[Share] Hangzhou Pyecharts visualization popular walking routes -

Foreword

This paper is organized as follows:

  • Hangzhou popular walking route
  • Scatter gradient effect

Are all examples of Echarts official, etc., this will be achieved by Pyecharts.

Hangzhou popular walking route

The complete code

from pyecharts import options as opts
from pyecharts.charts import BMap
from pyecharts.globals import ChartType, SymbolType, ThemeType
import requests

# 通过requests获取数据
r = requests.get('https://echarts.baidu.com/examples/data/asset/data/hangzhou-tracks.json')
data = r.json()

data_pair = []

# 新建一个BMap对象
bmap = BMap()

for i, item in enumerate([j for i in data for j in i ]):
    # 新增坐标点
    bmap.add_coordinate(i, item['coord'][0], item['coord'][1])
    data_pair.append((i, 1)) 

bmap.add_schema(
    # 需要申请一个AK
    baidu_ak='VtTfLEPhrSmI34foXXozmE441uDOSA7V',
    # 地图缩放比例
    zoom=14, 
    # 显示地图中心坐标点
    center=[120.13066322374, 30.240018034923])

# 添加数据
bmap.add("门店数", data_pair,
         type_='heatmap')

# 数据标签不显示
bmap.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bmap.set_global_opts(
    visualmap_opts=opts.VisualMapOpts(min_=0, max_=50, 
                                      # 颜色效果借用Echarts示例效果
                                      range_color=['blue', 'blue', 'green', 'yellow', 'red']),
    # 图例不显示
    legend_opts=opts.LegendOpts(is_show=False),
    title_opts=opts.TitleOpts(title="杭州热门步行路线"))

# notebook中渲染
# 其他运行环境使用bmap.render()
bmap.render_notebook()

Achieve results

Scatter gradient effect

The complete code

from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode

# 人均寿命于GDP
data = [[[28604,77,17096869,'Australia',1990],[31163,77.4,27662440,'Canada',1990],[1516,68,1154605773,'China',1990],[13670,74.7,10582082,'Cuba',1990],[28599,75,4986705,'Finland',1990],[29476,77.1,56943299,'France',1990],[31476,75.4,78958237,'Germany',1990],[28666,78.1,254830,'Iceland',1990],[1777,57.7,870601776,'India',1990],[29550,79.1,122249285,'Japan',1990],[2076,67.9,20194354,'North Korea',1990],[12087,72,42972254,'South Korea',1990],[24021,75.4,3397534,'New Zealand',1990],[43296,76.8,4240375,'Norway',1990],[10088,70.8,38195258,'Poland',1990],[19349,69.6,147568552,'Russia',1990],[10670,67.3,53994605,'Turkey',1990],[26424,75.7,57110117,'United Kingdom',1990],[37062,75.4,252847810,'United States',1990]],
    [[44056,81.8,23968973,'Australia',2015],[43294,81.7,35939927,'Canada',2015],[13334,76.9,1376048943,'China',2015],[21291,78.5,11389562,'Cuba',2015],[38923,80.8,5503457,'Finland',2015],[37599,81.9,64395345,'France',2015],[44053,81.1,80688545,'Germany',2015],[42182,82.8,329425,'Iceland',2015],[5903,66.8,1311050527,'India',2015],[36162,83.5,126573481,'Japan',2015],[1390,71.4,25155317,'North Korea',2015],[34644,80.7,50293439,'South Korea',2015],[34186,80.6,4528526,'New Zealand',2015],[64304,81.6,5210967,'Norway',2015],[24787,77.3,38611794,'Poland',2015],[23038,73.13,143456918,'Russia',2015],[19360,76.5,78665830,'Turkey',2015],[38225,81.4,64715810,'United Kingdom',2015],[53354,79.1,321773631,'United States',2015]]]

scatter = (Scatter()
           .add_xaxis([i[0] for i in data[0]])
           .add_yaxis("1990年", [[i[1],i[3], i[2]] for i in data[0]],
                      # 渐变效果实现部分
                      color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(251, 118, 123)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(204, 46, 72)'
                                    }])"""))
           .add_yaxis("2015年", [[i[1],i[3], i[2]]  for i in data[1]], 
                      # 渐变效果实现部分
                      color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(129, 227, 238)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(25, 183, 207)'
                                    }])"""))
           .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
           .set_global_opts(
               title_opts=opts.TitleOpts(title="1990 与 2015 年各国家人均寿命与 GDP"),
               tooltip_opts = opts.TooltipOpts(
                   # 通过执行js代码实现提示显示为国家
                   formatter=JsCode("function (param) {return param.data[2];}")),
               xaxis_opts=opts.AxisOpts(
                   # 设置坐标轴为数值类型
                   type_="value", 
                   # 显示分割线
                   splitline_opts=opts.SplitLineOpts(is_show=True)),
               yaxis_opts=opts.AxisOpts(
                   # 设置坐标轴为数值类型
                   type_="value",
                   # 默认为False表示起始为0
                   is_scale=True,
                   splitline_opts=opts.SplitLineOpts(is_show=True),),
               # 数据中第三个度量值通过图形的size来展示
               visualmap_opts=opts.VisualMapOpts(is_show=False, type_='size', min_=20194354, max_=1154605773)
    ))

scatter.render_notebook() 

Achieve results


Published 11 original articles · won praise 22 · views 531

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Origin blog.csdn.net/qq_27484665/article/details/104434108