Learn Plotly and Dash to build interactive visualizations and dashboards with Python. Explore customization, interactivity, and complex datasets to create dynamic, data-driven applications.

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Discover the compelling universe of Plotly and Dash, versatile Python libraries that enable interactive data visualization and web dashboard applications. These tools are essential for anyone seeking to effectively communicate data narratives and build interactive data visualizations.

You'll explore Plotly’s robust data visualization capabilities. You’ll create plots for univariate, bivariate and multivariate analysis before customizing and adding interactivity, using real world datasets. Then, you’ll build upon and use these skills when learning Dash as you build and customize analytical dashboard apps. You’ll journey through Dash's structure, from understanding layouts and HTML components to mastering Dash styling using CSS. You’ll learn the significance of callback functions, enhancing user interactivity, and experience the power of Dash's DataTable component.

By the end of this course, you'll be able to create engaging visualizations with Plotly and develop interactive web applications using Dash.

Mastering Plotly and Dash Data Visualization with Python

# Exercise 1

Create a scatter plot with the `Sales` variable plotted against `TV`. Include a red trendline of the line of best fit using Ordinary Least Squares (OLS). What is the $R^2$ value?

## Solution

+ A scatter plot is created using the `px.scatter()` function on **line 2** with the `advertising` DataFrame. The `x` parameter is set to `TV` to specify the x-axis data, and the `y` parameter is set to `Sales` to specify the y-axis data. The `trendline` parameter is set to `ols` to add a linear regression line to the plot. The `trendline_color_override` parameter is set to `red` to override the default color of the trendline with red color.

+ The `update_layout()` function on **line 7** is used to set the title of the figure to "Sales vs. TV Advertising (with OLS line estimate)."

> **Note:** From the graph, we see that the $R^2$ is 0.81.



Exercise: Visualizing Regression Plots

Introduction and Univariate Analysis

Exploratory Data Analysis - Bivariate and Multivariate Analysis

Customizing Plots

Advanced Customization

Interactivity in Plotly

Time Series Analysis and Visualization Using Python and Plotly

Visualizing Machine Learning Models with Plotly

Global Energy Consumption Visualization

Introducing Dash

Dash Layout

Dash Styling

Dash Callbacks

User Interactivity in Dash

Tabular data in Dash DataTable

Global Energy Consumption Dashboard

Conclusion

Build Data-centric Dashboards Using Dash

Exercise: Visualizing Regression Plots

Exercise 1

Solution