EN

Python Bokeh Tutorial | Create Interactive Plots, Multiplots & Grid Layouts

#AI Tools
2025-09-11 19:499 min read

Content Introduction

This video covers the use of interactive libraries for data visualization, focusing on the capabilities of 'bouquet' and 'plotly' for creating interactive plots in Python. It discusses the limitations of static plotting libraries like Matplotlib and Seaborn, and introduces the interactive features that allow users to manipulate plots, including setting dimensions, titles, labels, markers, and colors. The tutorial provides examples of generating various plots, including line and scatter plots, with attention to customization options such as legends and marker shapes. It also touches on embedding plots in Jupyter notebooks and outlines the process for creating a grid of multiple plots, highlighting the benefits of interaction in visual data representation. The video concludes with a promise of more advanced topics, including three-dimensional plotting, in future sessions.

Key Information

  • The video discusses the usage of `matplotlib` and `seaborn` libraries for creating static plots, highlighting their limitations for interactivity.
  • Interactive plotting libraries like `bouquet` and `plotly` provide functionalities that allow users to engage with their plots.
  • The video introduces `bouquet` and explains its interactive plotting capabilities by showcasing a demonstration.
  • It emphasizes that once a plot is created using static libraries, users cannot interact with them, whereas `bouquet` allows for interactive plotting.
  • The video transitions into showing how to set up interactive plots, including how to define plot dimensions, titles, and markers.
  • Multiple interactive plots can be combined and managed in the same notebook, enhancing the data visualization experience.
  • There are different modes of displaying plots, such as in separate files or embedded within notebooks.
  • The video also contrasts `bouquet` with `plotly`, which is highlighted as another library for interactive three-dimensional plotting.

Timeline Analysis

Content Keywords

Matplotlib and Seaborn

Both Matplotlib and Seaborn are static plotting libraries used extensively for creating plots. Once a plot is created, users cannot interact with it, but there are many interactive libraries available.

Interactive Libraries

Interactive libraries like Bokeh and Plotly allow users to create interactive plots where figures can be drawn and manipulated. This enhances user engagement and data analysis.

Bokeh

Bokeh is introduced as an interactive plotting library. The speaker demonstrates using Bokeh to set up a plot using Jupyter Notebook, showing specific code for imports and basic configurations.

Plot Customization

The video discusses customizing plots, including setting titles, dimensions, and the marker type. It also emphasizes interaction and adjustments available with the defined parameters.

Grid Plot

A grid plot feature in Bokeh allows multiple subplots to be arranged in a grid format, enabling users to interact with multiple plots simultaneously.

3D Plotting

The discussion transitions to 3D plotting, where the speaker indicates the necessity of interactive functionality in such plots, particularly for data manipulation across dimensions.

Plotly

Plotly is introduced as a library that facilitates interactive 3D plotting. The speaker hints at exploring this library in future videos to address the limitations of Bokeh for 3D visualization.

Related questions&answers

What is the focus of this course?

The course focuses on interactive libraries for creating visual plots and data visualization.

What are Matplotlib and Seaborn?

Matplotlib and Seaborn are libraries used for static plots in data visualization.

Can we interact with plots created using Matplotlib or Seaborn?

No, plots created with Matplotlib or Seaborn are static and do not allow for interaction.

Which interactive libraries are mentioned in the course?

The course mentions Bokeh and Plotly as interactive libraries.

What is Bokeh used for?

Bokeh is used for creating interactive plots that can be embedded in Jupyter notebooks.

How do you set up Bokeh for use in a Jupyter notebook?

You need to import Bokeh libraries such as figure and output_file, then set the output to be in a notebook.

What are the properties that you can set for a plot in Bokeh?

You can set properties such as plot width, height, title, axis labels, and legend.

How does one create interactive plots in Bokeh?

You can create interactive plots by defining the figure and adding various rendering functions with specific styles and data.

What functionality does Bokeh provide for three-dimensional plotting?

Bokeh provides capabilities for 3D plotting, though there might be limitations or challenges with direct interactive access.

What is Plotly used for?

Plotly is used for creating interactive three-dimensional plots.

What are some of the challenges with interactive three-dimensional plots in Bokeh?

Interactive three-dimensional plotting can be less straightforward in Bokeh, often requiring alternative libraries like Plotly.

What can you do with interactive plots in Bokeh?

With interactive plots, you can zoom, reset, set titles, and interact with individual components.

What is a grid plot in Bokeh?

A grid plot allows multiple plots to be organized in a grid layout, providing a way to visualize multiple datasets together.

What will be covered in the next video?

The next video will cover more examples of using grid plots and potentially introducing Plotly for 3D visualizations.

More video recommendations

Share to: