Python可视化神器pyecharts之绘制地理图表练习
更新时间:2022年07月06日 16:17:47 作者:王小王_123
这篇文章主要介绍了Python可视化神器pyecharts之绘制地理图表,文章围绕主题展开详细的内容介绍,具有一定的参考价值,需要的小伙伴可以参考一下
炫酷地图
前期我们介绍了很多的地图模板,不管是全球的还是中国的,其实我感觉都十分的炫酷,哈哈哈,可是还有更加神奇的,更加炫酷的地图模板,下面让我们一起一饱眼福吧!
3D炫酷地图模板系列
重庆市3D地图展示
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") )
中国3D地图
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") )
中国3D数据地图(适合做数据可视化)
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。
全国行政区地图(带城市名字)
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") )
地球展示
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") )
其实pyecharts还可以做百度地图,可以缩放定位到每一个区域,但是其实我们在日常生活中可能用不上,如果要用可以去百度地图展示效果或者学习练习也是可的
到此这篇关于Python可视化神器pyecharts之绘制地理图表的文章就介绍到这了,更多相关Python绘制地理图表内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!
相关文章
python+opencv图像分割实现分割不规则ROI区域方法汇总
这篇文章主要介绍了python+opencv图像分割实现分割不规则ROI区域方法汇总,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧2021-04-04
最新评论