오늘은 제가 일상생활에서 자주 사용하는 몇 가지 지도 그리기 방법에 대해 이야기해보겠습니다. 아래에서는 아름다운 시각적 그리기를 위한 다음 시각화 라이브러리의 지도 그리기 방법을 소개하겠습니다. 지도도 훌륭한 라이브러리가 많아서 다 나열할 수는 없습니다
pyecharts,plolly, folium,bokeh,basemap,geopandas,cartopy
우선, Boken 지도 그리기 방법을 소개하겠습니다
Bokeh는 지리 데이터 처리를 기반으로 기본 지도 시각화 및 지도 시각화 생성을 지원합니다
세계 지도 그리기
from bokeh.plotting import figure, show from bokeh.tile_providers import CARTODBPOSITRON, get_provider from bokeh.io import output_notebook output_notebook() tile_provider = get_provider(CARTODBPOSITRON) p = figure(x_range=(-2000000, 6000000), y_range=(-1000000, 7000000), x_axis_type="mercator", y_axis_type="mercator") p.add_tile(tile_provider) show(p)
중국 지도 다시 그리기
from bokeh.plotting import curdoc, figure from bokeh.models import GeoJSONDataSource from bokeh.io import show # 读入中国地图数据并传给GeoJSONDataSource with open("china.json", encoding="utf8") as f: geo_source = GeoJSONDataSource(geojson=f.read()) # 设置一张画布 p = figure(width=500, height=500) # 使用patches函数以及geo_source绘制地图 p.patches(xs='xs', ys='ys', source=geo_source) show(p)
GEO 지리적 데이터를 통해 그려보겠습니다. 지도도 매우 편리하지만 지도가 약간 단조로워 보입니다. 다양한 색상으로 다양한 지방을 그려서 살펴보겠습니다.
with open("china.json", encoding="utf8") as f: data = json.loads(f.read()) # 判断是不是 北京地区数据 def isBeijing(district): if 'beijing' in district['properties']['woe-name'].lower(): return True return False # data['features'] = list(filter(isInLondon, data['features'])) # 过滤数据 # 为每一个地区增加一个color属性 for i in range(len(data['features'])): data['features'][i]['properties']['color'] = ['red', 'blue', 'yellow', 'orange', 'gray', 'purple'][i % 6] data['features'][i]['properties']['number'] = random.randint(0, 20_000) geo_source = GeoJSONDataSource(geojson=json.dumps(data)) p = figure(width=500, height=500, tooltips="@name, number: @number") p.patches(xs='xs', ys='ys', fill_alpha=0.7, line_color='white', line_width=0.5, color="color", # 增加颜色属性,这里的"color"对应每个地区的color属性 source=geo_source) p.axis.axis_label = None p.axis.visible = False p.grid.grid_line_color = None show(p)
이미 내부 풍미가 있음을 알 수 있습니다. . 유일한 단점은 남중국해 13구간이 표시되지 않는다는 점입니다
GeoPandas 是基于 Pandas 的地图可视化工具,其数据结构完全继承自 Pandas,对于熟悉潘大师的同学来说还是非常友好的
还是先画一张世界地图
import pandas as pd import geopandas import matplotlib.pyplot as plt %matplotlib inline world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world.plot() plt.show()
这也是 geopandas 官网上的经典图片,可以看到非常简单,除去 import 代码,仅仅三行,就完成了地图的绘制
下面我们继续绘制中国地图,这次我们加上九段线信息
china_nine = geopandas.read_file(r"geojson/九段线GS(2019)1719号.geojson") china = geopandas.read_file('china-new.json') fig, ax = plt.subplots(figsize=(12, 8),dpi=80) ax = china.plot(ax=ax, column='number') ax = china_nine.plot(ax=ax) plt.show()
我们复用了前面处理的 china.json 数据,里面的 number 字段是随机生成的测试数据,效果与 Bokeh 不相上下
接下来我们介绍 plotly,这也是一个非常好用的 Python 可视化工具,如果要绘制地图信息,我们需要安装如下依赖
!pip install geopandas==0.3.0 !pip install pyshp==1.2.10 !pip install shapely==1.6.3
接下来我们先绘制一个世界地图
