def weather_show(path):
# 定义时间线标题
title = ["最高气温", "昼夜温差", "降水量"]
# 定义查询字段
field = ["high_temp", "differ_temp", "precipitation"]
# 定义标记单位
remarks = ["温度(℃)", "温度(℃)", "降水量(mm)"]
# 定义时间线对象
tl = Timeline()
for i in range(3):
# 查询数据
name, value = WeatherTrans().get_data(field[i], field[i])
# 统计表标题
bar_title = "2020年3月15日全国" + title[i] + "Top 10 城市"
# 定义柱状图对象
bar = Bar()
# 添加横坐标
bar.add_xaxis(name)
# 添加纵坐标
bar.add_yaxis(remarks[i],
value,
label_opts=opts.LabelOpts(position="right"))
# 绘制横向统计图
bar.reversal_axis()
# 添加标题
bar.set_global_opts(title_opts=opts.TitleOpts(bar_title))
# 将统计图添加到时间线中
tl.add(bar, title[i])
# 生成html文件
tl.render(path)
def create_likes(df):
# 排序，降序
df = df.sort_values('likes', ascending=False)
# 获取TOP10的数据
attr = df['name'][0:10]
v1 = [float('%.1f' % (float(i) / 100000000)) for i in df['likes'][0:10]]
# 初始化配置
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
# x轴数据
bar.add_xaxis(list(reversed(attr.tolist())))
# y轴数据
bar.add_yaxis("", list(reversed(v1)))
# 设置全局配置项，标题、工具箱(下载图片)、y轴分割线
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V点赞数TOP10(亿)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
# 设置系列配置项，标签样式
bar.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, position="right", color="black"))
bar.reversal_axis()
bar.render("抖音大V点赞数TOP10(亿).html")
def plt_feat_import(model_name, max_cols=9999, max_print=10):
from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB
from pyecharts import options as opts
from pyecharts.charts import Bar
# 处理数据并排序
x_name = model_name.feature_name()
y_val = [int(x) for x in model_name.feature_importance()]
val_pairs = [x for x in zip(x_name, y_val)]
val_pairs.sort(key=lambda x: x[1])
# 设置图像属性
bar = Bar()
bar.add_xaxis([x[0] for x in val_pairs][-max_cols:])
bar.add_yaxis("特征重要度", [x[1] for x in val_pairs][-max_cols:])
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, position='right'))
bar.set_global_opts(
title_opts=opts.TitleOpts(title="特征重要度"),
datazoom_opts=opts.DataZoomOpts(),
)
bar.reversal_axis()
print(val_pairs[-max_print:][::-1])
return bar
def create_avg_likes(df):
# 筛选
df = df[df['videos'] > 0]
# 计算单个视频平均点赞数
df.eval('result = likes/(videos*10000)', inplace=True)
df['result'] = df['result'].round(decimals=1)
df = df.sort_values('result', ascending=False)
# 取TOP10
attr = df['name'][0:10]
v1 = ['%.1f' % (float(i)) for i in df['result'][0:10]]
# 初始化配置
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
# 添加数据
bar.add_xaxis(list(reversed(attr.tolist())))
bar.add_yaxis("", list(reversed(v1)))
# 设置全局配置项，标题、工具箱(下载图片)、y轴分割线
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V平均视频点赞数TOP10(万)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
# 设置系列配置项
bar.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, position="right", color="black"))
# 翻转xy轴
bar.reversal_axis()
bar.render("抖音大V平均视频点赞数TOP10(万).html")
def show_score_top():
# 查找上映年份集合
showtime = select_showtime()
# 生成时间轴的图
timeline = Timeline(
init_opts=opts.InitOpts(page_title="豆瓣电影TOP250-评分TOP10的电影", ))
for year in showtime:
film_tuple = select_film(year)
date_list = select_data(year)
timeline.add_schema(is_auto_play=True, play_interval=1000)
# 柱状图初始化
bar = Bar()
# 横坐标
bar.add_xaxis(film_tuple[0])
# 纵坐标
bar.add_yaxis(
"",
date_list,
# 数据靠右显示
label_opts=opts.LabelOpts(is_show=True, position='right'))
# 横纵坐标翻转
bar.reversal_axis()
# 全局配置
bar.set_global_opts(
# 标题
title_opts=opts.TitleOpts(
title="豆瓣电影TOP250-第{}年评分TOP10的电影".format(year),
pos_left='center'),
# 横坐标隐藏
xaxis_opts=opts.AxisOpts(is_show=False,
max_=select_film(year)[2],
min_=(float(select_film(year)[3]) - 0.1),
split_number=10),
# 纵坐标
yaxis_opts=opts.AxisOpts(
max_=9,
# 字体大小
axislabel_opts=opts.LabelOpts(font_size=10),
# 隐藏坐标轴
axisline_opts=opts.AxisLineOpts(is_show=False),
# 隐藏刻度
axistick_opts=opts.AxisTickOpts(is_show=False)))
# 组合组件
grid = (Grid().add(bar,
grid_opts=opts.GridOpts(pos_top='8%',
pos_bottom='12%',
pos_left='25%')))
timeline.add(grid, "{}年".format(year))
timeline.add_schema(is_auto_play=True,
play_interval=1000,
is_loop_play=False,
width='820px',
pos_left='60px')
# 生成HTML
html = "pages/iframes/score_top.html"
timeline.render("./templates/" + html)
return html
def bar(x, y, x_name, y_name):
bar = Bar(init_opts=opts.InitOpts(height="600px", width="1200px"))
bar.add_xaxis(xaxis_data=x)
bar.add_yaxis(series_name='每年上架數量', yaxis_data=y)
bar.set_global_opts(title_opts=opts.TitleOpts(title='數量'),
xaxis_opts=opts.AxisOpts(name=x_name),
yaxis_opts=opts.AxisOpts(name=y_name))
bar.set_series_opts(label_opts=opts.LabelOpts(position='right'))
bar.reversal_axis()
return bar
def bar_reversal_axis():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("A", Faker.values())
obj_bar.add_yaxis("B", Faker.values())
# 设置翻转 XY 轴
obj_bar.reversal_axis()
# 设置标签靠右显示
obj_bar.set_series_opts(label_opts=opts.LabelOpts(position="right"))
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-翻转 XY 轴", subtitle="副标题"),)
return obj_bar
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 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 print_bar(confirm_frame, timelist, all_province_name):
tl = Timeline(init_opts=opts.InitOpts(
page_title='疫情可视化', width='1080px', height='480px'))
# 调节播放速率 ,是否自动播放，是否循环播放，是否显示时间轴
tl.add_schema(play_interval=300,
is_auto_play=True,
is_loop_play=False,
is_timeline_show=True)
l = len(timelist)
for i in range(0, l):
# 将每天的数据单独拿出来进行排序
s = {'province': all_province_name, 'confirm': confirm_frame.loc[:][i]}
s = pd.DataFrame(s)
# 翻转是镜像翻转所以 这里要从大到小排序
s = s.sort_values(by='confirm', ascending=1)
province_name = s[-10:]['province']
confirm = s[-10:]['confirm']
heal_list, dead_list = get_Top10info(province_name, l - i)
bar = Bar()
bar.add_xaxis(province_name.to_list())
# 名字，数据，柱状图间距， label标签显示在右侧|这样翻转的时候就在右侧
# gap 神奇的参数因为每个柱状图有固定位置|宽度l， 正代表向正向移动百分比l，负代表向负向移动百分比l
bar.add_yaxis('确诊人数',
confirm.to_list(),
label_opts=opts.LabelOpts(position='right'),
color='#696969')
bar.add_yaxis('治愈人数',
heal_list,
label_opts=opts.LabelOpts(position='right'),
color='#90EE90')
bar.add_yaxis('死亡人数',
dead_list,
label_opts=opts.LabelOpts(position='right'),
color='#F08080')
# 设置全局变量：x轴标签倾斜度，html主标题
bar.set_global_opts(legend_opts=opts.LegendOpts(is_show=True),
xaxis_opts=opts.AxisOpts(name_rotate=-15),
title_opts=opts.TitleOpts('当前日期：%s' % timelist[i],
subtitle='数据来自inews'))
bar.reversal_axis()
tl.add(bar, timelist[i])
name = '疫情柱状图.html'
print(name, '已成功生成到', os.getcwd())
tl.render(name)
def __plot_cat_notebook(self):
data = pd.value_counts(self.X)
x = list(data.index.values)
values = list(data.values / np.sum(data.values) * 100)
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
bar.load_javascript()
bar.add_xaxis([i for i in x])
bar.add_yaxis("1", [round(i, 2) for i in values])
bar.set_global_opts(title_opts=opts.TitleOpts(title=self.var_name),
xaxis_opts=opts.AxisOpts(name_location='end', name='%'),
yaxis_opts=opts.AxisOpts(name_location='end', name='Groups'),
)
bar.reversal_axis()
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="right"))
return bar
def create_comments(df):
df = df.sort_values('comments', ascending=False)
attr = df['name'][0:10]
