Instantly share code, notes, and snippets.


nicwhitehead / gist:4e359848dbbc9848b58eee020ea2ae75

  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Embed Embed this gist in your website.
  • Share Copy sharable link for this gist.
  • Clone via HTTPS Clone using the web URL.
  • Learn more about clone URLs


AgbaSparks commented Jun 22, 2023

The code runs but it doesn't display a graph... please what can I do?

Sorry, something went wrong.

Final Exam: Data Visualization with Python

  • 1 video | 32s
  • Includes Assessment
  • Earns a Badge


final assignment data visualization with python


Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.


final assignment data visualization with python


final assignment data visualization with python

Advisory boards aren’t only for executives. Join the LogRocket Content Advisory Board today →

LogRocket blog logo

  • Product Management
  • Solve User-Reported Issues
  • Find Issues Faster
  • Optimize Conversion and Adoption
  • Start Monitoring for Free

Data visualization with Python Dash

final assignment data visualization with python

Data visualization interfacing, also known as dashboarding , is an integral part of data analysts’ skillset. Dashboards and data apps are used everywhere now, from reporting your analysis through a series of visuals to showcasing your machine learning apps.

Visualized Data Bar Graph

When there is data involved, so is Python. Specifically, we are talking about its Dash library, which is built on top of one of the hottest graphing libraries, Plotly .

Dash makes it a breeze to create and share your data analysis through interactive dashboards using only Python code. There is no need to learn HTML, CSS, or complex JavaScript frameworks like React.js .

In this tutorial, you’ll get a glimpse of what Dash can do and how to integrate it into your workflow.

Installing Dash and Plotly

You can install dash with pip . You should also install pandas library to work with datasets:

The above command also installs plotly . Plotly is known for its interactive charts, and both Plotly and Dash are created by Plotly Software Foundation, so the libraries work pretty well together.

Requirements to use Dash

A powerful framework like Dash has a few requirements. First, you must know Plotly Python as Dash can only display Plotly’s interactive charts.

Next, you need a basic understanding of HTML and CSS. Dash is like React but in Python. It is a template framework where you can build a data website without JavaScript.

A dashboard contains multiple visuals, and it is up to the user how all these visuals are displayed on a single page. Plotly handles the visuals, but the layout aspect is all up to Dash and its HTML components.

Building a Dash app

Let’s create our Dash app. After the installation, we import the following libraries:

dash is the global library containing all the core features. The dash_core_components and dash_html_components are libraries that are installed with dash by default. They include Dash-specific features and Python representation of HTML components (tags). More on them later.

Any Dash app starts with the following command:

The above code creates all the boilerplate code for a blank website. We don’t want a blank page, so let’s populate it.

First, we load a built-in dataset from Plotly and create a simple scatterplot:

Then, we add this figure to our app’s layout attribute inside a div tag with a few texts:

Here, we create the HTML tags using the dash_html_components ( html ) library and the figure using the core components ( dcc ) library .

The H1 title tag is followed by a div that contains a simple text, then the graph itself using the Graph function of dcc . All of these are inside a single DIV tag’s children attribute.

In the end, we also add the command that runs our app in debug mode, meaning the changes take effect as the script changes. Here is the complete code so far:

Put it inside a Python script and run it. You will get a message in the terminal telling you to go to this link:

So, let’s go:

Hello Dash Scatterplot

In the next sections, we will dig into the details of what we did here.

final assignment data visualization with python

Over 200k developers use LogRocket to create better digital experiences

final assignment data visualization with python

Creating the app.layout

Let’s start with the layout attribute. It is the single attribute that contains all your HTML components and figures. You should pass all your visuals and HTML tags to it in a final DIV tag.

Depending on the size of your project, this attribute can get pretty large, so I recommend creating all your HTML tags and figures in separate variables, then pass them to the layout .

For example, here is what the above app would look like:

This is much neater and more compact, and this is one of the things that you won’t learn from the Dash docs. It is full of nested code instead of doing the above.

HTML and CSS components in Dash

Let’s discuss how HTML and CSS work in Dash. The dash_html_components sub-library contains the most common HTML tags such as divs, buttons, text boxes, captions, title tags (H1-6), etc.

These are implemented in Python code under relevant names as a representation of their HTML counterparts. So, a code like below:

Will be interpreted by your browser like this:

All HTML tags under this sub-library contain these general arguments:

  • id : the same as the the id attribute of HTML tags
  • className : the same as the class attribute HEYYYof HTML tags
  • style : the same as the style attribute of HTML tags but only accepts a dictionary of CSS styles
  • children : the first argument of most HTML components

Here is an example div, with a few customizations:

Simple Div Red Highlight

The children argument is unique — it can accept numbers and strings, but, most of the time, you pass other HTML components inside a list if it is a container tag like a div.

A note on CSS styles: most CSS style attributes use hyphens to break words. When you pass them to Dash’s style argument, they should follow camelCase like backgroundColor instead of background-color .

I highly recommend you learn about these HTML tags, as these are the only things that keep together the layout of your app. Here is a complete list of HTML tags Dash supports.

