def test_page_render_embed():
bar = _create_bar()
line = _create_line()
content = Page().add(bar, line).render_embed()
assert_true(len(content) > 8000)
def page_layout2() -> Page:
page = Page(layout=Page.SimplePageLayout)
page.add(level_distribution_bar(), level_distribution_pie())
return page
def analys_of_data(datas):
"""
对主播类型进行分类，统计
(dict)type_dict : {
'type' : {
'count': int,
'popularity': int,
}
'type' : str(popularity*10000 + count)
}
"""
type_dict = dict()
for data in datas:
if data['type'] not in type_dict.keys():
type_dict[data['type']] = data['popularity'] * 100000 + 1
else:
type_dict[data['type']] += data['popularity'] * 100000 + 1
num_list = sorted([num%100000 for num in list(type_dict.values())], reverse=True) # 获得类型数量的降序列表
popul_list = sorted([int(num/100000) for num in list(type_dict.values())], reverse=True) # 获得类型人气值的降序列表
# 人气前25名的直播内容统计
data_list = sorted(datas, key=lambda x: x['popularity'], reverse=True)
popul_dict = dict()
for data in data_list[:25]:
if data['type'] not in popul_dict.keys():
popul_dict[data['type']] = data['popularity'] * 100 + 1
else:
popul_dict[data['type']] += data['popularity'] * 100 + 1
# 创建统计前25位人气主播的网页
page_1 = Page(page_title='关于前25位人气主播分析')
a = (
Bar(init_opts=opts.InitOpts(width='100%'))
.add_xaxis([data_list[num]['name'] for num in range(25)])
.add_yaxis('',[round(data_list[num]['popularity']/10000,1) for num in range(25)], color='#fa0000')
.set_global_opts(
title_opts=opts.TitleOpts(title="前25位人气主播排名\n(单位：万)"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
.set_series_opts(
xaxis_opts=opts.AxisOpts(name='主播名称'),
title_opts=opts.TitleOpts(title="人气值占比",pos_left='10%',pos_top='2%'),
legend_opts=opts.LegendOpts(is_show=False),
)
)
b = (
Pie(init_opts=opts.InitOpts(width='100%'))
.add(
series_name="数量占比",
data_pair=[(list(popul_dict.keys())[num],list(popul_dict.values())[num]%100) for num in range(popul_dict.__len__())],
radius=50,
center=['90%','15%'],
)
.add(
series_name="人气占比",
data_pair=[(list(popul_dict.keys())[num],int(list(popul_dict.values())[num]/100)) for num in range(popul_dict.__len__())],
radius=50,
center=['90%','40%'],
)
.set_series_opts(
title_opts=opts.TitleOpts(title='数量占比情况'),
legend_opts=opts.LegendOpts(type_='scroll',pos_left="50%", pos_top="2%",is_show=True),
)
)
grid = (
Grid(init_opts=opts.InitOpts(width='100%',height='720px',theme=ThemeType.DARK))
.add(a, grid_opts=opts.GridOpts(pos_top="10%", pos_right="10%"))
.add(b, grid_opts=opts.GridOpts(pos_left="90%", pos_top='10%'))
)
page_1.add(grid)
page_1.render(f'{data_path}/analys_of_25.html')
# 提取类型前32（数量）的进行分类
source_num_1 = list()
source_num_2 = list()
source_num_3 = list()
source_num_4 = list()
source_num_1.append(["数量", "人气", "类型"])
source_num_2.append(["数量", "人气", "类型"])
source_num_3.append(["数量", "人气", "类型"])
source_num_4.append(["数量", "人气", "类型"])
for num in range(type_dict.__len__()):
type_num = list(type_dict.values())[num]%100000
type_popul = int(list(type_dict.values())[num]/100000)
type_name = list(type_dict.keys())[num]
if num_list[8] < type_num <= num_list[0]:
source_num_1.append([type_num, type_popul, type_name])
elif num_list[16] < type_num <= num_list[8]:
source_num_2.append([type_num, type_popul, type_name])
elif num_list[24] < type_num <= num_list[16]:
source_num_3.append([type_num, type_popul, type_name])
elif num_list[32] < type_num <= num_list[24]:
source_num_4.append([type_num, type_popul, type_name])
# 提取类型前32（人气）的进行分类
source_popul_1 = list()
source_popul_2 = list()
source_popul_3 = list()
source_popul_4 = list()
source_popul_other = {
'type': '其他',
'popularity': 0,
'num': 0,
}
source_popul_1.append(["人气", "数量", "类型"])
source_popul_2.append(["人气", "数量", "类型"])
source_popul_3.append(["人气", "数量", "类型"])
source_popul_4.append(["人气", "数量", "类型"])
for num in range(type_dict.__len__()):
type_num = list(type_dict.values())[num]%100000
type_popul = int(list(type_dict.values())[num]/100000)
type_name = list(type_dict.keys())[num]
if popul_list[8] < type_popul <= popul_list[0]:
source_popul_1.append([type_popul, type_num, type_name])
elif popul_list[16] < type_popul <= popul_list[8]:
source_popul_2.append([type_popul, type_num, type_name])
elif popul_list[24] < type_popul <= popul_list[16]:
source_popul_3.append([type_popul, type_num, type_name])
elif popul_list[32] < type_popul <= popul_list[24]:
source_popul_4.append([type_popul, type_num, type_name])
else:
source_popul_other['popularity'] += type_popul
source_popul_other['num'] += type_num
# # 创建一个网页实例，用于整合多张图表
page_2 = Page(page_title='直播类型一览图1')
page_3 = Page(page_title='直播类型一览图2')
# 创建横向柱状图
def create_bar(source,page,x_type,x_name,y_type,title,range,range_text,split_num=8):
c = (
Bar(init_opts=opts.InitOpts(width='100%',height='480px',theme=ThemeType.DARK))
.add_dataset(source=source)
.add_yaxis(
series_name="",
y_axis=[],
encode={"x": x_type, "y": y_type},
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title),
xaxis_opts=opts.AxisOpts(name=x_name),
yaxis_opts=opts.AxisOpts(type_="category"),
visualmap_opts=opts.VisualMapOpts(
orient="horizontal",
pos_left="center",
min_=range[0],
max_=range[1],
range_text=range_text,
dimension=0,
range_color=["#D7DA8B", "#E15457"],
is_piecewise=True,
split_number=split_num,
),
)
)
page.add(c)
create_bar(source_num_1,page_2,'人气','人气','类型','类型人气(数量:前8名)',(num_list[7],num_list[0]),('数量高','数量低'))
create_bar(source_num_2,page_2,'人气','人气','类型','类型人气(数量:第8~16名)',(num_list[15],num_list[8]),('数量高','数量低'))
create_bar(source_num_3,page_2,'人气','人气','类型','类型人气(数量:第16~24名)',(num_list[23],num_list[16]),('数量高','数量低'))
create_bar(source_num_4,page_2,'人气','人气','类型','类型人气(数量:第24～32名)',(num_list[31],num_list[24]),('数量高','数量低'))
page_2.render(f'{data_path}/type_of_num.html')
create_bar(source_popul_1,page_3,'数量','数量','类型','类型数量(人气:前8名)',(popul_list[7],popul_list[0]),('人气高','人气低'),split_num=5)
create_bar(source_popul_2,page_3,'数量','数量','类型','类型数量(人气:第8~16名)',(popul_list[15],popul_list[8]),('人气高','人气低'),split_num=5)
create_bar(source_popul_3,page_3,'数量','数量','类型','类型数量(人气:第16~24名)',(popul_list[23],popul_list[16]),('人气高','人气低'),split_num=5)
create_bar(source_popul_4,page_3,'数量','数量','类型','类型数量(人气:第24～32名)',(popul_list[31],popul_list[24]),('人气高','人气低'),split_num=5)
page_3.render(f'{data_path}/type_of_popular.html')
# 类型平均人气一览图
source = list()
for s in source_popul_1[1:]:
source.append([s[2],round(s[0]/s[1]/100000,1)])
for s in source_popul_2[1:]:
source.append([s[2],round(s[0]/s[1]/100000,1)])
for s in source_popul_3[1:]:
source.append([s[2],round(s[0]/s[1]/100000,1)])
source.append([source_popul_other['type'],round(source_popul_other['popularity']/100000/source_popul_other['num'],1)])
d = (
Bar(init_opts=opts.InitOpts(width='100%',height='720px',theme=ThemeType.DARK,page_title='直播平均人气排名'))
.add_xaxis([s[0] for s in source])
.add_yaxis('人气值(单位：万)',[s[1] for s in source], color='#fa0000')
.set_global_opts(
title_opts=opts.TitleOpts(title="直播类型平均人气排名"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
.set_series_opts(
xaxis_opts=opts.AxisOpts(name='直播类型'),
legend_opts=opts.LegendOpts(is_show=False),
)
.render(f'{data_path}/avg_of_popular.html')
)
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
Page().add(*[fn() for fn, _ in C.charts]).render('grid_example.html')
for i in range(len(tracelist)):
tracelistthree = tracelist[i][1:4]
if tracelistthree not in newtracelist:
newtracelist.append(tracelistthree)
for j in range(len(newtracelist)):
for i in range(len(tracelist)):
if newtracelist[j][0]==tracelist[i][1] and newtracelist[j][1]==tracelist[i][2] and newtracelist[j][2]==tracelist[i][3]:
newtracelist[j].append(tracelist[i][0])
break
verden = list(set(verden))
horden = list(set(horden))
newtracelist.insert(0,firstPoint)
newtracelist.append(endPoint)
print(len(newtracelist))
print(newtracelist)
print(horden)
print(verden)
print(newtracelist)
page = Page()# st
data = newtracelist
range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = Scatter3D() #设置图表的高和宽
scatter3D.add("", data,True, range_color) #视觉映射和颜色选择
page.add(scatter3D)
page.render()
from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line, Liquid, Page, Pie
def pie_echart():
keys = ['男性', '女性']
values = [7322452, 7438028]
data = zip(keys, values)
c = Pie()
c.add("饼状图", list(data))
c.set_global_opts(title_opts=opts.TitleOpts(title='男女比例情况'))
return c
def bar_echart():
c = Bar()
c.add_xaxis(['1978年', '1980年', '1990年', '2000年', '2017年', '2018年'])
c.add_yaxis("人均GDP", [449, 565, 2123, 11471, 98011, 105399])
c.add_yaxis("居民储蓄存款余额", [30, 52, 866, 8240, 84474, 90273])
return c
if __name__ == '__main__':
page = Page()
page.add(pie_echart(), bar_echart(), pie_echart(), bar_echart())
page.render("all.html")
page.add(cloud)
#判断变量类型的函数
def typeof(variate):
type = None
if isinstance(variate, int):
type = "int"
elif isinstance(variate, str):
type = "str"
elif isinstance(variate, float):
type = "float"
elif isinstance(variate, list):
type = "list"
elif isinstance(variate, tuple):
type = "tuple"
elif isinstance(variate, dict):
type = "dict"
elif isinstance(variate, set):
type = "set"
return type
if __name__ == '__main__':
name = sys.argv[1]
page = Page()
getyear(name)
getcloud()
#年度表格添加
page.render(r'views/process.handlebars')
def china_citys_history():
page1 = Page()
dwf.createFile(savaPath+'/中国各省的城市历史疫情信息')
path = readPath + '中国各省的城市历史疫情信息'
floder = glob.glob(os.path.join(path, "*"))
# 判断数据是否存在
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
for name in floder:
for root, dirs, files in os.walk(name, topdown=False):
for filename in files:
name = ''.join('%s' % id for id in name)
# print(type(filename))
# print(type(name))
filepath = name + '/' + filename
filepath = filepath.replace('\\','/')
data = pd.read_excel(filepath, index=False)
province_name = re.findall('([\u4e00-\u9fa5]*)\\\\[\u4e00-\u9fa5]*\.xlsx', filepath)
province_name = ''.join('%s' % id for id in province_name)
city_name = re.findall('([\u4e00-\u9fa5]*)\.xlsx', filepath)
city_name = ''.join('%s' % id for id in city_name)
# print(province_name+'and'+city_name)
checknan(data['confirm'])
checknan(data['confirm_add'])
checknan(data['heal'])
checknan(data['dead'])
y1_confirm = data['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_confirm_add = data['confirm_add']
y2_confirm_add = list(y2_confirm_add)
y2_confirm_add = [int(i) for i in y2_confirm_add]
y3_heal = data['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
y4_dead = data['dead']
y4_dead = list(y4_dead)
y4_dead = [int(i) for i in y4_dead]
date_list = []
for j in data['date']:
date_list.append(j)
x = date_list
lines = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name='确诊人数', y_axis=y1_confirm, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='增加确诊人数', y_axis=y2_confirm_add, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='治愈人数', y_axis=y3_heal, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='死亡人数', y_axis=y4_dead, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.set_global_opts(
title_opts=opts.TitleOpts(title="%s" % (city_name) + "疫情走势", subtitle="数据来源：腾讯新闻"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=10, interval=3)))
)
name1 = name.replace('data/中国各省的城市历史疫情信息\\', 'data/data_DaliyChangeView/中国各省的城市历史疫情信息/')
dwf.createFile(name1)
save_path = name1 + '%s.txt'
# lines.render(save_path % (city_name), index=False)
page1.add(lines) # 将图像加入同一页
if flag:
dwf.write_to_file(save_path % (city_name),
str(lines.dump_options_with_quotes()))
page.render(savaPath + '/中国各省的城市历史疫情信息/analyse_all.html')
def foregin_city_all():
path = readPath + '/各国各地区疫情信息'
path2 = savaPath + '/各国疫情严重程度排名前十地区信息'
dwf.createFile(path2)
Filelist = [] # 将当前文件夹内的所有表名存放此列表
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
# 判断数据是否存在
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
# 随机颜色生成用于制作南丁格尔玫瑰图
def randomcolor(kind):
colors = []
for i in range(kind):
colArr = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F']
color = ""
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append("#" + color)
return colors
for i in Filelist:
page1 = Page()
data = pd.read_excel(readPath + '/各国各地区疫情信息/%s' % (i), index=False)
data_sort = data.sort_values(axis=0, ascending=False, by=['confirm'])
data_message = data_sort.head(10) # 提取疫情严重程度排名前十地区的信息
n = i[:-5] # 只提取国家名，不要后缀（.xlsx）
checknan(data_message['confirm'])
checknan(data_message['heal'])
checknan(data_message['dead'])
y1_confirm = data_message['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_dead = data_message['dead']
y2_dead = list(y2_dead)
y2_dead = [int(i) for i in y2_dead]
y3_heal = data_message['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
name_list = []
for j in data_message['name']:
name_list.append(j)
x = name_list
color_series = randomcolor(len(x))
# Bars = (
# Bar(init_opts=opts.InitOpts(width='1080px',height='700px'))
# .add_xaxis(xaxis_data=x)
# .add_yaxis(series_name='确诊人数', yaxis_data=y1_confirm)
# )
#####画南丁格尔玫瑰图##########
# 画出确诊人数的图
fig = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig.add("", [list(z) for z in zip(x, y1_confirm)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的确诊人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
fig.set_colors(color_series)
# 画出死亡人数的图
fig1 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig1.add("", [list(z) for z in zip(x, y2_dead)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig1.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的死亡人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig1.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
# 画出治愈人数的图
fig2 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig2.add("", [list(z) for z in zip(x, y3_heal)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig2.set_global_opts(title_opts=opts.TitleOpts(title=n + '疫情严重程度排名前十地区的治愈人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig2.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}:{c}例', font_style='italic', font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
page1.add(fig) # 将图像加入同一页
page1.add(fig1) # 将图像加入同一页
page1.add(fig2) # 将图像加入同一页
page1.render(savaPath + '/各国疫情严重程度排名前十地区信息/%s.html' % (n), index=False)
if flag:
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（确诊）%s.txt' % (n),str(fig.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（死亡）%s.txt' % (n),str(fig1.dump_options_with_quotes()))
dwf.write_to_file(savaPath + '/各国疫情严重程度排名前十地区信息/（治愈）%s.txt' % (n),str(fig2.dump_options_with_quotes()))
min_=0,
max_=250,
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#5793f3")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
line = (Line().add_xaxis(x_data).add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
))
bar.overlap(line)
return Grid().add(bar,
opts.GridOpts(pos_left="5%", pos_right="20%"),
is_control_axis_index=True)
page = Page(layout=Page.SimplePageLayout)
# 需要自行调整每个 chart 的 height/width，显示效果在不同的显示器上可能不同
page.add(line_markpoint(), line_markpoint1())
page.render("test.html")
import pandas as pd
import numpy as np
import os,glob,re,random
from pyecharts.charts import Line,Bar,Pie#折线图绘制所需要的库
import pyecharts.options as opts#设置全局变量
from pyecharts.charts import Page#将所有图像放在同一页所需要的库
from util import dealWithFile as dwf
readPath = dwf.rootPath #读取数据的地方
savaPath = dwf.saveDaliyChangeView #生成结果的地方
flag = dwf.flag #是否生成数据
page=Page()#创建一个分页用于放所有的图像在这一分页上
##################中国历史疫情总体信息##############
def china_history_all():
China_all = pd.DataFrame(pd.read_excel(readPath+'/中国总体历史疫情信息/历史总体信息.xlsx')) # 读取信息
CNday_date = list(China_all['date'])
CNday_confirm = list(China_all['confirm'])
CNday_dead = list(China_all['dead'])
CNday_heal = list(China_all['heal'])
line_CNday = (
Line()
.add_xaxis(xaxis_data=CNday_date) # xaxis_data为x轴，y_axis为y轴
.add_yaxis(series_name='确诊', y_axis=CNday_confirm, label_opts=opts.LabelOpts(is_show=False))
.add_yaxis(series_name='死亡', y_axis=CNday_dead, label_opts=opts.LabelOpts(is_show=False))
.add_yaxis(series_name='治愈', y_axis=CNday_heal, label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title='中国疫情历史总体信息', pos_left='50%', pos_top='10%'),
tooltip_opts=opts.TooltipOpts(axis_pointer_type='cross'))
from pyecharts.charts import Bar, Line, Grid, Page
from pyecharts import options as opts
from pyecharts.faker import Faker
x = Faker.choose()
y = Faker.values()
bar = (Bar().add_xaxis(x).add_yaxis("销量", y))
line = (Line().add_xaxis(x).add_yaxis("销量", y))
#grid,并行多图，它可以使得多个图表并行的排成一排
# grid = (
# #设置页面的宽度
# Grid(init_opts=opts.InitOpts(width='1200px'))
# #GridOpts就是为grid布局而生的一个配置项,常用的几个参数pos_top，pos_bottom，pos_left，pos_right
# .add(bar,grid_opts=opts.GridOpts(is_show=True,pos_top="50%",background_color='red',border_color='black'))
# .add(line,grid_opts=opts.GridOpts(is_show=True,pos_bottom='50%',border_color='blue'))
# .render()
# )
#page 顺序多图,有3种布局管理器：默认，SimplePageLayout，GraggablePageLayout
page = (
#布局管理器会自动寻找最佳的布局方案并给与适当的间隔
# Page(layout=Page.SimplePageLayout)
#布局管理器可以拖拽
Page(layout=Page.DraggablePageLayout).add(bar, line).render())
def show_factor(self,
name,
value,
file_html,
freq="30分钟",
open_in_browser=True):
"""查看因子某个值的结果
:param freq:
:param name: str
因子名
:param value: any
因子值
:param file_html: str
:param open_in_browser: bool
:return:
"""
freq_m = {"1分钟": 1, "5分钟": 5, "30分钟": 30}
rows = [x for x in self.factors if x[name] == value]
value1 = [{
"x": s['dt'],
"y": k,
"heat": v
} for s in rows for k, v in s.items() if "百分位" in k]
value2 = [{
"x": s['dt'],
"y": k,
"heat": v
} for s in rows for k, v in s.items() if "均收益" in k]
x_label = [s['dt'] for s in rows]
y_label1 = [k for k, _ in rows[0].items() if "百分位" in k]
y_label2 = [k for k, _ in rows[0].items() if "均收益" in k]
hm1 = heat_map(value1,
x_label=x_label,
y_label=y_label1,
title="{}历史表现评估（N周期区间百分位）".format(name),
width="1400px",
height="480px")
hm2 = heat_map(value2,
x_label=x_label,
y_label=y_label2,
title="{}历史表现评估（N周期区间收益）".format(name),
width="1400px",
height="480px")
bs = [{
"dt": bar_end_time(s['dt'], m=freq_m[freq]),
"mark": "buy",
"price": round(s['close'], 2)
} for s in rows]
chart_kline = self.show_bs(bs,
freq=freq,
width="1400px",
height='480px')
page = Page(layout=Page.DraggablePageLayout,
page_title="{}".format(name))
page.add(hm1, hm2, chart_kline)
if file_html:
page.render(file_html)
if open_in_browser:
webbrowser.open(file_html)
return page
def test_page_render_notebook():
page = Page()
page.add(_create_line(), _create_bar(), _create_table())
html = page.render_notebook().__html__()
assert_in("City name", html)
def get_all_chart():
page = Page()
page.add(get_language_proportion())
page.add(get_salary_proportion())
page.add(get_map())
#
page.add(get_education_background())
page.render("temp/all.html")
# get_education_background()
pass
def china_provinces_history():
page1 = Page()
path2 = savaPath + '/中国各省市历史疫情信息'
dwf.createFile(path2)
path = readPath + '/中国各省市历史疫情信息'
Filelist = []
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
# print(Filelist)
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
for i in Filelist:
data = pd.read_excel(readPath + '/中国各省市历史疫情信息/%s' % (i), index=False)
n = i[:-5]
checknan(data['confirm'])
checknan(data['confirm_add'])
checknan(data['heal'])
checknan(data['dead'])
y1_confirm = data['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_confirm_add = data['confirm_add']
y2_confirm_add = list(y2_confirm_add)
y2_confirm_add = [int(i) for i in y2_confirm_add]
y3_heal = data['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
y4_dead = data['dead']
y4_dead = list(y4_dead)
y4_dead = [int(i) for i in y4_dead]
date_list = []
for j in data['date']:
date_list.append(j)
x = date_list
linse12 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name='确诊人数', y_axis=y1_confirm, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='增加确诊人数', y_axis=y2_confirm_add, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='治愈人数', y_axis=y3_heal, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.add_yaxis(series_name='死亡人数', y_axis=y4_dead, is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"), ]))
.set_global_opts(title_opts=opts.TitleOpts(title="%s" % (n) + "疫情走势", subtitle="数据来源：腾讯新闻"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=10, interval=3)))
)
# lines.render(savaPath + '\\daliy_changes_view\\中国各省市历史疫情信息\\%s.html' % (n), index=False)
page1.add(linse12) # 将图像加入同一页
if flag:
dwf.write_to_file(savaPath + '/中国各省市历史疫情信息/%s.txt' % (n), str(linse12.dump_options_with_quotes()))
page.render(savaPath + '/中国各省市历史疫情信息/analyse_all.html')
def plot_covid_19_world_situation(world_data):
"""
绘制全球新冠疫情情况:
1. 包含新增确诊人数
2. 新增死亡人数
3. 累计确诊人数
4. 累计死亡人数
5. 累计检测人数
6. 全病例比例
Args:
world_data:pd.DataFrame
全球数据
Returns:html
绘制成html
"""
last_date = max(world_data['Date'])
# 1.全球单日新增确诊病例
now_date_confirmed_info = world_data[[
'Difference', 'Country_Region'
]][(world_data[world_data.columns[0]] == 'Confirmed')
& (world_data['Date'] == last_date)]
now_date_confirmed_info = now_date_confirmed_info.groupby(
['Country_Region']).agg({
'Difference': 'sum'
}).reset_index()
# 2.全球单日新增死亡病例
now_date_deaths_info = world_data[[
'Difference', 'Country_Region'
]][(world_data[world_data.columns[0]] == 'Deaths')
& (world_data['Date'] == last_date)]
now_date_deaths_info = now_date_deaths_info.groupby(['Country_Region'
]).agg({
'Difference':
'sum'
}).reset_index()
# 3.全球总共确诊病例
total_confirmed_info = world_data[[
'Difference', 'Country_Region'
]][world_data[world_data.columns[0]] == 'Confirmed']
total_confirmed_info = total_confirmed_info.groupby(['Country_Region'
]).agg({
'Difference':
'sum'
}).reset_index()
# 4.全球总共死亡病例
total_deaths_info = world_data[[
'Difference', 'Country_Region'
]][world_data[world_data.columns[0]] == 'Deaths']
total_deaths_info = total_deaths_info.groupby(['Country_Region']).agg({
'Difference':
'sum'
}).reset_index()
# 四种指标字典
# express_dict = {
# 'day_confirmed':
# {
# 'data': now_date_confirmed_info,
# 'case_type': 'confirmed',
# 'date_type': 'day',
# },
# 'day_deaths':
# {
# 'data': now_date_deaths_info,
# 'case_type': 'deaths',
# 'date_type': 'day',
# },
# 'total_confirmed':
# {
# 'data': total_confirmed_info,
# 'case_type': 'confirmed',
# 'date_type': 'total',
# },
# 'total_deaths':
# {
# 'data': total_deaths_info,
# 'case_type': 'deaths',
# 'date_type': 'total',
# },
# }
express_dict = {
'total_confirmed': {
'data': total_confirmed_info,
'case_type': 'confirmed',
'date_type': 'total',
},
}
for name, info in express_dict.items():
data = info['data']
max_num = max(data['Difference'])
total_num = sum(data['Difference'])
data_pair = [(country, int(confirmed_num)) for country, confirmed_num
in zip(data['Country_Region'], data['Difference'])]
express_dict[name]['max_num'] = max_num
express_dict[name]['total_num'] = total_num
express_dict[name]['data_pair'] = data_pair
# 初始化pages
pages = Page(page_title=f'全球{last_date}情况', layout=opts.PageLayoutOpts())
# 绘制地图
for plot_name, plot_data in express_dict.items():
# 初始化地图
map = Map(init_opts=opts.InitOpts(
width='1000px', height='800px', bg_color="#000f1a"))
# 加载数据:其中maptype可以初始化地图类型,而geo中需要单独设置
if plot_data['case_type'] == 'confirmed':
case_name = '确诊'
else:
case_name = '死亡'
if plot_data['date_type'] == 'day':
date_type = '当日'
else:
date_type = '累计'
map.add(
series_name=f'全球{last_date}{date_type}{case_name}人数分布',
data_pair=plot_data['data_pair'],
maptype='world',
label_opts=opts.LabelOpts(is_show=True, color='white'),
# itemstyle_opts是设置每个项目中标注点的样式 area_color是设置项目的背景色,color是设置项目的点的颜色
itemstyle_opts=opts.ItemStyleOpts(area_color='#2a2a28',
color="#d3d307"))
# 配置
map.set_global_opts(
title_opts=opts.TitleOpts(
title=f'全球{last_date}{date_type}{case_name}人数分布',
subtitle=f"全球新增确诊:{plot_data['total_num']}"),
# # 视觉配置
# visualmap_opts=opts.VisualMapOpts(
# type_='color',
# min_=0,
# max_=plot_data['max_num'],
# ),
# tooltip_opts=opts.TooltipOpts(is_show=True)
# 图形配置
)
# 添加图形系列
pages.add(map)
"""
# 绘制美国和中国每日新增确诊病例趋势线
us_day_confirmed_data = world_data[
(world_data['Country_Region'] == 'United States') & (world_data['Case_Type'] == 'Confirmed')]
us_day_confirmed_grouped = us_day_confirmed_data[['Date', 'Difference']].groupby(['Date']).agg(
{'Difference': 'sum'}).reset_index()
gc.collect()
us_day_confirmed_grouped_x = pd.to_datetime(us_day_confirmed_grouped['Date'], dayfirst=False)
us_day_confirmed_grouped_x = list(us_day_confirmed_grouped_x.apply(lambda x: x.date()))
us_day_confirmed_grouped_dict = {date: confirmed for date, confirmed in
zip(us_day_confirmed_grouped_x, list(us_day_confirmed_grouped['Difference']))}
us_day_confirmed_grouped_x = sorted(us_day_confirmed_grouped_x)
us_day_confirmed_grouped_y = [us_day_confirmed_grouped_dict[date] for date in us_day_confirmed_grouped_x]
total_confirmed_num = sum(us_day_confirmed_grouped_y)
# 绘制折线图
line = Line(init_opts=opts.InitOpts())
# 添加X,Y数据
line.add_xaxis(xaxis_data=us_day_confirmed_grouped_x)
line.add_yaxis(series_name='美国确诊病例趋势', y_axis=us_day_confirmed_grouped_y)
# 添加折线图配置
line.set_global_opts(title_opts=opts.TitleOpts(title='美国新增确诊病例趋势', subtitle=f'美国累计确诊病例{total_confirmed_num}'))
pages.add(line)
# 绘制美国累计确诊病例
us_add_confirmed_grouped = us_day_confirmed_data[['Date', 'Cases']].groupby(['Date']).agg(
{'Cases': 'sum'}).reset_index()
us_add_confirmed_grouped_x = pd.to_datetime(us_add_confirmed_grouped['Date'], dayfirst=False)
us_add_confirmed_grouped_x = list(us_add_confirmed_grouped_x.apply(lambda x: x.date()))
us_add_confirmed_grouped_dict = {date: case_num for date, case_num in
zip(us_add_confirmed_grouped_x, list(us_add_confirmed_grouped['Cases']))}
us_add_confirmed_grouped_x = sorted(us_add_confirmed_grouped_x)
us_add_confirmed_grouped_y = [us_add_confirmed_grouped_dict[date] for date in us_add_confirmed_grouped_x]
# 绘制折线图
line = Line(init_opts=opts.InitOpts())
# 添加X,Y数据
line.add_xaxis(xaxis_data=us_add_confirmed_grouped_x)
line.add_yaxis(series_name='美国累计确诊病例趋势', y_axis=us_add_confirmed_grouped_y)
# 添加折线图配置
line.set_global_opts(title_opts=opts.TitleOpts(title='美国累计确诊病例趋势'))
pages.add(line)
"""
# 输出pages
pages.render('全球新冠疫情情况.html')
def draw_multiple_map(month, day):
page = Page(layout=Page.SimplePageLayout)
city_map = {
"西双版纳": "西双版纳傣族自治州",
"大理": "大理白族自治州",
"红河": "红河哈尼族自治州",
"德宏": "德宏傣族景颇族自治州",
"甘孜州": "甘孜藏族自治州",
"凉山": "凉山彝族自治州",
"阿坝州": "阿坝藏族羌族自治州",
"黔东南州": "黔东南苗族侗族自治州",
"黔西南州": "黔西南依族苗族自治州",
"黔南州": "黔南布依族苗族自治州",
"湘西自治州": "湘西土家族苗族自治州",
"恩施": "恩施土家族苗族自治州"
}
for p in get_province_data(month, day):
title = '%s-%d例' % (p['provinceShortName'], p['confirmedCount'])
# 城市
labels = []
for city in p['cities']:
city_name = city['cityName']
if p['provinceShortName'] in ['重庆', '上海', '北京', '天津']:
labels.append(city_name)
elif city_name in city_map:
labels.append(city_map[city_name])
else:
labels.append(city_name + "市")
# 数量
counts = [city['confirmedCount'] for city in p['cities']]
if len(labels) == 0:
continue
province_map = Map(
init_opts=opts.InitOpts(width='350px', height='380px'))
province_map.add("", [list(z) for z in zip(labels, counts)],
p['provinceShortName'])
province_map.set_series_opts(label_opts=opts.LabelOpts(font_size=8))
province_map.set_global_opts(
title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(pieces=[
{
'min': 1000,
'color': '#450704'
},
{
'max': 999,
'min': 500,
'color': '#75140B'
},
{
'max': 499,
'min': 200,
'color': '#AD2217'
},
{
'max': 199,
'min': 10,
'color': '#DE605B'
},
{
'max': 9,
'color': '#FFFEE7'
},
],
is_piecewise=True,
is_show=False),
)
page.add(province_map)
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
page.render('%s/省份地图.html' % root)
site_counts_211 = df_211['site'].value_counts()
dict_site_211 = {'name': site_counts_211.index, 'counts': site_counts_211.values}
data_211 = pd.DataFrame(dict_site_211)
sum_211_counts = data_211['counts'].sum()
data_211['rate'] = data_211['counts'].apply(lambda x: x / sum_211_counts)
df_985_211 = pd.concat([df_211, df_985], ignore_index=True)
site_counts_985_211 = df_985_211['site'].value_counts()
dict_site_985_211 = {'name': site_counts_985_211.index, 'counts': site_counts_985_211.values}
data_985_211 = pd.DataFrame(dict_site_985_211)
sum_985_211_counts = data_985_211['counts'].sum()
data_985_211['rate'] = data_985_211['counts'].apply(lambda x: x / sum_985_211_counts)
# 柱状图页面
page = Page()
# 各地区高校数量柱状图
bar = Bar()
bar.add_xaxis(data['name'].values.tolist())
bar.add_yaxis("", data['counts'].values.tolist())
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
title_opts=opts.TitleOpts(title="各地区高校数量"),
datazoom_opts=opts.DataZoomOpts()
)
bar.set_series_opts(itemstyle_opts={
"normal": {
