Choropleth Maps - 101 using Plotly. An Introduction to Choropleth Maps using Plotly graphing library. This is an attempt in that direction. You can use this as a guide to choropleth maps, or plotly, or both.
I have been working as a Data Analyst for almost 5 years now but, in this time I have mostly used business intelligence software for all my data visualization tasks. So my experience with visualization has been limited to knowing when to plot a bar chart vs a line chart. To correct this, I have recently taken up to learn matplotlib and other plotting and graphing libraries. This is an attempt in that direction. You can use this as a guide to choropleth maps, or plotly, or both. Hope this helps.
Map by Our World in Data
Choropleth Maps are a type of thematic map in which areas are shaded or patterned in proportion to a statistical variable (from Wikipedia). This map for example shows the share of adults who were obese in 2016.
Plotly is a company based in Canada, that develops analytics and data visualization tools. They have created an open-source scientific graphing library for Python, which I am using here.
For this task, I am looking at data for Religious Adherents in the United States (Source: theARDA.com). The data contains County-level information for different religious groups for the years 1980, 1990, 2000 & 2010. It also includes the population of the counties in that year, along with some other information.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
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Interactive choropleth maps in python. A Step by step guide to create interactive map visuals in python using opensource libraries - Altair, Plotly, and Folium.
Choropleth map is one of the most effective methods to visualize geographic data. The most popular methods to construct such maps are sophisticated software like QGIS, ArcGIS, and so on. However, these types of visualizations are usually a small part of my entire workflow. Hence, I don’t like to switch to specialized software to render these maps. In this article, I will explore how to make these maps using Python. using GeoPandas and Matplotlib