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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plotlydash.tar.gz

ch1_01-the-plotly-layout

codingExercises

Ch3_04-hover-types

ch1_l1_all.ipynb

CH1_02-categorical-univariate-analysis-lab_Q

CH1_02-categorical-univariate-analysis-lab_A

CH1_03-numeric_univariate_analysis-lab_Q

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CH2_01-continuous-vs-categorical-lab_Q

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CH3_01-title-axes-lab_Q

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CH3_03-sizing-color-lab_Q.ipynb

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CH4_01_subplots_lab_Q

CH4_01_subplots_lab_A

CH4_03_date_buttons_line_charts_lab_Q

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CH5_01_Adding_custom_sliders_lab_Q

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CH5_02_custom_buttons_and_dropdowns_lab_Q

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CH6_01_scatterplot_smoothers_and_regression_lab_Q

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CH6_02_Visualising_Classification_Model_Outputs_lab_Q

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CH6_03_dimensionality_reduction_lab_Q

CH7_01-simple-example

CH6_03_dimensionality_reduction_lab_A

CH7_01-dash-and-plotly_CODE

CH7_02-the-dash-architecture_CODE1_layout

CH7_02-the-dash-architecture_CODE2_callback

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CH7_03-simple-dash-app_CODE

CH8_01_html_in_dash_CODE

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CH8_02_dash_graph_components_LAB

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CH8_03_using_and_reusing_dash_components_LAB

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CH8_04_dash_inputs_CODE1_dropdown

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CH9_01_intro_to_css_and_css_in_dash_CODE1_inline

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CH9_03_css_with_functions_CODE

CH10_01_callback_functions_CODE1_callbackHTML

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CH11_02_updating_plots_from_user_interactivity_CODE1_markers

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CH12_05_data_manipulation_CODE1_sort

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CH12_05_data_manipulation_CODE3_delete

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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

How can you update the title of a plot, as well as the labels of the x and y axes?

How can you update the title of a plot, as well as the labels of the x and y axes?

Which specific arguments are used in Plotly to update the title of the x and y axes of a plot?

Which specific arguments are used in Plotly to update the title of the x and y axes of a plot?

When color is set to a continuous variable, the legend is comprised of discrete colors, while for a categorical variable, the legend represents a color bar.

When color is set to a categorical variable, the legend is comprised of discrete colors, while for a continuous variable, the legend represents a color bar.

When color is set to a continuous variable, the legend represents a color bar, while for a categorical variable, the legend is comprised of discrete colors.

When color is set to a categorical variable, the legend represents a color bar, while for a continuous variable, the legend is comprised of discrete colors.

What is the difference in the appearance of the legend in a Plotly Express scatter plot when the color argument column is continuous versus categorical?

What is the difference in the appearance of the legend in a Plotly Express scatter plot when the color argument column is continuous versus categorical?

A marker is a line that connects data points in a Plotly Express plot, used to show trends over time.

A marker is an individual symbol that represents a data point in a plotly express plot, customized with various attributes such as color, shape, and size.

A marker is a label that provides additional information about a data point in a Plotly Express plot, used to highlight specific data values.

A marker is a region on a Plotly Express plot that indicates the range of values for a given dataset, used to compare multiple datasets.

What is a marker, and how is it used in Plotly Express visualizations?

What is a marker, and how is it used in Plotly Express visualizations?

To set the minimum size of the markers used to represent data points on the plot

To set the maximum size of the markers used to represent data points on the plot

To set the default size of the markers used to represent data points on the plot

What is the purpose of the `size_max` argument in a Plotly Express scatter plot?

What is the purpose of the <code>size_max</code> argument in a Plotly Express scatter plot?

These arguments are the same, you can use either.

The `hovermode` determines the type of data that will be displayed on hover, while `hover_name` determines the size of the hover text.

The `hovermode` sets the type of hover style and `hover_name` property controls which column is displayed in bold as the tooltip title.

The `hovermode` determines the size and color of the hover markers, while `hover_name` specifies the size of the marker on hover.

What is the primary difference between the `hovermode` and `hover_name` arguments?

What is the primary difference between the <code>hovermode</code> and <code>hover_name</code> arguments?

The annotations argument is used, which takes a list of annotation dictionaries.

How can you add an annotation to a plot in Plotly, and what argument is used to specify the properties of the annotation object?

How can you add an annotation to a plot in Plotly, and what argument is used to specify the properties of the annotation object?

Quiz: Customizing Plots

Quiz: Customizing Plots

Test your understanding of techniques and concepts to format your Plotly figures, including titles, axes, colors, hovers, and annotations.

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

Quiz: Customizing Plots