def wordcloud_base() -> WordCloud:
c = (WordCloud().add(
"", words, word_size_range=[20, 100],
shape=SymbolType.ARROW).set_global_opts(
title_opts=opts.TitleOpts(title="A gift for you!"),
toolbox_opts=opts.ToolboxOpts()))
return c
def coincedence(place) -> Bar:
place = np.array(place)
foo = place[:, 1].astype(np.float)
order = np.argsort(foo)
k = 0
place1 = np.copy(place)
for i in order:
place1[k] = place[i, :]
k = k + 1
place = place1
bar = Bar()
bar.add_xaxis(list(place[:, 0]))
bar.add_yaxis("热度", list(place[:, 1]))
bar.set_global_opts(title_opts=opts.TitleOpts(title="外来旅游人口相关度"))
bar.set_global_opts(xaxis_opts=opts.AxisOpts(boundary_gap=1,
interval=0,
axislabel_opts={"rotate":
45}),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=0,
range_end=100,
is_zoom_lock=False)
])
#bar.set_global_opts(xaxis_opts=opts.AxisTickOpts(is_align_with_label=0))
bar.set_series_opts(label_opts=opts.LabelOpts(font_size=9))
bar.width = 1200
bar.render(path="外来旅游人口相关度.html")
return True
def map_data(data, tableName, titleName) -> Map:
"""
地图数据生成函数
:Param data: 地图填充数据
"""
try:
name, value = data[0], data[1]
new_name, new_value = [], []
for index, temp in enumerate(data[1]):
if temp > 0:
new_name.append(name[index])
new_value.append(value[index])
name, value = new_name, new_value
c = (Map(init_opts=opts.InitOpts(page_title="Luwei")).add(
tableName, [list(z) for z in zip(name, value)],
"world",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title=titleName),
visualmap_opts=opts.VisualMapOpts(),
toolbox_opts=opts.ToolboxOpts(),
))
return c
except Exception as ex:
log.error("Map create failed：{0}".format(str(ex)))
return None
def heliu() -> Bar:
a = [1,10,100,150,180,200] #模拟确诊
b = [0,2,20,50,100,102] #模拟治愈
c = [0,1,10,15,18,18] #模拟死亡
v = list(map(lambda x: x[0]-x[1]-x[2], zip(a, b, c))) #等待治愈
x = [0,1,2,3,4,5] #模拟日
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)) #设置主题
.add_xaxis(x) #直接列表
#.add_yaxis('确诊',v,stack="stack1")
.add_yaxis('死亡',c,stack="stack3")
.add_yaxis('治愈',b,stack="stack3")
.add_yaxis('确诊',v,stack="stack3")
.set_global_opts(
title_opts=opts.TitleOpts(title="2019-nCov 海外国家疫情分析", subtitle=""),
toolbox_opts = opts.ToolboxOpts(is_show = True), #
datazoom_opts=opts.DataZoomOpts(),
)
.set_series_opts(
# 设置系列配置
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(max_=80)
)
)
return c
def drawline(list1,list4,name):
# 图表初始化配置
init_opts = opts.InitOpts(page_title=name)
line = Line(init_opts=init_opts)
# 标题配置
title = opts.TitleOpts(title=name,
pos_left="10%")
# 图例配置
legend_opts = opts.LegendOpts(orient="horizontal",
pos_top="5%",
pos_right="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_bottom="15%",
pos_left="90%",
)
line.set_global_opts(title_opts=title,
legend_opts=legend_opts,
toolbox_opts=toolbox_opts,
yaxis_opts=opts.AxisOpts(name="单位：岁",
# axislabel_opts=opts.LabelOpts(formatter="{value}例",
),
xaxis_opts=opts.AxisOpts(name="日期"),
datazoom_opts = opts.DataZoomOpts(orient="vertical"),
)
line.add_xaxis(list4, )
line.add_yaxis(name, list1, is_smooth=True, linestyle_opts=opts.LineStyleOpts(color="#E83132", width="4"))
line.render('{0}.html'.format(name))
def bar_base_j() -> Bar:
j全国 = j['全国'].tolist()
j农村 = j['农村'].tolist()
j城镇 = j['城镇'].tolist()
j东部 = j['东部'].tolist()
j东北部 = j['东北部'].tolist()
j中部 = j['中部'].tolist()
j西部 = j['西部'].tolist()
