This course provides a comprehensive beginner through to advanced learning pathway to gain skills in data visualization with both Plotly and Dash in Python.

Plotly and Dash in Python Cover Art Iteration 02.png

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

CH1_03-numeric_univariate_analysis-lab_A

CH2_01-continuous-vs-categorical-lab_Q

CH2_01-continuous-vs-categorical-lab_A

CH3_01-title-axes-lab_Q

CH3_01-title-axes-lab_A

CH3_03-sizing-color-lab_Q.ipynb

CH3_03-sizing-color-lab_A

CH4_01_subplots_lab_Q

CH4_01_subplots_lab_A

CH4_03_date_buttons_line_charts_lab_Q

CH4_03_date_buttons_line_charts_lab_A

CH5_01_Adding_custom_sliders_lab_Q

CH5_01_Adding_custom_sliders_lab_A

CH5_02_custom_buttons_and_dropdowns_lab_Q

CH5_02_custom_buttons_and_dropdowns_lab_A

CH6_01_scatterplot_smoothers_and_regression_lab_Q

CH6_01_scatterplot_smoothers_and_regression_lab_A

CH6_02_Visualising_Classification_Model_Outputs_lab_Q

CH6_02_Visualising_Classification_Model_Outputs_lab_A

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

CH7_02-the-dash-architecture_LAB

CH7_03-simple-dash-app_CODE

CH8_01_html_in_dash_CODE

CH8_01_html_in_dash_LAB

CH8_02_dash_graph_components_LAB

CH8_02-dash_graph_components_CODE

CH8_03_using_and_reusing_dash_components_LAB

CH8_03-using-and-reusing-dash-components_CODE

CH8_04_dash_inputs_CODE1_dropdown

CH8_04_dash_inputs_CODE2_radio

CH9_01_intro_to_css_and_css_in_dash_CODE1_inline

CH9_01_intro_to_css_and_css_in_dash_CODE2_stylesheets

CH9_02_css_as_stylesheets_CODE

CH9_03_css_with_functions_CODE

CH10_01_callback_functions_CODE1_callbackHTML

CH10_01_callback_functions_CODE2_textarea

CH10_01_callback_functions_CODE3_date

CH10_01_callback_functions_CODE4_image

CH10_01_callback_functions_LAB

CH10_02_Multiple_in_out_callbacks_CODE1_date

CH10_02_Multiple_in_out_callbacks_CODE2_plot.ipynb

CH10_02_Multiple_in_out_callbacks_CODE3_complex_plt.ipynb

CH10_02_Multiple_in_out_callbacks_LAB

CH10_03_chained_callbacks_CODE

CH10_03_chained_callbacks_LAB

CH11_01_interactivity_attributes_CODE1_simple

CH11_01_interactivity_attributes_CODE2_hover_cb

CH11_01_interactivity_attributes_CODE3_hover_harder

CH11_01_interactivity_attributes_CODE4_click_interact

CH11_02_updating_plots_from_user_interactivity_LAB

CH11_03_updating_layouts_styles_userint_CODE1_titlefont

CH11_03_updating_layouts_styles_userint_CODE2_axes

CH11_03_updating_layouts_styles_userint_CODE3_theme

CH11_03_updating_layouts_styles_userint_CODE4_theme_col

CH11_02_updating_plots_from_user_interactivity_CODE1_markers

CH11_02_updating_plots_from_user_interactivity_CODE2_color

CH11_02_updating_plots_from_user_interactivity_CODE3_marker_size

CH11_02_updating_plots_from_user_interactivity_CODE4_symbol

CH12_01_Properties_of_the_DataTable_CODE1_id_data

CH12_01_Properties_of_the_DataTable_CODE2_column

CH12_01_Properties_of_the_DataTable_CODE3_column2

CH12_01_Properties_of_the_DataTable_CODE4_row

CH12_01_Properties_of_the_DataTable_CODE5_style

CH12_01_Properties_of_the_DataTable_CODE6_page

CH12_02_DataTable-Layout_CODE1_nopag

CH12_02_DataTable-Layout_CODE2_autopag

CH12_02_DataTable-Layout_CODE3_limit_r

CH12_02_DataTable-Layout_CODE4_format

CH12_02_DataTable-Layout_CODE5_in_dash

CH12_03_Configuring-columns-and-scroll_CODE1_default

CH12_03_Configuring-columns-and-scroll_CODE2_scrollb

CH12_03_Configuring-columns-and-scroll_CODE3_elipses

CH12_03_Configuring-columns-and-scroll_CODE4_newlines

CH12_03_Configuring-columns-and-scroll_CODE5_freeze

CH12_03_Configuring-columns-and-scroll_CODE6_same_size_c

CH12_04_DataTable_style_and_formatting_CODE1_Default

CH12_04_DataTable_style_and_formatting_CODE2_headers

CH12_04_DataTable_style_and_formatting_CODE3_data_cells

CH12_04_DataTable_style_and_formatting_CODE4_user_int

CH12_04_DataTable_style_and_formatting_CODE5_formatting

CH12_05_data_manipulation_CODE1_sort

CH12_05_data_manipulation_CODE2_select

CH12_05_data_manipulation_CODE3_delete

CH12_05_data_manipulation_CODE4_filter

CH12_05_data_manipulation_CODE5_edit

CH9_01_intro_to_css_and_css_in_dash_CODE3_both

CH9_02_css_as_stylesheets_CODE1_YETI

CH9_02_css_as_stylesheets_CODE2_MORPH

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

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

Quiz: Customizing Plots