def create_gender(data):
df = data.copy()
# 修改数值
df.loc[df.gender == '0', 'gender'] = '未知'
df.loc[df.gender == '1', 'gender'] = '男性'
df.loc[df.gender == '2', 'gender'] = '女性'
# 根据性别分组
gender_message = df.groupby(['gender'])
# 对分组后的结果进行计数
gender_com = gender_message['gender'].agg(['count'])
gender_com.reset_index(inplace=True)
# 饼图数据
attr = gender_com['gender']
v1 = gender_com['count']
# 初始化配置
pie = Pie(init_opts=opts.InitOpts(width="800px", height="400px"))
# 添加数据，设置半径
pie.add("", [list(z) for z in zip(attr, v1)], radius=["40%", "75%"])
# 设置全局配置项，标题、图例、工具箱(下载图片)
pie.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V性别分布情况",
pos_left="center"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True, feature={"saveAsImage": {}}))
# 设置系列配置项，标签样式
pie.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, formatter="{b}:{d}%"))
pie.render("抖音大V性别分布情况.html")
def get_pie(city, center1, center2):
data = df.loc[df['City'] == city, ['Date', 'AQI_Bucket']]
rank_message = data.groupby(['AQI_Bucket'])
rank_com = rank_message['AQI_Bucket'].agg(['count'])
rank_com.reset_index(inplace=True)
rank_com_last = rank_com.sort_values('count', ascending=False)
x = rank_com_last['AQI_Bucket']
y = rank_com_last['count']
pie = Pie(init_opts=opts.InitOpts(width='400px', height='300px'))
input_data = [list(z) for z in zip(x, y)]
pie.add(city, input_data, center=center1, radius=['15%', '30%'])
pie.set_series_opts(label_opts=opts.LabelOpts(formatter='{b}：{d}%'))
pie.set_global_opts(title_opts=opts.TitleOpts(
title=city,
pos_left=center2[0],
pos_top=center2[1],
),
legend_opts=opts.LegendOpts(is_show=False))
return pie
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 draw_pie_chart(groupby_gpu, GPU_count=7) -> Pie:
global pie, data
base_radius = int(250 // GPU_count * 1.5)
loc_x = 10 #起始位置
step = 10 #调整饼图的间距
loc_y = 20
pie = Pie(init_opts=opts.InitOpts(
width="1600px", height="900px", theme=ThemeType.ROMA))
all_gpu_info = groupby_gpu['MEM'].sum()
for i in range(GPU_count):
if i in all_gpu_info.keys():
current_gpu_list = groupby_gpu.get_group(i)[["PID_PATH", "MEM"
]].values.tolist()
aval_mem = round(11 - all_gpu_info[i], 2)
data = [['可用', aval_mem]]
data.extend(current_gpu_list)
else:
aval_mem = 11
data = [['可用', aval_mem]]
pie.add(
"GPU:%s" % i,
data,
radius='%s' % base_radius,
center=[str(loc_x + step * i) + '%',
str(loc_y) + '%'],
rosetype=None,
label_opts=opts.LabelOpts(position='inside',
font_size=10,
is_show=False),
)
pie = pie.set_global_opts(title_opts=opts.TitleOpts(title="GPU Stat"))
return pie
def Pie_base(sql, titl) -> Pie:
pie = Pie()
# engine = create_engine("mysql+pymysql://root:root@localhost:3306/mysql", encoding="utf-8")
# session = sessionmaker(bind=engine)
# # sql= "select gzlx as 故障类型,count(1) as 数量 from b_epos where rq BETWEEN '%s' and '%s' group by gzlx"% (rq1, rq2)
# df = pd.read_sql(sql, engine)
# rows=df.values.tolist()
# headers=df.columns.tolist()
with SQLPoll() as db:
students = db.fetch_all(sql, None)
headers = []
for student in students:
headers = list(student.keys())
break
c = []
rows = []
for student in students:
c = list(student.values())
rows += [c]
if len(rows) == 0:
rows = [['无', 0]]
pie.add("", rows)
pie.set_global_opts(title_opts=opts.TitleOpts(title=titl))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
return pie
def v_positions(self, date=yesterdayobj(), rendered=True):
"""
pie chart visualization of positions ratio in combination
"""
sdata = sorted(
[
(fob.name, fob.briefdailyreport(date).get("currentvalue", 0))
for fob in self.fundtradeobj
],
key=lambda x: x[1],
reverse=True,
)
pie = Pie()
pie.add(
series_name="总值占比",
data_pair=sdata,
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_global_opts(
legend_opts=opts.LegendOpts(
pos_left="left", type_="scroll", orient="vertical"
)
).set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
)
if rendered:
return pie.render_notebook()
else:
return pie
def MutiplePieCharts(datas):
height_i = '450'
if len(datas) > 3:
height_i = height_i + (350 * (len(datas) // 3))
height = str(height_i) + 'px'
pie = Pie(init_opts=opts.InitOpts(width="100%", height=height))
left_i = 1
right_i = 1
for data in datas:
right = str(300 * right_i) + 'px'
if left_i == 1:
left = '20%'
left_i = 2
elif left_i == 2:
left = '50%'
left_i = 3
else:
left = '80%'
right_i += 1
left_i = 1
pie.add(
data[0],
data[1],
center=[left, right],
radius=120,
)
pie.set_global_opts(title_opts=opts.TitleOpts(title="磁盘使用率"), )
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"), )
return pie.render_embed()
def statistics_friends():
# 初始化
unknown, known_male, known_female, known_other = 0, 0, 0, 0
# 遍历
for user in friends:
# 备注不为空
if ((user.remark_name).strip()):
if (user.sex == 1):
known_male += 1
elif (user.sex == 2):
known_female += 1
else:
known_other += 1
else:
unknown += 1
name_list = ['未设置备注的好友', '设置备注的男性好友', '设置备注的女性好友', '设置备注的其他好友']
num_list = [unknown, known_male, known_female, known_other]
pie = Pie()
pie.add("你认识的好友比例", [list(z) for z in zip(name_list, num_list)])
pie.set_global_opts(title_opts=opts.TitleOpts(title="你认识的好友比例"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie.render('data/你认识的好友比例.html')
def analyze_remark_name():
close_partner_dict = {
'宝宝,糙糙,仙女,亲爱,老婆': 0,
'老公': 0,
'父亲,爸': 0,
'母亲,妈': 0,
'闺蜜,死党,基友': 0
}
# 遍历好友数据
for user in friends:
for key in close_partner_dict.keys():
# 判断该好友备注名是否包含close_partner_dict中的任意一个key
name = key.split(',')
for sub_name in name:
if (sub_name in user.remark_name):
close_partner_dict[key] += 1
break
name_list = ['最重要的她', '最重要的他', '爸爸', '妈妈', '死党']
num_list = [x for x in close_partner_dict.values()]
pie = Pie()
pie.add("可能是你最亲密的人", [list(z) for z in zip(name_list, num_list)])
pie.set_global_opts(title_opts=opts.TitleOpts(title="可能是你最亲密的人"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie.render('data/你最亲密的人.html')
def analyze_remark_name():
close_partner_dict = {
'宝宝,猪,仙女,亲爱,老婆': 0,
'老公': 0,
'父亲,爸': 0,
'母亲,妈': 0,
'闺蜜,死党,基友': 0
}
# 遍历好友数据
for user in friends:
for key in close_partner_dict.keys():
# 判断该好友备注名是否包含close_partner_dict中的任意一个key
name = key.split(',')
for sub_name in name:
if (sub_name in user.remark_name):
close_partner_dict[key] += 1
break
name_list = ['最重要的她', '最重要的他', '爸爸', '妈妈', '死党']
num_list = [x for x in close_partner_dict.values()]
pie = Pie("可能是你最亲密的人")
pie.add("", name_list, num_list, is_label_show=True, is_legend_show=False)
pie.render('data/你最亲密的人.html')
def draw_usage_pie(payout, budget, title) -> Pie:
"""
list [(title1, num1), (title2, num2)]
