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这篇文章主要讲解了“Python怎么使用pyecharts绘制地理图表”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“Python怎么使用pyecharts绘制地理图表”吧！
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
# 经纬度
example_data = [
[[119.107078, 36.70925, 1000], [116.587245, 35.415393, 1000]],
[[117.000923, 36.675807], [120.355173, 36.082982]],
[[118.047648, 36.814939], [118.66471, 37.434564]],
[[121.391382, 37.539297], [119.107078, 36.70925]],
[[116.587245, 35.415393], [122.116394, 37.509691]],
[[119.461208, 35.428588], [118.326443, 35.065282]],
[[116.307428, 37.453968], [115.469381, 35.246531]],
]
c = (
Map3D(init_opts=opts.InitOpts(width="1400px", height="700px"))
.add_schema(
maptype="重庆",
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
is_main_shadow=False,
main_alpha=55,
main_beta=10,
ambient_intensity=0.3,
),
view_control_opts=opts.Map3DViewControlOpts(center=[-10, 0, 10]),
post_effect_opts=opts.Map3DPostEffectOpts(is_enable=False),
)
.add(
series_name="",
data_pair=example_data,
type_=ChartType.LINES3D,
effect=opts.Lines3DEffectOpts(
is_show=True,
period=4,
trail_width=3,
trail_length=0.5,
trail_color="#f00",
trail_opacity=1,
),
linestyle_opts=opts.LineStyleOpts(is_show=False, color="#fff", opacity=0),
)
.set_global_opts(title_opts=opts.TitleOpts(title="Map3D"))
.render("区县3D地图.html")
)
数组里面分别代表：经纬度，数值
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
from pyecharts.commons.utils import JsCode
example_data = [
("黑龙江", [127.9688, 45.368, 100]),
("内蒙古", [110.3467, 41.4899, 100]),
("吉林", [125.8154, 44.2584, 100]),
("辽宁", [123.1238, 42.1216, 100]),
("河北", [114.4995, 38.1006, 100]),
("天津", [117.4219, 39.4189, 100]),
("山西", [112.3352, 37.9413, 100]),
("陕西", [109.1162, 34.2004, 100]),
("甘肃", [103.5901, 36.3043, 100]),
("宁夏", [106.3586, 38.1775, 100]),
("青海", [101.4038, 36.8207, 100]),
("新疆", [87.9236, 43.5883, 100]),
("西藏", [91.11, 29.97, 100]),
("四川", [103.9526, 30.7617, 100]),
("重庆", [108.384366, 30.439702, 100]),
("山东", [117.1582, 36.8701, 100]),
("河南", [113.4668, 34.6234, 100]),
("江苏", [118.8062, 31.9208, 100]),
("安徽", [117.29, 32.0581, 100]),
("湖北", [114.3896, 30.6628, 100]),
("浙江", [119.5313, 29.8773, 100]),
("福建", [119.4543, 25.9222, 100]),
("江西", [116.0046, 28.6633, 100]),
("湖南", [113.0823, 28.2568, 100]),
("贵州", [106.6992, 26.7682, 100]),
("广西", [108.479, 23.1152, 100]),
("海南", [110.3893, 19.8516, 100]),
("上海", [121.4648, 31.2891, 100]),
]
c = (
Map3D(init_opts=opts.InitOpts(width="1400px", height="700px"))
.add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + " " + data.value[2];}"),
),
emphasis_label_opts=opts.LabelOpts(
is_show=False,
color="#fff",
font_size=10,
background_color="rgba(0,23,11,0)",
),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
main_shadow_quality="high",
is_main_shadow=False,
main_beta=10,
ambient_intensity=0.3,
),
)
.add(
series_name="Scatter3D",
data_pair=example_data,
type_=ChartType.SCATTER3D,
bar_size=1,
shading="lambert",
label_opts=opts.LabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + ' ' + data.value[2];}"),
),
)
.set_global_opts(title_opts=opts.TitleOpts(title="Map3D"))
.render("中国3D地图.html")
)
如果说前面的那个你看起来不太舒服，那么这个绝对适合做数据可视化展示哟！
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
