def show_bar(id):
fix_data()
bar = Bar(init_opts=opts.InitOpts(
width='1200px', height='800px', page_title='page'))
bar.add_xaxis(fix_id)
bar.add_yaxis('线程', fix_time, stack="stack1", color=Faker.rand_color())
bar.reversal_axis()
bar.set_global_opts(title_opts=opts.TitleOpts(title="压测时间图"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"))
bar.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"),
opts.MarkPointItem(type_="average", name="平均值"),
]))
bar.render('压力测试{}.html'.format(id + 1))
def bar(keywords):
xaxis = []
yaxis = []
for i in keywords:
xaxis.append(i[0])
yaxis.append(i[1])
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.WALDEN))
bar.add_xaxis(xaxis[:25])
bar.add_yaxis("重要性", yaxis[:25])
bar.set_global_opts(title_opts=opts.TitleOpts(title='TF-IDF Ranking'))
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts(is_show=True))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.reversal_axis()
return bar
def analyze_country():
#lambda表达式进行以空格分开，并返回列表
countryList = reduce(lambda x, y: x + y, list(df['Country'].apply(lambda x: x.split(' '))))
#进行分类统计
countryDic = Counter(countryList)
#将字典转为DataFrame
country_dataframe = pd.DataFrame.from_dict(countryDic, orient='index')
print(country_dataframe)
country = list(country_dataframe.index)
num = list(country_dataframe.iloc[:, 0])
bar = Bar()
bar.add_xaxis(country[:10])
bar.add_yaxis("各国家电影数量Top10", num[:10])
bar.set_global_opts(title_opts=opts.TitleOpts(title="各国家电影数量Top10"))
bar.render("./html_files/country.html")
def create_percent_stack_bar(title, x_list, data_list, data_unit):
"""
:param title: 图表名称
:param x_list: 横坐标内容为时间序列，纵坐标高度为多个(同级)指标的总和，具体各个指标的数值、比例以不同颜色标出
:param data_list: 字典{key1:value1,key2:value2...}，构成柱状图的多个不同类型的bar
:param data_unit: y轴的数值单位
:return:
"""
# 数据格式重构：
count = len(x_list) # 数据期数
sumList = [] # 汇总
for i in range(count):
sumList.append(0)
for key in data_list.keys():
for i in range(count):
if data_list[key][i] is not None: # 空数据处理
sumList[i] = sumList[i] + data_list[key][i]
for key in data_list.keys():
value_list = []
for i in range(count):
new_dic = {}
new_dic["value"] = data_list[key][i]
if data_list[key][i] is not None: # 空数据处理,sumList[i]不会为None或为0，指标选择时已经做了规避
new_dic["percent"] = data_list[key][i]/sumList[i]
else:
new_dic["percent"] = None
value_list.append(new_dic)
data_list[key] = value_list
c = Bar(init_opts=opts.InitOpts(theme=THEME_TYPE))
c.add_xaxis(x_list)
for key in data_list.keys():
c.add_yaxis(key, data_list[key],stack="stack1") # stack参数设置层叠样式
c.set_series_opts(label_opts=opts.LabelOpts(position="right",
formatter=JsCode(
"function(x){console.log(x);return Number(x.data.percent * 100).toFixed() + '%';}"
)))
c.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="right"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit))
src_path = "./指标-年度-图片生成/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
make_snapshot(snapshot, c.render(html_file_name), img_file_name)
print(img_file_name+":生成完毕...")
