Voiced by Amazon Polly |
Introduction
Heatmaps or Density Maps are popular tools for displaying the distribution of a variable over a geographic area. They help in identifying trends, detecting outliers, and presenting data in a visually appealing way. Python provides several mapping libraries for creating heatmaps, including Plotly Express and Folium.
Folium is a feature-rich library that simplifies the process of visualizing geospatial data. It is built on top of Leaflet.js, a popular JavaScript mapping library, and is designed to work on any platform. Folium provides a variousools and functionality for creating interactive and dynamic maps.
Plotly, on the other hand, is a widely-used library for generating interactive data visualizations with minimal coding. Plotly can be used to create interactive maps that incorporate MapBox functionality. Plotly Express is a simple and user-friendly wrapper library for Plotly, designed to make it easier to create a wide range of interactive visualizations, including heatmaps.
Importing libraries and reading the dataset
We will import the pandas library to load our data from a CSV file. The dataset we will be working with is https://www.kaggle.com/competitions/covid19-global-forecasting-week-4 COVID19 Global Forecasting (Week 4) competition dataset, which will be predicting the cumulative number of confirmed COVID-19 cases in various locations across the world, as well as the number of resulting fatalities, for future dates.
Pioneers in Cloud Consulting & Migration Services
- Reduced infrastructural costs
- Accelerated application deployment
Heatmap with Plotly Express
To get started on generating a heatmap using Plotly Express, we will first import the library into our notebook using the following code:
Next, we will create a new figure by calling the density_mapbox function from Plotly Express https://plotly.com/graphing-libraries/. To generate the map, we need to specify several parameters:
df: the name of the data frame containing the data
lat: the name of the column containing latitude values
lon: the name of the column containing longitude values
z: the name of the column containing the data we want to display on the heatmap
center: the coordinates that the map will be centered on, which we can obtain by calculating the mean of the latitude and longitude columns
zoom: the initial zoom level of the map
mapbox_style: the background mapping layer
height: the height of the plot
We can create an informative and visually appealing heatmap by setting these parameters.
Examining the generated map, we can observe that there are higher Importance values in the middle part of the area, and various factors may influence this trend.
Heatmap with Folium
To start working with Folium, we must first import it. However, we must import the Heatmap plugin from folium Plugins if we wish to create heatmaps.
After importing Folium, we can create a base map by calling the folium.Map() method, and pass several arguments within the parentheses. For this example, we will use the following parameters:
location: the coordinates where the map will be centered
zoom_start: the initial zoom level of the map
control_scale: whether the scale controls are displayed on the map
If you want to learn more about the parameters available for the map function, refer to the help documentation at https://python-visualization.github.io/folium/.
The next step is to create the heatmap layer. To accomplish this, we must first convert the latitude, longitude, and value data to a list, which can then be passed into the Heatmap function.
To customize the heatmap’s appearance, we can set several parameters, such as the minimum and maximum opacity, the radius of the color gradient around each data point, and others.
Conclusion
Heatmaps are an effective way to visualize and analyze geographical data, particularly petrophysical and well-log properties. Python offers several mapping libraries to create heatmaps, including Folium and Plotly Express. These libraries are user-friendly and enable the mapping of large regions, allowing for identifying trends and variations in the data. The insights gained from analyzing these maps can help in decision-making processes related to resource exploration and extraction. Therefore, heatmaps are an essential tool for the geoscience community, and Folium and Plotly Express are valuable libraries for creating them.
Empowering organizations to become ‘data driven’ enterprises with our Cloud experts.
- Reduced infrastructure costs
- Timely data-driven decisions
About CloudThat
CloudThat is an official AWS (Amazon Web Services) Advanced Consulting Partner and Training partner and Microsoft Gold Partner, helping people develop knowledge of the cloud and help their businesses aim for higher goals using best-in-industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.
Drop a query if you have any questions regarding Python, I will get back to you quickly.
To get started, go through our Consultancy page and Managed Services Package, CloudThat’s offerings.
FAQs
1. What is Plotly library?
ANS: – Plotly is a data visualization library that allows users to create interactive charts, graphs, and other visualizations. It provides a web-based platform for creating and sharing data visualizations, as well as an open-source Python library that can be used to create custom visualizations in Python.
2. How does Folium help in visualization?
ANS: – Folium is a Python library used for visualizing geospatial data. It is built on the Leaflet JavaScript library and allows users to create interactive maps and visualizations directly from Python code. Folium also supports various data formats, including GeoJSON, TopoJSON, and Pandas DataFrames, allowing users to easily import and manipulate their geospatial data within Python.
3. What is Heatmap graph?
ANS: – A Heatmap is a type of data visualization that uses color-coded squares or rectangles to represent values in a matrix or table. The color of each square or rectangle is chosen to represent the intensity or magnitude of the corresponding value, with darker colors typically indicating higher values.
WRITTEN BY Daneshwari Mathapati
Comments