10 行 Python 代碼創建可視化地圖

import

vincent

world_countries =

r'world-countries.json'

world =

vincent.Map(width=1200,
height=1000)

world.geo_data(projection='winkel3',
scale=200,
world=world_countries)

world.to_json(path)

當我開始建造Vincent時, 我的一個目的就是使得地圖的建造儘可能合理化. 有一些很棒的python地圖庫-參見Basemap 和 Kartograph能讓地圖更有意思. 我強烈推薦這兩個工具, 因爲他們都很好用而且很強大. 我想有更簡單一些的工具,能依靠Vega的力量並且允許簡單的語法點到geoJSON文件,詳細描述一個投影和大小/比列,最後輸出地圖.

hwang30
翻譯於 1年前

0人頂

頂翻譯的不錯哦!

例如, 將地圖數據分層來建立更復雜的地圖:

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,等值線地圖需綁定Pandas數據,需要數據列直接映射到地圖要素.假設有一個從geoJSON到列數據的1:1映射,它的語法是非常簡單的:

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#'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)

hwang30
翻譯於 1年前

0人頂

頂翻譯的不錯哦!

我們的數據並非沒有爭議無需改造——用戶需要確保 geoJSON 鍵與熊貓數據框架之間具有1:1的映射。下面就是之前實例所需的簡明的數據框架映射：我們的國家信息是一個列有FIPS 碼、國家名稱、以及經濟信息（列名省略）的 CSV 文件：

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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，我們的國家形狀是以 FIPS 碼爲id 的（感謝 fork 自 Trifacta 的相關信息）。爲了簡便，實際形狀已經做了簡略，在示例數據可以找到完整的數據集：

{"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"}

我們需要匹配 FIPS 碼，確保匹配正確，否則 Vega 無法正確的壓縮數據：

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 don't 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 NA's 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

現在，我們可以快速生成不同的等值線：

1 2	`vis.tabular_data(merged, columns=['FIPS_Code',` `'Civilian_labor_force_2011'])` `vis.to_json(path)`

Khiyuan
翻譯於 1年前

0人頂

頂翻譯的不錯哦!

這隻能告訴我們 LA 和 King 面積非常大，人口非常稠密。讓我們再看看中等家庭收入：

1 2	`vis.tabular_data(merged, columns=['FIPS_Code',` `'Median_Household_Income_2011'])` `vis.to_json(path)`

明顯很多高收入區域在東海岸或是其他高密度區域。我敢打賭，在城市層級這將更加有趣，但這需要等以後發佈的版本。讓我們快速重置地圖，再看看國家失業率：

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#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)