JY = j['阶段'].tolist()
CO = (
Bar()
.add_xaxis(JY)
.add_yaxis("全国", j全国)
.add_yaxis("农村", j农村)
.add_yaxis("城镇", j城镇)
.add_yaxis("东部", j东部)
.add_yaxis("东北部", j东北部)
.add_yaxis("中部", j中部)
.add_yaxis("西部", j西部)
.set_global_opts(
title_opts=opts.TitleOpts(title="学科类校外培训参与率", subtitle="全国中小学生（%）"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
return CO.render_embed()
def set_process() -> Bar:
"""
进程调度比较图
:return:
"""
index = ['时间片轮转调度', '短进程优先调度', '非剥夺优先级调度']
data = [217, 459, 324]
bar = (
Bar()
.add_xaxis(index)
.add_yaxis(series_name='三大调度算法', yaxis_data=data)
.set_global_opts(title_opts=opts.TitleOpts(title='三大算法调度性能分析'),
xaxis_opts=opts.AxisOpts(type_='category', name='算法名称'),
yaxis_opts=opts.AxisOpts(type_='value', name='获胜次数',
axislabel_opts=opts.LabelOpts(formatter='{value}/次')),
toolbox_opts=opts.ToolboxOpts(),
tooltip_opts=opts.TooltipOpts(axis_pointer_type='cross')
)
.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name='最大值', type_='min'),
opts.MarkPointItem(name='最大值', type_='max')]),
itemstyle_opts={
"normal": {
"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor": 'rgb(0, 160, 221)',
}}
)
)
return bar
def showAqi():
# 对空气质量aqi，进行排序
for row in mycol2.find().sort("name", -1):
# print("空气质量")
# print(row)
# print(row['num'])
weather_aqis_num.append(row['num'])
a = []
b = []
for i in mycol2.find():
a.append(i['name'])
b.append(i['aqi'])
c = (Bar().add_xaxis(a).add_yaxis("AQI", b).set_global_opts(
title_opts=opts.TitleOpts(title="空气质量AQI柱状排名图"),
datazoom_opts=opts.DataZoomOpts(range_start=0, range_end=10),
toolbox_opts=opts.ToolboxOpts(),
visualmap_opts=opts.VisualMapOpts(min_=50, max_=100)))
page.add(c)
# 展示折线图，空气质量的各种因素
name = []
aqi = []
co = []
no2 = []
o3 = []
pm10 = []
pm2_5 = []
so2 = []
for i in mycol2.find():
# print(i['name'])
name.append(i['name'])
aqi.append(i['aqi'])
co.append(i['co'])
no2.append(i['no2'])
o3.append(i['o3'])
pm10.append(i['pm10'])
pm2_5.append(i['pm2_5'])
so2.append(i['so2'])
data = []
for i in range(len(name)):
t = [aqi[i], co[i], no2[i]]
data.append(t)
print(t)
c = (Line().add_xaxis(name).add_yaxis("aqi(空气质量指数)", aqi).add_yaxis(
"co", co).add_yaxis("no2", no2).add_yaxis("o3", o3).add_yaxis(
"pm10",
pm10).add_yaxis("pm2_5",
pm2_5).add_yaxis("so2", so2).set_global_opts(
title_opts=opts.TitleOpts(title="空气质量"),
datazoom_opts=opts.DataZoomOpts(is_show=True,
range_start=0,
range_end=3)))
page.add(c)
def hotpoint(movie_name):
#读取城市数据
with open(movie_name + '\\' + movie_name + 'dbplaces.txt',
'r+',
encoding='utf-8-sig') as f:
heat_1 = f.read()
for i in heat_1:
if i in string.punctuation:
heat_1 = heat_1.replace(i, "")
heat = heat_1.split()
heat_dic = Counter(heat)
#整理数据
heat_list = []
for place, times in heat_dic.items():
c_word = (place, times)
heat_list.append(c_word)
#出图
heat_map = (Geo().add_schema(maptype="china").add(
"", heat_list).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title=movie_name + " 豆瓣观影热点图",
subtitle=None),
toolbox_opts=opts.ToolboxOpts(is_show=True)))
heat_map.render(movie_name + '\\' + movie_name + 'dbhotpoint.html')
def post(self, request, *args, **kwargs):
'''
导出Echart报表
'''
ret = {"code": 1000}
datas = request.data
content = json.loads(datas.get("request", ""))
url_list = []
duration_list = []
for i in content:
url_list.append(i["url"].split("/")[-1])
duration_list.append(i["duration"])
print(url_list)
duration_list = [0 if i == None else i for i in duration_list]
print(duration_list)
try:
# 纵向柱状图
c = (Bar().add_xaxis(url_list).add_yaxis(
"Duration", duration_list).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=-15)),
title_opts=opts.TitleOpts(title="接口响应时长分布图",
subtitle="单位：毫秒（ms）"),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
datazoom_opts=opts.DataZoomOpts(),
).render("./easy/templates/pyEchartReport.html"))
ret["msg"] = "生成报表成功"
except Exception as e:
print(e)
ret["code"] = 1001
ret["error"] = "生成报表异常"
return Response(ret)
def plot_one_y(df, title: str):
df.index = pd.to_datetime(df['date'])
df.sort_index(inplace=True)
df = df.drop(['date'], axis=1)
return (Line(
init_opts=opts.InitOpts(width="1200px", height="400px")).add_xaxis(
xaxis_data=df.index.strftime(
'%Y-%m-%d').values.tolist()).add_yaxis(
series_name=title.upper(),
y_axis=np.round(df['r2'].values, 2).tolist(),
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).set_global_opts(
datazoom_opts=opts.DataZoomOpts(),
legend_opts=opts.LegendOpts(pos_bottom="0%",
pos_right='45%'),
title_opts=opts.TitleOpts(
title=title.upper(),
pos_left='0%',
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='Max'),
opts.MarkPointItem(type_='min', name='Min')
]), ))
def print_bar(self):
bar = Bar(init_opts=opts.InitOpts(width='1080px', height='480px'))
bar.add_xaxis(self.timedata)
# category 是同系列直接的距离，gap是不同系列之间的距离
bar.add_yaxis('治愈人数',
self.heal,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#90EE90'))
if self.name == '湖北':
bar.add_yaxis('死亡人数',
self.dead,
gap='100%',
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#696969'))
# 设置全局变量：x轴标签倾斜度，html主标题
bar.set_global_opts(datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_='inside')
],
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(name_rotate=-15),
title_opts=opts.TitleOpts(self.name,
subtitle='数据来自inews'))
line = Line()
line.add_xaxis(self.timedata)
line.add_yaxis('确诊人数',
self.confirm,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color='#F08080'))
print(self.name + '.html', '已成功生成到', os.getcwd())
# 在bar的基础上画line
bar.overlap(line).render(self.name + '.html')
def creat_pivot_chart(pivot, index, column, agg, value):
index_list = pivot.index.tolist()
column_list = pivot.columns.tolist()
if pivot.max().tolist():
visualmap_max = max(pivot.max().tolist())
range_text = ['最大值', '最小值']
else:
visualmap_max = 1
range_text = ['无', '数据']
# print(pivot.max())
data = [(i, j, int(pivot.iloc[i, j])) for i in range(len(index_list))
for j in range(len(column_list))]
c = (Bar3D().add(
series_name=agg + '(' + value + ')',
data=data,
shading="lambert",
xaxis3d_opts=opts.Axis3DOpts(index_list, type_="category", name=index),
yaxis3d_opts=opts.Axis3DOpts(column_list,
type_="category",
name=column),
zaxis3d_opts=opts.Axis3DOpts(type_="value",
name=agg + '(' + value + ')'),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=visualmap_max,
range_text=range_text),
title_opts=opts.TitleOpts(title="数据透视三维图"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(type_="inside")))
return c
def line_base(l1, l2) -> Line:
lh_list = [y["total_box"] for y in l2]
lh_list.extend([0 for _ in range(3)]) # 前面三天为0
c = (Line(
init_opts=opts.InitOpts(bg_color="", page_title="总票房")).add_xaxis(
[y["date"] for y in reversed(l1)]).add_yaxis(
"哪吒之魔童降世", [y["total_box"] for y in reversed(l1)],
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")])).add_yaxis(
"烈火英雄",
reversed(lh_list),
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(
type_="max")])).set_global_opts(
title_opts=opts.TitleOpts(
title="总票房",
subtitle_textstyle_opts={
"color": "red"
},
subtitle="单位: 万元"),
toolbox_opts=opts.ToolboxOpts()))
return c.render("line.html")
def redis_qps(request, redis_type, ins_id, redis_ip, redis_port):
"""
TODO: 目前查看 QPS的趋势图是硬编码，后台只能查看最近60秒的趋势
:param request:
:param redis_type:
:param ins_id:
:param redis_ip:
:param redis_port:
:return:
"""
real_time_qps = RealTimeQps.objects.all()
if redis_type == 'sentinel':
running_ins_time = RunningInsSentinel.objects.all()
elif redis_type == 'standalone':
running_ins_time = RunningInsStandalone.objects.all()
elif redis_type == 'cluster':
running_ins_time = RunningInsCluster.objects.all()
real_time_obj = real_time_qps.filter(redis_running_monitor_id=ins_id, redis_ip=redis_ip, redis_port=redis_port).order_by('-collect_date')[:60]
running_ins = running_ins_time.filter(redis_ip=redis_ip, running_ins_port=redis_port)
running_ins_name = running_ins.values('running_ins_name').first()
real_time = [real_time.__dict__['collect_date'] for real_time in real_time_obj]
redis_qps = [redis_qps.__dict__['redis_qps'] for redis_qps in real_time_obj]
c = (
Line()
.add_xaxis(real_time)
.add_yaxis(running_ins_name['running_ins_name'], redis_qps, is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="{0}:{1}".format(redis_ip,
redis_port),
subtitle="Redis QPS图"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],)
)
return HttpResponse(c.render_embed())
def redis_qps(request, ins_id):
real_time_qps = RealTimeQps.objects.all()
running_ins_time = RunningInsTime.objects.all()
real_time_obj = real_time_qps.filter(
redis_running_monitor_id=ins_id).order_by('-collect_date')[:60]
running_ins = running_ins_time.filter(id=ins_id)
running_ins_name = running_ins.values('running_ins_name').first()
running_ins_ip = running_ins.values('redis_ip').first()
running_ins_port = running_ins.values('running_ins_port').first()
real_time = [
real_time.__dict__['collect_date'] for real_time in real_time_obj
]
redis_qps = [
redis_qps.__dict__['redis_qps'] for redis_qps in real_time_obj
]
c = (Line().add_xaxis(real_time).add_yaxis(
running_ins_name['running_ins_name'], redis_qps,
is_smooth=True).set_global_opts(
title_opts=opts.TitleOpts(title="{0}:{1}".format(
running_ins_ip['redis_ip'],
running_ins_port['running_ins_port']),
subtitle="Redis QPS图"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")
],
))
return HttpResponse(c.render_embed())
def bar_base_h() -> Bar:
学前 = h['学前'].tolist()
小学 = h['小学'].tolist()
初中 = h['初中'].tolist()
普通高中 = h['普通高中'].tolist()
中职 = h['中职'].tolist()
HY = h['指标'].tolist()
CO = (
Bar()
.add_xaxis(HY)
.add_yaxis("学前", 学前)
.add_yaxis("小学", 小学)
.add_yaxis("初中", 初中)
.add_yaxis("普通高中", 普通高中)
.add_yaxis("中职", 中职)
.set_global_opts(
title_opts=opts.TitleOpts(title="2017城农家庭教育支出水平", subtitle="教育支出水平（元）"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
return CO.render_embed()
def bar_datazoom_inside() -> Timeline:
tl = Timeline()
for i in range(2014, 2020):
c = (
Bar(
init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"
