Python運用webbrowser打開PyEcharts生成的html檔案

話不多說，直接上代碼

這裡

也有更多pyecharts的代碼~

示例

調用庫

import pandas as pd
import time
from functools import partial
from PyQt5.QtWidgets import *
from PyQt5 import QtCore, QtGui, QtWidgets
from pyecharts import options as opts
from pyecharts.charts import Kline, Line, Bar, Grid
import webbrowser as wb      

K線圖、輸出在預設浏覽器顯示

# 移動平均數計算
def moving_average(data, day_count):
    data = data.values[:, 0]
    result = []
    for i in range(len(data)):
        start_day_index = i - day_count + 1
        if start_day_index <= 0:
            start_day_index = 0
        justified_day_count = i - start_day_index + 1
        mean = data[start_day_index:i + 1].sum() / justified_day_count
        result.append(mean)
    return result

# k線             --項目需求：已實作--
def show_kline(csv_name):
    # 讀取.csv檔案，
    stock_code = 'Brent_OIL'

    stock_data = pd.read_csv(csv_name, encoding='gb2312')
    # 将檔案内容按照by=[‘date’]内容進行排序
    stock_data = stock_data.sort_values(by=["date"], ascending=[True], inplace=False)

    stock_data_cleared = stock_data[stock_data['close'] > 0]

    stock_name = stock_data_cleared["position"][0]

    stock_data_extracted = stock_data_cleared[["open", "close", "low", "high", "volume", "date"]]

    kline = (
        Kline()
            .add_xaxis(stock_data_extracted["date"].values.tolist())
            .add_yaxis("K線圖", stock_data_extracted.iloc[:, :4].values.tolist())
            .set_global_opts(
            xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
            # axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y軸起始坐标可以設為0
            yaxis_opts=opts.AxisOpts(is_scale=True),  # y軸起始坐标可自動調整
            #title_opts=opts.TitleOpts(title="價格", subtitle=stock_name + "\n" + stock_code, pos_top="20%"),
            axispointer_opts=opts.AxisPointerOpts(
                is_show=True,
                link=[{"xAxisIndex": "all"}],
                label=opts.LabelOpts(background_color="#777"),
            ),
            datazoom_opts=[  # 設定zoom參數後即可縮放
                opts.DataZoomOpts(
                    is_show=True,
                    type_="inside",
                    xaxis_index=[0, 1],  # 設定第0軸和第1軸同時縮放
                    range_start=0,
                    range_end=100,
                ),
                opts.DataZoomOpts(
                    is_show=True,
                    xaxis_index=[0, 1],
                    type_="slider",
                    pos_top="90%",
                    range_start=0,
                    range_end=100,
                ),
            ],

        )
    )

    # 移動平均線
    line = (
        Line()
            .add_xaxis(xaxis_data=stock_data_extracted["date"].values.tolist())
            .add_yaxis(
            series_name="MA5",
            y_axis=moving_average(stock_data_extracted[["close"]], 5),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA10",
            y_axis=moving_average(stock_data_extracted[["close"]], 10),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA30",
            y_axis=moving_average(stock_data_extracted[["close"]], 30),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA60",
            y_axis=moving_average(stock_data_extracted[["close"]], 60),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA120",
            y_axis=moving_average(stock_data_extracted[["close"]], 120),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA240",
            y_axis=moving_average(stock_data_extracted[["close"]], 240),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .add_yaxis(
            series_name="MA360",
            y_axis=moving_average(stock_data_extracted[["close"]], 360),
            is_smooth=True,
            is_hover_animation=False,
            linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
            .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
    )

    # 将K線圖和移動平均線顯示在一個圖内
    kline.overlap(line)

    # 成交量柱形圖
    x = stock_data_extracted[["date"]].values[:, 0].tolist()
    y = stock_data_extracted[["volume"]].values[:, 0].tolist()

    bar = (
        Bar()
            .add_xaxis(x)
            .add_yaxis("成交量", y, label_opts=opts.LabelOpts(is_show=False),
                       itemstyle_opts=opts.ItemStyleOpts(color="#008080"))
            .set_global_opts(title_opts=opts.TitleOpts(title="成交量", pos_top="70%"),
                             legend_opts=opts.LegendOpts(is_show=False),
                             )
    )

    # 使用網格将多張圖示組合到一起顯示
    grid_chart = Grid()

    grid_chart.add(
        kline,
        grid_opts=opts.GridOpts(pos_left="15%", pos_right="8%", height="55%"),
    )

    grid_chart.add(
        bar,
        grid_opts=opts.GridOpts(pos_left="15%", pos_right="8%", pos_top="70%", height="20%"),
    )
    htl = csv_name + ".html"
    grid_chart.render(htl)


    wb.open(htl)


      

主函數

def click_success(self):

    print("資料擷取成功！")
    csv_name1 = 'outside_brent_oil.csv'
    outside_history_brent_oil_data().to_csv(csv_name1, index=False)
    show_kline(csv_name1)
    # html_success()

def click_success_3(self):

    print("資料擷取成功！")
    csv_name2 = 'outside_newyork_oil.csv'
    outside_history_newyork_oil_data().to_csv(csv_name2, index=False)
    show_kline(csv_name2)


def click_success_4(self):

    print("資料擷取成功！")

    csv_name3 = 'outside_newyork_gas.csv'
    outside_history_newyork_natural_gas_data().to_csv(csv_name3, index=False)
    show_kline(csv_name3)


if __name__ == '__main__':

    app = QApplication(sys.argv)
    MainWindow = QMainWindow()
    ui = Ui_MainWindow()
    ui.setupUi(MainWindow)
    MainWindow.show()
     # 外盤期貨
    ui.pushButton.clicked.connect(click_success)           # 布倫特原油期貨分析圖
    ui.pushButton_3.clicked.connect(click_success_3)       # 紐約原油期貨分析圖
    ui.pushButton_4.clicked.connect(click_success_4)       # 紐約天然氣期貨分析圖

    sys.exit(app.exec_())      

其中實作跳轉代碼為

htl = csv_name + ".html"
    grid_chart.render(htl)
    
    wb.open(htl)