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One of the features that makes this API so powerful is its integration with Jupyter Notebook. Jupyter Notebook is a web-based integrated development environment IDE for executing Python code. Unlike other traditional IDEs that are designed for developers, Jupyter Notebook provides a simple and easy-to-use interface that encourages the Read-Eval-Print Loop REPL process that is central to learning how to code in Python. Jupyter Notebook is a way to explore spatial data. Content can be added with its default symbology or—using smart mapping—the API can figure out how best to symbolize the data. This approach originated with IPython Notebook. Project Jupyter took over the open-source project and opened it up to the Julia, Python, and R languages hence the name Jupyter. Now many more languages are supported. Because these notebooks are free, open source, and platform independent, they can be run on any device that has a browser, allowing them to be easily shared. Python and Jupyter Notebook are immensely popular in the data science community. Jupyter Notebook combines code execution, rich text, mathematics, plots, and rich media and has become the de facto medium for teaching Python and data science at schools and in online training programs. The interactive Jupyter Notebook environment is built around the concept of cells that can contain executable code or text and illustrative graphics written in Markdown format. Cells can be run in any order and any number of times. When a cell is run, its output is displayed immediately below the cell. This encourages tweaking and rerunning code until the perfect solution is found—illustrating the REPL paradigm in action. Because it is a web application running in a browser, Jupyter Notebook supports the display of graphic outputs. Write a snippet of Python code to plot a bar chart of household income of a county, and the chart will be displayed right below the cell containing that code. 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