Data Discovery and Visualization with Python
The ability to explore and visualize data is an important skill in the age of data-driven decision-making. Python, with its versatile ecosystem of libraries, has become a powerhouse for data analysis, manipulation, and visualization. In this blog, we will delve into the world of Python-based data discovery and visualization, focusing on examples and techniques for bringing data to life.
Come with me to explore the hidden gems of programming……..
Python’s popularity can be attributed to a variety of factors, including its ease of learning, clean syntax, rich standard library, and a large number of high-quality third-party packages. Python has become a go-to language for data-related tasks, including those in modern web development with frameworks like FastAPI, due to its emphasis on data manipulation and introspection.
The blog begins by introducing PSV (pipe separated values) as a separate data representation format. We investigate different ways to load and display data from a fictional database of creatures from around the world.
Examples include reading a PSV file with the standard Python csv package, using python-tabulate for tabular output, and leveraging the power of pandas for more advanced data manipulation.
SQLite Data Source and Web Output
SQLite is a data source as it transitions from text output to web-based interaction. The focus shifts to FastAPI, where we explore how to create web routes to serve data and integrate it into a growing cryptid website. The importance of structuring web, service, and data levels is emphasized.
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Charting and Graphing with Plotly
The narrative takes a graphical turn with the introduction of charting and graphing. Introduces various Python packages for visualizations, including Matplotlib, Plotly, Dash, Seaborn, and Bokeh. For illustrative purposes, Plotly is chosen to create diverse charts, such as scatter plots, line charts, bar charts, histograms, and more.
Maps and Geospatial Visualization
Spatial data and mapping take center stage as the blog explores Python packages like PyGIS, PySAL, Cartopy, Folium, and GeoPandas. With a focus on Plotly for mapping examples, the blog demonstrates how to represent creature distribution across countries using choropleth maps. The nuances of handling ISO country codes and mapping between different code standards are discussed.
The blog concludes with a comprehensive example that brings together data loading, web development, and visualization. A FastAPI web application is created, showcasing how to integrate data discovery and visualization seamlessly. Readers are encouraged to explore the example, tweak parameters, and adapt the techniques to their own datasets.