import plotly.graph_objects as go fig = go.Figure(go.Scattermapbox( mode = "markers+lines", lon = [10, 20, 30], lat = [10, 20,30], marker = {'size': 10})) fig.add_trace(go.Scattermapbox( mode = "markers+lines", lon = [-50, -60,40], lat = [30, 10, -20], marker = {'size': 10})) fig.update_layout( margin ={'l':0,'t':0,'b':0,'r':0}, mapbox = { 'center': {'lon': 113.65000, 'lat': 34.76667}, 'style': "stamen-terrain", 'center': {'lon': -20, 'lat': -20}, 'zoom': 1}) fig.show()
这里我们使用底层 API plotly.graph_objects.Choroplethmapbox
来绘制
下面我们继续绘制中国地图,使用一个高级 API plotly.express.choropleth_mapbox
import pandas as pd import plotly.express as px import numpy as np import json with open(r"china_province.geojson", encoding='utf8') as f: provinces_map = json.load(f) df = pd.read_csv(r'data.csv') df.确诊 = df.确诊.map(np.log) fig = px.choropleth_mapbox( df, geojson=provinces_map, color='确诊', locations="地区", featureidkey="properties.NL_NAME_1", mapbox_style="carto-darkmatter", color_continuous_scale='viridis', center={"lat": 37.110573, "lon": 106.493924}, zoom=3, ) fig.show()
可以看出绘制出的交互式地图还是非常漂亮的,不过渲染速度有些感人,这个就看个人的需求了,如果你对渲染速度有要求,那么 Ployly 可能不是最好的选择~
之所以把这两个库放到一起,是因为他们都是基于 Matplotlib 之上的,而随着 Python2 的不再维护,Basemap 也被 Matplotlib 放弃,Cartopy 随之转正,下面我们主要介绍 Cartopy 工具
Cartopy 利用了强大的 PROJ.4、NumPy 和 Shapely 库,并在 Matplotlib 之上构建了一个编程接口,用于创建发布高质量的地图
先来绘制一个世界地图
%matplotlib inline import cartopy.crs as ccrs import matplotlib.pyplot as plt ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() plt.show()
这是一个 cartopy 绘制的非常经典且常见的世界地图,形式比较简单,下面我们增强该地图
import datetime import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.feature.nightshade import Nightshade fig = plt.figure(figsize=(10, 5)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) date = datetime.datetime(2021, 12, 2, 21) ax.set_title(f'Night time shading for {date}') ax.stock_img() ax.add_feature(Nightshade(date, alpha=0.2)) plt.show()
我们通过上面的代码,绘制了当前时间世界昼夜图,还是很强的
下面我们继续绘制中国地图
import cartopy.io.shapereader as shpreader import numpy as np import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter import cartopy.io.shapereader as shapereader import matplotlib.ticker as mticker #从文件中加载中国区域shp shpfile = shapereader.Reader(r'ne_10m_admin_0_countries_chn\ne_10m_admin_0_countries_chn.shp') # 设置 figure 大小 fig = plt.figure(figsize=[8, 5.5]) # 设置投影方式并绘制主图 ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180)) ax.add_geometries( shpfile.geometries(), ccrs.PlateCarree()) ax.set_extent([70, 140, 0, 55],crs=ccrs.PlateCarree()) plt.show()
使用 cartopy 绘制地图最大的特点就是灵活度高,那么相对应的代价就是编写代码也会更难一些,比如如果想要给不同省份填充不同颜色,我们需要编写的代码就有点多
import matplotlib.patches as mpatches import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import shapely.geometry as sgeom import cartopy.crs as ccrs import cartopy.io.shapereader as shpreader font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) def sample_data(): # lons = [110, 115, 120, 122, 124 ] lons = [124, 122, 120, 115, 110 ] lats = [33, 32, 28, 30, 28 ] return lons, lats #ax = plt.axes([0, 0, 1, 1], projection=ccrs.LambertConformal()) ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([70, 140, 0, 55],crs=ccrs.Geodetic()) shapename = 'admin_1_states_provinces' states_shp = shpreader.natural_earth(resolution='10m', category='cultural', name=shapename) lons, lats = sample_data() # to get the effect of having just the states without a map "background" # turn off the outline and background patches ax.background_patch.set_visible(False) ax.outline_patch.set_visible(False) plt.title(u'China Province Level', fontproperties=font) # turn the lons and lats into a shapely LineString track = sgeom.LineString(zip(lons, lats)) track_buffer = track.buffer(1) for state in shpreader.Reader(states_shp).geometries(): # pick a default color for the land with a black outline, # this will change if the storm intersects with our track facecolor = [0.9375, 0.9375, 0.859375] edgecolor = 'black' if state.intersects(track): facecolor = 'red' elif state.intersects(track_buffer): facecolor = '#FF7E00' ax.add_geometries([state], ccrs.PlateCarree(), facecolor=facecolor, edgecolor=edgecolor) # make two proxy artists to add to a legend direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red") within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00") labels = [u'省份level1', '省份level2'] plt.legend([direct_hit, within_2_deg], labels, loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True, prop=font) ax.figure.set_size_inches(14, 9) plt.show()
folium 是建立在 Python 生态系统的数据应用能力和 Leaflet.js 库的映射能力之上的高级地图绘制工具,通过 Python 操作数据,然后在 Leaflet 地图中可视化,可以灵活的自定义绘制区域,并且展现形式更加多样化
首先是三行代码绘制世界地图
import folium # define the world map world_map = folium.Map() # display world map world_map
接下来绘制中国地图
# 绘制边界 import json df = pd.read_csv(r'plotly-choropleth-mapbox-demo-master/data.csv') # read china border with open(r"plotly-choropleth-mapbox-demo-master/china_province.geojson", encoding='utf8') as f: china = json.load(f) chn_map = folium.Map(location=[40, 100], zoom_start=4) folium.Choropleth( geo_data=china, name="choropleth", data=df, columns=["地区", "确诊"], key_on="properties.NL_NAME_1", fill_color="YlGn", fill_opacity=0.7, line_opacity=0.2, legend_name="新冠确诊", ).add_to(chn_map) folium.LayerControl().add_to(chn_map) chn_map
作为专业地图工具,不仅渲染速度快,自定义程度也是非常高的,值得使用尝试
最后我们介绍 PyEcharts,这款国产的精良可视化工具
绘制世界地图
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map() .add("测试数据", [list(z) for z in zip(Faker.country, Faker.values())], "world") .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="Map-世界地图"), visualmap_opts=opts.VisualMapOpts(max_=200), ) ) c.render_notebook()
通过 Pyecharts 绘制地图的一个好处就是不需要处理 GEO 文件,我们直接出入国家名称,就可以自动匹配到地图上,非常方便
再绘制中国地图
c = ( Map() .add("测试数据", [list(z) for z in zip(Faker.provinces, Faker.values())], "china") .set_global_opts( title_opts=opts.TitleOpts(title="Map-VisualMap(中国)"), visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True), ) ) c.render_notebook()
我们只需要把参数替换成 ”china“ 就可方便的绘制中国地图,真的很给力,当然对于 Pyecharts 还有很多种玩法,就不一一介绍了
综合上面的示例,我们可以看出, Pyecharts 绘制地图最为简单,非常适合新手学习使用;而 folium 和 cartopy 则胜在自由度上,它们作为专业的地图工具,留给了使用者无限可能;至于 Plotly 和 Bokeh 则属于更高级的可视化工具,它们胜在画质更加优美,API 调用也更加完善
今天我们介绍了几种比较常用的绘制地图的类库,每一个工具都有其优缺点,我们只需要在选择的时候,明确目标,用心探索就好!
参考:https://gitee.com/kevinqqnj/cartopy_trial/blob/master/cartopy_province.py
https://zhuanlan.zhihu.com/p/112324234
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