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

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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

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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

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

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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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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

A univariate box plot displays the distribution of one variable, while a bivariate box plot displays the distribution of two variables.

A univariate box plot shows the median, quartiles, and outliers of a dataset, while a bivariate box plot displays only the median and quartiles.

 A univariate box plot displays the distribution of categorical data, while a bivariate box plot displays the distribution of numerical data.

A univariate box plot is used to compare two or more groups, while a bivariate box plot is used to compare only two groups.

What is the main difference between a univariate and bivariate box plot?

What is the main difference between a univariate and bivariate box plot?

What Plotly Express function is used to create a histogram where the color differentiates categories?

What Plotly Express function is used to create a histogram where the color differentiates categories?

The `y` argument represents the numerical variable `Age` that will be plotted on the y-axis.

The `y` argument specifies the variable `Age` that will be used to determine the width of the violin plot.

The `y` argument represents the categorical variable `Geography` that will be plotted on the y-axis.

The `y` argument determines the vertical position of each observation in the violin plot based on the `Age` variable.

Which statement about the `y` argument in the following function is *true* when creating a violin plot using Plotly Express?

`px.violin(churn, 
                x="Geography", 
                y="Age", 
                color="Geography", 
                box=True, 
                points="all")`

Which statement about the <code>y</code> argument in the following function is true when creating a violin plot using Plotly Express?
<code>px.violin(churn, x=&quot;Geography&quot;, y=&quot;Age&quot;, color=&quot;Geography&quot;, box=True, points=&quot;all&quot;)</code>

It displays the distribution of one variable over time vs. the trend of one variable over time.


It compares the change in two different variables over time vs. the change in a single variable over time.

It displays the relationship between two categorical variables vs. the relationship between two numerical variables.

It displays the distribution of a single variable at two different time points vs. the distribution of a single variable over a continuous time period.

What is the primary purpose of a dumbbell plot and how does it differ from a line plot?

What is the primary purpose of a dumbbell plot and how does it differ from a line plot?

Scatterplots can be misleading when there are too many data points.

Scatterplots can only show linear relationships.

Scatterplots cannot determine causality between two variables.

What is the main limitation of using scatterplots to understand the relationship between two variables?

What is the main limitation of using scatterplots to understand the relationship between two variables?

By using a logarithmic scale for one of the axes

By coloring and sizing the points based on a third or fourth variable

How can scatterplots be made multivariate?

How can scatterplots be made multivariate?

Which top-level function of Plotly Express is used in to plot a correlation matrix?

Which top-level function of Plotly Express is used in to plot a correlation matrix?

When we wish to compare the values of different categories or groups.

When we want to display the parts of a whole.

When we wish to visualize patterns and trends of a continuous variable over time.

When we want to show the relationship between two numerical variables.

When is a line chart particularly useful for data visualization?

When is a line chart particularly useful for data visualization?

Quiz: Bivariate and Multivariate Analysis

Quiz: Bivariate and Multivariate Analysis

Test your understanding of techniques and Plotly code to visualize different combinations of continuous and categorical variables.

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: Bivariate and Multivariate Analysis