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10行Python代码创建可视化地图

亿华云2025-10-02 16:33:17【域名】0人已围观

简介importvincentworld_countries=rworld-countries.jsonworld=vincent.Map(width=1200,height=1000)world.geo

import vincent world_countries = rworld-countries.json world = vincent.Map(width=1200,代地图 height=1000) world.geo_data(projection=winkel3, scale=200, world=world_countries) world.to_json(path)

当我开始建造Vincent时, 我的一个目的就是使得地图的建造尽可能合理化. 有一些很棒的python地图库-参见Basemap 和 Kartograph能让地图更有意思. 我强烈推荐这两个工具, 因为他们都很好用而且很强大. 我想有更简单一些的工具,能依靠Vega的力量并且允许简单的语法点到geoJSON文件，详细描述一个投影和大小/比列,码创***输出地图。

例如,视化将地图数据分层来建立更复杂的地图:

vis = vincent.Map(width=1000, height=800) #Add the US county data and a new line color vis.geo_data(projection=albersUsa, scale=1000, counties=county_geo) vis + (2B4ECF, marks, 0, properties, enter, stroke, value) #Add the state data, remove the fill, write Vega spec output to JSON vis.geo_data(states=state_geo) vis - (fill, marks, 1, properties, enter) vis.to_json(path)

加之,等值线地图需绑定Pandas数据,需要数据列直接映射到地图要素.假设有一个从geoJSON到列数据的1:1映射,它的语法是非常简单的:

#merged is the Pandas DataFrame vis = vincent.Map(width=1000, height=800) vis.tabular_data(merged, columns=[FIPS_Code, Unemployment_rate_2011]) vis.geo_data(projection=albersUsa, scale=1000, bind_data=data.id, counties=county_geo) vis + (["#f5f5f5","#000045"], scales, 0, range) vis.to_json(path)

我们的数据并非没有争议无需改造——用户需要确保 geoJSON 键与熊猫数据框架之间具有1:1的映射。下面就是代地图之前实例所需的简明的高防服务器数据框架映射：我们的国家信息是一个列有FIPS 码、国家名称、码创以及经济信息(列名省略)的视化 CSV 文件：

00000,US,United States,154505871,140674478,13831393,9,50502,100 01000,AL,Alabama,2190519,1993977,196542,9,41427,100 01001,AL,Autauga County,25930,23854,2076,8,48863,117.9 01003,AL,Baldwin County,85407,78491,6916,8.1,50144,121 01005,AL,Barbour County,9761,8651,1110,11.4,30117,72.7

在 geoJSON 中，我们的代地图国家形状是以 FIPS 码为id 的(感谢 fork 自 Trifacta 的相关信息)。为了简便，码创实际形状已经做了简略，视化在示例数据可以找到完整的代地图数据集：

{ "type":"FeatureCollection","features":[ { "type":"Feature","id":"1001","properties":{ "name":"Autauga"} { "type":"Feature","id":"1003","properties":{ "name":"Baldwin"} { "type":"Feature","id":"1005","properties":{ "name":"Barbour"} { "type":"Feature","id":"1007","properties":{ "name":"Bibb"} { "type":"Feature","id":"1009","properties":{ "name":"Blount"} { "type":"Feature","id":"1011","properties":{ "name":"Bullock"} { "type":"Feature","id":"1013","properties":{ "name":"Butler"} { "type":"Feature","id":"1015","properties":{ "name":"Calhoun"} { "type":"Feature","id":"1017","properties":{ "name":"Chambers"} { "type":"Feature","id":"1019","properties":{ "name":"Cherokee"}

我们需要匹配 FIPS 码，确保匹配正确，码创否则 Vega 无法正确的视化压缩数据：

import json import pandas as pd #Map the county codes we have in our geometry to those in the #county_data file, which contains additional rows we dont need with open(county_geo, r) as f: get_id = json.load(f) #Grab the FIPS codes and load them into a dataframe county_codes = [x[id] for x in get_id[features]] county_df = pd.DataFrame({ FIPS_Code: county_codes}, dtype=str) #Read into Dataframe, cast to string for consistency df = pd.read_csv(county_data, na_values=[ ]) df[FIPS_Code] = df[FIPS_Code].astype(str) #Perform an inner join, pad NAs with data from nearest county merged = pd.merge(df, county_df, on=FIPS_Code, how=inner) merged = merged.fillna(method=pad) >>>merged.head() FIPS_Code State Area_name Civilian_labor_force_2011 Employed_2011 \ 0 1001 AL Autauga County 25930 23854 1 1003 AL Baldwin County 85407 78491 2 1005 AL Barbour County 9761 8651 3 1007 AL Bibb County 9216 8303 4 1009 AL Blount County 26347 24156 Unemployed_2011 Unemployment_rate_2011 Median_Household_Income_2011 \ 0 2076 8.0 48863 1 6916 8.1 50144 2 1110 11.4 30117 3 913 9.9 37347 4 2191 8.3 41940 Med_HH_Income_Percent_of_StateTotal_2011 0 117.9 1 121.0 2 72.7 3 90.2 4 101.2

现在，我们可以快速生成不同的代地图等值线：

vis.tabular_data(merged, columns=[FIPS_Code, Civilian_labor_force_2011]) vis.to_json(path)

这只能告诉我们 LA 和 King 面积非常大，云南idc服务商人口非常稠密。码创让我们再看看中等家庭收入：

vis.tabular_data(merged,视化 columns=[FIPS_Code, Median_Household_Income_2011]) vis.to_json(path)

明显很多高收入区域在东海岸或是其他高密度区域。我敢打赌，在城市层级这将更加有趣，但这需要等以后发布的版本。让我们快速重置地图，再看看国家失业率：

#Swap county data for state data, reset map state_data = pd.read_csv(state_unemployment) vis.tabular_data(state_data, columns=[State, Unemployment]) vis.geo_data(bind_data=data.id, reset=True, states=state_geo) vis.update_map(scale=1000, projection=albersUsa) vis + ([#c9cedb, #0b0d11], scales, 0, range) vis.to_json(path)