Creating a Hangzhou Walking Routes Heatmap and a Gradient Scatter Chart with Pyecharts

This article shows how to implement two visualization examples using the Pyecharts library: a heatmap of popular walking routes in Hangzhou displayed on a Baidu map, and a gradient scatter plot illustrating the relationship between life expectancy and GDP for various countries in 1990 and 2015.

Hangzhou Walking Routes Heatmap

Before running the code you need a Baidu Map developer AK, which can be obtained from the Baidu Map Open Platform. The data source is the JSON file hangzhou-tracks.json .

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

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

data_pair = []

# Create BMap object
bmap = BMap()

for i, item in enumerate([j for i in data for j in i]):
    # Add coordinate points
    bmap.add_coordinate(i, item['coord'][0], item['coord'][1])
    data_pair.append((i, 1))

bmap.add_schema(
    # You must provide your own AK
    baidu_ak='VtTfLEPhrSmI34foXXozmE441uDOSA7V',
    # Map zoom level
    zoom=14,
    # Center coordinates
    center=[120.13066322374, 30.240018034923])

# Add data as heatmap
bmap.add("门店数", data_pair, type_='heatmap')

# Hide data labels
bmap.set_series_opts(label_opts=opts.LabelOpts(is_show=False))

bmap.set_global_opts(
    visualmap_opts=opts.VisualMapOpts(min_=0, max_=50, range_color=['blue', 'blue', 'green', 'yellow', 'red']),
    legend_opts=opts.LegendOpts(is_show=False),
    title_opts=opts.TitleOpts(title="杭州热门步行路线"))

# Render in notebook (or use bmap.render() in other environments)
bmap.render_notebook()
</code>

The resulting map visualizes the density of walking routes across Hangzhou.

Gradient Effect Scatter Plot

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

# Data: life expectancy, GDP, population, country, year
data = [
    [
        [[28604,77,17096869,'Australia',1990], [31163,77.4,27662440,'Canada',1990], ...],
        [[44056,81.8,23968973,'Australia',2015], [43294,81.7,35939927,'Canada',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]],
        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]],
        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(formatter=JsCode("function (param) {return param.data[2];}")),
        xaxis_opts=opts.AxisOpts(type_="value", splitline_opts=opts.SplitLineOpts(is_show=True)),
        yaxis_opts=opts.AxisOpts(type_="value", is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)),
        visualmap_opts=opts.VisualMapOpts(is_show=False, type_='size', min_=20194354, max_=1154605773)
    ))

scatter.render_notebook()
</code>

The scatter chart uses radial gradients to differentiate the two years and encodes population size via point size, providing a clear visual comparison of life expectancy and GDP across countries.

Both visualizations are intended for learning purposes; the original images are shown in the article.