Adding a legend
Next, we are going to convert the area in sq. km by dividing it to 10^6 i.e (1000000). Output can be seen in variable explorer in the “world_data” variable.
We can add a legend to our world map along with a label using plot() arguments
- legend: bool (default False). Plot a legend. Ignored if no column is given, or if color is given.
- legend_kwds: dict (default None). Keyword arguments to pass to matplotlib.pyplot.legend() or matplotlib.pyplot.colorbar(). Additional accepted keywords when scheme is specified:
- fmt: string. A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: {“fmt”: “{:.0f}”}.
- labels: list-like. A list of legend labels to override the auto-generated labels. Needs to have the same number of elements as the number of classes (k).
- interval: boolean (default False). An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend.
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'] world_data.plot() # Changing the projection current_crs = world_data.crs world_data.to_crs(epsg=3857, inplace=True) world_data.plot(column='NAME', cmap='hsv') # Re-calculate the areas in Sq. Km. world_data['area'] = world_data.area/1000000 # Adding a legend world_data.plot(column='area', cmap='hsv', legend=True, legend_kwds={'label': "Area of the country (Sq. Km.)"}, figsize=(7, 7)) |
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.
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