from pyecharts import options as opts
# 导入模块
from pyecharts.charts import Pie
# 导入theme
from pyecharts.globals import ThemeType
import pandas as pd
# 加载数据
data = pd.read_excel('./Python地区分校人数.xlsx')
print('data:\n', data)
# 绘制饼图
pie = Pie()
pie.add(
series_name='分校人数',
data_pair=data.values.tolist(), #
radius=[
'50%',
'75%' # 内外半径 内径 外径
])
# 添加全局配置
pie.set_global_opts(
title_opts=opts.TitleOpts(title='分校占比图', pos_left='center'),
legend_opts=opts.LegendOpts(is_show=False),
)
# 添加系列配置
# 建立柱状图
bar_3 = (
Bar()
.add_xaxis(x)
.add_yaxis('', y)
.set_global_opts(
title_opts=opts.TitleOpts(title='用户一周内各天领卷数量累计'), # title
)
)
######################
# 优惠卷类型比例
v1=['折扣','满减']
v2=list(offline[offline['Date_received'].notnull()]['is_manjian'].value_counts(True))
pie_1=(
Pie()
.add("",[list(v) for v in zip(v1,v2)])
.set_global_opts(title_opts={"text":"各类优惠卷数量占比饼图"})
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)
path = './tmp/task2_output'
if not os.path.exists(path):
os.makedirs(path)
# render会生成本地HTML文件，默认在当前目录生成render.html文件
bar_1.render(path + '/bar_1.html')
bar_2.render(path + '/bar_2.html')
bar_3.render(path + '/bar_3.html')
pie_1.render(path + '/pie_1.html')
def grid_test():
Mode_pie = (
Pie()
.add("Mode",
df_mode.values.tolist(),
center=["12%", "35%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(is_show=False, position="center") )
.set_colors(["#fc636b", "#ffb900", "#6a67ce", "#50667f", "#1aafd0", "#3be8b0"])
.set_global_opts(title_opts=opts.TitleOpts(title="Mode", pos_left="10%", pos_top="30%"),
legend_opts=opts.LegendOpts(orient="vertical", pos_left="0%", pos_top="10%") )
.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)") )
)
Language_pie = (
Pie()
.add("Language",
df_Language.values.tolist(),
center=["32%", "35%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(is_show=False, position="center") )
.set_colors(["#fc636b", "#ffb900", "#6a67ce", "#50667f", "#1aafd0", "#3be8b0"])
.set_global_opts(title_opts=opts.TitleOpts(title="Language", pos_left="28.7%", pos_top="30%"),
legend_opts=opts.LegendOpts(orient="vertical", pos_left="20%", pos_top="10%") )
.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)") )
)
Genre_pie = (
Pie()
.add("Genre",
df_genre.values.tolist(),
center=["52%", "35%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(is_show=False, position="center") )
.set_colors(["#fc636b", "#ffb900", "#6a67ce", "#50667f", "#1aafd0", "#3be8b0"])
.set_global_opts(title_opts=opts.TitleOpts(title="Genre", pos_left="50%", pos_top="30%"),
legend_opts=opts.LegendOpts(orient="vertical", pos_left="40%", pos_top="10%") )
.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)") )
)
Length_pie = (
Pie()
.add("Length",
df_length.values.tolist(),
center=["72%", "35%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(is_show=False, position="center") )
.set_colors(["#fc636b", "#ffb900", "#6a67ce", "#50667f", "#1aafd0", "#3be8b0"])
.set_global_opts(title_opts=opts.TitleOpts(title="Length", pos_left="69.6%", pos_top="30%"),
legend_opts=opts.LegendOpts(orient="vertical", pos_left="60%", pos_top="10%") )
.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)") )
)
Star_Rating_pie=(
Pie()
.add("Star Rating",
df_star.values.tolist(),
center=["92%", "35%"],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(is_show=False, position="center") )
.set_colors(["#fc636b", "#ffb900", "#6a67ce", "#50667f", "#1aafd0", "#3be8b0"])
.set_global_opts(title_opts=opts.TitleOpts(title="Star Rating", pos_left="88.4%", pos_top="30%"),
legend_opts=opts.LegendOpts(orient="vertical", pos_left="80%", pos_top="10%") )
.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)") )
)