)
)
)
.add_xaxis(list(zip(list(data总.set_index('类别').index))))
.add_yaxis("显示",list(data总["{}".format(i)]))
.set_global_opts(
title_opts=opts.TitleOpts(title="纵横小说月票榜"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
visualmap_opts=opts.VisualMapOpts(type_="color", max_=250000, min_=200,pos_right='20',pos_top='middle'),
toolbox_opts=opts.ToolboxOpts(),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
)
)
tl.add(c, "{}年".format(i))
return tl
def court_pri(keys, values1):
dict = {}
for result in results:
if (result["court"] in keys) and result["court"] not in dict.keys():
dict[result["court"]] = [result["unit_Price"]]
elif (result["court"] in keys) and result["court"] in dict.keys():
dict[result["court"]].append(result["unit_Price"])
keys1 = list(dict.keys())
values = list(dict.values())
list1 = []
for i in range(len(values)):
arr_mean = np.mean(values[i])
list1.append(int(arr_mean))
# print(values)
# print(keys)
# print("长度" + str(len(keys)))
# print(list1)
# print("长度" + str(len(list1)))
b = Bar(init_opts=opts.InitOpts(width="900px", height="900px"))
b.add_xaxis(keys1)
b.add_yaxis("小区价格", list1)
# b.add_yaxis("小区房源",values1)
b.set_global_opts(title_opts=opts.TitleOpts(title="新乡市",
subtitle="各小区平均价格"),
datazoom_opts=opts.DataZoomOpts(),
toolbox_opts=opts.ToolboxOpts())
return b
def draw_map_world(data, to_file, svg_name, label_name, number_max):
"""
画地图
:param data:
:param to_file:
:param svg_name:
:param label_name: 图例名称
:param number_max: 最大值
:return:
"""
geo = Map(init_opts=opts.InitOpts(width="800px", height="600px", bg_color='rgb(255, 255, 255)')) \
.add(label_name, data, maptype="world") \
.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
showLegendSymbol=False) \
.set_global_opts(legend_opts=opts.LegendOpts(item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
visualmap_opts=opts.VisualMapOpts(min_=0,
max_=int(number_max),
background_color='rgb(255, 255, 255)',
is_piecewise=True,
item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)
),
)
# geo.render(to_file)
make_snapshot(snapshot, geo.render(to_file), svg_name) # 生成svg图片
def set_lagou_financing(financing_rank):
"""
绘制拉勾网企业融资图，采用倒漏斗图
:param financing_rank:
:return:漏斗图对象
"""
# 可以用sorted直接将Counter转化为列表形式，里面嵌套字典
data = [
[key, value]
for key, value in financing_rank.items()
]
funnel = (
Funnel(init_opts=opts.InitOpts(width='700px', height='500px'))
.add('企业融资图', data_pair=data, gap=20)
.set_global_opts(
title_opts=opts.TitleOpts(title='拉勾网30000+招聘的企业的技术融资情况'),
toolbox_opts=opts.ToolboxOpts(is_show=False),
legend_opts=opts.LegendOpts(type_='scroll', orient='vertical',
align='right', legend_icon='circle',
pos_left=1, pos_top=150,
textstyle_opts=opts.TextStyleOpts(font_family='楷体',
font_size=16))
)
)
return funnel
def picture4(self):
df = pd.read_sql(
f"select * from {self.table_name} where 检查时间 LIKE '%{self.last_month}%'",
self.conn)
group = df.groupby(by=df['责任人岗位']).count()['性质']
x = group.index.tolist()
y = group.values.tolist()
c = (Line(init_opts=opts.InitOpts()).add_xaxis(x).add_yaxis(
"数量", y).set_global_opts(
title_opts=opts.TitleOpts(title="现场安全信息责任人岗位统计分布",
subtitle=f"统计时间：{self.last_month}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={
"saveAsImage": {
'backgroundColor':
'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
