How to use Color Maps (cmap) In Python
We can color each country in the world using a head column and cmap. To find out head column type “world_data.head()” in console. We can choose different color maps(cmap) available in matplotlib. In the following code, we have colored countries using plot() arguments column and cmap.
Example:
Python3
import geopandas as gpd # Reading the world shapefile world_data = gpd.read_file(r 'world.shp' ) world_data = world_data[[ 'NAME' , 'geometry' ]] # Calculating the area of each country world_data[ 'area' ] = world_data.area # Removing Antarctica from GeoPandas GeoDataframe world_data = world_data[world_data[ 'NAME' ] ! = 'Antarctica' ] # Changing the projection current_crs = world_data.crs world_data.to_crs(epsg = 3857 , inplace = True ) world_data.plot(column = 'NAME' , cmap = 'hsv' ) |
Output:
Working with Geospatial Data in Python
Spatial data, also known as geospatial data, GIS data, or geodata, is a type of numeric data that defines the geographic location of a physical object, such as a building, a street, a town, a city, a country, or other physical objects, using a geographic coordinate system. You may determine not just the position of an object, but also its length, size, area, and shape using spatial data.
To work with geospatial data in python we need the GeoPandas & GeoPlot library
GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the data types used by pandas to allow spatial operations on geometric types. Geometric operations are performed shapely. Geopandas further depends on fiona for file access and matplotlib for plotting. GeoPandas depends on its spatial functionality on a large geospatial, open-source stack of libraries (GEOS, GDAL, and PROJ). See the Dependencies section below for more details.
Required dependencies:
- numpy
- pandas (version 0.24 or later)
- shapely (interface to GEOS)
- fiona (interface to GDAL)
- pyproj (interface to PROJ; version 2.2.0 or later)
Further, optional dependencies are:
- rtree (optional; spatial index to improve performance and required for overlay operations; interface to libspatialindex)
- psycopg2 (optional; for PostGIS connection)
- GeoAlchemy2 (optional; for writing to PostGIS)
- geopy (optional; For plotting, these additional for geocoding)
packages may be used:
- matplotlib (>= 2.2.0)
- mapclassify (>= 2.2.0)
Geoplot is a geospatial data visualization library for data scientists and geospatial analysts that want to get things done quickly. Below we’ll cover the basics of Geoplot and explore how it’s applied. Geoplot is for Python 3.6+ versions only.
Note: Please install all the dependencies and modules for the proper functioning of the given codes.