def dsp_gcc(): # 显示遗传相关
import xlrd
# file = "breeding.xlsx"
f1 = xlrd.open_workbook(file)
sheet1 = f1.sheet_by_name('Sheet1')
lst = gcc(file)[0] # 接收计算的早选指数
y = []
colorArr = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E',
'F'
]
for i in range(len(lst)):
color = "#"
for j in range(6):
color += colorArr[random.randint(0, 14)]
y.append(
opts.BarItem(
value=lst[i],
itemstyle_opts=opts.ItemStyleOpts(color=str(color)),
# itemstyle_opts=opts.ItemStyleOpts(color=color[i])
))
x_ax = gcc(file)[1]
y_ax = []
bar = Bar(init_opts=opts.InitOpts( #bg_color='rgba(255,250,205,0.2)',
width="1800px", height="800px"))
bar.set_colors(["#FF6347", "black"])
bar.add_xaxis(x_ax)
bar.add_yaxis("遗传相关", y, category_gap="60%")
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max", name="最大值")]))
# bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
bar.set_global_opts(title_opts=opts.TitleOpts(title="显示已有数据遗传相关"))
for i in range(sheet1.nrows - 1):
y_ax.append(i)
line = Line(init_opts=opts.InitOpts(width="1800px", height="800px"))
line.add_xaxis(y_ax)
line.add_yaxis(series_name="",
y_axis=lst,
label_opts=opts.LabelOpts(is_show=True))
picture = bar.overlap(line)
return render_template("display.html",
picture_options=picture.dump_options())
def bar_chart():
# 柱状图
bar = Bar()
x_y_axis = get_chart_data()
bar.add_xaxis(x_y_axis[0])
for j in x_y_axis[1]:
for key, value in j.items():
bar.add_yaxis(key, value, label_opts=opts.LabelOpts(is_show=False))
bar.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]), )
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="API耗时统计",
subtitle="生产环境",
pos_left="30%",
title_textstyle_opts=opts.TextStyleOpts(color='red'),
subtitle_textstyle_opts=opts.TextStyleOpts(color='blue')),
xaxis_opts=opts.AxisOpts(
name="运行时间",
type_="category",
boundary_gap=True,
# axislabel_opts=opts.LabelOpts(rotate=15)
),
yaxis_opts=opts.AxisOpts(name="实际的响应时间（单位：秒）",
# min_=0,
# max_=20
),
legend_opts=opts.LegendOpts(type_='scroll',
selected_mode='multiple',
pos_left='right',
pos_top='10%',
orient='vertical'),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
toolbox_opts=opts.ToolboxOpts(is_show=True, pos_left='right'),
visualmap_opts=opts.VisualMapOpts(is_show=True,
type_="size",
min_=0,
max_=20,
range_text=["最大值", "最小值"]),
datazoom_opts=[opts.DataZoomOpts(range_start=50, range_end=100)],
)
bar = bar.dump_options_with_quotes()
return bar
def workhour_get():
p = WorkhourInfo()
# 获取最近的12月
df = p.df.dropna()[-12:]
xdata = df['月份'].tolist()
# fig = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
fig = Bar()
fig.add_xaxis(xdata)
fig.add_yaxis('总投入',
df['实际投入工时'].tolist(),
label_opts=opts.LabelOpts(position='inside'))
for v in df.columns[6:]:
fig.add_yaxis(v,
df[v].tolist(),
stack='detail',
is_selected=False,
label_opts=opts.LabelOpts(is_show=False))
fig.set_global_opts(
title_opts=opts.TitleOpts(title="最近12月工时投入"),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
yaxis_opts=opts.AxisOpts(name='工时'),
)
#"""
df['投入总工时比率'] = (df['投入总工时比率'] * 100).round(0)
maxratio = df['投入总工时比率'].max()
maxratio = 100 if maxratio <= 100 else maxratio
ratio = df['投入总工时比率'].tolist()
line = Line()
line.add_xaxis(xdata)
line.add_yaxis('投入工时率', ratio, yaxis_index=1)
fig.extend_axis(yaxis=opts.AxisOpts(name="投入工时率",
type_="value",
min_=0,
max_=maxratio,
axislabel_opts=opts.LabelOpts(
formatter="{value} %")))
fig.overlap(line)
#"""
return fig.dump_options_with_quotes()
def process_statistics():
para = {'size': 80}
data_source = requests.post("http://localhost:7000/es_virus_data",
data=json.dumps(para)).json()
# 得出地理信息/感染数量/
confirmedCount_province_list = {}
suspectedCount_province_list = {}
curedCount_province_list = {}
deadCount_province_list = {}
for source in province_souce['hits']['hits']:
base = source['_source']
provinceShortName = base['provinceShortName']
confirmedCount = base['confirmedCount']
suspectedCount = base['suspectedCount']
