Created by: Dani Arribas-Bel & Contexily Contributors
Welcome to the taster guide for contextily, the package for contextual tiles in Python. In this notebook, we will show the basic functionality available in contextily, a package to work with web-tiles for background maps. To do that, we will use additional data to illustrate contextily can be integrated with other libraries such as geopandas and rasterio.
The examples below show GeoCAT-comp functions being utilized in real-world use cases. They also demonstrate how GeoCAT-comp can be used to make plots with Matplotlib (using Cartopy) and PyNGL (work in progress).
The user guide provides a detailed introduction to the API and features of hvPlot. In the Introduction you will learn how to activate the plotting API and start using it. Next you will learn to use the API for tabular data and get an overview of the types of plots you can generate and how to customize them; including how to customize interactivity using widgets. Next is an overview on how to display and save plots in the notebook, on the commandline, and from a script. Another section will introduce you to generating subplots from your data. Once the basics are covered you can learn how to use the plotting API for specific types of data including streaming data, gridded data network graphs, geographic data, and timeseries data.
MetPy is a modern meteorological open-source toolkit for Python. It is a maintained project of Unidata to serve the academic meteorological community. MetPy consists of three major areas of functionality: plots, calculations, and file i/o.
An introduction to Xarray through the Unidata Python Workshop that asks, “What is XArray and how does XArray fit in with Numpy and Pandas?”” by creating a DataArray, openning netCDF data using XArray, and subsetting the data.