v1 = ['%.1f' % (float(i) / 100000000) for i in df['comments'][0:10]]
bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
bar.add_xaxis(list(reversed(attr.tolist())))
bar.add_yaxis("", list(reversed(v1)))
bar.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V评论数TOP10(亿)",
pos_left="center",
pos_top="18"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)))
bar.set_series_opts(label_opts=opts.LabelOpts(
is_show=True, position="right", color="black"))
bar.reversal_axis()
bar.render("抖音大V评论数TOP10(亿).html")
def draw_chart(x_data, y_data, title, page_title, series_name, floder_name,
res):
bar = Bar(
InitOpts(width="1500px",
height="4600px",
page_title=page_title,
animation_opts=opts.AnimationOpts(
animation_easing='exponentialInOut')))
bar.add_xaxis(xaxis_data=x_data)
bar.add_yaxis(yaxis_data=y_data,
series_name=series_name,
category_gap="30%")
bar.set_global_opts(title_opts=opts.TitleOpts(title=title + '粉丝数变化',
subtitle=res[0] + '到' +
res[1]),
xaxis_opts=AxisOpts(boundary_gap=['5%', '10%']),
toolbox_opts=opts.ToolboxOpts())
# bar.set_global_opts(xaxis_opts=AxisOpts(boundary_gap=['5%', '10%']), toolbox_opts=opts.ToolboxOpts())
bar.reversal_axis()
bar.set_series_opts(label_opts=opts.LabelOpts(position="right"))
bar.render("serve/chart/{0}/{1}.html".format(floder_name, title))
def hot_city_bar(df_data):
"""绘制热门行业Top10"""
x = df_data[0]
y = df_data[1]
bar = Bar(init_opts=opts.InitOpts(
width='720px', height='320px', theme=ThemeType.LIGHT))
bar.add_xaxis(xaxis_data=x[::-1])
bar.add_yaxis(series_name='热门行业的用人需求Top10', yaxis_data=y[::-1])
bar.set_global_opts(
# reversal_axis : 是否反向坐标轴
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(), ),
# range_start : 开始值 百分之零到百分百
# range_end : 结束值
datazoom_opts=opts.DataZoomOpts(is_show=True,
range_start=0,
range_end=100),
# tooltip_opts=opts.ToolBoxFeatureOpts()
toolbox_opts=opts.ToolboxOpts(is_show=True))
bar.reversal_axis()
return bar
def create_reversal_bar(title, x_list, data_list, data_unit):
"""
:param title: 图表名称
:param x_list: 横坐标内容，具有多种情况：1.时间序列 2.指标序列
: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: 数值的单位
:return:
"""
unit = "单位:" + data_unit
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])
c.reversal_axis()
c.set_series_opts(label_opts=opts.LabelOpts(position="right"))
c.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="right"),
xaxis_opts=opts.AxisOpts(name=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 bar_render_general(self):
c = Bar()
c.add_xaxis(self.x_value)
c.add_yaxis(self.y_name,
self.y_value,
label_opts=opts.LabelOpts(is_show=self.is_show))
if self.reversal_axis == True:
c.reversal_axis()
c.set_series_opts(label_opts=opts.LabelOpts(position="right"))
c.set_global_opts(
title_opts=opts.TitleOpts(title=self.title,
subtitle=self.subtitle),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
legend_opts=opts.LegendOpts(pos_left="center",
pos_top=self.pos_top,
legend_icon=self.legend_icon),
)
if len(self.args) > 0:
for num in range(len(self.args[0])):
c.add_yaxis(series_name=self.args[0][num],
y_axis=self.args[1][num],
label_opts=opts.LabelOpts(is_show=self.is_show))
return c
def make_bar_voter_star(filename):
"""
生成水平直方图，根据影评有用数
:param filename:
:return:
"""
# 定义各个文件名
# title = filename.split('/')[-1].split('.')[0]
title = os.path.basename(filename).split('.')[0]
target_dir = os.path.join(
os.path.abspath(os.path.dirname(os.path.dirname(__file__))),
"analyse_data", "bar_voter_star_pic")
if not os.path.exists(target_dir):
os.mkdir(target_dir)
print("分析结果保存在 ", target_dir, " 文件夹下...")
htmlName = title + '.html'
pngName = title + '.png'
htmlPath = os.path.join(target_dir, htmlName)
pngPath = os.path.join(target_dir, pngName)
# 导入数据，并删除无关列
rows = pd.read_csv(filename, encoding='utf-8', dtype=str)
to_drop = ['是否看过', '评论时间', '评分', '评论']
rows.drop(to_drop, axis=1, inplace=True)
# 数据清洗
rows['有用数'] = rows['有用数'].astype('int')
# 数据统计
result_voter_star = rows.sort_values(by='有用数', ascending=True).tail(15)
# 转换类型 pd.Series -> py.list
values = [(i[1], i[2]) for i in result_voter_star.itertuples()]
# pyecharts 生成水平柱状图
bar = Bar()
bar.add_xaxis([i[0] for i in values])
bar.add_yaxis('影评者获认同数', [i[1] for i in values])
bar.reversal_axis()
bar.set_global_opts(title_opts=opts.TitleOpts(title="电影<" + title +
">影评者影响力排行"))
bar.render(htmlPath)
def timeline_bar_reversal(title="Timeline-Bar-Reversal", is_auto_play=True):
'''
动效数据增长图
'''
data = getExcelToData1()
tl = Timeline()
tl.add_schema(
is_auto_play=is_auto_play,
play_interval=100,
is_loop_play=True,
is_inverse=False,
# axis_type='time',
is_timeline_show=True, # 是否展示播放按钮
)
# for i in range(1950, 2020):
# bar = Bar()
# bar.add_xaxis(Faker.choose())
# bar.add_yaxis("商家A", Faker.values(), label_opts=opts.LabelOpts(position="right"))
# bar.add_yaxis("商家B", Faker.values(), label_opts=opts.LabelOpts(position="right"))
# bar.reversal_axis()
# bar.set_global_opts(title_opts=opts.TitleOpts("{}: {} ".format(title,i)))
# tl.add(bar, "{}年".format(i))
for i in range(1, len(data['date'])):
bar = Bar()
bar.add_xaxis(data['kanaal'])
bar.add_yaxis('新增',
data['values'][i - 1],
label_opts=opts.LabelOpts(position="right"))
bar.reversal_axis()
bar.set_global_opts(
title_opts=opts.TitleOpts("{}: {} ".format(title, i)))
tl.add(bar, "{}".format(i))
return tl
from pyecharts.charts import Bar
# In[47]:
# 条形图
bar1 = Bar(init_opts=opts.InitOpts(width='1280px', height='1000px'))
bar1.add_xaxis(province_num.index.tolist())
bar1.add_yaxis("省份", province_num.values.tolist())
bar1.set_global_opts(title_opts=opts.TitleOpts(title="调剂信息发布数省份分布"),
toolbox_opts=opts.ToolboxOpts(),
visualmap_opts=opts.VisualMapOpts(max_=110))
bar1.set_series_opts(label_opts=opts.LabelOpts(position='right')) # 标签
bar1.reversal_axis()
bar1.render_notebook()
# In[48]:
from pyecharts.charts import Map
# In[49]:
c = Map(init_opts=opts.InitOpts(width='800px', height='750px'))
c.add('',[list(z) for z in zip(province_num.index.tolist(), province_num.values.tolist())], 'china')
c.set_global_opts(title_opts=opts.TitleOpts('调剂信息省份分布地图'),
tiji_top10 = tiji_top10.sort_values('tiji_num')
tiji_top10
# 产生数据
x_data = tiji_top10['actor_name'].values.tolist()
y_data = tiji_top10['tiji_num'].values.tolist()
# 条形图
bar3 = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
bar3.add_xaxis(x_data)
bar3.add_yaxis('', y_data)
bar3.set_global_opts(title_opts=opts.TitleOpts(title='弹幕中演员提及次数排行Top10'),
visualmap_opts=opts.VisualMapOpts(max_=int(
max(tiji_top10['tiji_num'])),
is_show=False))
bar3.set_series_opts(label_opts=opts.LabelOpts(position='right'))
bar3.reversal_axis()
bar3.render()
page = Page()
page.add(bar1, bar2, bar3)
page.render('../image/演员请就位2弹幕分析.html')
def get_cut_words(x_series):
# 读入停用词表
stop_words = []
with open(
r"C:\Users\wzd\Desktop\CDA\CDA_Python\Python文本分析\10.文本摘要\stop_words.txt",
'r',
encoding='utf-8') as f:
def pyechart_Bar_plot(self, filedata, para):
from pyecharts.charts import Bar, Timeline
file_name = '条形图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
df = pd.read_excel(filedata, sheet_name='Sheet1')
# -----------------------------------------------------------------------
if not para['TimeLineOn']:
bar = (
Bar(init_opts=opts.InitOpts(theme=costumeTheme,
width=para['width'],
height=para['height']))
# ThemeType.LIGHT para['theme']
)
chart = bar
columns = df.columns.values.tolist()
xlabel = columns[0]
xdata = df[xlabel].values.tolist()
chart.add_xaxis(xaxis_data=xdata)
for i in range(1, len(columns)):
ylabel = columns[i]
ydata = df[ylabel].values.tolist()
chart.add_yaxis(series_name=ylabel, y_axis=ydata)
if para['x2y']:
chart.reversal_axis()
self.Common_code(chart, path_plotly, para)
else:
Timewhole = df['时间'].values.tolist()
Timevalid = df['时间'].dropna().tolist()
index = []
for i in Timevalid:
index.append(Timewhole.index(i))
index.append(len(df))
print(index)
columns = df.columns.values.tolist()
Timel = Timeline().add_schema(
symbol='arrow',
play_interval=1000 # 单位为毫秒
)
for j in range(len(index) - 1):
chart = Bar()
# 数据切片
m = index[j]
n = index[j + 1]
