This notebook provides a comprehensive guide to various data visualization techniques using the Matplotlib library in Python. We've demonstrated these techniques using both sample data and real-world banking data.
- Introduction
- Basic Visualizations with Sample Data
- Visualizations with Banking Data
- -Contributing
- -Contact Information
Data visualization is an essential component of data analysis. It allows us to quickly understand the structure of our data and draw preliminary insights. In this notebook, we utilize Matplotlib, a powerful library in Python, to create a variety of plots.
A histogram showcases the distribution of a dataset. In our sample, we have displayed the test scores of students.
Boxplots give a five-number summary of our dataset: the minimum, first quartile (Q1), median, third quartile (Q3), and maximum. Our sample displays a set of random values.
Line charts are excellent for understanding trends in a dataset. Our sample consists of two line charts comparing two different datasets.
A scatter plot displays values of two numerical variables as coordinates in two-dimensional space. Our example showcases a random distribution of points.
Bar charts help visualize categorical data with rectangular bars. We've included vertical and horizontal bar charts for a sample dataset.
Pie charts provide a circular statistical graphic which is divided into slices to illustrate numerical proportions. We've shown the distribution of four sample categories.
A histogram that provides insights into how bank account balances are distributed among customers.
This boxplot showcases the range and spread of loan amounts among those who have taken loans.
A line chart that depicts how account balances trend across different customer indices.
A scatter plot representing the relationship between the account balances of customers and the amount of loan they have taken.
A pie chart displaying the proportion of different types of bank accounts.
This bar chart provides insights into the distribution of loan statuses among customers, e.g., active, completed, or defaulted.
We welcome contributions to this project. To contribute:
- Fork the project.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -m 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a Pull Request.
For any questions or inquiries, please contact support@pyfi.com - Subject: Github Repo Q, Visualization-tools-in-Python.
For a full article walkthrough please visit > https://www.pyfi.com/blog < and learn more about PyFi's award winning Python for Finance courses which have been trusted by the top financial institutions in the United States and Canada multiple years running here >> https://www.pyfi.com <<
We hope this notebook helps you understand the basics of data visualization with Matplotlib and inspires you to create your own visualizations.