Exploring Data Visualization with Matplotlib and Pandas in Jupyter Notebook
When it comes to analyzing data from databases, visual representation is key to understanding trends and patterns. In this article, we will delve into creating different types of pie charts using Matplotlib in a Jupyter Notebook with Pandas, showcasing the results of database analysis in a visually appealing manner.
Introduction
Pie charts are a popular choice for visualizing data when dealing with categorical information, providing a clear comparison of relative proportions within a dataset. They are particularly useful when there are a limited number of categories to represent parts of a whole. In this exploration, we will demonstrate how to create four distinctive pie chart variations using a dataset focused on heart disease research publications between 2002 and 2020.
Getting Started
Before diving into the data visualization, we first import the necessary libraries and extensions to set the stage for our analysis. Once we have our tools in place, we prepare the CSV file containing the dataset to be processed in our Jupyter Notebook.
Pie Chart Variations
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Exploding Slices: For smaller datasets, exploding slices in a pie chart can draw attention to specific categories by slightly separating them from the rest of the chart. This technique highlights key categories and their proportional representation within the dataset.
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Donut Chart: Similar to a pie chart but with a hole in the center, a donut chart offers a cleaner visual representation. It allows for an additional layer of data presentation in the center, making it easier to compare relative sizes of slices.
- Nested Donut Chart: Building on the traditional donut chart, a nested version incorporates an additional layer to display multiple sets of data. In our exploration, we utilize this technique to showcase gender distribution among the top 10 journals for heart disease research publications.
Conclusion
In conclusion, pie charts serve as effective tools for quickly grasping the essential categories or dominant proportions within a dataset. By utilizing four different types of pie charts, we shed light on the gender distribution among first authors in heart disease research publications. Our visual analysis reveals a higher percentage of male authors across the top journals during the specified period.
For a hands-on experience, you can access the Jupyter Notebook and dataset used in this article on GitHub. Explore the power of data visualization with Matplotlib and Pandas to gain deeper insights from your datasets.
Thank you for exploring the world of data visualization with us.
Diana Rozenshteyn