These parameters can be fill the color of polygons, edge color, edge width, etc.īelow we have created a world map by giving world GeoDataFrame which we loaded earlier using geopandas. All other extra parameters provided will be passed to polygons or multi-polygon objects which are present in geometry column.The ax parameter accepts matplotlix Axes object.The figsize parameter accepts tuple specifying figure size.The extent parameter takes as input tuple of 4 values (min_longitude, min_latitude, max_longitude, max_latitude) specifying bounding box of map.It can be used to change the projection of the map. The projection parameter takes as input instance of any projection available from geoplot.crs module.The GeoDataFrame is an data frame from geopandas which has column named geomtry holding information about geometries (polygons, multi-polygons, points, etc.). polyplot(df,projection=None,extent=None,figsize=(8,6),ax=None,**kwargs) - This method takes as input GeoDataFrame and creates map from it.If we want to create simple maps without any information presented through them then we can use polyplot() method available from geoplot. We have also printed the versions of the libraries that we are using. We'll start by importing the necessary libraries. We'll try to explain the usage of geoplot API with simple examples. We'll be creating choropleth maps using geoplot as a part of this tutorial. If you want to create scatter and bubble maps using geoplot then please check our other tutorial where we cover them. Cartopy - Basic Maps (Scatter Map, Bubble Map and Connection Map).Plotting Static Maps using geopandas (Working with Geospatial data).If you don't have a background in geopandas and cartopy then please feel free to check our tutorials on them. As geoplot is built on top of geopandas and cartopy which itself are built on top of matplotlib, we can add details to charts using matplotlib as well. We'll be using datasets from geopandas as well in our tutorial. It let us create a choropleth map with just one line of code. As a part of this tutorial, we'll introduce one new library named geoplot which is built on top of geopandas and cartopy. Python has a list of libraries (geopandas, bokeh, cartopy, folium, ipyleaflet, etc.) that let us create choropleth maps. population density, GDP per capita of countries, etc). It can be used to analyze the distribution of data in geographical regions (e.g. The Choropleth map is one such representation of geospatial data. Visualizing geospatial data can give meaningful insights during data analysis. The geospatial data can be names of locations (cities, states, countries, etc.) or it can be exact geolocations (longitude and latitude) as well. Geospatial data is generally available in datasets nowadays.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |