小册子朱皮特情节颜色密度热图 [英] leaflet jupyter plot color density heat map

问题描述

我正在研究一个熊猫csv数据框，并且使用 ipyleaflet 在jupyter中了解到，可以绘制到地图上.

I'm working on a pandas csv dataframe and came to know in jupyter using ipyleaflet you can plot to a map.

到目前为止，我的代码是这样的

My code so far looks like this

from ipyleaflet import Map, Marker, MarkerCluster

longitudes = df['Longitude'].values.tolist()
latitudes = df['Latitude'].values.tolist()

markers = []

for lon,lat in zip(longitudes, latitudes):
    markers.append(Marker(location=(lat, lon)))

m = Map(center=(latitudes[0], longitudes[0]), zoom=10)

marker_cluster = MarkerCluster(
    markers=markers
)

m.add_layer(marker_cluster);

m

哪个很好，但是后来我看到了

Which is nice but then I saw this

我也有相同的字段 Economic Need Index (经济需求索引)，所以我也想这样做，也很好奇如何也可以切换到不太繁忙的 CartoDB 地图./p>

I also have the same field Economic Need Index so I also want to do the same and also curious how I can also switch to the CartoDB less busy map.

推荐答案

由于

Since the last version of ipyleaflet it is now possible to create a HeatMap:

from ipyleaflet import Map, Heatmap
from random import uniform

m = Map(center=[0, 0], zoom=2)

# Create a random heatmap
locations = [
    [uniform(-80, 80), uniform(-180, 180), uniform(0, 1000)] # lat, lng, intensity 
    for i in range(1000)
]
heat = Heatmap(locations=locations, radius=20, blur=10)
m.add_layer(heat)

# Change some attributes of the heatmap
heat.radius = 30
heat.blur = 50
heat.max = 0.5
heat.gradient = {0.4: 'red', 0.6: 'yellow', 0.7: 'lime', 0.8: 'cyan', 1.0: 'blue'}

m

此外，如果要切换到不太繁忙的地图"，则可以在创建底图时更改底图:

Also, if you want to switch to a "less busy map", you can change the basemap when creating it:

from ipyleaflet import Map, basemaps

m = Map(center=(52, 10), zoom=8, basemap=basemaps.CartoDB.DarkMatter)
m

您还可以在给定图块的URL的情况下创建TileLayer，您可以在文档

And you can also create a TileLayer given the url of the tiles, you can find examples in the documentation

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