18.3 Plotly for interactive and web-based visualizations

Plotly is a powerful Python library for creating interactive, web-based visualizations. It offers a wide range of chart types and customization options, allowing you to create stunning, publication-quality graphs that users can interact with directly in their web browsers.

With Plotly, you can easily add interactivity to your visualizations, create animations, and even build dynamic dashboards. This makes it an essential tool for data scientists and analysts who want to create engaging, exploratory data visualizations that can be easily shared and embedded in web pages.

Interactive Plots with Plotly

Creating Plots with Plotly

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• Plotly is a Python graphing library used to create interactive, publication-quality graphs
  • Supports various chart types (line charts, scatter plots, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D graphs)
• Plotly graphs are defined by data, layout and frames parameters
  • data parameter takes a list of dictionaries specifying the data for each trace
  • layout parameter configures the visual elements of the graph (title, axes, annotations, shapes, legends)
  • frames parameter allows for the creation of animated graphs
• Line charts are created using the

  go.Scatter

  function with

  mode='lines'

  • Data should be in a dictionary with keys for x and y values
  • Example:

    {'x': [1, 2, 3], 'y': [4, 5, 6]}

• Bar charts are created using the

  go.Bar

  function
  • Data should be in a dictionary with keys for x (categorical variables) and y (numerical variables) values
  • Example:

    {'x': ['A', 'B', 'C'], 'y': [10, 20, 30]}

• Scatter plots are created using the

  go.Scatter

  function with

  mode='markers'

  • Data should be in a dictionary with keys for x and y values
  • Additional keys can control marker properties (color, size, symbol)
  • Example:

    {'x': [1, 2, 3], 'y': [4, 5, 6], 'marker': {'color': 'red', 'size': 10, 'symbol': 'circle'}}

Plot Customization and Interactivity

Customizing Plot Layouts

• The layout parameter is used to customize non-data elements of the graph
  • Defined as a dictionary
  • Can customize title, axes, annotations, shapes, and legends
  • Example:

    {'title': 'My Plot', 'xaxis': {'title': 'X Axis'}, 'yaxis': {'title': 'Y Axis'}}

• Subplots can be created using the

  [make_subplots](https://www.fiveableKeyTerm:make_subplots)

  function
  • Returns a figure with a grid of subplots that can be populated with traces
  • Allows for the creation of complex layouts with multiple plots

Adding Interactivity

• Hover information can be customized using the

  [hoverinfo](https://www.fiveableKeyTerm:hoverinfo)

  ,

  [hovertext](https://www.fiveableKeyTerm:hovertext)

  , and

  [hovertemplate](https://www.fiveableKeyTerm:hovertemplate)

  parameters in the data traces
  • Allows for the display of additional information when hovering over data points
  • Example:

    {'x': [1, 2, 3], 'y': [4, 5, 6], 'hovertext': ['A', 'B', 'C']}

• Plotly supports click, hover, and selection events that can trigger changes to the graph
  • Events are configured using the

    [on_click](https://www.fiveableKeyTerm:on_click)

    ,

    [on_hover](https://www.fiveableKeyTerm:on_hover)

    , and

    [on_selection](https://www.fiveableKeyTerm:on_selection)

    parameters
  • Can update data or layout properties in response to user interactions
• Interactive features like zooming, panning, and box selection can be enabled
  • Controlled using the

    [layout.xaxis.autorange](https://www.fiveableKeyTerm:layout.xaxis.autorange)

    ,

    [layout.yaxis.autorange](https://www.fiveableKeyTerm:layout.yaxis.autorange)

    , and

    [layout.dragmode](https://www.fiveableKeyTerm:layout.dragmode)

    parameters
  • Allows users to explore data by zooming in on specific regions or selecting subsets of data

Animated and Updateable Visualizations

Creating Animations

• Animations are created by passing a list of frames to the

  frames

  parameter
  • Each frame is a dictionary specifying the data and layout properties for a specific point in the animation
  • Frames are displayed sequentially to create the animation effect
• The

  [layout.updatemenus](https://www.fiveableKeyTerm:layout.updatemenus)

  parameter is used to create menus that allow the user to control the animation
  • Can include play, pause, and frame selection buttons
  • Allows users to interact with the animation and explore the data at their own pace

Updating Visualizations Dynamically

• Plotly supports updating the data and layout of a graph without redrawing the entire graph
  • Uses the

    update

    and

    [restyle](https://www.fiveableKeyTerm:restyle)

    methods of the figure object
  • Allows for efficient updates of large datasets
  • Example:

    fig.update(data=[{'y': [1, 2, 3]}])

• The Plotly

  [FigureWidget](https://www.fiveableKeyTerm:figurewidget)

  class can be used to create interactive graphs that update in response to changes in the data or layout properties
  • Useful for creating dynamic dashboards and data exploration tools
  • Widgets can be used to control the data and layout of the graph
  • Example:

    fig = go.FigureWidget(data=[{'y': [1, 2, 3]}])

Embedding Visualizations in Web Pages

Embedding Plotly Graphs in HTML

• Plotly graphs can be embedded in web pages using HTML

  iframe

  elements
  • Graph is first saved as an HTML file using the

    [write_html](https://www.fiveableKeyTerm:write_html)

    method
  • HTML file is then embedded in the web page using an

    iframe

  • Allows for easy sharing of interactive visualizations online
• Plotly graphs can also be embedded using the

  [plotly.js](https://www.fiveableKeyTerm:plotly.js)

  JavaScript library
  • Graph is defined using JavaScript code and rendered in a

    div

    element on the page
  • Provides more flexibility and customization options than the

    iframe

    approach

Building Dashboards with Dash

• Plotly Dash is a Python framework for building analytical web applications
  • Allows for the creation of interactive dashboards that can be deployed as web applications
  • Consists of two parts: the layout and the callbacks
    • Layout defines the HTML elements of the application
    • Callbacks define the interactivity between the elements
• Dash components are pre-built UI elements that can be used to quickly build interactive dashboards
  • Includes dropdowns, sliders, graphs, and more
  • Can be easily customized and combined to create complex layouts
• Plotly graphs can be integrated into Dash applications using the

  [dcc.Graph](https://www.fiveableKeyTerm:dcc.graph)

  component
  • Allows for the creation of interactive dashboards that update in real-time as the underlying data changes
  • Provides a powerful tool for exploring and sharing data insights online