curedCount = base['curedCount']
deadCount = base['deadCount']
# print('{}-{}-{}-{}-{}'.format(provinceShortName,confirmedCount,suspectedCount,curedCount,deadCount))
if provinceShortName not in confirmedCount_province_list.keys():
confirmedCount_province_list[provinceShortName] = int(
confirmedCount)
if provinceShortName not in suspectedCount_province_list.keys():
suspectedCount_province_list[provinceShortName] = int(
suspectedCount)
if provinceShortName not in curedCount_province_list.keys():
curedCount_province_list[provinceShortName] = int(curedCount)
if provinceShortName not in deadCount_province_list.keys():
deadCount_province_list[provinceShortName] = int(deadCount)
provinces = list(confirmedCount_province_list.keys())
confirm_list = list(confirmedCount_province_list.values())
suspect_list = list(suspectedCount_province_list.values())
cure_list = list(curedCount_province_list.values())
dead_list = list(deadCount_province_list.values())
# bar = Line()
bar = Bar()
bar.add_xaxis(provinces)
bar.add_yaxis("确诊", confirm_list, stack="stack1")
bar.add_yaxis("疑似", suspect_list, stack="stack1")
bar.add_yaxis("治愈", cure_list, stack="stack1")
bar.add_yaxis("死亡", dead_list, stack="stack1")
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
bar.set_global_opts(title_opts=opts.TitleOpts(title="省份疫情统计"))
return bar
# if __name__ == '__main__':
# process_statistics()
def member_month_workhour():
p = PJinfo()
m = p.member_workhour_get()
tl = Timeline()
li_month = sorted(m.keys(), reverse=True)
for i in li_month:
month = m[i].sort_values(0, axis=1, ascending=False)
fig = Bar()
fig.add_xaxis(month.columns.tolist())
fig.add_yaxis('投入工时', month.values.flatten().tolist())
fig.set_global_opts(
title_opts=opts.TitleOpts(title=f"2020年{i}月人员工时投入"),
yaxis_opts=opts.AxisOpts(name='工时'),
)
tl.add(fig, time_point=f'{i}月')
return tl.dump_options_with_quotes()
def do_baidu_charts():
nvshen = pd.read_csv('nvshen_new.csv')
nvshen.sort_values('aip_score', ascending=False, inplace=True)
bar = Bar()
name_top = nvshen['name'][0:10]
name_bottom = nvshen['name'][-10:-1]
name = name_top.values.tolist() + name_bottom.values.tolist()
score_top = nvshen["aip_score"][0:10]
score_bottom = nvshen["aip_score"][-10:-1]
score = score_top.values.tolist() + score_bottom.values.tolist()
bar.add_xaxis(name)
bar.add_yaxis("女神新得分/百分制", score)
bar.set_global_opts(
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('女神新排名.html')
def draw_balance_bar(xaxis,
yaxis,
difference=None,
title="消费统计",
markline=None,
width=2000) -> Bar:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param difference: 差值 (比如：收入100，消费80，差值就是20) : ['title', [1,2,3,4]]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Bar
"""
bar = Bar()
bar.add_xaxis(xaxis)
for name, axis in yaxis:
bar.add_yaxis(name, axis, category_gap="20%", gap="0%")
bar.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")
],
tooltip_opts=opts.TooltipOpts(
trigger='axis', axis_pointer_type='shadow'))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if difference:
name, diff_yaxis = difference
line = Line().add_xaxis(xaxis).add_yaxis(
name,
diff_yaxis,
symbol_size=15,
z_level=1,
is_symbol_show=False,
)
bar.overlap(line)
if markline is not None:
bar.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=markline, name='预算')]))
return bar
def bar_markline_type():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("商家A",Faker.values())
obj_bar.add_yaxis("商家B",Faker.values())
obj_bar.set_series_opts(
label_opts=opts.LabelOpts(is_show = False),
markline_opts=opts.MarkPointOpts(
data = [
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]
)
)
obj_bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkLine（指定类型）", subtitle="平均值的线"))
return obj_bar
def bar_datazoom_slider(xaxis,yaxis_name,yaxis,title_name) -> Bar:
if isinstance(yaxis_name,str):
c=(
Bar()
.add_xaxis(xaxis)
.add_yaxis(yaxis_name,yaxis)
