Data Visualisation

Elevate Your Dashboard Interactivity In Dash

Last week, I shared with you how to make a dashboard to track the spread of coronavirus using Dash in python, from which you can have a real-time overview of the numbers of global coronavirus cases, including confirmed, recovered and deaths cases, and their distribution on a world map. As for the first version, we implemented basic dash functions and obtained a static application interface. In other words, except for the native interactions offered by plotly (e.

Build A Dashboard To Track The Spread of Coronavirus Using Dash

Last month, I published four posts to share with you my experience in using matplotlib. Benefit from its full control of elements on a given graph, matplotlib is deemed as a fundamental python library for data visualisation and used by many other libraries (e.g. seaborn and pandas) as plotting module. This is also why I think learning matplotlib is an essential part for being a practitioner in data science, which helps to build up in-depth understanding about logic behind data visualisation tools.

Create An Infographic Using Matplotlib

From my previous posts about the hierarchical structure of matplotlib plotting and the many ways to instantiate axes, we can see that these features render matplotlib a great potential for creating highly complex and customisable visualisations. To demonstrate this and also improve my own understanding of matplotlib, I set out to make an infographic using matplotlib this week. An infographic generally combines visual imagery, data charts, and minimal text together.

The Many Ways To Call Axes In Matplotlib

Although matplotlib is extremely powerful and the only limitation might be our imagination, it is a bit challenging for new users to find the right path as there are always more than one way to achieve the same goal in matplotlib. Calling axes is one of them. Let’s say you just decide to make plots using object-oriented interface (aka artist layer plotting) in matplotlib. However, I bet you will be soon running into problems when trying to instantiate axes to start your plotting., or, That Is The Question In Matplotlib

As a newbie converted recently from using R to Python, data visualisation via the standard Python visualisation library matplotlib is an inevitable part through the learning process. At the very beginning, after following several easy-to-find matplotlib tutorials, I had an impression that “en, it’s not as hard as I thought”. After import matplotlib.pyplot as plt, I can basically plot everything by calling…) (e.g. plt.plot(…), plt.scatter(…),…), plt.xlabel(…), plt.ylabel(…), plt.

Build an Interactive Choropleth Map with Plotly and Dash

Last week, I finished my final assignment in IBM Data Science course, which is to find an ideal suburb for opening an Italian restaurant based on location data. During the process, I web-scrapped property median price (i.e. House buy/rent and Unit buy/rent) for each suburb in Sydney and plotted them on Choropleth maps, respectively. However, I was wondering if it is possible to combine all these maps in one and select one of them just by clicking name from a dropdown menu.