geoplot is a high-level Python geospatial plotting library. It’s an extension to cartopy and matplotlib which makes mapping easy: like seaborn for geospatial.
PyNGL (pronounced “pingle”) is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations.
This is a Python rewrite of the code used to create the Visualizing Facebook Friends visualization in 2010.
The original code was written in R and was built specifically around the Facebook dataset. This rewrite is as a Python module and is built to work on top of any dataset.
If you are looking to visualize a few (<10,000) coordinate pairs, matplotlib with basemap will be more flexible. The visualization implemented by this module is useful when the data alone are sufficient to show the geography.
The algorithm uses a heuristic which attempts to visualize the structure of the pairs rather than their relative importance. In interpreting the results, you should not come to any conclusions about the relative importance of different coordinate pairs.
Raster –> vector surface creation tools in python
The GeoRasters package is a python module that provides a fast and flexible tool to work with GIS raster files. It provides the GeoRaster class, which makes working with rasters quite transparent and easy. In a way it tries to do for rasters what GeoPandas does for geometries.
It includes tools to
- Merge rasters
- Plot rasters
- Extract information from rasters
- Given a point (lat,lon) find its location in a raster
- Aggregate rasters to lower resolutions
- Align two rasters of different sizes to common area and size
- Get all the geographical information of raster
- Create GeoTiff files easily
- Load GeoTiff files as masked numpy rasters
Pycoast is a Python package to add coastlines, borders and rivers to raster images using data from the GSHHS and WDBII datasets
Simplifies drawing logos, text labels, color scales and legends onto PIL image objects.
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 on a Leaflet map via Folium.