"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
min_=0,
max_=2000,
pos_top='center',
), #调整图例参数
title_opts=opts.TitleOpts(title="全国Java招聘数量分布图",
pos_left='center')))
return c
def geo_heatmap() -> Geo:
c = (Geo().add_schema(maptype="china").add(
"",
[list(z) for z in zip(citys[:120], City_Avg_Salary[:120])],
type_=ChartType.HEATMAP,
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
pos_top='center',
min_=0,
max_=15000,
),
title_opts=opts.TitleOpts(title="全国Java薪资分布图", pos_left='center'),
))
return c
page = Page()
page.add(geo_base(), geo_heatmap())
page.render('全国岗位薪资分布图.html')
def pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
data = chart_dao.workflow_by_date(30)
today = date.today()
one_month_before = today - relativedelta(days=+30)
attr = chart_dao.get_date_list(one_month_before, today)
_dict = {}
for row in data['rows']:
_dict[row[0]] = row[1]
value = [_dict.get(day) if _dict.get(day) else 0 for day in attr]
bar1 = Bar()
bar1.add_xaxis(attr)
bar1.add_yaxis("月统计", value)
bar1.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(数量)'),
datazoom_opts=opts.DataZoomOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(is_show=True))
# 工单按组统计
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1 = Pie()
pie1.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(组)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie1.add("", [list(z) for z in zip(attr, value)])
# 工单按人统计
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2 = Bar()
bar2.add_xaxis(attr)
bar2.add_yaxis("月统计", value)
bar2.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(用户)'))
# SQL语句类型统计
data = chart_dao.syntax_type()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2 = Pie()
pie2.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(类型)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie2.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie2.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(每日检索行数)
attr = chart_dao.get_date_list(one_month_before, today)
effect_data = chart_dao.querylog_effect_row_by_date(30)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [
effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr
]
count_data = chart_dao.querylog_count_by_date(30)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [
count_dict.get(day) if count_dict.get(day) else 0 for day in attr
]
line1 = Line()
line1.set_global_opts(title_opts=opts.TitleOpts(title='SQL查询统计'),
legend_opts=opts.LegendOpts(selected_mode='single'))
line1.add_xaxis(attr)
line1.add_yaxis("检索行数",
effect_value,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="average")]))
line1.add_yaxis("检索次数",
count_value,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average")
]))
# SQL查询统计(用户检索行数)
data = chart_dao.querylog_effect_row_by_user(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4 = Pie()
pie4.set_global_opts(title_opts=opts.TitleOpts(title='SQL查询统计(用户检索行数)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie4.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie4.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(DB检索行数)
data = chart_dao.querylog_effect_row_by_db(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5 = Pie()
pie5.set_global_opts(title_opts=opts.TitleOpts(title='SQL查询统计(DB检索行数)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie5.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}", position="left"))
pie5.add("", [list(z) for z in zip(attr, value)])
# 可视化展示页面
page = Page(layout=Page.SimplePageLayout)
page.add(bar1, pie1, bar2, pie2, line1, pie4, pie5)
return render(request, "dashboard.html", {"chart": page.render_embed()})
def theme_walden() -> Bar:
c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.WALDEN)).add_xaxis(
Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).add_yaxis("商家C", Faker.values()).add_yaxis(
"商家D", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("Theme-walden")))
return c
@C.funcs
def theme_westeros() -> Bar:
c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS)).add_xaxis(
Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).add_yaxis("商家C", Faker.values()).add_yaxis(
"商家D", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("Theme-westeros")))
return c
@C.funcs
def theme_wonderland() -> Bar:
c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND)).add_xaxis(
Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).add_yaxis("商家C", Faker.values()).add_yaxis(
"商家D", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("Theme-wonderland")))
return c
put_html(Page().add(*[fn() for fn, _ in C.charts]).render_notebook())
data = [('广东', 54558), ('台湾', 49564), ('上海', 37677), ('北京', 14690),
('浙江', 9062), ('江苏', 4226), ('河北', 4216), ('天津', 4017), ('福建', 2358),
('香港', 1667), ('山东', 1427), ('江西', 1119), ('湖南', 863), ('河南', 847),
('湖北', 377), ('黑龙江', 341), ('海南', 226), ('四川', 101), ('山西', 38),
('安徽', 36), ('辽宁', 34), ('陕西', 32), ('贵州', 28), ('吉林', 27), ('重庆', 1)]
class Collector:
charts = []
@staticmethod
def funcs(fn):
Collector.charts.append((fn, fn.__name__))
C = Collector()
@C.funcs
def map_world() -> Map:
c = (Map().add("中国省份分布", data, "china").set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（连续型）"),
visualmap_opts=opts.VisualMapOpts(max_=54558),
))
return c
Page().add(*[fn()
for fn, _ in C.charts]).render('./output/map_china_province.html')
"%s_vehicles" % site_list[i]))
y_data_duration.append(
generate_y_axis(x_data, res_duration_list[i],
"%s_duration" % site_list[i]))
y_data_distance.append(
generate_y_axis(x_data, res_distance_list[i],
"%s_distance" % site_list[i]))
# sys.stdout.write("Type of the whole result is: ")
# 使用stdout可以不换行输出
# print(type(res_vehicle_disney))
# sys.stdout.write("Type of the a single result is: ")
# print(type(res_vehicle_disney[0]))
# sys.stdout.write("The result is: \n")
# print(res_vehicle_disney)
# print(type(res_vehicle_disney[0]['yearweek(begin_timestamp,1)']))
# print(x_data)
# print(x_data_temp)
page = Page()
line1 = draw_test(x_data_temp, y_data_vehicle, "每周投入车辆（单位：辆）", "投入车辆数")
page.add(line1)
line2 = draw_test(x_data_temp, y_data_duration, "每周总测试时间（单位：小时）", "测试小时数")
page.add(line2)
line3 = draw_test(x_data_temp, y_data_distance, "每周总测试里程（单位：公里）", "公里")
page.add(line3)
page.render("111.html")
def page_layout1() -> Page:
page = Page(layout=Page.SimplePageLayout)
page.add(sex_distribution_bar(), sex_distribution_pie())
return page
datazoom_opts=opts.DataZoomOpts(range_start= start,range_end= 100))
#.set_global_opts()
#.render("China_1_Daily_C_vs_P.html")
)
e= (
Line()
.add_xaxis(mydate)
.add_yaxis("Positive", pos,is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),)
.add_yaxis("Negative", neg,is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),)
.set_global_opts(title_opts=opts.TitleOpts(title="Weekly Media Effect In HongKong"),
#tooltip_opts=opts.TooltipOpts(is_show=False),
#legend_opts=opts.LegendOpts(textstyle_opts={"fontSize": 20}),
#graphic_textstyle_opts = opts.GraphicTextStyleOpts(font='2em "STHeiti", sans-serif'),
datazoom_opts=opts.DataZoomOpts(range_start= start,range_end= 100))
#.set_global_opts()
#.render("China_1_Daily_C_vs_P.html")
)
page = Page(layout=Page.SimplePageLayout)
page.add(c,d,e)
page.render('./res/'+str(time.time())+'WeeklySuicide.html')
from example.commons import Collector, Faker
from pyecharts import options as opts
from pyecharts.charts import Bar, Page
"""
Created on 19-5-09
@title: ''
@author: XuChao
"""
# 直方图
if __name__ == "__main__":
C = Collector()
@C.funcs
def bar_base() -> Bar:
num_people_py = [37, 240, 1, 22]
# java
num_people_java = [44, 239, 2, 15]
degree = ['大专', '本科', '硕士', '不限']
c = (Bar().add_xaxis(degree).add_yaxis(
"java", num_people_java).add_yaxis(
"python",
num_people_py).set_global_opts(title_opts=opts.TitleOpts(
title="对学历要求", subtitle="数据由拉勾网提供")))
return c
Page().add(*[fn() for fn, _ in C.charts]).render("d1.html")
},
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")],
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
visualmap_opts=opts.VisualMapOpts(max_=120))
data_pair = [list(z) for z in zip(x, y)]
data_pair.sort(key=lambda x: x[1])
pie = Pie()
pie.add(
series_name="电影国家分布",
data_pair=data_pair[:],
radius="70%",
)
pie.set_global_opts(
title_opts=opts.TitleOpts(title="电影国家分布饼状图"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_left="5%",
pos_top="20%"),
# visualmap_opts=opts.VisualMapOpts(max_=120)
)
pie.set_series_opts(tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"))
page = Page(layout=Page.SimplePageLayout)
page.add(pie)
page.add(bar)
page.render('templates/test.html')
data = []
for i in range(101):
theta = i / 100 * 360
r = 5 * (1 + math.sin(theta / 180 * math.pi))
data.append([r, theta])
hour = [i for i in range(1, 25)]
c = (Polar().add_schema(angleaxis_opts=opts.AngleAxisOpts(
data=hour, type_="value", boundary_gap=False, start_angle=0)).add(
"love", data,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Polar-Love")))
return c
@C.funcs
def polar_flower() -> Polar:
data = []
for i in range(361):
t = i / 180 * math.pi
r = math.sin(2 * t) * math.cos(2 * t)
data.append([r, i])
c = (Polar().add_schema(
angleaxis_opts=opts.AngleAxisOpts(start_angle=0, min_=0)).add(
"flower", data,
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Polar-Flower")))
return c
Page().add(*[fn() for fn, _ in C.charts]).render()
def test_page_jshost_default():
bar = _create_bar()
line = _create_line()
page = Page().add(bar, line)
assert_equal(page.js_host, "https://assets.pyecharts.org/assets/")