return Grid(init_opts=opts.InitOpts(width="1500px", height="250px")).add(Mode_pie, grid_opts=opts.GridOpts()).add(Language_pie, grid_opts=opts.GridOpts()).add(Genre_pie, grid_opts=opts.GridOpts()).add(Length_pie, grid_opts=opts.GridOpts()).add(Star_Rating_pie, grid_opts=opts.GridOpts())
def analyse_worldhistory(self):
world=pd.DataFrame(pd.read_excel(self.file+'\\data\\世界总体疫情信息\\世界总体疫情信息.xlsx'))
confirmAdd=int(world['confirmAdd'].sum())
confirm=int(world['confirm'].sum())
confirmAddCut=int(world['confirmAddCut'].sum())
suspect=int(world['suspect'].sum())
dead=int(world['dead'].sum())
heal=int(world['heal'].sum())
nowConfirm=int(world['nowConfirm'].sum())
confirmCompare=int(world['confirmCompare'].sum())
nowConfirmCompare=int(world['nowConfirmCompare'].sum())
healCompare=int(world['healCompare'].sum())
deadCompare=int(world['deadCompare'].sum())
healRate=round(heal/confirm*100,2)
deadRate=round(dead/confirm*100,2)
aliveRate=round(100-healRate-deadRate,2)
ratedata=[deadRate,healRate,aliveRate]
ratecolumns=['死亡率/% ','治愈率/% ','存活率/% ']
bar_world=\
(
Bar()
.add_xaxis(" ")
.add_yaxis('新增确诊',[confirmAdd],color='pink',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('累计确诊',[confirm],color='green',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('疑似病例',[suspect],color='black',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('死亡病例',[dead],color='orange',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('治愈病例',[heal],color='red',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('现有确诊',[nowConfirm],color='grey',label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(title_opts=opts.TitleOpts(title='世界疫情信息'),
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(name='日期',axislabel_opts={"interval":"0"}),
yaxis_opts=opts.AxisOpts(name='人数',splitline_opts=opts.SplitLineOpts(is_show=True),)
)
)#柱形图
bar_worldother=\
(
Bar()
.add_xaxis(" ")
.add_yaxis('confirmAddCut',[confirmAddCut],color='pink',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('confirmCompare',[confirmCompare],color='green',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('nowConfirmCompare',[nowConfirmCompare],color='orange',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('healCompare',[healCompare],color='black',label_opts=opts.LabelOpts(is_show=True))
.add_yaxis('deadCompare',[deadCompare],color='red',label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(title_opts=opts.TitleOpts(title='世界疫情其他信息',pos_left='37%',pos_top='5%'),
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(name='日期',axislabel_opts={"interval":"0"}),
yaxis_opts=opts.AxisOpts(name='人数',splitline_opts=opts.SplitLineOpts(is_show=True),)
)
) #柱形图
pie_worldrate=\
(
Pie()
.add("", [list(z) for z in zip(ratecolumns,ratedata)],
radius=["20%", "60%"],
center=["55%", "50%"],
rosetype="area"
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="inside", font_size=12,formatter="{b}:{c}", font_style="italic",font_weight="bold", font_family="Microsoft YaHei"),)
.set_global_opts(title_opts=opts.TitleOpts(title='世界总的死亡率、治疗率、存活率'),
tooltip_opts=opts.TooltipOpts(axis_pointer_type= 'cross'),
xaxis_opts=opts.AxisOpts(name='日期'),
yaxis_opts=opts.AxisOpts(name='百分比'))
)#饼图
self.page.add(bar_world)#添加到同一页面
self.page.add(bar_worldother)#添加到同一页面
self.page.add(pie_worldrate)#添加到同一页面
bar_world.render(self.path+'\\world.html')#在analysis生成html文件
bar_worldother.render(self.path+'\\worldother.html')#在analysis生成html文件
pie_worldrate.render(self.path+'\\worldrate.html')#在analysis生成html文件
"""
Pie()
.add(
series_name="访问来源",
data_pair=inner_data_pair,
radius=[0, "30%"],
label_opts=opts.LabelOpts(position="inner"),
)
.add(
series_name="访问来源",
radius=["40%", "55%"],
data_pair=outer_data_pair,
label_opts=opts.LabelOpts(
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {"color": "#999", "lineHeight": 22, "align": "center"},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {"fontSize": 16, "lineHeight": 33},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
)
.set_global_opts(legend_opts=opts.LegendOpts(pos_left="left", orient="vertical"))
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
)
)
.render("nested_pies.html")
'max': 99,
'min': 10,
'color': '#f27777'
},
{
'max': 9,
'min': 1,
'color': '#f7adad'
},
{
'max': 0,
'color': '#f7e4e4'
},
]
m = Pie()
m.add("累计确诊", [list(z) for z in zip(labels, counts)],
center=["50%", "70%"],
radius='55%')
#m.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
m.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情饼图（COVID-19 CHINA）" + today),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="85%"),
visualmap_opts=opts.VisualMapOpts(
pieces=pieces,
is_piecewise=True, #是否为分段型
is_show=True)) #是否显示视觉映射配置)
m.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
def get_year_chart(year: str):
map_data = [
[[x["name"], x["value"]] for x in d["data"]] for d in MapData if d["time"] == year
][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (
Map()
.add(
series_name="",
data_pair=map_data,
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {"areaColor": "#323c48", "borderColor": "#404a59"},
"emphasis": {
"label": {"show": Timeline},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="" +
str(year) + "全国各省份NCP实时动态(数据来源:丁香园; 数据仓库:BlankerL/DXY-2019-nCoV-Data)",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
line_chart = (
Line()
.add_xaxis(time_list)
.add_yaxis("", total_num)
.add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(
title="全国各省份NCP实时动态(单位: 百人)", pos_left="72%", pos_top="5%"
)
)
)
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (
Bar()
.add_xaxis(xaxis_data=bar_x_data)
.add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(
is_show=True, position="right", formatter="{b} : {c}"
),
)
.reversal_axis()
.set_global_opts(
xaxis_opts=opts.AxisOpts(
max_=maxCount, axislabel_opts=opts.LabelOpts(is_show=False)
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (
Pie()
.add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="rgba(0,0,0,0.5)"
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
)
)
grid_chart = (
Grid()
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="5"
),
)
.add(
line_chart,
grid_opts=opts.GridOpts(
pos_left="65%", pos_right="80", pos_top="10%", pos_bottom="50%"
),
)
.add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%"))
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
sum_price = 0
number = len(price_list)
for price in price_list:
sum_price += int(price[0].replace('元/㎡', ''))
average = sum_price / number
average_list.append(format(average, '.2f'))
return average_list
price_list = select_average_price()
print(price_list)
# 柱状图
bar_obj = (
Bar(init_opts=options.InitOpts(theme=ThemeType.MACARONS)) \
.add_xaxis((city_list)) \
.add_yaxis("", price_list) \
.set_global_opts(title_opts=options.TitleOpts("买房平均价格元/㎡", subtitle='仅供参考'),
toolbox_opts=options.ToolboxOpts(), # 工具栏选项
brush_opts=options.BrushOpts() # 工具刷对象
)
)
# 饼状图
b_list = [list(i) for i in zip(city_list, price_list)]
pie_ogj = (Pie().add("", b_list).set_series_opts(label_opts=options.LabelOpts(
formatter="{b}: {c}")))
# page对象用于整合地图
p = Page(layout=Page.SimplePageLayout)
p.add(pie_ogj, bar_obj)
p.render("一线城市买房信息整合.html")
from pyecharts.charts import Pie
import pyecharts.options as opts
import pandas as pd
vote_result = pd.read_csv('vote_result.csv')
'''
Pie要求输入的数据格式为
[
[name1, value1],
[name2, value2],
[name3, value3],
...
]
故需要转换格式
'''
data = [list(z) for z in zip(vote_result['感兴趣的领域'], vote_result['票数'])]
pie = Pie()
pie.add('', data)
pie.set_global_opts(title_opts=opts.TitleOpts(title='饼图示例'),
legend_opts=opts.LegendOpts(is_show=False))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter='{b}：{d}%'))
pie.render('pie(1).html')
# 5.绘图
(
Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK))
.add(
"",
result,
radius=["30%","75%"],
center=["50%", "50%"],
rosetype="area"
)
# 全局配置项
.set_global_opts(
title_opts=opts.TitleOpts(
title="户型分布",
pos_left="center",
pos_top="20v",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# 系列配置项
.set_series_opts(
label_opts=opts.LabelOpts(
formatter="{b}:{d}%"
),
)
.render("户型分布.html")
)
labels1 = [
'0-100', '100-1000', '1000-5000', '5000-10000', '10000-100000', '100000以上'
]
sizes1 = [
len(df[(df['粉丝'] <= 100)]),
len(df[(df['粉丝'] >= 100) & (df['粉丝'] < 1000)]),
len(df[(df['粉丝'] >= 1000) & (df['粉丝'] < 5000)]),
len(df[(df['粉丝'] >= 5000) & (df['粉丝'] < 10000)]),
len(df[(df['粉丝'] >= 10000) & (df['粉丝'] < 100000)]),
len(df[(df['粉丝'] >= 100000)])
]
print(sizes1)
c = (Pie(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)).add(
"", [list(z) for z in zip(labels1, sizes1)]).set_global_opts(
title_opts=opts.TitleOpts(title="知乎用户粉丝数量分布图", pos_bottom='center'),
legend_opts=opts.LegendOpts(pos_left="15%"),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%")))
c.render("知乎用户粉丝数量分布图.html")
#知乎用户获赞数分布图
labels2 = [
'0-100', '100-1000', '1000-5000', '5000-10000', '10000-100000', '100000以上'
]
sizes2 = [
len(df[(df['获赞'] <= 100)]),
len(df[(df['获赞'] >= 100) & (df['获赞'] < 1000)]),
len(df[(df['获赞'] >= 1000) & (df['获赞'] < 5000)]),
len(df[(df['获赞'] >= 5000) & (df['获赞'] < 10000)]),
len(df[(df['获赞'] >= 10000) & (df['获赞'] < 100000)]),
len(df[(df['获赞'] >= 100000)])
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c = (
Pie()
.add(
"",
[
list(z)
for z in zip(
Faker.choose() + Faker.choose() + Faker.choose(),
Faker.values() + Faker.values() + Faker.values(),
)
],
center=["40%", "50%"],
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-Legend 滚动"),
legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("pie_scroll_legend.html")
)
Pie(init_opts=opts.InitOpts(
bg_color='#031f2d',
))
.set_global_opts(
title_opts=opts.TitleOpts(
title="%d年\n\n不同空军机型统计" % (years[i],),
pos_left='center',
pos_top='40%',
padding=[24, 0],
title_textstyle_opts=opts.TextStyleOpts(
color='#fff',
font_size=18,
align='center'
)
),
legend_opts=opts.LegendOpts(
type_="scroll",
pos_left="80%",
orient="vertical",
textstyle_opts=opts.TextStyleOpts(
color='#fff',
font_size=16,
rich=rich
),
)
)
.add(
"xilie1",
datalist[i],
radius=['42%', '50%'],
label_opts=opts.LabelOpts(
position="outside",
rich=rich,
formatter="{white|{b}} \n{hr|}\n{yellow|{c}}\n{blue|{d}%}"
# 参数说明：{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）
)
)
def WriteImgTxt(IPdata,filename):
alivehosts = len(IPdata)
openports = 0
weburls = 0
portdict = {}
servicedict = {}
for i in IPdata:
weburls += (len(i.get('urls')))
openports += (len(i.get('ports')))
service = (i.get('services'))
for k, v in service.items():
portdict[k] = 0
servicedict[v.upper()] = 0
for i in IPdata:
service = (i.get('services'))
for k, v in service.items():
portdict[k] = portdict[k] + 1
servicedict[v.upper()] = servicedict[v.upper()] + 1
inner_x_data = ["存活主机", "开放端口", "部署网站"]
inner_y_data = [alivehosts, openports, weburls]
inner_data_pair = [list(z) for z in zip(inner_x_data, inner_y_data)]
mid_data_pair = list(portdict.items())
outer_data_pair = list(servicedict.items())
c=(
Pie(init_opts=opts.InitOpts(width="2200px", height="900px"))
.add(
series_name="总体资产",
data_pair=inner_data_pair,
radius=[0, "20%"],
label_opts=opts.LabelOpts(position="inner",formatter="{b}:{c}个"),
)
.add(
series_name="开放端口",
data_pair=mid_data_pair,
radius=["25%", "50%"],
label_opts=opts.LabelOpts(position="inner",formatter="端口:{b}|总数:{c}"),
)
.add(
series_name="部署服务",
radius=["55%", "80%"],
data_pair=outer_data_pair,
label_opts=opts.LabelOpts(formatter="{a}:{b}|占比:{d}%"),
)
.set_global_opts(legend_opts=opts.LegendOpts(pos_left="mid", orient="vertical"))
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
)
)
.render(filename)
)
# words = mid_data_pair + outer_data_pair
# c = (
# WordCloud(init_opts=opts.InitOpts(width="1200px", height="800px"))
# .add("", words, word_size_range=[30, 80])
# # .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-基本示例"))
# ).render('test.txt')
if os.path.exists(filename):
with open(filename, 'r', encoding='utf-8')as a:
res1 = re.search('(<body>.*?</body>)', a.read(), re.S | re.I).group(1)
os.remove(filename)
return res1
# with open('test.txt', 'r', encoding='utf-8')as a:
# res2 = re.search('(<body>.*?</body>)', a.read(), re.S | re.I).group(1)
# os.remove('test.txt')
# res3 = '<div class="col-sm-6">{}</div><div class="col-sm-6">{}</div>'.format(res1,res2)
# return res3
else:
Log('生成效果图失败')
def drawpie(attr,value,name):
list1 = [list(z) for z in zip(attr,value)]
# 图表初始化配置
init_opts = opts.InitOpts(page_title=name)
pie = Pie(init_opts=init_opts)
# 标题配置
title = opts.TitleOpts(title=name,
pos_left='center')
# 图例配置
legend_opts = opts.LegendOpts(orient="vertical",
pos_top="20%",
pos_left="15%")
# 工具箱配置
# feature = opts.ToolBoxFeatureOpts(save_as_image=True, restore=True, data_view=True, data_zoom=True)
# 工具箱配置
toolbox_opts = opts.ToolboxOpts(orient="vertical",
pos_top="25%",
pos_right="15%"
)
pie.set_global_opts(title_opts=title,
legend_opts=legend_opts,
toolbox_opts=toolbox_opts
)
# 标签配置项
pie.add("",
list1,
radius=[30, 75],
center=['50%', '70%'],
rosetype="area",
label_opts=opts.LabelOpts(
position="outside",
formatter="{b|{b}: }{c} {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {"color": "#999", "lineHeight": 22, "align": "center"},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {"fontSize": 16, "lineHeight": 33},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
)
pie.render('{0}.html'.format(name))
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: yanghuizhi
# Time: 2020/3/15 6:59 下午
# // 导入饼图Pie
# // 设置主标题与副标题，标题设置居中，设置宽度为900
from pyecharts.charts import Pie
pie = Pie()
# // 加入数据，设置坐标位置为【25，50】，上方的colums选项取消显示
# pie.add("降水量", data_pair= /center=[25, 50] )
# // 加入数据，设置坐标位置为【75，50】，上方的colums选项取消显示，显示label标签
# pie.add("蒸发量", columns, data2, center=[75, 50], is_legend_show=False, is_label_show=True)
#
# // 保存图表
pie.render()
df = pd.DataFrame(color, columns=['color'])
analyse_color = df['color'].value_counts()
# 颜色可视化
bar = Bar()
bar.add_xaxis(analyse_color.index.values.tolist())
bar.add_yaxis("", analyse_color.values.tolist())
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="颜色分布"),
# datazoom_opts=opts.DataZoomOpts(),
)
bar.render_notebook()
pie = Pie()
pie.add("", [
list(z) for z in zip(analyse_color.index.values.tolist(),
analyse_color.values.tolist())
],
radius=["30%", "75%"],
center=["50%", "50%"],
rosetype="area")
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
pie.set_global_opts(title_opts=opts.TitleOpts(title="各颜色占比"),
legend_opts=opts.LegendOpts(type_="scroll",
pos_left="80%",
orient="vertical"))
pie.render_notebook()
# 尺寸可视化
def Pie1():
pie = (Pie().add("", newdata).set_global_opts(
title_opts=opts.TitleOpts(title="Pie-演员"),
legend_opts=opts.LegendOpts(pos_left=160)).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")))
return pie
# 此处不可以用索引访问：row[0]
dataX.append(row["category_name"])
dataY.append(row["电影类型"])
return dataX, dataY
except:
print("错误：数据查询操作失败")
finally:
connection.close()
# 执行主函数
if __name__ == '__main__':
print(order_category_sum_query())
dataX, dataY = order_category_sum_query()
data_pair = [list(z) for z in zip(dataX, dataY)]
pie = Pie()
pie.add("", data_pair)
pie.set_global_opts(
title_opts=opts.TitleOpts(title="商品类型构成图"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_right="-4.5%"),
)
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"),
position="outside",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4)
pie.render('pie.html')
1、迷之颜色映射的问题
"""
x_data = ["直接访问", "邮件营销", "联盟广告", "视频广告", "搜索引擎"]
y_data = [335, 310, 274, 235, 400]
data_pair = [list(z) for z in zip(x_data, y_data)]
data_pair.sort(key=lambda x: x[1])
(Pie(init_opts=opts.InitOpts(
width="1600px", height="800px", bg_color="#2c343c")).add(
series_name="访问来源",
data_pair=data_pair,
rosetype="radius",
radius="55%",
center=["50%", "50%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Customized Pie",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
).set_series_opts(
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{a} <br/>{b}: {c} ({d}%)"),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
).render("customized_pie.html"))
# In[11]:
risk = ['01 위험회피형','02 위험중립형','03 위험감수형']
color = ['#fc6472', '#f4b2a6', '#eccdb3', '#bcefd0', '#a1e8e4', '#23c8b2', '#7f5a7c']
label = label_opts=opts.LabelOpts(color=["black"], formatter="{b}:\n{d}% \n ", font_size = 11)
h_20 = list(high_20.risk.value_counts().sort_index())
m_20 = list(middle_20.risk.value_counts().sort_index())
l_20 = list(low_20.risk.value_counts().sort_index())
h_30 = list(high_30.risk.value_counts().sort_index())
m_30 = list(middle_30.risk.value_counts().sort_index())
l_30 = list(low_30.risk.value_counts().sort_index())
tl = Timeline()
pie_0 = (Pie().add("", [list(z) for z in zip(risk, l_20)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 20대")).set_series_opts(label))
tl.add(pie_0, "20대 자산 하위그룹")
pie_1 = (Pie().add("", [list(z) for z in zip(risk, m_20)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 20대")).set_series_opts(label))
tl.add(pie_1, "20대 자산 중위그룹")
pie_2 = (Pie().add("", [list(z) for z in zip(risk, h_20)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 20대")).set_series_opts(label))
tl.add(pie_2, "20대 자산 상위그룹")
tl2 = Timeline()
pie_0 = (Pie().add("", [list(z) for z in zip(risk, l_30)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 30대")).set_series_opts(label))
tl2.add(pie_0, "30대 자산 하위그룹")
pie_1 = (Pie().add("", [list(z) for z in zip(risk, m_30)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 30대")).set_series_opts(label))
tl2.add(pie_1, "30대 자산 중위그룹")
def paint_confirmed_distributed_chart(country):
date = get.get_today()
result = get.get_confirmed_distribution(country, date)
if result:
charts = (Pie().add("", result, radius=[60, 160]).set_global_opts(
title_opts=opts.TitleOpts(title="累计确诊分布",
pos_left="3%",
pos_top="10",
title_textstyle_opts=opts.TextStyleOpts(
font_size=22, )),
legend_opts=opts.LegendOpts(type_="scroll",
pos_left='80%',
pos_top='20%',
orient="vertical"),
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c} ({d}%)",
font_size=15,
)))
return charts
else:
data = get.get_word_epidemic(date)[2]
flag = False
for index in range(0, len(data)):
if data[index][0] == country:
data = data[index][1][0]
flag = True
break
if not flag:
title_charts = (Pie().add(
"", [[country, -1]], radius=[0, 0],
center=['100%', '100%']).set_global_opts(
title_opts=opts.TitleOpts(
title="暂无数据",
pos_left="40%%",
pos_top="44%",
title_textstyle_opts=opts.TextStyleOpts(
font_size=30, )),
legend_opts=opts.LegendOpts(is_show=False),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
return title_charts
draw_data = [[country, data]]
empty_charts = (
Pie().add(
"",
draw_data,
radius=[60, 160],
# center=['50%', '65%']
).set_global_opts(
title_opts=opts.TitleOpts(
title="确诊人数",
pos_left="3%",
pos_top="10",
title_textstyle_opts=opts.TextStyleOpts(font_size=22, )),
legend_opts=opts.LegendOpts(type_="scroll",
pos_left='80%',
pos_top='20%',
orient="vertical"),
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c} ({d}%)",
font_size=15,
)))
return empty_charts
.add_xaxis(Faker.choose())
.add_yaxis("商家C", Faker.values(), stack="stack2")
.add_yaxis("商家D", Faker.values(), stack="stack2")
.set_global_opts(
title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
)
c = (
Pie()
.add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
radius=["40%", "75%"],
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-Radius"),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="15%", pos_left="2%"
),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)
grid = (
Grid()
.add(bar1, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(c, grid_opts=opts.GridOpts(pos_top="60%"))
)
grid.render('grid.html')
# # json_personnal_information = personnal_information1(uid)
# text = personnal_information2(uid)
# birthday = re.findall('<p id="J_birthday" style="display:none;">(.*?)</p></div>', text)
# location = re.findall('<p id="J_location" class="J_label"[^>]*>([^<]*)</p>', text)
# name = re.findall('<p id="J_name" class="J_label" isCompany="">(.*?)</p></div>', text)
# agender = re.findall('<span class="L-selected-select">(.*?)</span></div>', text)
# else:
# print('登陆失败')
""""""
# if os.path.exists('weibo.csv'):
# os.remove('weibo.csv')
# for i in range(1000):
# time.sleep(random.random() * 3)
# print('第%d页' % (i + 1))
# spider_weibo()
""""""
dic = read_csv_to_dict(2)
# 生成二维数组
gender_count_list = [list(z) for z in zip(dic.keys(), dic.values())]
print(gender_count_list)
pie = (
Pie()
.add('', gender_count_list)
.set_colors(['red', 'blue'])
.set_global_opts(title_opts=options.TitleOpts(title='性别分析'))
.set_series_opts(label_opts=options.LabelOpts(formatter='{b}: {c}'))
)
pie.render('gender.html')
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c = (Pie().add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
center=["35%", "50%"],
).set_global_opts(
title_opts=opts.TitleOpts(title="Pie-调整位置"),
legend_opts=opts.LegendOpts(pos_left="15%"),
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c}")).render("Pie_position.html"))
c = (Pie().add(
"",