.set_global_opts(
title_opts=opts.TitleOpts(title=title_name),
datazoom_opts=[opts.DataZoomOpts()],
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(name="我是 Y 轴"),
xaxis_opts=opts.AxisOpts(name="我是 X 轴"),
)
)
elif isinstance(yaxis,list):
c=Bar()
c.add_xaxis(xaxis)
for i in range(len(yaxis_name)):
c.add_yaxis(yaxis_name[i],yaxis[i], gap="0%",category_gap="10%",is_selected=True)
c.set_global_opts(
title_opts=opts.TitleOpts(title=title_name),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
#datazoom_opts=opts.DataZoomOpts(orient="vertical"),
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(name="我是 Y 轴"),
xaxis_opts=opts.AxisOpts(name="我是 X 轴"),
)
c.set_series_opts(
label_opts=opts.LabelOpts(is_show=True),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值"),
]
)
)
return c
def table(self):
file = open(self.name + "_title.txt", "r", encoding="utf-8")
title = []
read = []
list = file.readlines()
for i in list:
pos = None
end = i.__len__() - 1
while end >= 0:
if (i[end] == " "):
pos = end
break
end -= 1
pos2 = None
while end >= 0:
if (i[end] != " "):
pos2 = end + 1
break
end -= 1
title_str = i[:pos2]
j = title_str.__len__()
j = int(j / 2)
title_str = title_str[:j] + "\n" + title_str[j:]
title.append(title_str)
read_str = i[pos:]
read_str = read_str.strip()
read.append(int(read_str))
read_data = []
title_data = []
j = 0
for i in read:
if i > self.num:
read_data.append(i)
title_data.append(title[j])
j += 1
bar = Bar()
bar.add_xaxis(title_data)
bar.add_yaxis("访问量大于" + str(self.num) + "的博客", read_data)
bar.reversal_axis()
bar.render(self.name + ".html")
def bar_markpoint_custom():
x, y = Faker.choose(), Faker.values()
obj_bar = Bar()
obj_bar.add_xaxis(x)
obj_bar.add_yaxis("A",
y,
# 自定义标注点,设置在这里obj_bar.add_yaxis，只有一个系列图标注，
# 如果该参数设置到obj_bar.set_series_opts，所有系列柱的指定柱就会标注该点，但值是一样的是第一个系列柱的值
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(name="自定义标记点", coord=[x[2], y[2]], value=y[2])]
))
obj_bar.add_yaxis("B", Faker.values())
# 设置标签不显示
obj_bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
obj_bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkPoint（自定义）", subtitle="副标题"))
return obj_bar
def get_education_background():
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
bar.set_global_opts(title_opts=opts.TitleOpts(title="学历占比", ),
legend_opts=opts.LegendOpts(pos_left="20%"))
bar.add_xaxis(education)
value = []
for i in education:
where = {
'experience': {
'$regex': ".*" + i + ".*"
},
}
count = mongodb.get_count_by_where(where)
value.append(count)
bar.add_yaxis("要求学历招聘", value, bar_width=20)
bar.set_colors("blue")
return (bar)
def bar_base():
# 今天日期
todaydate = datetime.date.today()
# 时间周期-横坐标
date_periods = []
for i in range(6, 0, -1):
date_i = todaydate - datetime.timedelta(days=i)
date_periods.append(date_i)
date_periods_day = [str(date.day) + '日' for date in date_periods]
bar = Bar()
bar.add_xaxis(date_periods_day)
bar.add_yaxis("商家A", [randrange(0, 100) for _ in range(6)])
bar.add_yaxis("商家B", [randrange(0, 100) for _ in range(6)])
title_options = opts.TitleOpts(title="群发数据统计", subtitle="最近7日数据")
bar.set_global_opts(title_opts=title_options)
return bar
def vw_pyecharts():
data = xlrd.open_workbook('./data/xlrd-data.xlsx')
sheet_table = data.sheets()[0]
xdata = []
ydata = []
for i in range(1, sheet_table.nrows):
print(sheet_table.row_values(i))
xdata.append(sheet_table.row_values(i)[0])
ydata.append(sheet_table.row_values(i)[1])
print(xdata)
print(ydata)
# 数据可视化，柱状图
bar = Bar()
bar.add_xaxis(xdata)
bar.add_yaxis("名称1", ydata)
bar.render("show.html")
def bar_graphic_component():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("商家A", Faker.values())
obj_bar.add_yaxis("商家B", Faker.values())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-Graphic 组件示例", subtitle="副标题"),
graphic_opts=opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text="pyecharts bar chart",
