def order_chart():
# 今日各状态订单总金额
pie2 = Pie()
pie2.add("", list(order_status_total.items()))
pie2.set_global_opts(title_opts=opts.TitleOpts(title="今日各状态订单总金额"))
pie2.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
return pie2.dump_options_with_quotes()
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()))