Dash core components

Another essential part of Dash is its core components. The dash_core_components library contains a few other HTML tags but is already built-in with a few CSS and JavaScript flairs.

Some examples include dropdowns, sliders, download and upload functionalities, and a component to display Plotly Graphs.

Here are a few examples of these components, starting with a dropdown:

A multi-dropdown:

A slider with labeled breakpoints:

There’s a universal value attribute that represents the default value when a component is rendered for the first time.

You can see the complete list of core components here .

Creating the final data visualization interface with Python Dash

As a final example, take a look at the below app:

We import the Diamonds dataset from Seaborn and create three charts :  a scatterplot, a histogram, and a violin plot. We want to display the scatterplot and violin plot next to each other and put the violin plot right below in the center.

For this, we create two divs to contain the scatter plot and violin plot, left_figure and right_figure . Then, these two divs are put into another div – upper_div for simplicity.

We set a CSS style of flex-box to this div, which puts the figures side-by-side.

Then, we create the central div that contains the violin plot, and we center it using flex-box and its justify-content attribute.

Finally, we put everything into the layout inside a final DIV and run the script. Here is the final output:

Here is a summary of the steps to create a basic Dash app:

  • Create the app with dash.Dash and give it any name
  • Sketch the layout of your plots in the dashboard before writing the actual code
  • Create the plots that go into your dashboard
  • Create the boilerplate layout using HTML Dash components
  • Add your figures to their relevant containers
  • Finally, add all HTML components to the layout attribute

Even though we have covered many basics like HTML, core components, and app layout, we have barely scratched the surface of what Dash can do.

I showed you many examples of interactive HTML components, but not how to integrate them into your app. How can you update your graphs based on user input like sliders, text inputs, or something similar?

That’s where callbacks come in. It’s  a powerful and central feature of Dash. To use callbacks, you define functions in Dash that fire off when a component is changed by the user, and the function modifies another component based on this event.

Dash devotes a large section in the docs just to explain callbacks, as they are hard to wrap your head around at first. I suggest you go there as the next step.

Check out the example gallery where you can see and enjoy some very cool projects built with Dash. The best part is that many of them are open-source, meaning you can learn much from their source codes and get inspired. Thanks for reading!

Get set up with LogRocket's modern error tracking in minutes:

  • Visit to get an app ID

Install LogRocket via npm or script tag. LogRocket.init() must be called client-side, not server-side

Share this:

  • Click to share on Twitter (Opens in new window)
  • Click to share on Reddit (Opens in new window)
  • Click to share on LinkedIn (Opens in new window)
  • Click to share on Facebook (Opens in new window)

final assignment data visualization with python

Stop guessing about your digital experience with LogRocket

Recent posts:.

11 Planetscale Alternatives With Free Tiers

11 Planetscale alternatives with free tiers

Planetscale is getting rid of their free plan. Let’s explore 11 alternatives with free plans for backend and database operations.

final assignment data visualization with python

Redux Toolkit adoption guide: Overview, examples, and alternatives

Redux Toolkit is the officially recommended way to build Redux apps. Let’s explore why it’s a great tool to leverage in your next project.

final assignment data visualization with python

CSS Hooks and the state of CSS-in-JS

Explore the evolution of CSS-in-JS and CSS Hooks, a React-based solution that simplifies dynamic styling and component encapsulation.

final assignment data visualization with python

How Waku compares to Next.js

Waku is a new lightweight React framework built on top of React Server Components (RSCs). Let’s compare Waku with Next.js in this article.

final assignment data visualization with python

Leave a Reply Cancel reply


  1. Here’s Your Guide to IBM’s “Data Visualization with Python” Final

    final assignment data visualization with python

  2. Data-Visualization-with-Python/Week 5

    final assignment data visualization with python

  3. Transcripts for Python Data Visualization: Facetting

    final assignment data visualization with python

  4. The Best Python Data Visualization Libraries

    final assignment data visualization with python

  5. Data Visualization with Python

    final assignment data visualization with python

  6. Quick guide to Visualization in Python

    final assignment data visualization with python


  1. Coursera: IBM

  2. Coursera

  3. Data Visualization in Python using Matplotlib

  4. Coursera: IBM

  5. Page View Time Series Visualizer



  1. Data Visualization with Python

    Data Visualization with Python - Final Assignment Import required libraries import pandas as pd import dash import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output, State import plotly.graph_objects as go import as px from dash import no_update

  2. GitHub

    Final project from coursera: As a data analyst, you have been given a task to monitor and report US domestic airline flights performance. Goal is to analyze the performance of the reporting airline to improve fight reliability thereby improving customer relaibility. - nominizim/Data-Visualization-with-Python

  3. 1965Eric/IBM-DV0101EN-Visualizing-Data-with-Python

    Final Final ... The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using ...

  4. 5.1 Data Visualisation with Python

    5.1 Data Visualisation with Python - Week 5 Final Assignment (code) Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs. Dataframes to create graph. This function takes in airline data and selected year as an input and performs computation for creating ...