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
)
]
)
)
return obj_bar
def most_appear_per_chapter():
"""
每一回出场次数最多的角色
用柱状图进行可视化
:return:
"""
jieba.load_userdict("character.txt") # 加载人物 nr
character_list = []
with open('character.txt', encoding='utf-8') as r:
lines = r.readlines()
for line in lines:
name = line.split(',')[0]
character_list.append(name)
counts_per_chapter = []
char_per_chapter = []
for i in range(120):
with open('chapters/' + str(i + 1) + '.txt', encoding='utf-8') as r:
text = r.read()
p = pseg.cut(text) # return: generator
fre_char_dist = OrderedDict()
for word, tag in p:
if tag == 'nr' and word in character_list:
if word in fre_char_dist.keys():
fre_char_dist[word] += 1
else:
fre_char_dist[word] = 1
key = list(fre_char_dist.keys())[0]
counts_per_chapter.append(fre_char_dist[key])
char_per_chapter.append(key + '-第' + str(i + 1) + '回')
fre_char_dist.clear()
# print(len(counts_per_chapter))
# print(len(char_per_chapter))
bar = Bar()
bar.add_xaxis(char_per_chapter)
bar.add_yaxis('频次', counts_per_chapter)
bar.set_global_opts(title_opts=opts.TitleOpts(title="红楼梦每一回出场次数最多的人物"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts()])
bar.render('most_appear_per_chapter.html')
def grid_vertical() -> Grid:
#grid=Grid()
#for item in name_list:
tl = Timeline()
for t in time_range:
bar = Bar()
bar.add_xaxis(name_list[0])
for name in name_list[0]:
price_list = list(data[(data['name'] == name)
& (data['dates'] == t)]['price'])
if len(price_list) == 0:
bar.add_yaxis(name, [0], category_gap='5%', gap='5%')
else:
bar.add_yaxis(name, price_list, category_gap='5%', gap='5%')
bar.set_global_opts(title_opts=opts.TitleOpts("某商店{}营业额".format(t)))
tl.add(bar, "{}年".format(t))
#grid.add(tl, grid_opts=opts.GridOpts(pos_bottom="60%"))
tl.add_schema(is_auto_play=True, play_interval=1000)
return tl #grid
def _draw_bar(data_list: typing.List[ClassifierResult]) -> Bar:
# draw bar chart
bar = Bar()
x_axis = sorted(list(set([i.stage for i in data_list])))
y_axis = list()
offset = data_list[1].timestamp - data_list[0].timestamp
for each_stage_name in x_axis:
each_stage = sorted([i for i in data_list if i.stage == each_stage_name], key=lambda x: x.frame_id)
time_cost = each_stage[-1].timestamp - each_stage[0].timestamp + offset
y_axis.append(time_cost)
bar.add_xaxis(x_axis)
bar.add_yaxis('time cost', y_axis)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="Time Cost"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
logger.debug(f'time cost: {dict(zip(x_axis, y_axis))}')
return bar
def draw_balance_bar(balance_axis: BalanceAxis, title="收支统计", markline=None, width=2000) -> Bar:
bar = Bar()
bar.add_xaxis(balance_axis.x_axis)
for name, axis in balance_axis.y_axis.items():
bar.add_yaxis(name, axis, category_gap="20%", gap="0%")
bar.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")],
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='shadow'))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
bar.set_series_opts(markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(y=markline, name='预算')]),
)
return bar
def average_salary_bar(self):
option_list = []
option_dict = {}
for i in self.data.col_values(8, 1):
if i not in option_list:
option_list.append(i)
if not option_dict.get(i):
option_dict[i] = int(0)
for i in self.data.col_values(8, 1):
option_dict[i] = option_dict[i] + 1
option_value_list = list(option_dict.values())
bar = Bar()
bar.add_xaxis(option_list)
print(type(option_value_list))
bar.add_yaxis("平均薪资", option_value_list)
bar.set_global_opts(title_opts=opts.TitleOpts(title="平均薪资"))
bar.render("average_salary.html")
def draw_bar(x, mid, legends, name):
bar = Bar(init_opts={"width": "3000px", "height": "600px"})
# bar.add_xaxis(x)
assert len(mid) == len(legends)
bar.add_xaxis(x)
for j in range(len(mid)):
bar.add_yaxis(mid[j], legends[j], stack="stack")
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=18, font_size=10)),
