This project is dedicated to explore data through visual representations, by using different tools that python offer us. The goal of this project is to demonstrate that when a representation of a data set is simple and visually appealing, its meaning becomes so clear to viewers that they can see patterns in the data sets that they never knew existed.
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Web Visualization
Python Crash Course is the world's best selling guide to Python programming language. This fast-paced thorough introduction to programming with Python will have you writing programs, solving problems, and making things that work in no time.
In the first half of the book, you'll learn basic programming concepts, such as variables, list, classes, and loops, and practice writing clean code with excercises for each topic. You'll also learn how to make your programs interactive and test your code safely before adding it to a project.
In the second half of the, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, a set of data visualizations with Python's handy labraries, and a simple web app you can deploy online.
In this chapter, you'll learn to generate data sets and create visualizations of that data. You'll create simple plots with Matplotlib and use scatter plots to explore random walks. Also, you'll create a histogram with Plotly and use a histogram to explore the results of rolling three times a dice. Generating your own data sets with code is and interesting and powerful way, to model and explore a wide variety of real-world situations. As you continue to work thorough data visualization projects that follow, keep an eye out for situations you might be able to model with code. Look at visualizations you see in the news media, and see if you can identify those that were generated using methods similar to the ones you're learning in these projects.
| Squares Graph | Pollen Grain on the Surface |
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In this chapter, you'll learn to work with real-world data sets and process CSV and JSON files. You'll use historical weather data, to learn more about Matplotlib and its capabilities, including how to use the datetime module, and how to plot multiple data series on one chart. Also, you'll learn how to plot geographical data, by using Plotly maps. As you gain experience working with CSV and JSON files, you'll be able to process almost any data you want to analyze. You can download most online data sets in either or both these formats. By working with these formats, you'll be able to learn how to work with other data formats more easily as well.
| Sitka Temperatures | Death Valley Temperatures |
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In this chapter, you'll learn how to use APIs to write self-contained programs that automatically gather the data they need and use data to create a visualization. You'll use the GitHub API to explore the most-starred Python projects on GitHub. You'll learn how to use the 'Requests' package to automatically issue and API call to GitHub and how to process the results of that call.
- Name: Raul Maya Salazar
- Phone: +52 833 159 7006
- E-mail: [email protected]
- GitHub: https://github.com/RaulMaya
- LinkedIn: https://www.linkedin.com/in/raul-maya/