}),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=20), name="责任人岗位")))
print('已画图成功')
return c
def plot(self):
self.chart = Bar(init_opts=opts.InitOpts(page_title=self.title))
# load data
self.chart.add_xaxis(self.data[0][0])
index = 0
for data_set in self.data:
if len(data_set) > 2: # get label from data set
label = data_set[2]
else:
label = self.yaxis_name + str(index)
# self.chart.add_yaxis(label, data_set[1], stack="stack"+str(index))
self.chart.add_yaxis(label, data_set[1], stack="stack")
index += 1
# set options
self.chart.set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100),
xaxis_opts=opts.AxisOpts(name=self.xaxis_name,
name_location='end',
name_gap=15),
yaxis_opts=opts.AxisOpts(
name=self.yaxis_name,
name_location='center',
name_gap=25,
max_=None if self.count_y <= 1 else int(self.max_sum_y * 1.1)))
self.chart.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, position='inside'))
return self
def do_charts():
nvshen = pd.read_csv('nvshen.csv')
nvshen.sort_values('weight_score', ascending=False, inplace=True)
bar = Bar()
count_top = nvshen['count'][0:10].values.tolist()
count_bottom = nvshen['count'][-10:-1].values.tolist()
count = [''.join(list(filter(str.isdigit, i))) for i in count_top] + \
[''.join(list(filter(str.isdigit, i))) for i in count_bottom]
name_top = nvshen['name'][0:10]
name_bottom = nvshen['name'][-10:-1]
name = name_top.values.tolist() + name_bottom.values.tolist()
score_top = nvshen["weight_score"][0:10]
score_bottom = nvshen["weight_score"][-10:-1]
score = score_top.values.tolist() + score_bottom.values.tolist()
bar.add_xaxis(name)
bar.add_yaxis("女神得分/百分制", score, gap="0%")
bar.add_yaxis("打分人数/万", count, gap="0%")
bar.set_global_opts(
title_opts=opts.TitleOpts(title="女神大会", subtitle="女神大排名-top10"),
datazoom_opts=opts.DataZoomOpts(is_show=True, orient="vertical"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
toolbox_opts=opts.ToolboxOpts())
bar.render('女神大排名-top10.html')
word_name = nvshen['name'].values.tolist()
word_value = nvshen["weight_score"].values.tolist()
words = [i for i in zip(word_name, word_value)]
wordcloud = WordCloud()
wordcloud.add("", words, word_size_range=[10, 40], shape='circle')
wordcloud.render("女神词云.html")
def scatter_muti_diemnsion_data():
c = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis(
"商家A",
[list(z) for z in zip(Faker.values(), Faker.choose())],
label_opts=opts.LabelOpts(
formatter=JsCode(
"function (params) {return params.value[1] +' : '+ params.value[2];}"
)
),
)
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
formatter=JsCode(
"function (params) {return params.name +' : ' +params.value[2];}"
)
),
visualmap_opts=opts.VisualMapOpts(
type_="color", max_=150, min_=20, dimension=1
),
)
)
return c
def bar_datazoom_slider(title='Bar-DataZoom（slider-水平）',width="1200px",x_data=['前天','昨天','今天'], \
y_data = [{'name':'商家A','values':[1,6,7]}],
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts()]
) -> Bar:
'''
柱状图
y_data 几根柱子，默认1根
y_data = [{'name':'商家A','values':[1,6,7]}]
toolbox_opts 是否展示右上方的小工具，默认展示，不展示传 None
datazoom_opts 是否展示图表下方可拖动的进度条，默认展示，不展示传 None
'''
b = Bar(init_opts=opts.InitOpts(width=width))
if len(y_data) == 1:
b.add_yaxis(y_data[0]['name'], y_data[0]['values'])
if len(y_data) == 2:
b.add_yaxis(y_data[0]['name'], y_data[0]['values'])
