Arctern pandas入門-前後端渲染

    Arctern基於開源大數據生態，構建靈活、強大、高性能的時空數據分析平臺，幫助用戶應對5G/IoT帶來的新型數據挑戰,加速時空數據的處理、分析、模型預測與呈現。本文中將會介紹Arctern pandas安裝和簡單使用

Arctern安裝

    參照官網安裝教程，。激活arctern環境，進入python,列印arctern版本就可以查看是否安裝成功

Arctern後端渲染展示

    後端展示基本是參照官網安裝教程，但是有兩個地方要注意：一：以散點圖為例,原來的給出來的代碼會在我的電腦上一直不能加載出來圖片，我們需要自己修改下contextily選擇的底圖如contextily.providers.CartoDB.Voyager，其他的可視化案例同樣需要注意；二：icon展示圖，原文中的圖標地址已經不存在，可使用下面地址https://user-gold-cdn.xitu.io/2020/6/21/172d46470736bad7?w=19&h=38&f=png&s=674保存下來圖標或者用任意圖標代替。 下面是示例代碼和點圖、帶權點圖、熱力圖、輪廓圖、圖標圖、漁網圖效果圖

import pandas as pdimport randomimport arcternfrom arctern.util import save_png, vegaimport matplotlib.pyplot as pltimport matplotlib.image as mpimgimport contextily as cxdef gen_data(num_rows, bbox): pickup_longitude = [(bbox[2]-bbox[0])*random.random()+bbox[0] for i in range(num_rows)] pickup_latitude = [(bbox[3]-bbox[1])*random.random()+bbox[1] for i in range(num_rows)] fare_amount = [100*random.random() for i in range(num_rows)] tip_amount = [fare*(random.random()*0.05+0.15) for fare in fare_amount] total_amount = [fare_amount[i]+tip_amount[i] for i in range(num_rows)] return pd.DataFrame({&34;:pickup_longitude, &34;:pickup_latitude, &34;:fare_amount, &34;:total_amount})num_rows=200bbox=[-73.991504, 40.770759, -73.945155, 40.783434]df=gen_data(num_rows,bbox)fig, ax = plt.subplots(figsize=(10, 6), dpi=200)arctern.plot.pointmap(ax,arctern.GeoSeries.point(df.pickup_longitude,df.pickup_latitude),bbox,point_size=6,point_color=&2DEF4A&34;EPSG:4326&arctern.plot.pointmaphttps://contextily.readthedocs.io/en/latest/intro_guide.html?highlight=providers openstreetmap mapnikhttps://arctern.io/docs/versions/v0.2.x/development-doc-cn/html/feature_description/visualization/backend_visualization/arctern_plot.html