Python运用webbrowser打开PyEcharts生成的html文件
【摘要】
话不多说，直接上代码
这里 也有更多pyecharts的代码~
示例
调用库
import pandas as pd
import time
from functools import parti...
话不多说，直接上代码
这里 也有更多pyecharts的代码~
示例
调用库
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
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K线图、输出在默认浏览器显示
# 移动平均数计算
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线 --项目需求：已实现--
def show_kline(csv_name):
# 读取.csv文件，
stock_code = 'Brent_OIL'
stock_data = pd.read_csv(csv_name, encoding='gb2312')
# 将文件内容按照by=[‘date’]内容进行排序
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线图", 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轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(is_scale=True), # y轴起始坐标可自动调整
#title_opts=opts.TitleOpts(title="价格", 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=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1], # 设置第0轴和第1轴同时缩放
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,
),
],
)
)
# 移动平均线
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线图和移动平均线显示在一个图内
kline.overlap(line)
# 成交量柱形图
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),
)
)
# 使用网格将多张图标组合到一起显示
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)
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主函数
def click_success(self):
print("数据获取成功！")
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("数据获取成功！")
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("数据获取成功！")
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()
# 外盘期货
ui.pushButton.clicked.connect(click_success) # 布伦特原油期货分析图
ui.pushButton_3.clicked.connect(click_success_3) # 纽约原油期货分析图
ui.pushButton_4.clicked.connect(click_success_4) # 纽约天然气期货分析图
sys.exit(app.exec_())
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其中实现跳转代码为
htl = csv_name + ".html"
grid_chart.render(htl)
wb.open(htl)
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"
所以不用老去纠结每件事
集中精力完成眼前的事情的过程中
愿望自然就会实现了吧。
"
文章来源: blog.csdn.net，作者：府学路18号车神，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/weixin_44333889/article/details/117920091
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