python中pyecharts绘制地图
pyecharts中的Geo 地理坐标系组件用于地图的绘制，可直接使用全国的城市信息。
使用前先安装相关地图扩展包：
pip install echarts-countries-pypkg
pip install echarts-china-provinces-pypkg
pip install echarts-china-cities-pypkg
pip install echarts-china-counties-pypkg
pip install echarts-china-misc-pypkg
pip install echarts-cities-pypkg1，全国地图：
from pyecharts import Geo
data = [
("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)
]
data2 = [
("北京", 9), ("上海", 12), ("拉萨", 12), ("重庆", 12), ("乌鲁木齐", 14), ("昆明", 15), ("西宁", 22), ("兰州", 17)
]
geo = Geo(
"全国主要城市空气质量",
"data from pm2.5",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
attr2, value2 = geo.cast(data2)
geo.add(
"",
attr,
value,
type="effectScatter",
# is_random=True,
symbol_size=8,
effect_scale=5,
effect_period=3.5)
geo.add(
"",
attr2,
value2,
type="effectScatter",
is_random=True,
symbol="pin",
symbol_size=10,
effect_scale=5,
effect_period=2.5,
is_more_utils=True)
geo.show_config()
geo.render(path="Geo.html")
结果如图：全国地图中右上角的那块有个小区域比较特别，在黑龙江省和内蒙古自治区的挨着那片区域，是黑龙江省的“飞地”——加格达奇。http://wemedia.ifeng.com/91014790/wemedia.shtml
2，使用Visualmap的代码：
from pyecharts import Geo
data = [
("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15),
("北京", 9), ("上海", 12), ("拉萨", 12), ("重庆", 12), ("乌鲁木齐", 14), ("昆明", 15), ("西宁", 22), ("兰州", 17)
]
geo = Geo(
"全国主要城市空气质量",
"data from pm2.5",
title_color="#fff",
title_pos="left",
width=1200,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
geo.add(
" ", # 注意与""的区别，在图顶部中间的scatter点
attr,
value,
type="effectScatter",
is_random=True,
# symbol="pin",
symbol_size=10,
effect_scale=5,
effect_period=2.5,
is_more_utils=True,
is_visualmap=True,
visual_range=[0, 25],
visual_text_color="#fff",
)
geo.show_config()
geo.render(path="Geo.html")
结果如图：
3，广东省地图：
from pyecharts import Geo
data = [("汕头市", 50), ("汕尾市", 60), ("揭阳市", 35), ("阳江市", 44), ("肇庆市", 72),
("湛江市", 13), ("韶关市", 88), ("广州市", 12), ("佛山市", 24), ("清远市", 66)]
geo = Geo(
"广东城市空气质量",
"data from pm2.5",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
maptype="广东",
type="effectScatter",
# is_random=True,
effect_scale=5,
# is_legend_show=False,
is_visualmap=True,
visual_range=[0, 100],
visual_text_color='#000',
# is_label_show=True, # 这个显示的是市的维度
is_more_utils=True,
)
geo.show_config()
geo.render(path="Geo.html")结果如图：
4，北京市地图：
from pyecharts import Geo
data = [("海淀区", 20), ("丰台区", 29), ("西城区", 25), ("东城区", 24), ("房山区", 22),
("昌平区", 13), ("密云县", 11), ("怀柔区", 16), ("通州区", 15), ("大兴区", 9)]
geo = Geo(
"北京城市空气质量",
"data from pm2.5",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
maptype="北京",
type="effectScatter",
# is_random=True,
effect_scale=5,
# is_legend_show=False,
is_visualmap=True,
visual_range=[0, 30],
visual_text_color='#000',
# is_label_show=True, # 这个显示的是市的维度
is_more_utils=True,
)
geo.show_config()
geo.render(path="Geo.html")
结果如图：
注意：
is_label_show=True时，地图上显示的是每个市的维度，
每个市是按照经纬度进行定位的，第3个值才是数据值，其数据组织形式如下：
"data": [
{
"name": "\u6d77\u6dc0\u533a",
"value": [
116.3,
39.95,
20
]
},
{
"name": "\u4e30\u53f0\u533a",
"value": [
116.28,
39.85,
29
]
},
{
"name": "\u897f\u57ce\u533a",
"value": [
116.37,
39.92,
25
]
},
{
"name": "\u4e1c\u57ce\u533a",
"value": [
116.42,
39.93,
24
]
},
{
"name": "\u623f\u5c71\u533a",
"value": [
116.13,
39.75,
22
]
},
{
"name": "\u660c\u5e73\u533a",
"value": [
116.23,
40.22,
13
]
},
{
"name": "\u5bc6\u4e91\u53bf",
"value": [
116.83,
40.37,
11
]
},
{
"name": "\u6000\u67d4\u533a",
"value": [
116.63,
40.32,
16
]
},
{
"name": "\u901a\u5dde\u533a",
"value": [
116.65,
39.92,
15
]
},
{
"name": "\u5927\u5174\u533a",
"value": [
116.33,
39.73,
9
]
}
],参考：
http://pyecharts.org/#/zh-cn/charts_base?id=geo%EF%BC%88%E5%9C%B0%E7%90%86%E5%9D%90%E6%A0%87%E7%B3%BB%EF%BC%89