Geopandas Plot - Python

Geopandas is a library in Python that allows users to analyze and visualize geospatial data. One of the most useful tools in the Geopandas library is the ability to create maps using the plot function. In this introductory guide, we will discuss the basics of how to use the Geopandas plot function to create interactive and informative maps.

1. Installing Geopandas

Before we can start using Geopandas, we need to install it in our system. Geopandas can be installed using pip. In your terminal, execute the following command:

pip install geopandas

2. Reading Spatial Data

Once we have installed Geopandas, we can start reading our spatial data into our Python environment. Geopandas supports several file formats such as shapefiles, GeoJSON, KML, and others. For the purpose of this tutorial, we will use a shapefile containing the world map.

import geopandas as gpd

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

Here, we read the shapefile using the read_file function and store it in a variable called world. We can explore this data using normal Pandas functions.

world.head()

3. Basic Plotting

We can start by plotting our spatial data to get an idea of what we are dealing with. Geopandas comes with a built-in plot function that allows us to create plots easily.

world.plot()

This creates a basic map of the world with default settings. The function creates a matplotlib figure under the hood and returns it as an object. We can customize this plot by passing additional arguments to the plot function.

world.plot(column='gdp_md_est', cmap='OrRd', legend=True)

Here, we have passed the column argument to plot the GDP values for each country. We have also set the colormap to OrRd and enabled the legend. We can pass any column from our dataset to this function to create different types of maps.

4. Advanced Plotting

Geopandas supports advanced plotting functionality that makes it easy to create customized and informative maps. We can customize maps by passing arguments to the plot function.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(20, 10))

world.plot(column='pop_est', cmap='Blues',
           linewidth=0.8, edgecolor='0.8',
           ax=ax,
           legend=True)

ax.axis('off')

plt.show()

Here, we have customized the plot by setting a larger size using figsize, setting the column to pop_est, and using the Blues colormap. We have also set the linewidth, edge color, and turned off the axis. We can customize our plots further by exploring the documentation and experimenting with different arguments.

Conclusion

Geospatial plotting is an essential task in analyzing and visualizing spatial data. Geopandas provides a simple and efficient interface to plot spatial data with customizable options. In this tutorial, we have explored the basics of using Geopandas plot function to create informative maps. With Geopandas, we can easily create maps that convey meaningful insights and inform better decision-making.