:param payout: 消费
:param budget: 预算
:param title: 标题
:return: Pie
"""
pie = Pie()
pie.add(
series_name=title,
data_pair=budget,
radius=["0%", "40%"],
label_opts=opts.LabelOpts(position="inner"),
)
pie.add(
series_name=title,
radius=["40%", "50%"],
data_pair=payout,
label_opts=opts.LabelOpts(
position="outside",
formatter="{b}:\n{c}({d}%)",
border_width=1,
border_radius=4,
),
)
# pie.set_global_opts(
# legend_opts=opts.LegendOpts(pos_left="left", orient="vertical"), )
# pie.set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item",
# formatter="{a} <br/>{b}: {c} ({d}%)"))
return pie
def statistics_friends():
# 初始化
unknown, known_male, known_female, known_other = 0, 0, 0, 0
# 遍历
for user in friends:
# 备注不为空
if ((user.remark_name).strip()):
if (user.sex == 1):
known_male += 1
elif (user.sex == 2):
known_female += 1
else:
known_other += 1
else:
unknown += 1
name_list = ['未设置备注的好友', '设置备注的男性好友', '设置备注的女性好友', '设置备注的其他好友']
num_list = [unknown, known_male, known_female, known_other]
pie = Pie("你认识的好友比例", title_pos='center')
pie.add("",
name_list,
num_list,
is_label_show=True,
legend_orient="vertical",
legend_pos="left")
pie.render('data/你认识的好友比例.html')
def draw_multiple_pie(month, day):
max_width, max_height = 1400, 3000
pie = Pie(init_opts=opts.InitOpts(width='{}px'.format(max_width),
height='{}px'.format(max_height)))
pie.set_global_opts(legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(
title='2020-%02d-%d 全国各省份城市确诊病例' % (month, day)))
h_center, v_center = 10, 40
horizontal_step, vertical_step = 350, 320
for p in get_province_data(month, day):
title = '%s-%d例' % (p['provinceShortName'], p['confirmedCount'])
labels = [city['cityName'] for city in p['cities']]
counts = [city['confirmedCount'] for city in p['cities']]
if len(labels) == 0:
continue
pie.add(title, [list(z) for z in zip(labels, counts)],
radius=[5, 80],
center=[h_center + 150, v_center + 110]).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}"),
tooltip_opts=opts.TooltipOpts())
h_center += horizontal_step
if h_center + 200 > max_width:
h_center = 10
v_center += vertical_step
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
pie.render('%s/省份信息.html' % root)
def draw_category_pie(inner,
outside,
inner_title="分类报表",
outer_title='小类报表',
width=2000) -> Pie:
pie = Pie()
inner_radius = "70%"
outer_radius = "80%"
pie.add(
series_name=inner_title,
data_pair=inner,
radius=["0%", inner_radius],
label_opts=opts.LabelOpts(position="inner"),
)
pie.add(
series_name=outer_title,
radius=[inner_radius, outer_radius],
data_pair=outside,
label_opts=opts.LabelOpts(
position="outside",
formatter="{b}:\n{c}({d}%)",
border_width=1,
border_radius=4,
),
)
pie.set_global_opts(legend_opts=opts.LegendOpts(pos_left="left",
orient="vertical"), )
pie.set_series_opts(tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"))
return pie
def pie_two_html(self, chart_id, title, sub_title1, sub_title2, key1, key2, val1, val2):
# 实例化饼状图
pie = Pie(init_opts=opts.InitOpts(width="100%", height="300px"))
pie.set_global_opts(
title_opts=opts.TitleOpts(
title="{}-{}".format(self.dt, title),
title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=14),
pos_left="center"
),
legend_opts=opts.LegendOpts(is_show=False),
)
pie.chart_id = chart_id
