Creating Choropleth Maps using Plotly | Working on Plotly | Codegnan | Part-9

Creating Choropleth Maps using Plotly

Data visualization is an important skill to possess for anyone trying to extract and communicate insights from data. In the field of Machine Learning, visualization plays a vital role throughout the entire process of analysis. Python has some of the most interactive data visualization tools. In the previous videos, we have started analyzing Life Expectancy vs GDP by using Bubble Chart, Facet Plots and we have also added Animations to the Bubble chart. Gapminder data is about all the countries over the years and their GDPs, life expectancy, and population.

In this video, well-known, we will create a Choropleth Map using Plotly as it is a map composed of colored polygons. It is used to represent spatial variations of a quantity. Making choropleth maps requires two main types of input:

1.Geometry information: This can either be a supplied GeoJSON file where each feature has either an id field or some identifying value in properties; or one of the built-in geometries within plotly

2.A list of values indexed by feature identifier.
Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on various types of data and produces easy-to-style figures.

We have analyzed the data using Plotly, which provides the interactivity to our graphs. We have learned many things in this entire series of videos. From analyzing data with pandas to adding animations and bubbles in our graphs

To learn more about the Importance of Data Analysis, check out at

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