  5. Data Visualization with Python Course (IBM)

    You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash. Introduction to Data Visualization Tools. ... For the final assignment you will analyze historical automobile sales data covering periods of recession and non-recession. You will bring your analysis to life using visualization ...

  6. Here's Your Guide to IBM's "Data Visualization with Python" Final

    This article will guide you to accomplish the final assignment of Data Visualization with Python, a course created by IBM and offered by Coursera.Nevertheless, this tutorial is for anyone— enrolled in the course or not — who wants to learn how to code an interactive dashboard in Python using Plotly's Dash library.

  7. Data Visualization With Python (Learning Path)

    Applied Data Visualization. In this final section, apply your data visualization skills in Python on real world tasks. Learn to build interactive web applications with Dash, and interactive web maps using Folium. Then, explore the creative side of data visualization by drawing the Mandelbrot set, a famous fractal, using Matplotlib and Pillow.

  8. Final Assignment

    Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources. code. New Notebook. table_chart. New Dataset. tenancy. New Model. emoji_events. New Competition. corporate_fare. New Organization. No Active Events. Create notebooks and keep track of their status here. add New Notebook. auto_awesome_motion.

  9. Data Visualization in Python with matplotlib, Seaborn, and Bokeh

    Data visualization is an important aspect of all AI and machine learning applications. You can gain key insights into your data through different graphical representations. In this tutorial, we'll talk about a few options for data visualization in Python. We'll use the MNIST dataset and the Tensorflow library for number crunching and data manipulation. To […]

  10. Complete Guide to Data Visualization with Python

    4. Let's see the main libraries for data visualization with Python and all the types of charts that can be done with them. We will also see which library is recommended to use on each occasion and the unique capabilities of each library. We will start with the most basic visualization that is looking at the data directly, then we will move on ...

  11. Data Visualization with Python

    This is the Final Assignment for the course Data Visualization with Python, as part of the IBM Data Science Professional Certification on Coursera, taught by Alex Aklson (Ph.D., Data Scientist / IBM Developer Skills Network). Even though the report has not been requested, I took the chance to practice with it.

  12. Final Exam: Data Visualization with Python

    Data Visualization | Intermediate. 1 video | 32s. Includes Assessment. Earns a Badge. 3.8 (11) From Journey: Data Visualization Mastery. Final Exam: Data Visualization with Python will test your knowledge and application of the topics presented throughout the Data Visualization with Python track of the Skillsoft Aspire Data Visualization Journey.

  13. Data Visualization with Python

    There are 4 modules in this course. In today's data-driven world, the ability to create compelling visualizations and tell impactful stories with data is a crucial skill. This comprehensive course will guide you through the process of visualization using coding tools with Python, spreadsheets, and BI (Business Intelligence) tooling.

  14. Data Visualization with Python

    For the final assignment you will analyze historical automobile sales data covering periods of recession and non-recession. You will bring your analysis to life using visualization techniques and then display the plots and graphs on dashboards. Finally, you will submit your assignment for peer review and you will review an assignment from one ...

  15. Data visualization with Python Dash

    Dash is a powerful Python library that lets you create and share interactive dashboards for data visualization. In this blog post, you will learn how to use Dash to build a simple app that displays a chart and a table from a CSV file. You will also see how LogRocket, a frontend monitoring solution, can help you improve your app's performance and user experience.

  16. Data Visualization with Python. Final project to Coursera's data…

    Sep 21, 2020. 51. Final project to Coursera's data visualization with Python course. Data visualization is trying to understand data by representing it in a visual context so that the trends and correlation between the features in the dataset can be clearly expressed. Python offers excellent libraries that comes with a lot of feature for ...

  17. Python Data Visualization

    This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data.

  18. IBM Data Visualization Python_Final Assignment

    Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.

  19. Develop Data Visualization Interfaces in Python With Dash

    Dash is an open-source framework for building data visualization interfaces. Released in 2017 as a Python library, it's grown to include implementations for R, Julia, and F#. Dash helps data scientists build analytical web applications without requiring advanced web development knowledge.

  20. NakulLakhotia/Data-Visualization-with-Python

    This repository contains all the jupyter notebooks and the final peer graded assignment for the course - "Data Visualization with Python" offered by Coursera Topics data-science datavisualization

  21. 5 Common Python Gotchas (And How To Avoid Them)

    So always use the == operator to check if any two Python objects have the same value. 4. Tuple Assignment and Mutable Objects . If you're familiar with built-in data structures in Python, you know that tuples are immutable. So you cannot modify them in place. Data structures like lists and dictionaries, on the other hand, are mutable.

  22. Data-Visualization-with-Python_Final-Assignment

    If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from Topic Servey Dataset.

  23. Data-Visualization-with-Python/ at main

    peer graded assignment. Contribute to bscully8/Data-Visualization-with-Python development by creating an account on GitHub.

  24. nicole-zhen/IBM-Data-visualization-with-Python-Final-Assignment

    You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window.