xlabel = columns[1]
xdata = df[xlabel].values.tolist()[m:n]
chart.add_xaxis(xaxis_data=xdata)
for i in range(2, len(columns)):
ylabel = columns[i]
ydata = df[ylabel].values.tolist()[m:n]
chart.add_yaxis(series_name=ylabel, y_axis=ydata)
if para['x2y']:
chart.reversal_axis()
Timel.add(chart, "{}".format(Timevalid[j]))
self.Common_code(chart, path_plotly, para)
Timel.render(path_plotly)
print('成功绘制条形图')
return path_plotly # 返回该HTML文件路径
x_vals2 = ['POLO', '篮球鞋', '羽绒服', '皮鞋', '领带', '睡衣']
x_vals3 = ['羽毛球服', '羽毛球鞋', '护腕', '护膝', '护踝', '毛巾']
y_vals = [random.randint(10, 100) for _ in range(18)]
bar = Bar().add_xaxis(x_vals1 + x_vals2 + x_vals3)
bar.add_yaxis('商家A',
y_vals,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='average'),
opts.MarkPointItem(type_='max'),
opts.MarkPointItem(type_='min')
],
symbol_size=80))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right'))
bar.set_global_opts(
title_opts=opts.TitleOpts(title='条形图示例-商品销量', subtitle='条目较多条形图比较好看点'))
bar.reversal_axis() #翻转XY轴，将柱状图转换为条形图
bar.render('条形图.html')
# 直方图
import random
import pyecharts.options as opts
from pyecharts.charts import Bar
x_vals = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
xlen = len(x_vals)
# 设置成两种颜色
y_vals = []
for idx, item in enumerate(x_vals):
if idx % 2 == 0:
y_vals.append(
opts.BarItem(
def plot_ground_truth_analysis(
all_score_columns: typing.List[str],
title: str,
output_path: str,
df: pd.DataFrame,
add_wrong_candidates: bool = False,
wrong_candidates_score_column: str = None,
) -> None:
"""
use pyechart to plot html interactive figure
"""
df_processed = copy.deepcopy(df)
for each_col in df_processed.columns:
df_processed[each_col] = pd.to_numeric(df_processed[each_col],
errors='ignore')
xaxis_labels = []
memo = defaultdict(list)
groupby_res = df_processed[df_processed["evaluation_label"] ==
1].groupby(["column", "row"])
for key, each_group in reversed(tuple(groupby_res)):
if add_wrong_candidates:
df_wrong_examples = df_processed[(df_processed["column"] == key[0]) &
(df_processed["row"] == key[1]) &
(df_processed["evaluation_label"] == -1)] \
.sort_values(by=[wrong_candidates_score_column], ascending=False).iloc[:3, :]
# add wrong candidate information
for _, each_row in df_wrong_examples.iterrows():
longest_string = max(each_row["kg_labels"].split("|"),
key=len)
xaxis_labels.append(each_row["label_clean"] +
" \n({})".format(longest_string))
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_row[each_score_column]))
# add ground truth information
xaxis_labels.append(each_group["label_clean"].iloc[0])
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_group[each_score_column].iloc[0]))
# build figure
bar = Bar()
bar.add_xaxis(xaxis_labels)
for k, v in memo.items():
bar.add_yaxis(k, v)
# set the global options
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left='40%'),
legend_opts=opts.LegendOpts(pos_left="center",
pos_top="bottom",
orient='horizontal'),
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="save as image"),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
line_title="switch to line chart",
bar_title="switch to bar chart",
stack_title="switch to stacked values",
tiled_title="switch to tiled values"),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
zoom_title="zoom in", back_title="zoom reset"),
restore=opts.ToolBoxFeatureRestoreOpts(title="reset"),
data_view=opts.ToolBoxFeatureDataViewOpts(
title="Data table view",
lang=["Table view", "Close", "Refresh"],
),
brush=opts.ToolBoxFeatureBrushOpts(
rect_title="rectangle choice",
polygon_title="polygon choice",
clear_title="clear choices",
keep_title="keep choices"))),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
# yaxis_opts=opts.AxisOpts(name='labels', name_gap=5000, name_rotate=15),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(
is_inside=True,
length=850,
linestyle_opts=opts.LineStyleOpts(type_="dotted",
opacity=0.5))))
# do not shown bar label values
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.reversal_axis()
grid = (Grid(init_opts=opts.InitOpts(
width="1500px",
height="1000px",
page_title="Table Linker visualization page")).add(
bar,
grid_opts=opts.GridOpts(pos_top='5%',
pos_bottom="10%",
pos_right='5%',
pos_left="20%")))
grid.render(output_path + ".html")
def draw_bar_reverse_picture(xaxis_data,
data,
to_file,
unit,
svg_name,
stack,
y_name,
pos_left='25%'):
"""
多个y列，是否堆叠要确认下
:param pos_left: "25%"
:param is_reverse: xy轴是否反转
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Bar(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis( # 1250px
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
stack='1',
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
)
else:
bar = bar.add_yaxis(
series_name=series_name,
color="#61a0a8",
category_gap='50%',
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="category",
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=20),
),
xaxis_opts=opts.AxisOpts(
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=20),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=20),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(is_show=False),
)
bar = bar.reversal_axis()
grid = (
Grid(init_opts=opts.InitOpts(width="800px",
height="600px",
bg_color='rgb(255, 255, 255)')) # 800px
.add(bar,
grid_opts=opts.GridOpts(pos_left=pos_left),
is_control_axis_index=True))
make_snapshot(snapshot, grid.render(to_file), svg_name) # 生成svg图片
def bilibiliData_demo():
# 获取B站排行榜数据
bilibilidata.getRank()
os.chdir(r'C:\Users\William\Desktop\bili\bilibili_data')
rank_data = pd.read_csv('all.csv')
print('成功获取排行榜数据')
rank_url = rank_data.url
rank_title = rank_data.title
url_head = r'https:'
for i in range(len(rank_url)):
rank_url[i] = url_head + rank_url[i]
# 爬取排行榜每一个视频的基本信息
# print('正在爬取排行榜每一个视频的基本信息')
# process_num = 1
# for each in rank_url:
# try:
# os.chdir(r'C:\Users\William\Desktop\bili')
# video = VideoData(each)
# print(i)
# i += 1
# except:
# i+=1
danmaku_list = list()
replies_list = list()
favorite_list = list()
coin_list = list()
like_list = list()
for each in rank_title:
each_video_info = list()
try:
os.chdir(
r'C:\Users\William\Desktop\bili\videos_data\{}'.format(each))
info = pd.read_csv('video_info.csv')
except Exception as exc:
print('无法打开文件')
continue
print('成功获取文件')
info = pd.DataFrame(info.values.T)
danmaku_list.append(int(info[5][1])) # 弹幕
replies_list.append(int(info[6][1])) # 评论
favorite_list.append(int(info[7][1])) # 收藏
coin_list.append(int(info[8][1])) # 投币
like_list.append(int(info[9][1])) # 喜爱
x_axis = list(range(100, 0, -1))
bar = Bar(init_opts=opts.InitOpts(
width='1600px', height='900px', theme=ThemeType.WALDEN))
bar.add_xaxis(x_axis)
bar.add_yaxis('弹幕', danmaku_list)
bar.add_yaxis('评论', replies_list, is_selected=False)
bar.add_yaxis('收藏', favorite_list, is_selected=False)
bar.add_yaxis('投币', coin_list, is_selected=False)
bar.add_yaxis('喜爱', like_list, is_selected=False)
bar.reversal_axis()
bar.set_global_opts(datazoom_opts=opts.DataZoomOpts(orient='vertical'))
os.chdir(r'C:\Users\William\Desktop\bili')
bar.render('排行榜数量分布图.html')
make_snapshot(snapshot, bar.render(), '排行榜数量分布图.png')
def barChart(df):
type_of_plot = st.selectbox("Select Type of Plot",["BAR1","BAR2","BAR3","BAR4","BAR5"])
all_columns = df.columns
#st.success("Generating Customizable Plot of {} for {}".format(type_of_plot))
if type_of_plot == 'BAR1':
columns_to_plot = st.selectbox("Select 1 column",all_columns,key='a' )
columns_to_plot1 = st.selectbox("Select 2 column",[col for col in df.columns if col not in [columns_to_plot]], key='b' )
columns_to_plot2 = st.selectbox("Select 3 column",[col for col in df.columns if col not in [columns_to_plot, columns_to_plot1]], key='c' )
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
other_var1 = df[columns_to_plot2].tolist()
bar = Bar()
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var)
bar.add_yaxis(columns_to_plot2, other_var1)
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts(), legend_opts=opts.LegendOpts(is_show=False))
bar.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'))
bar.reversal_axis()
bar.set_series_opts(label_opts=opts.LabelOpts(position="right"), markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min"),
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average"),
]
),)
#bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-Test Rendered Pictures"))
# st.button("Generate Plot")
return bar
if type_of_plot == 'BAR2':
columns_to_plot = st.selectbox("Select 1 column",all_columns,key='a' )
columns_to_plot1 = st.selectbox("Select 2 column",[col for col in df.columns if col not in [columns_to_plot]], key='b' )
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
bar = Bar()
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False))
bar.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')
)
bar.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min"),
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average"),