b.add_yaxis(y_data[1]['name'], y_data[1]['values'])
b.add_xaxis(x_data)
b.set_global_opts(
title_opts=opts.TitleOpts(title=title),
datazoom_opts=datazoom_opts,
toolbox_opts=toolbox_opts,
)
return b
def bar_gdp(data):
"""柱状图"""
try:
x_data = data[0]
c = (Bar().add_xaxis(x_data).add_yaxis(
"增长率(%)",
list(map(lambda x: round(x, 1), data[1])),
category_gap="20%",
color="#675bba").set_global_opts(
title_opts=opts.TitleOpts(title="人均GDP年增长率"),
toolbox_opts=opts.ToolboxOpts(),
yaxis_opts=opts.AxisOpts(offset=0),
xaxis_opts=opts.AxisOpts(
type_="category",
offset=int(min(data[1]) * 15),
axislabel_opts=opts.LabelOpts(rotate=30,
font_weight="bold",
interval=0,
font_size=12,
border_width=5),
),
))
log.info("柱状图创建成功")
return c
except Exception as ex:
log.error("柱状图创建失败: {0}".format(str(ex)))
return None
def line_base():
c = (
Line()
# 传入X轴数据,选择七个品类中的一种中的七样东西
.add_xaxis(Faker.choose())
# 传入Y轴数据，第一个参数是系列的名称，第二个参数是数据(随机选择20-150之间的整数)
# 平滑曲线加上is_smooth=True即可自动平滑
.add_yaxis('商家A', Faker.values(), is_smooth=True)
# 隐藏某系列数据False,隐藏后图例是灰色，点击就可以显示出来,默认参数是True显示
.add_yaxis('商家B', Faker.values(), is_smooth=True, is_selected=True)
# 设置数据配置项
# 每个商品的数量是显示在点的上部，改变数据的颜色
.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, color='red', position='top'))
# 设置全局配置项
.set_global_opts(
# 显示主副标题
title_opts=opts.TitleOpts(title='主标题:商家', subtitle='副标题:销售记录'),
# 显示工具栏,工具栏用于刷新图片，下载图片，查看图片数据等
toolbox_opts=opts.ToolboxOpts(),
# 隐藏图例False，默认显示图例True,没有该设置，默认所有图例都显示
legend_opts=opts.LegendOpts(is_show=True),
# 设置Y轴坐标刻度
yaxis_opts=opts.AxisOpts(min_=0, max_=180)))
# 绘制图片
pic = c.render()
return pic
def musics_rating_bar(id,num) -> Bar:
(key,val,num) = musics_rating(id,num)
if key == 0:
return 0
c = (
Bar()
.add_xaxis([x + '分' for x in key])
.add_yaxis("音乐数", list(map(str, val)))
.set_global_opts(title_opts=opts.TitleOpts(title="各评分的音乐数", subtitle="数据量：" + str(num),),
yaxis_opts=opts.AxisOpts(name_gap=50,name_rotate=90,name_location="center",name="音乐数（个）"),
xaxis_opts=opts.AxisOpts(name="评分"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
# orient="vertical",
pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"magicType": {
"show": True,
"title": "切换",
"type": ['line', 'bar'],
# 启用的动态类型，包括'line'（切换为折线图）, 'bar'（切换为柱状图）, 'stack'（切换为堆叠模式）, 'tiled'（切换为平铺模式）
},
"restore": {},
"saveAsImage": {},
},
),
)
.dump_options_with_quotes()
)
return c
def get_month_data(search, condition="month"):
# 获取每个月的数据
q = Q()
condition = "date__" + condition
# print(condition,search)
q.children.append((condition, search))
source_info = models.Customer.objects.filter(q).values_list(
"source").annotate(num=Count("pk")) # queryset
# 替换来源称对应的中文名
source_list = list(
source_info) # [('baidu',28)],source_type = (("qq","qq"),)
source_list = [list(i) for i in source_list]
for i in range(len(source_list)):
for j in range(len(models.source_type)):
if source_list[i][0] == models.source_type[j][0]:
source_list[i][0] = models.source_type[j][1]
source_list = [tuple(i) for i in source_list]
# print(source_list)
pie = (Pie().add(
"客户来源",
source_list,
radius=["30%", "75%"],
center=["50%", "50%"],
rosetype="area",
).set_global_opts(title_opts=opts.TitleOpts(
title="2019年客户月流量分析", )).set_series_opts(
label_opts=opts.LabelOpts(
formatter="{b}: {c}")).set_global_opts(
toolbox_opts=opts.ToolboxOpts(orient="vertical")))
return pie
