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.
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.
A dashboard built to track the spread of recent outbreak coronavirus (COVID-19).
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.