# print("key1: %s" % key1)
# print("val1: %s" % val1)
pie.add(
sub_title1,
[list(z1) for z1 in zip(key1, val1)],
center=[300, 160],
radius=[30, 75],
)
pie.add(
sub_title2,
[list(z2) for z2 in zip(key2, val2)],
center=[1100, 160],
radius=[30, 75],
)
return pie.render_embed()
def mkhtml2(name1):
"""
饼图
:return:生成豆瓣top250电影产源国家数量占比分析图html文件
"""
# ORM查询
myresult = Test.objects.values('address').annotate(
count=Count('name')).order_by('-count')[:10]
# fetchall() 获取所有记录
namelist = []
# 将变量存在列表里
numlist = []
for name in myresult:
namelist.append(name['address'])
numlist.append(name['count'])
pie = Pie()
pie.add(
"占比",
[list(z) for z in zip(namelist, numlist)],
center=["40%", "60%"],
)
pie.set_global_opts(
title_opts=opts.TitleOpts(title="豆瓣top250电影产源国家数量占比"),
legend_opts=opts.LegendOpts(pos_left="35%"),
)
# 设置显示的样子，加入了百分比
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"))
pie.render(f'{name1}')
print(f'{name1}已生成')
def data_visualized(user_id):
project = Project.query.filter_by(user_id=user_id).first()
duration = (project.expected_end_date - project.start_date).days
tasks = Task.query.filter_by(project_id=project.project_id).all()
task_name = []
task_duration = []
for task in tasks:
task_name.append("task " + str(task.task_id))
# start_date = datetime.datetime(task.start_date)
# end_date = datetime.datetime(task.end_date)
interval = (task.end_date - task.start_date).days
duration -= interval
task_duration.append(interval)
task_name.append('Unused time')
task_duration.append(duration)
time_usage_chart = Pie({
'width': '400px',
'height': '400px',
'chart_id': project.project_name + '_usage_chart'
})
# time_usage_chart.add_xaxis(task_name)
# time_usage_chart.add_yaxis('duration', task_duration)
time_usage_chart.add(
project.project_name,
[list(z) for z in zip(task_name, task_duration)],
radius=["30%", "55%"],
)
return render_template('data_visualized.html',
time_usage=time_usage_chart.render_embed())
def get_github_id_sum(self):
select_sql = "select github_id,sum(zh_sum) as total from zh_trans where flag = " \
+ str(self.flag) + " and merge_time between '" + self.begin_time + "' and '" + \
self.end_time + "' group by github_id order by total desc"
# 连接数据库
conn = sqlite3.connect(self.db)
# 创建一个 cursor
cursor = conn.cursor()
cursor.execute(select_sql)
data = cursor.fetchall()
all_data = []
all_author = []
all_value = []
total = 0
for zh in data:
all_author.append(zh[0])
all_value.append((zh[1]))
all_data.append([zh[0], zh[1]])
total += zh[1]
if self.flag == 0:
series_name = "开始时间：" + self.begin_time + " 结束时间：" + self.end_time + "\n istio.io 新增中文翻译字数统计 总参与人数为：" + str(
len(all_data)) + "\n istio.io 新增中文翻译字数统计 总翻译字数为：" + str(total)
else:
series_name = "开始时间：" + self.begin_time + " 结束时间：" + self.end_time + "\n istio.io 更新中文翻译字数统计 总参与人数为：" + str(
len(all_data)) + "\n istio.io 更新中文翻译字数统计 总翻译字数为：" + str(total)
generated_log(series_name, self.flag)
# 饼图
pie = Pie()
pie.add(
"",
data_pair=all_data,
center=["40%", "62%"],
radius=["0%", "45%"],
rosetype='radius').set_global_opts(
title_opts=opts.TitleOpts(title=""),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="5%",
pos_right="6%")).set_series_opts(label_opts=opts.LabelOpts(
horizontal_align=True, formatter="{b}: {c}"))
html_time = str(time.strftime('%Y-%m-%d',
time.localtime(time.time()))).replace(
"-", "")
if self.flag == 0:
pie.render(html_time + "page_pie_add.html")
else:
pie.render(html_time + "page_pie_update.html")
# 柱状图
bar = (Bar().add_xaxis(all_author).add_yaxis(
"github账户", all_value).set_global_opts(title_opts=opts.TitleOpts(
title="翻译统计")))
if self.flag == 0:
bar.render(html_time + "page_bar_add.html")
else:
bar.render(html_time + "page_bar_update.html")
cursor.close()
conn.commit()
conn.close()
def bingtu(shopNames, nums):
attr = shopNames
v1 = nums
pie = Pie("笔记本店铺饼图展示")
pie.add("", attr, v1, is_label_show=True)
pie.show_config()
pie.render("D:\\scrapy\\jingdong\\shops.html")
def show_pie_charts(df):
score_perc = df['star'].value_counts() / df['star'].value_counts().sum(
) # 评分占比 = 评分1-5的个数(分别) / 评分总个数
score_perc = np.round(score_perc * 100, 2)
score_perc = score_perc.sort_index() # Series也可以排序
# 聚合后类型为Series，转成Dataframe
# df_score = pd.DataFrame(list(zip(score_perc.index, score_perc.values)), columns=['score', 'percentage']).sort_values('score')
pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))
pie1.add(
series_name='评分占比',
data_pair=[*zip(score_perc.index, score_perc.values)
], # Series: zip打包为一个元组对象，用*解压成元组，再[]转成列表
# data_pair=[*zip(df_score['score'], df_score['percentage'])], # Dataframe
label_opts=opts.LabelOpts(formatter='{c}%'), # formatter='{c}%'百分比
radius=['35%', '70%'] # 是什么意思
)
pie1.set_global_opts(
title_opts=opts.TitleOpts(title='总体评分分布'),
legend_opts=opts.LegendOpts(orient='vertical',
pos_top='15%',
pos_left='2%'), # 图例的位置：水平/垂直
toolbox_opts=opts.ToolboxOpts()) # 右上角的工具栏，保存图片等等
pie1.set_colors(['#D7655A', '#FFAF34', '#3B7BA9', '#EF9050', '#6FB27C'])
pie1.render('charts/pie_chart.html')
def pie_set_colors():
obj_pie = Pie()
obj_pie.add("",[list(z) for z in zip(Faker.choose(), Faker.values())])
obj_pie.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
obj_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
obj_pie.set_global_opts(title_opts=opts.TitleOpts(title="Pie-设置颜色",subtitle ="副标题"))
return obj_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 sexAnalyse(itemname_, itemnamelist_, itemnumlist_):
totle = itemnumlist_[0] + itemnumlist_[1] + itemnumlist_[2]
subtitle = "共有:%d个好友" % totle
pie = Pie()#新建饼图对象
pie.add("",[list(z) for z in zip(itemnamelist_,itemnumlist_)],center=["35%", "50%"])#饼图对象数据添加
pie.set_global_opts(title_opts=opts.TitleOpts(title=subtitle),legend_opts=opts.LegendOpts(pos_left="15%"),)
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
outFileName = itemname_+ '.html'
pie.render(outFileName)#保存饼图对象
def render_html(self) -> str:
"""渲染html"""
pie = Pie(init_opts=InitOpts(theme=ThemeType.LIGHT))
pie.add('KeyWords', self.result.items())
# pie.set_global_opts(title_opts=TitleOpts(title='主标题', subtitle='附标题'))
html = CacheModule.create_cache_path('html')
pie.render(html)
self.htmlpath = html
return html
def get_pie(labels, counts, title, size):
pie = Pie(init_opts=opts.InitOpts(width=size[0], height=size[1]))
pie.set_global_opts(legend_opts=opts.LegendOpts(is_show=False), title_opts=opts.TitleOpts(title))
pie.add(
title,
[list(z) for z in zip(labels, counts)],
radius=[5, 80],
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"), tooltip_opts=opts.TooltipOpts())