[list(z) for z in zip(["剧情", "其他"], [25, 75])],
center=["20%", "30%"],
radius=[60, 80],
label_opts=new_label_opts(),
).add(
"",
[list(z) for z in zip(["奇幻", "其他"], [24, 76])],
center=["55%", "30%"],
radius=[60, 80],
label_opts=new_label_opts(),
).add(
"",
[list(z) for z in zip(["爱情", "其他"], [14, 86])],
center=["20%", "70%"],
radius=[60, 80],
label_opts=new_label_opts(),
).add(
"",
[list(z) for z in zip(["惊悚", "其他"], [11, 89])],
center=["55%", "70%"],
radius=[60, 80],
label_opts=new_label_opts(),
).set_global_opts(
title_opts=opts.TitleOpts(title="Pie-多饼图基本示例"),
legend_opts=opts.LegendOpts(type_="scroll",
pos_top="20%",
pos_left="80%",
orient="vertical"),
).render("Mutiple_pie.html"))
c = (Pie().add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
radius=["40%", "55%"],
label_opts=opts.LabelOpts(
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {
"color": "#999",
"lineHeight": 22,
"align": "center"
},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {
"fontSize": 16,
"lineHeight": 33
},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
).set_global_opts(title_opts=opts.TitleOpts(
title="Pie-富文本示例")).render("pie_rich_label.html"))
from pyecharts import options as opts
from pyecharts.charts import Pie, Line, Bar, Liquid
engine = create_engine(
'mysql://*****:*****@172.16.122.25:3306/test?charset=utf8')
# PU 占比
sql = """
select sum(case when pay_price > 0 then 1 else 0 end) as `付费用户`,
sum(case when pay_price > 0 then 0 else 1 end) as `非付费用户`
from age_of_barbarians
"""
data = pd.read_sql(con=engine, sql=sql)
c1 = (Pie().add(
"",
[list(z) for z in zip(data.columns, data.values[0])],
).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c} 占比: {d}%")).render("pie_pu.html"))
os.system("pie_pu.html")
# DNU 柱形图
sql = """
select cast(register_time as date) as day,
count(1) as dnu
from age_of_barbarians
group by cast(register_time as date)
order by day;
"""
data = pd.read_sql(con=engine, sql=sql)
c2 = (Bar().add_xaxis(list(data['day'])).add_yaxis("新增用户数", list(
from pyecharts.charts import Pie
from pyecharts import options as ops
f = open('PF.txt', 'r', encoding='utf8')
l = []
for i in f.readlines():
l.append(i.strip('\n'))
print(l)
data = []
for s in l:
data.append(s.replace("%", '').split('\t'))
data = [[d[0], float(d[1])] for d in data]
print(data)
c = (Pie().add("", [z for z in data], radius=[
'30', '200'
]).set_global_opts(title_opts=ops.TitleOpts(title='评分')).set_series_opts(
label_opts=ops.LabelOpts(formatter='{b}:{c}')).render('./pf.html'))
# 3、生成饼状图
cut_words = jieba.cut(' '.join(short_comments.Comment))
keywords_count_list = []
for i in reversed(keywords_count_list_TR[:20]):
keywords_count_list.append(i[0])
print(keywords_count_list)
keywords_count_result = [0 for x in range(20)]
for word in cut_words:
for j in range(20):
if word == keywords_count_list[j]:
keywords_count_result[j] = keywords_count_result[j] + 1
print(keywords_count_result)
keywords_count_pie = (Pie().add(
"", [list(z) for z in zip(keywords_count_list, keywords_count_result)
]).set_global_opts(title_opts=opts.TitleOpts(
title="上海堡垒短评热词TOP20")).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {c}")))
keywords_count_pie.render('comment-word-count-pie.html')
# 4、评星统计
rating = [0 for x in range(5)]
for result in short_comments.Rating:
if result == '很差':
rating[0] += 1
elif result == '较差':
rating[1] += 1
elif result == '还行':
rating[2] += 1
elif result == '推荐':
rating[3] += 1
