Visualize data with Python


Built for anyone that uses data to create Jupyter Notebooks and other artifacts, this pattern shows the power of open source libraries like pandas, PixieDust, and folium. pandas introduced data frames and series to Python and is an essential part of using Python for data analysis. With PixieDust, hosted on IBM Watson™ Studio, you can quickly create charts, graphs, and tables without complex code, in an interactive and dynamic manner. In addition, PixieApps are used to embed UI elements directly in the Jupyter Notebook. Given an open source data provider like the USGS, PixieDust, and Watson Studio can empower you to analyze and share data visualizations. folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map through folium.


The USGS is a scientific agency of the United States government. Their scientists study the landscape of the United States, its natural resources, and the natural hazards that threaten it, providing numerous sources of open source data, including the site.

This code pattern uses some standard techniques for data science and data engineering running on Watson Studio to analyze publicly available data for flooding in Houston, Texas, in 2017. Watson Studio is an interactive, collaborative, cloud-based environment where data scientists, developers, and others that are interested in data science can use tools (for example, RStudio, Jupyter Notebooks, and Spark) to collaborate, share, and gather insight from their data.

When you have completed this pattern, you will understand how to:


Visualize Data with Python

  1. Load the Jupyter Notebook onto the Watson Studio platform.
  2. USGS data from the Houston flood of 2017 is loaded into the Notebook.
  3. The Notebook is used to clean the data and then display it.
  4. A PixieApp dashboard is created and can be interacted with.
  5. Mapbox and folium are used for map visualizations.


Find the detailed steps for this pattern in the README. The steps show you how to:

  1. Sign up for Watson Studio.
  2. Create the Notebook.
  3. Run the Notebook.
  4. Analyze the results.
  5. Save and share.