]
),
)
return bar
if type_of_plot == 'BAR3':
columns_to_plot = st.selectbox("Select 1 column",all_columns,key='a' )
columns_to_plot1 = st.selectbox("Select 2 column",[col for col in df.columns if col not in [columns_to_plot]], key='b' )
columns_to_plot2 = st.selectbox("Select 3 column",[col for col in df.columns if col not in [columns_to_plot, columns_to_plot1]], key='c' )
columns_to_plot3 = st.selectbox("Select 4 column",[col for col in df.columns if col not in [columns_to_plot, columns_to_plot1,columns_to_plot2]], key='d' )
columns_to_plot4 = st.selectbox("Select 5 column",[col for col in df.columns if col not in [columns_to_plot, columns_to_plot1,columns_to_plot2,columns_to_plot3]], key='e' )
cje = df[columns_to_plot].tolist()
other_var1 = df[columns_to_plot1].tolist()
other_var2 = df[columns_to_plot2].tolist()
other_var3 = df[columns_to_plot3].tolist()
other_var4 = df[columns_to_plot4].tolist()
bar = Bar()
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var1)
bar.add_yaxis(columns_to_plot2, other_var2)
bar.add_yaxis(columns_to_plot3, other_var3)
bar.add_yaxis(columns_to_plot4, other_var4)
bar.reversal_axis()
#bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=65)),
brush_opts=opts.BrushOpts())
# bar.set_series_opts(label_opts=opts.LabelOpts( position='inside', font_size=8,
# formatter='{b}:{c}',font_style='italic', font_weight='bold', font_family='Microsoft YaHei')
# )
return bar
def barChart(df):
type_of_plot = st.selectbox("Select Type of Plot",
["BAR1", "BAR2", "BAR3", "BAR4", "BAR5"])
all_columns = df.columns
#st.success("Generating Customizable Plot of {} for {}".format(type_of_plot))
if type_of_plot == 'BAR1':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
columns_to_plot2 = st.selectbox("Select 3 column",
all_columns,
key='c')
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
other_var1 = df[columns_to_plot2].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1200px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var)
bar.add_yaxis(columns_to_plot2, other_var1)
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts())
bar.reversal_axis()
bar.set_series_opts(
label_opts=opts.LabelOpts(position="right"),
markline_opts=opts.MarkLineOpts(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"),
]),
)
#bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-Test Rendered Pictures"))
# st.button("Generate Plot")
return bar
if type_of_plot == 'BAR2':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1800px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=65)),
brush_opts=opts.BrushOpts())
bar.set_series_opts(markline_opts=opts.MarkLineOpts(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"),
]), )
return bar
if type_of_plot == 'BAR3':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
columns_to_plot2 = st.selectbox("Select 3 column",
all_columns,
key='c')
columns_to_plot3 = st.selectbox("Select 4 column",
all_columns,
key='d')
columns_to_plot4 = st.selectbox("Select 5 column",
all_columns,
key='e')
cje = df[columns_to_plot].tolist()
other_var1 = df[columns_to_plot1].tolist()
other_var2 = df[columns_to_plot2].tolist()
other_var3 = df[columns_to_plot3].tolist()
other_var4 = df[columns_to_plot4].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1200px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var1)
bar.add_yaxis(columns_to_plot2, other_var2)
bar.add_yaxis(columns_to_plot3, other_var3)
bar.add_yaxis(columns_to_plot4, other_var4)
bar.reversal_axis()
#bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=65)),
brush_opts=opts.BrushOpts())
grid = Grid()
grid.add(bar, grid_bottom="60%")
return bar
# 添加一组纵轴数据
bar.add_yaxis(series_name='商家B', yaxis_data=[57, 134, 137, 129, 145, 60, 49])
# b、添加横轴数据
bar.add_xaxis(
xaxis_data=['衬衫', '毛衣', '领带', '裤子', '风衣', '高跟鞋', '袜子'], # 横轴数据
)
# 3、全局配置
# 可以通过 set_global_opts 来设置全局配置
bar.set_global_opts(
# 标题配置项
title_opts=opts.TitleOpts(
title='柱状图绘制', # 标题名称
))
# 4、系列配置
# 可以通过 set_series_opts 来设置全局配置项
bar.set_series_opts(
# 标签配置项
label_opts=opts.LabelOpts(
is_show=True, # 显示标签
position='right', # 设置标签位置
))
# 注意：翻转坐标系
bar.reversal_axis()
# 5、生成图像
bar.render('./html/柱状图绘制.html')
def find_difference_render(data,X,query,table_path,answer):
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff',"#ca8622","#726930",]
if len(data)==2:
bar1=Bar()
bar1.add_xaxis(X[1])
bar1.add_yaxis("",data[0][1],label_opts=opts.LabelOpts(position="insideLeft"))
bar1.reversal_axis()