from pyecharts.commons.utils import JsCode
example_data = [
("黑龙江", [127.9688, 45.368, 100]),
("内蒙古", [110.3467, 41.4899, 300]),
("吉林", [125.8154, 44.2584, 300]),
("辽宁", [123.1238, 42.1216, 300]),
("河北", [114.4995, 38.1006, 300]),
("天津", [117.4219, 39.4189, 300]),
("山西", [112.3352, 37.9413, 300]),
("陕西", [109.1162, 34.2004, 300]),
("甘肃", [103.5901, 36.3043, 300]),
("宁夏", [106.3586, 38.1775, 300]),
("青海", [101.4038, 36.8207, 300]),
("新疆", [87.9236, 43.5883, 300]),
("西藏", [91.11, 29.97, 300]),
("四川", [103.9526, 30.7617, 300]),
("重庆", [108.384366, 30.439702, 300]),
("山东", [117.1582, 36.8701, 300]),
("河南", [113.4668, 34.6234, 300]),
("江苏", [118.8062, 31.9208, 300]),
("安徽", [117.29, 32.0581, 300]),
("湖北", [114.3896, 30.6628, 300]),
("浙江", [119.5313, 29.8773, 300]),
("福建", [119.4543, 25.9222, 300]),
("江西", [116.0046, 28.6633, 300]),
("湖南", [113.0823, 28.2568, 300]),
("贵州", [106.6992, 26.7682, 300]),
("广西", [108.479, 23.1152, 300]),
("海南", [110.3893, 19.8516, 300]),
("上海", [121.4648, 31.2891, 1300]),
]
c = (
Map3D(init_opts=opts.InitOpts(width="1400px", height="700px"))
.add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + " " + data.value[2];}"),
),
emphasis_label_opts=opts.LabelOpts(
is_show=False,
color="#fff",
font_size=10,
background_color="rgba(0,23,11,0)",
),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
main_shadow_quality="high",
is_main_shadow=False,
main_beta=10,
ambient_intensity=0.3,
),
)
.add(
series_name="数据",
data_pair=example_data,
type_=ChartType.BAR3D,
bar_size=1,
shading="lambert",
label_opts=opts.LabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + ' ' + data.value[2];}"),
),
)
.set_global_opts(title_opts=opts.TitleOpts(title="城市数据"))
.render("带有数据展示地图.html")
)
看完直呼这个模板，适合做城市之间的数据对，同时也展示了经纬度。
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
c = (
Map3D(init_opts=opts.InitOpts(width="1400px", height="700px"))
.add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=True,
text_style=opts.TextStyleOpts(
color="#fff", font_size=16, background_color="rgba(0,0,0,0)"
),
),
emphasis_label_opts=opts.LabelOpts(is_show=True),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
is_main_shadow=False,
main_alpha=55,
main_beta=10,
ambient_intensity=0.3,
),
)
.add(series_name="", data_pair="", maptype=ChartType.MAP3D)
.set_global_opts(
title_opts=opts.TitleOpts(title="全国行政区划地图-Base"),
visualmap_opts=opts.VisualMapOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
.render("全国标签地图.html")
)
import pyecharts.options as opts
from pyecharts.charts import MapGlobe
from pyecharts.faker import POPULATION
data = [x for _, x in POPULATION[1:]]
low, high = min(data), max(data)
c = (
MapGlobe(init_opts=opts.InitOpts(width="1400px", height="700px"))
.add_schema()
.add(
maptype="world",
series_name="World Population",
data_pair=POPULATION[1:],
is_map_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
min_=low,
max_=high,
range_text=["max", "min"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
)
)
.render("地球.html")
)
感谢各位的阅读，以上就是“Python怎么使用pyecharts绘制地理图表”的内容了，经过本文的学习后，相信大家对Python怎么使用pyecharts绘制地理图表这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是亿速云，小编将为大家推送更多相关知识点的文章，欢迎关注！
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