Python Data Visualization & Interactive Dashboards

Self-paced lessons

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  Course Intro

  Lesson 1: Environment Setup & Foundations

  0:59

  Learn to build interactive data visualization apps using Dash by Plotly from setup to advanced interactivity.

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  Getting Our Class Files

  Lesson 1: Environment Setup & Foundations

  3:13

  Download the class files from GitHub as a ZIP, unzip them directly into your Downloads folder (avoiding nested folders), and prepare for Anaconda installation.

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  Installing Anaconda

  Lesson 1: Environment Setup & Foundations

  2:31

  Download and install Anaconda from anaconda.com, selecting the correct version for your operating system and following the installation instructions.

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  Our First Terminal Command

  Lesson 1: Environment Setup & Foundations

  4:23

  Learn to open and use a terminal emulator to run basic text-based commands like echo in a command-response cycle.

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  Understanding the Prompt

  Lesson 1: Environment Setup & Foundations

  5:34

  Understand the terminal prompt as an indicator of your current Python environment, user, computer, and directory, signaling when it’s ready to accept commands.

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  Creating Our Python Environment

  Lesson 1: Environment Setup & Foundations

  7:22

  Create and activate a Python virtual environment using conda to isolate project-specific dependencies.

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  Configuring Our Python Environment

  Lesson 1: Environment Setup & Foundations

  4:42

  Configure the DVENV Python environment to use CondaForge as the exclusive package source and install the latest versions of pandas, dash, and dash-bootstrap-components.

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  Opening Our Class Files

  Lesson 1: Environment Setup & Foundations

  3:37

  Open your curriculum folder in Visual Studio Code, navigate to app.py in Notebook 1, and prepare to write and run code.

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  Running Our Dash App

  Lesson 2: Building Core Dash Visuals

  5:49

  Navigate to your project directory in the terminal, activate the dvenv environment, run your app.py file using the python command, and open the provided local URL in your browser to view your Dash app.

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  Intro to Dash

  Lesson 2: Building Core Dash Visuals

  5:48

  Understand how Dash, a Python library, builds interactive web dashboards by converting Python code into HTML, CSS, and JavaScript, and learn how to run, modify, and refresh a basic Dash app using app layout definitions and live server updates.

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  an Html Primer

  Lesson 2: Building Core Dash Visuals

  7:55

  Understand basic HTML structure and tags like H1-H6 for headings, P for paragraphs, and DIV/SPAN for layout and inline text, focusing on how elements nest as parent and child.

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  Formatting Our Data

  Lesson 2: Building Core Dash Visuals

  11:45

  Import stock data from a CSV file using Pandas, filter the top five stocks by trading volume, convert volume figures to millions, round them to one decimal place, and prepare the data for visualization.

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  Creating a Pie Chart

  Lesson 2: Building Core Dash Visuals

  6:32

  Create a pie chart using Plotly Express to visualize the top five stocks by volume and embed it into the dashboard using Dash core components.

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  Why Data Viz and Why Dash

  Lesson 2: Building Core Dash Visuals

  4:03

  Explain how simple data visualizations like pie charts, built with Dash, help reveal insights—such as Apple’s dominance in trading volume—and offer advantages over tools like Jupyter Notebooks for creating interactive, shareable web applications.

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  Composing Elements

  Lesson 2: Building Core Dash Visuals

  4:55

  Add a heading, author name, and pie chart as a list of children to an HTML.Div to structure the dashboard layout.

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  Creating a Bar Chart

  Lesson 2: Building Core Dash Visuals

  4:47

  Compare company stock volumes using a bar chart to highlight relative differences versus overall proportions shown in a pie chart.

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  Adding Further Visualizations

  Lesson 2: Building Core Dash Visuals

  7:45

  Explore different data visualizations—horizontal bar and scatter plots—to assess data relationships and determine the most effective format for analysis.

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  a Quick Demo of Dash Interactivity

  Lesson 2: Building Core Dash Visuals

  2:51

  Explore interactive Dash features that allow users to zoom, filter, and analyze specific data segments directly within visualizations.

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  Summing Up Section 1

  Lesson 2: Building Core Dash Visuals

  1:44

  Build a functional dashboard using Dash with interactive layouts and data visualizations.

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  VS Code Jupyter Setup

  Lesson 3: Pandas & Function Essentials

  5:09

  Review basic Python and pandas skills in Jupyter Notebooks using VS Code with the correct extensions and environment setup to prepare for advanced data visualization with Dash.

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  Reading in Our Data to a Notebook

  Lesson 3: Pandas & Function Essentials

  4:18

  Import pandas, read a CSV of company stock and market data into a DataFrame, and interact with it in a Jupyter notebook using VS Code.

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  Pandas Practice

  Lesson 3: Pandas & Function Essentials

  15:00

  Practice pandas by transforming market cap values, dropping unnecessary columns and zero-priced rows, filtering for specific companies, calculating statistics like max and average prices, counting entries under a threshold, and retrieving targeted data points.

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  Creating a Pandas and Pyplot Pie Chart

  Lesson 3: Pandas & Function Essentials

  7:34

  Group companies by country, sum their market caps, sort to find the top five, and plot them in a pie chart using matplotlib.

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  Viewing Our Pie Chart

  Lesson 3: Pandas & Function Essentials

  1:51

  Install missing packages as needed during development, then proceed with data formatting and plotting in the notebook.

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  Reintroducing Functions

  Lesson 3: Pandas & Function Essentials

  5:54

  Learn how to define and use functions, including lambdas and apply, for advanced data manipulation in a data frame.

