comprehensive stock chart
- Three-dimensional (3D) plots
- 3D scatter plot
- 3D bar chart
- Caveats of Matplotlib 3D
- Summary
- Adding Interactivity and Animating Plots
- Scraping information from websites
- Non-interactive backends
- Interactive backends
- Tkinter-based backend
- Interactive backend for Jupyter Notebook
- Plot.ly-based backend
- Creating animated plots
- Installation of FFmpeg
- Creating animations
- Summary
- A Practical Guide to Scientific Plotting
- General rules of effective visualization
- Planning your figure
- Do we need the plot?
- Choosing the right plot
- Targeting your audience
- Crafting your graph
- The science of visual perception
- The Gestalt principles of visual perception
- Getting organized
- Ordering plots and data series logically
- Grouping
- Giving emphasis and avoiding clutter
- Color and hue
- Size and weight
- Spacing
- Typography
- Use minimal marker shapes
- Styling plots for slideshows posters and journal articles
- Display time
- Space allowed
- Distance from the audience
- Adaptations
- Summary of styling plots for slideshows posters and journal articles
- Visualizing statistical data more intuitively
- Stacked bar chart and layered histogram
- Replacing bar charts with mean-and-error plots
- Indicating statistical significance
- Methods for dimensions reduction
- Principal Component Analysis (PCA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Summary
- Exploratory Data Analytics and Infographics
- Visualizing population health information
- Map-based visualization for geographical data
- Combining geographical and population health data
- Survival data analysis on cancer
- Summary 更新時間：2021-07-02 19:35:17
