How to draw dynamic and interactive geographic charts with Python

How to draw dynamic and interactive geographic charts with Python

Introduction: In data visualization, geographic charts are a common and powerful tool that can help us Better understand spatial distribution patterns and trends in data sets. As a general-purpose programming language, Python has powerful data processing and visualization capabilities, and can also be used to draw dynamic and interactive geographical charts. This article will introduce how to use Python to draw dynamic and interactive geographical charts, and provide specific code examples.

1. Preparation
Before using Python to draw geographical charts, we need to install some necessary libraries. The most commonly used libraries are Matplotlib (for drawing static charts) and Plotly (for drawing dynamic and interactive charts). These libraries can be installed through the following command:

pip install matplotlib
pip install plotly

2. Draw static geographical charts
First, let us learn how to use Python to draw static geographical charts. We will use the Matplotlib library and the Basemap module to achieve this goal. The following is a simple code example that demonstrates how to draw a world map and mark some points on the map:

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap

# 创建地图
map = Basemap(projection='mill', llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180, resolution='c')

# 绘制海岸线
map.drawcoastlines()

# 绘制国家边界
map.drawcountries()

# 绘制州界线
map.drawstates()

# 绘制城市
lons = [116.4074, 139.6917, -73.935242, -99.133178]
lats = [39.9042, 35.6895, 40.712776, 19.432608]
x, y = map(lons, lats)
map.plot(x, y, 'bo', markersize=8)

# 显示地图
plt.show()

In the above code, first we create a Basemap object and Specifies the map's projection method, latitude and longitude range, and resolution. We then draw coastlines, country boundaries, and state lines using methods such as drawcoastlines(), drawcountries(), and drawstates(). Finally, we plotted some points on the map using the plot() method.

Run the above code and you will get a static world map with the locations of four cities marked on the map.

3. Draw dynamic and interactive geographic charts
If we need to draw dynamic and interactive geographic charts, we usually choose to use the Plotly library. The following is a sample code that demonstrates how to use Plotly to draw a dynamic world map and display the GDP per capita of each country on the map:

import plotly.express as px

# 加载数据集
data = px.data.gapminder()

# 创建动态地理图表
fig = px.scatter_geo(data, locations="iso_alpha", color="continent", hover_name="country", size="gdpPercap",
                     animation_frame="year",
                     projection="natural earth")

# 显示地图
fig.show()

In the above code, we use px.data .gapminder()Loads a sample data set, which contains per capita GDP data for various countries in different years. Then, we created a dynamic geographic chart using the px.scatter_geo() method, where the locations parameter is used to specify the ISO code of the country, and the color parameter is used to Color distinction according to different continents, hover_name parameter is used to display the country name when the mouse hovers, size parameter is used to adjust the size of the point according to the size of GDP per capita, The animation_frame parameter is used to specify a dynamic time series.

Run the above code, you will get a dynamic world map, and the per capita GDP of each country will be represented according to different colors and point sizes.

Conclusion:
This article introduces how to use Python to draw dynamic and interactive geographical charts. By using Matplotlib and Basemap libraries, we can draw static geographical charts and mark different points on the map. By using the Plotly library, we can draw dynamic and interactive geographical charts and display dynamic effects based on changes in data. Geographic charts, whether static or dynamic, can help us better understand the spatial distribution patterns and trends of data. I hope this article can help you use Python to draw geographical charts in data visualization.

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