在这里顺便安利一下jupyter，pyecharts在v0.1.9.2版本开始，在jupyter上可以直接调用实例（例如上方直接调用bar.render_notebook()）就可以将图表直接展示出来，非常方便 。
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如果脚本在非jupyter环境运行，图表渲染方法需改为:
bar.render()默认情况下，pycharts生成图表为html格式，也支持生成png图片格式，如下：from snapshot_selenium import snapshot as driverfrom pyecharts import options as optsfrom pyecharts.charts import Barfrom pyecharts.render import make_snapshotdef bar_chart() -> Bar: c = ( Bar() .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]) .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105]) .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49]) .reversal_axis() .set_series_opts(label_opts=opts.LabelOpts(position="right")) .set_global_opts(title_opts=opts.TitleOpts(title="Bar-测试渲染图片")) ) return c# 需要安装 snapshot-selenium 或者 snapshot-phantomjsmake_snapshot(driver, bar_chart().render(), "bar.png")
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6. Pyecharts几种高频使用的可视化图表在上面官方示例中的柱状图表我们已经能感受到pycharts可视化功能的强大，最后再介始几种日常工作中常用的可视化图表及对应示例 。
6.1 Pie饼状图
from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerpie = ( Pie() .add("", [list(z) for z in zip(Faker.choose(), Faker.values())]) .set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"]) .set_global_opts(title_opts=opts.TitleOpts(title="Pie-设置颜色")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))pie.render_notebook()
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6.2 仪表盘
from pyecharts import options as optsfrom pyecharts.charts import Gaugeg = ( Gauge() .add("", [("完成率", 66.6)]) .set_global_opts(title_opts=opts.TitleOpts(title="Gauge-基本示例")))g.render_notebook()
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6.3 折线图
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerc = ( Line() .add_xaxis(Faker.choose()) .add_yaxis("商家A", Faker.values(), is_smooth=True) .add_yaxis("商家B", Faker.values(), is_smooth=True) .set_global_opts(title_opts=opts.TitleOpts(title="Line-smooth")))c.render_notebook()
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6.4 K线图
from pyecharts import options as optsfrom pyecharts.charts import Klinedata = [ [2320.26, 2320.26, 2287.3, 2362.94], [2300, 2291.3, 2288.26, 2308.38], [2295.35, 2346.5, 2295.35, 2345.92], [2347.22, 2358.98, 2337.35, 2363.8], [2360.75, 2382.48, 2347.89, 2383.76], [2383.43, 2385.42, 2371.23, 2391.82], [2377.41, 2419.02, 2369.57, 2421.15], [2425.92, 2428.15, 2417.58, 2440.38], [2411, 2433.13, 2403.3, 2437.42], [2432.68, 2334.48, 2427.7, 2441.73], [2430.69, 2418.53, 2394.22, 2433.89], [2416.62, 2432.4, 2414.4, 2443.03], [2441.91, 2421.56, 2418.43, 2444.8], [2420.26, 2382.91, 2373.53, 2427.07], [2383.49, 2397.18, 2370.61, 2397.94], [2378.82, 2325.95, 2309.17, 2378.82], [2322.94, 2314.16, 2308.76, 2330.88], [2320.62, 2325.82, 2315.01, 2338.78], [2313.74, 2293.34, 2289.89, 2340.71], [2297.77, 2313.22, 2292.03, 2324.63], [2322.32, 2365.59, 2308.92, 2366.16], [2364.54, 2359.51, 2330.86, 2369.65], [2332.08, 2273.4, 2259.25, 2333.54], [2274.81, 2326.31, 2270.1, 2328.14], [2333.61, 2347.18, 2321.6, 2351.44], [2340.44, 2324.29, 2304.27, 2352.02], [2326.42, 2318.61, 2314.59, 2333.67], [2314.68, 2310.59, 2296.58, 2320.96], [2309.16, 2286.6, 2264.83, 2333.29], [2282.17, 2263.97, 2253.25, 2286.33], [2255.77, 2270.28, 2253.31, 2276.22],]k = ( Kline() .add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)]) .add_yaxis("k线图", data) .set_global_opts( yaxis_opts=opts.AxisOpts(is_scale=True), xaxis_opts=opts.AxisOpts(is_scale=True), title_opts=opts.TitleOpts(title="K线图-基本示例"), ))k.render_notebook()
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