title_opts=opts.TitleOpts(title=f"X={name}"))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
SAVE_PATH = ".." + os.sep + "img" + os.sep + f"{name}.html"
bar.render(SAVE_PATH)
a = 1
def main():
refresh_cookie()
provices = {}
for date in DATE:
result = query(date)
for item in result:
name = item['name']
provices.setdefault(name, [])
provices[name].append(item['total'])
if len(provices.keys()) == 0:
return
bar = Bar()
bar.add_xaxis(DATE)
for p in provices.keys():
bar.add_yaxis(p, provices[p])
bar.render('housing_sales_area.html')
def bar_markpoint_type():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("A", Faker.values())
obj_bar.add_yaxis("B", Faker.values())
# 设置标签不显示
obj_bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
# 标注指定类型的点
markpoint_opts=opts.MarkPointOpts(
data =[
opts.MarkPointItem(type_="max",name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
]
)
)
obj_bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkPoint（指定类型）", subtitle="副标题"))
return obj_bar
def bar_base(ts_codes) -> Bar:
assert ts_codes is not None, '股票代码为空'
# if ts_codes is None:
# ts_codes = ['000001.SZ']
df = readProfits(ts_codes)
print(df)
c = Bar()
stocks = []
for key, data in df.iteritems():
if key == 'end_date':
c.add_xaxis(data.to_list())
else:
name = readStockName(key)
title = f'{key} {name}'
stocks.append(title)
c.add_yaxis(title, data.to_list())
c.set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例",
subtitle=', '.join(stocks)))
return c
def paintGraph(contestInfo, starttime='2017-09', endtime='2020-07'):
# 先根据指定的起始日期和截止日期构造需要的数据
x, cf, ac, nc, jsk, total = [], [], [], [], [], []
for key, value in sorted(contestInfo.items()):
if key >= starttime and key <= endtime:
x.append(key)
cf.append({'value': value.get('cf', 0)})
ac.append({'value': value.get('ac', 0)})
nc.append({'value': value.get('nc', 0)})
jsk.append({'value': value.get('jsk', 0)})
total.append(value.get('total', 0))
# print(total)
# 绘制图表
bar = Bar(
init_opts=opts.InitOpts(theme=ThemeType.LIGHT, page_title='各年月比赛数量统计'))
bar.set_global_opts(title_opts=opts.TitleOpts(title='各年月比赛数量统计'),
xaxis_opts=opts.AxisOpts(name='比赛年月'),
yaxis_opts=opts.AxisOpts(name='比赛数量统计'))
bar.add_xaxis(x)
bar.add_yaxis("CodeForces", cf, stack="stack1", category_gap="50%")
bar.add_yaxis("NowCoder", nc, stack="stack1", category_gap="50%")
bar.add_yaxis("AtCoder", ac, stack="stack1", category_gap="50%")
bar.add_yaxis("计蒜客", jsk, stack="stack1", category_gap="50%")
bar.set_series_opts(label_opts=opts.LabelOpts(
position="inside",
formatter=JsCode(
"function(x){return x.data.value ? Number(x.data.value) : '';}"),
))
line = Line()
line.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
line.add_xaxis(xaxis_data=x)
line.add_yaxis(
series_name="total",
y_axis=total,
symbol="Circle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=True),
)
# 将柱状图与折线图一起输出到html文件中
bar.overlap(line).render('acmersite02.html')
def timeline_bar():
cursor = connection.cursor()
cursor.execute("""SELECT
DATE_FORMAT( rev.create_time, '%Y-%m-%d' ) create_time,
revt.`name`,
count( * )
FROM
resource_event rev
LEFT JOIN resource_eventtype revt ON rev.event_type_id = revt.id
GROUP BY
create_time,
event_type_id""")
rows = cursor.fetchall()
name_list = []
event_type_list = []
for row in rows:
name = row[0]
if name not in name_list:
name_list.append(name)
event_type = row[1]
if event_type not in event_type_list:
event_type_list.append(event_type)
tl = Timeline(init_opts=opts.InitOpts(height="350px"))
for day in name_list:
bar = Bar()
bar.add_xaxis(['违章记录'])
for i in range(len(event_type_list)):
event_type_name = event_type_list[i]
num = 0
for row in rows:
if day == row[0] and event_type_name == row[1]:
num = row[2]
break
bar.add_yaxis(event_type_name, [num])
bar.set_global_opts(title_opts=opts.TitleOpts("{}".format(day)))
tl.add(bar, "{}".format(day))
return tl.render_embed()