bar1.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10,range_end=90,orient="vertical"), opts.DataZoomOpts(type_="inside")],
xaxis_opts=opts.AxisOpts(
position="top"
),
yaxis_opts=opts.AxisOpts(
is_inverse=True
),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
bar2=Bar()
bar2.add_xaxis(X[1])
bar2.add_yaxis("",data[1][1],label_opts=opts.LabelOpts(position="insideRight"))
bar2.reversal_axis()
bar2.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10, range_end=90, orient="vertical"),
opts.DataZoomOpts(type_="inside")],
xaxis_opts=opts.AxisOpts(
position="top",
is_inverse=True
),
yaxis_opts=opts.AxisOpts(
is_inverse=True,
position="right"
),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
grid1 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ) )
grid1.add(bar1, grid_opts=opts.GridOpts(
pos_left="60%",pos_bottom="30%"
))
grid1.add(bar2, grid_opts=opts.GridOpts(
pos_right="60%",pos_bottom="30%"
))
grid1.render("a.html")
option1 = grid1.dump_options_with_quotes()
option1 = json.loads(option1)
lable=[]
for i in range(len(X[1])):
lable.append(opts.RadarIndicatorItem(name=X[1][i]))
# r_data=[]
# for record in data:
# r_data.append(record[1])
radar=Radar(init_opts=opts.InitOpts(bg_color="#CCCCCC"))
radar.add_schema(
schema=lable,
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
for i in range(len(data)):
radar.add(
series_name=data[i][0],
label_opts=opts.LabelOpts(is_show=False),
data=[data[i][1]],
linestyle_opts=opts.LineStyleOpts(
color=colorList[i],
width=3
)
)
radar.set_global_opts(
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n'+"\n" + 'A:' + ' ' + answer],
font="10px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
grid2 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid2.add(radar, grid_opts={'left': '20%', 'bottom': '40%',"top":"1%"})
option2 = grid2.dump_options_with_quotes()
option2 = json.loads(option2)
bar3=Bar()
bar3.add_xaxis(X[1])
for i in range(len(data)):
bar3.add_yaxis(data[i][0],data[i][1],label_opts=opts.LabelOpts(is_show=False))
bar3.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")],
xaxis_opts=opts.AxisOpts(
name=X[0]
),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
grid3 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid3.add(bar3, grid_opts={'left': '20%', 'bottom': '30%'})
option3 = grid3.dump_options_with_quotes()
option3 = json.loads(option3)
option = {"option": [option1,option2,option3], "query": query}
return option
elif len(data)>2 and len(data[0][1])>1:
lable=[]
for i in range(len(X[1])):
lable.append(opts.RadarIndicatorItem(name=X[1][i]))
# r_data=[]
# for record in data:
# r_data.append(record[1])
radar=Radar(init_opts=opts.InitOpts(bg_color="#CCCCCC"))
radar.add_schema(
schema=lable,
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.set_global_opts(
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
for i in range(len(data)):
radar.add(
series_name=data[i][0],
label_opts=opts.LabelOpts(is_show=False),
data=[data[i][1]],
linestyle_opts=opts.LineStyleOpts(
color=colorList[i],
width=3
)
)
grid1 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid1.add(radar, grid_opts={'left': '20%', 'bottom': '30%'})
option1 = grid1.dump_options_with_quotes()
option1 = json.loads(option1)
bar = Bar()
bar.add_xaxis(X[1])
for i in range(len(data)):
bar.add_yaxis(data[i][0], data[i][1],label_opts=opts.LabelOpts(is_show=False))
bar.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")],
xaxis_opts=opts.AxisOpts(
name=X[0]
),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
grid2 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid2.add(bar, grid_opts={'left': '20%', 'bottom': '30%'})
option2 = grid2.dump_options_with_quotes()
option2 = json.loads(option2)
option = {"option": [option1,option2], "query": query}
return option
elif len(data)>2 and len(data[0][1])==1:
bar = Bar()
bar.add_xaxis(X[1])
for i in range(len(data)):
bar.add_yaxis(data[i][0], data[i][1],label_opts=opts.LabelOpts(is_show=False))
bar.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")],
xaxis_opts=opts.AxisOpts(
name=X[0]
),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n' + "Q:" + ' ' + query + '\n' +"\n"+ 'A:' + ' ' + answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
grid2 = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid2.add(bar, grid_opts={'left': '20%', 'bottom': '34%'})
option2 = grid2.dump_options_with_quotes()
option2 = json.loads(option2)
option = {"option": [option2], "query": query}
return option