return pie
def pie_render_air(self):
"""
* 实心圆一个，仅支持一份参数；
"""
# c = Pie(init_opts=opts.InitOpts(width="1600px", height="800px"))
c = Pie()
c.add(
series_name=self.y_name,
# radius=["40%", "55%"], # 半圆的尺寸；
radius=[0, "55%"],
data_pair=self.inner_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,
},
},
),
)
c.set_global_opts(title_opts=opts.TitleOpts(title=self.title),
legend_opts=opts.LegendOpts(pos_left=self.pos_left,
orient="vertical"))
c.set_series_opts(tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"))
return c
def search():
page = Pie()
bar = get_bar()
page.add(bar)
keyword = request.args.get('keyword').replace(' ', '') # 获得前端传回的keyword
print(keyword)
if '：' in keyword: # 判断是否是高级搜索
key = keyword.split('：')[0] # 获取传回的高级搜索关键字，如"诗人：李白"，此时获取‘诗人’
value = keyword.split('：')[1] # 获取‘李白’
if key == '诗人' and value: # 判断高级搜索为‘诗人’，并且搜索内容不为空
poet = Poet.query.filter_by(
name=value).first() # 在数据库中查询姓名为”李白“的诗人数据
if poet: # 如果查询到
context = {
'poet': poet, # 诗人字段信息
'poem': poet.poems # 诗人的是诗的字段信息
}
return render_template('search.html',
**context,
chart=page.render_embed())
else: # 如果没有查询到
context = {
'message': '没有找到相关内容' # 返回信息
}
return render_template('search.html', **context) # 渲染页面
elif key == '诗句' and value: # 判断高级搜索为‘诗句’，并且搜索内容不为空 如”诗句：清明“
poem = Poem.query.filter(
Poem.content.contains(value)).all() # 获取全部诗句内容中包含”清明“诗句
if poem: # 如果查询到
context = {
'poem': poem # 诗的字段信息
}
return render_template(
'search.html',
**context,
chart=page.render_embed(),
host='/static/js',
script_list=page.get_js_dependencies()) # 渲染页面
else: # 如果没有查询到
context = {'message': '没有找到相关内容'}
return render_template('search.html', **context) # 渲染页面
else: # 普通搜索，直接搜索诗的名字 如：‘江南’
poem = Poem.query.filter_by(title=keyword).all() # 获取所有诗的名字为‘江南’的数据
if poem: # 如果查询的到
context = {
'poem': poem # 所有诗的名字为‘江南’字段信息
}
return render_template(
'search.html',
**context,
chart=page.render_embed(),
host='/static/js',
script_list=page.get_js_dependencies()) # 渲染页面
else: # 如果没有查询的到
context = {'message': '没有找到相关内容'}
return render_template('search.html', **context) #渲染页面
def show_pie(id):
attr = ["成功（含熔断）", "失败", "限流"]
counts = [len(sync.success), len(sync.fail), sync.limit]
c = Pie(init_opts=opts.InitOpts(
width='1200px', height='800px', page_title='page'))
c.add("", [list(attr) for attr in zip(attr, counts)])
c.set_colors(["green", "red", "blue"])
c.set_global_opts(title_opts=opts.TitleOpts(title="成功失败数量饼图"))
c.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
c.render('成功失败数量饼图{}.html'.format(id + 1))
def pie_render_genre(self):
c = Pie()
c.add(self.y_name, self.inner_data_pair, center=["35%", "50%"])
c.set_global_opts(title_opts=opts.TitleOpts(title=self.title),
legend_opts=opts.LegendOpts(pos_left=self.pos_left,
orient="vertical"))
c.set_series_opts(tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),
label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
return c
def sex_chart(data, movie_id):
list = []
data = data['gender'].value_counts()
for key, v in enumerate(data):
list.append([sex[key], v])
pie = Pie()
pie.add("", list)
pie.set_global_opts(title_opts=opts.TitleOpts(title="评论用户性别分布饼图"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie.render('user_sex_' + movie_id + '.html')
