Data visualization examples in Python: map visualization

With the continuous development of data science, data visualization has become an increasingly important research field. As a feature-rich and free programming language, Python is also widely used in data visualization. In this article, we will introduce how to use Python for map visualization to show the spatial distribution of data.

In Python, there are a variety of libraries that can be used to implement map visualization functions, among which the more commonly used ones include geopandas, folium, and plotly. Below we will introduce the basic usage of these three packages respectively.

1. geopandas

geopandas is a geospatial data processing library based on pandas, which can read geographic information data in various formats and provides various map-based Data visualization methods. The following are the basic steps for using geopandas to draw maps:

(1) Install geopandas and related dependent libraries:

!pip install geopandas !pip install descartes

(2) Import the required libraries:

import geopandas as gpd import matplotlib.pyplot as plt

( 3) Read the geographical information data file. Here is an example of reading the boundary data file pandas.DataFrame of each state in the United States:

us_states = gpd.read_file('states.shp')

(4) Draw the map:

us_states.plot(figsize=(10, 10)) plt.show()

2. folium

folium is a free library for Python that can create interactive maps on the web. If we want to display data in various forms, folium can help us achieve it, such as color blocks, pop-up boxes, heat maps, etc. The following are the basic steps to draw a map using folium:

(1) Install folium:

!pip install folium

(2) Import the required libraries:

import folium

(3) Create the map Object and specify the center position of the map:

m = folium.Map(location=[45.523, -122.675], zoom_start=13)

(4) Add a marker on the map:

folium.Marker( location=[45.523, -122.675], popup='Portland, OR', icon=folium.Icon(icon='cloud') ).add_to(m)

(5) Draw the map:

m

3. plotly

plotly is a Python data visualization library based on an open source JavaScript library that can be used to create interactive charts, graphs, and other visualization applications. The following are the basic steps to draw a map using plotly:

(1) Install plotly:

!pip install plotly

(2) Import the required libraries:

import plotly.express as px

(3) Read Data and plotting maps:

df = px.data.gapminder().query("year == 2007") fig = px.scatter_geo(df, locations="iso_alpha", color="continent", hover_name="country", size="pop", projection="natural earth") fig.show()

Summary

In this article, we introduced three Python packages for map visualization. geopandas is a geospatial data processing library based on pandas, suitable for drawing map colors and patterns in different areas. folium is a Python library for creating interactive maps on the Web. Plotly is a Python data visualization library for JavaScript libraries that can draw customized, highly interactive map visualization charts.

Of course, these libraries are only some of the map visualization tools available in Python, and with the continuous advancement of technology, other more advanced programming tools with certain advantages will appear, so we need to use them in practical applications. Choose based on your needs.

The above is the detailed content of Data visualization examples in Python: map visualization. For more information, please follow other related articles on the PHP Chinese website!