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  Challenge Writing Your Own Function

  Lesson 3: Pandas & Function Essentials

  4:46

  Define a function addCents that returns a given dollar amount plus a random number of cents rounded to two decimal places using random.random.

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  Named Lambdas

  Lesson 3: Pandas & Function Essentials

  3:58

  Use lambda functions to create concise, one-line operations that take inputs and return computed values without requiring formal function definitions.

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  Apply

  Lesson 3: Pandas & Function Essentials

  4:28

  Create a DataFrame with numbers from 10,000 to 10,010 and use apply to transform the column with function calls, including chaining and anonymous lambdas.

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  Anonymous Lambdas With Apply

  Lesson 3: Pandas & Function Essentials

  6:01

  Use both a named function and an anonymous lambda with apply to convert numeric values into formatted dollar strings, optionally adding commas for readability.

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  Apply Challenge Solution

  Lesson 3: Pandas & Function Essentials

  4:08

  Format dollar amounts with commas using a custom function or lambda and apply it to a DataFrame column.

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  Using Apply With Real Data

  Lesson 3: Pandas & Function Essentials

  8:31

  Convert string-based percentage and volume columns from a stock CSV file into usable numeric formats using pandas, apply, lambda functions, and string manipulation.

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  Lambdas With Ternaries

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  9:27

  Create a column called advice using apply with a lambda that returns "buy" if last_price < 200, else "sell".

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  Exploring The Chipotle Data

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  7:21

  Analyze Chipotle order data to extract item prices, identify the most expensive item, and prepare for revenue calculations.

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  Grouping To Get Total Revenue

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:00

  Group Chipotle order data by item name to calculate total quantities and total revenue for each item, then extract specific values (like for "chicken bowl") using .loc for further analysis and visualization.

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  Graphing Our Revenue

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:55

  Identify the top-selling items by revenue using n-largest, then plot the top 5 as a bar chart with proper labels and styling.

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  Dash App Setup Redux

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  5:27

  Set up your Dash app by activating the correct Python environment and navigating to the proper project directory.

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  Manipulating The Data In A Python File

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  5:31

  Run the Dash app from the Notebook2 directory, load and clean the Chipotle CSV data, compute revenue by item, extract the top five items, and prepare figures for dashboard display.

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  Adding A Pie Graph

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  4:54

  Replace the header with a div containing a dcc.Graph that displays a Plotly Express pie chart based on the top five item prices.

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  Adding A Second Graph

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  3:26

  Add a bar chart alongside the pie chart using a list of multiple dcc.Graph components to display both figures with built-in interactivity.

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  Adding All Graphs

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  2:55

  Add additional bar and scatter plots, adjust their orientation and visual properties, then address layout issues by organizing the graphs using Bootstrap for better presentation.

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  Bootstrap

  Lesson 4: Data Analysis & Multi-Graph Dashboards

  10:35

  Use Bootstrap’s responsive layout system with containers, rows, and columns to arrange Dash components cleanly and control their sizing across screen sizes.

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  Final Dashboard Intro

  Lesson 5: Interactive Dashboards & Final Project

  1:46

  Create an interactive Dash dashboard that updates visualizations based on user input using car sales data from carsales.csv.

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  A Basic Scatterplot

  Lesson 5: Interactive Dashboards & Final Project

  5:36

  Create a Dash app in app.py that reads car-sales.csv, builds a Plotly Express scatter plot of horsepower vs fuel efficiency with color indicating price, and displays it in a web layout using Dash components.

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  Running Our Dash App

  Lesson 5: Interactive Dashboards & Final Project

  4:13

  Run the app in the DVEnv environment, view the interactive graph in a browser, and prepare to add company filtering, engine size slider, and detailed tooltips in the next step.

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  Creating A Dropdown

  Lesson 5: Interactive Dashboards & Final Project

  5:14

  Create a dropdown using Dash to list unique car manufacturers from a dataset and prepare it to filter the graph based on selection.

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  Using Dash Callbacks

  Lesson 5: Interactive Dashboards & Final Project

  13:54

  Use a Dash callback decorator to update a graph dynamically based on a user's dropdown selection, replacing the initial figure on page load.

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  Outputting From Our Callback

  Lesson 5: Interactive Dashboards & Final Project

  3:34

  Implement interactive filtering of a car dataset by manufacturer using a dropdown, and update the graph accordingly with an option to display all manufacturers.

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  Adding An All Option

  Lesson 5: Interactive Dashboards & Final Project

  7:05

  Insert an "All Manufacturers" option at the top of the drop-down list, set it as the default selection, and adjust the data filtering logic to display all cars when it's selected.

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  Adding A Range Slider

  Lesson 5: Interactive Dashboards & Final Project

  4:31

  Create a range slider using Pandas min and max engine size values to filter engine sizes from minimum to maximum in step increments of one.

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  Making The Slider Interactive

  Lesson 5: Interactive Dashboards & Final Project

  7:21

  Connect the engine size slider to the graph update function by adding it as an input, filter the data based on the selected range, and update the graph accordingly.

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  Hover Data Intro

  Lesson 5: Interactive Dashboards & Final Project

  2:25

  Enable interactive Dash dashboards by using user actions like hovering to dynamically display additional data in designated output sections.

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  The Final Interactive Element

  Lesson 5: Interactive Dashboards & Final Project

  22:43

  Create a new Dash callback that updates an info panel with detailed car data based on hover events from a graph.

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  Course Outro

  Lesson 5: Interactive Dashboards & Final Project

  0:40

  Explore data that interests you and apply your Dash skills to create interactive visualizations.

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