def TimelinePie(data_nums: dict):
'''
我自己选的八个出版社的年出版量对比\n
:param data_nums: 字典,键为年份,值是列表,存放各出版社的出版数量
:return: 不存在
'''
global dir_to_save
attr = ['机械工业出版社', '人民邮电出版社', '电子工业出版社',
'清华大学出版社', '人民文学出版社', '上海译文出版社',
'生活·读书·新知三联书店', '广西师范大学出版社']
tl = Timeline()
for i in tqdm(range(2002, 2015)):
pie = (
Pie()
.add(
"",
[list(z) for z in zip(attr, data_nums[i])],
rosetype="radius",
radius=["30%", "55%"],
)
.set_global_opts(title_opts=opts.TitleOpts("8个出版社的{}年出版量对比".format(i),
pos_top='15%')
)
)
tl.add(pie, "{}年".format(i))
tl.add_schema(is_auto_play=True, play_interval=1000)
tl.render(dir_to_save + "TypicalPublishers_timeline_pie.html")
print("Successfully drew TypicalPublishers_timeline_pie")
def print_gauge():
timeline3 = Timeline(init_opts=opts.InitOpts(bg_color="black"))
for i in range(len(Grid)):
b = batteryenergy[i] / 900 * 100
gauge0 = (Gauge(init_opts=opts.InitOpts(bg_color="black")).add(
series_name="grid",
data_pair=[["", round(b, 1)]],
max_=100,
min_=0,
start_angle=210,
split_number=5,
end_angle=-30,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[(
0.3, "#67e0e3"), (0.7, "#37a2da"), (1, "#fd666d")],
width=20)),
detail_label_opts=opts.LabelOpts(formatter="{value}%", ),
radius="80%").set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True,
formatter="{a} <br/>{b} : {c}%"),
title_opts=opts.TitleOpts(
"Percentage of battery energy at time {}".format(time[i]),
title_textstyle_opts=opts.TextStyleOpts(color="white"))))
timeline3.add(gauge0, "{}年".format(i)).add_schema(
is_timeline_show=True).add_schema(play_interval=2000,
is_auto_play=True,
is_timeline_show=False)
timeline3.render("C:/Users/suqi/Desktop/allcode/gaugenew.html")
def dong_scatter(data, info='', path0=''):
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Timeline
# endlist = ['canshu', 'celue_name', '预测_s_Time', '未来end_3', '最终预测值1', 'hg最终预测值1']
data['aim'] = data['canshu'] + data['celue_name']
data.rename(columns={
'hg最终预测值1': '预测值',
'未来end_3': '未来=月终收益',
'预测_s_Time': 's_Time'
},
inplace=True)
print(data.columns)
print(data.tail())
title = '动态图'
df = data
# data['预测值'] = data['预测值'].apply(lambda x: int(x))
min_pre = min(data['预测值'].values.tolist())
max_pre = max(data['预测值'].values.tolist())
# df['未来=月终收益']=df['未来=月终收益'].apply(lambda x: int(x))
# df['预测值']=df['预测值'].apply(lambda x: int(x))
df['s_Time'] = pd.to_datetime(
df['s_Time']) #.apply(lambda x:x.strftime(format="%Y-%m-%d"))
df.sort_values(by=['s_Time'], ascending=True, inplace=True)
tl = Timeline()
timelist = list(set(df['s_Time'].tolist()))
print(list(set(df['s_Time'].tolist())))
sorted(timelist)
# df_date = [time.strftime('%Y-%m-%d',time.localtime(i/1000000000) ) for i in timelist]
# print(df_date)
for k, i in enumerate(sorted(timelist)):
# print(k,i)
xdata = df.loc[df['s_Time'] == i, '预测值'].values.tolist()
ydata = df.loc[df['s_Time'] == i, ['未来=月终收益', '预测值']].values.tolist()
# print(ydata)
Scatter0 = (Scatter().add_xaxis(xdata).add_yaxis(
'未来=月终收益', ydata, label_opts=opts.LabelOpts(is_show=False)
).set_series_opts().set_global_opts(
xaxis_opts=opts.AxisOpts(
name='预测值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
name='真实值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}月份的数据"),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '真实：'+params.value[1] +' == 预测：'+ params.value[2];}"
)),
visualmap_opts=opts.VisualMapOpts(min_=min_pre, max_=max_pre),
))
tl.add(Scatter0, "{}月".format(i))
tl.render(path0 + f"{info}.html")
print(path0 + f"{info}.html")
def charts():
fp = open('./oldData.txt', 'r')
# Timeline
timeline = Timeline(init_opts=opts.InitOpts(
width="1660px",
height="708px",
))
# 48 bar charts
for i in range(48):
X, Y, Color = getSta(fp)
# y-axle with color
y = []
for j in range(22):
y.append(
opts.BarItem(
name=X[j],
value=Y[j],
itemstyle_opts=opts.ItemStyleOpts(color=Color[j])))
# single bar chart
bar = (
Bar().add_xaxis(X).add_yaxis(
"Youbike 使用率", y).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(
title="南港區Youbike的一天",
pos_left="center",
),
legend_opts=opts.LegendOpts(pos_left="left"),
graphic_opts=[
opts.GraphicGroup( # add a block
graphic_item=opts.GraphicItem(left="80%",
top="80%"),
children=[
opts.GraphicText( # add text into the block
graphic_item=opts.GraphicItem(
left="center",
top="center",
# z=100
),
graphic_textstyle_opts=opts.
GraphicTextStyleOpts(
text="{}".format(invTime[i]),
font="48px Verdana",
),
)
])
]).set_series_opts( # number showed at right of the bar
label_opts=opts.LabelOpts(position="right",
color="#333")))
timeline.add(bar, "") # add single bar chart into timeline
timeline.add_schema(play_interval=1000,
is_loop_play=True) # play settings
# export into HTML
timeline.render("YoubikeUsingRate.html")
fp.close()
def render_map(date_list, cy_name_list, ncov_data) -> Map:
tl = Timeline()
tl.add_schema(is_auto_play=True,
play_interval=60,
is_loop_play=False,
width=700)
for date_str in date_list:
map_ = (Map(init_opts=opts.InitOpts(
bg_color='rgba(255,250,205,0.2)',
width='1000px',
height='600px',
page_title='page',
theme=ThemeType.MACARONS)).add(
"全球疫情变化",
[list(z) for z in zip(cy_name_list, ncov_data[date_str])],
"world",
is_map_symbol_show=False,
name_map=countries_dict).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="19-nCoV\n%s" %
date_str),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=3000)))
tl.add(map_, "%s" % date_str)
tl.render("nCoV全球疫情地图({}).html".format(datetime.date.today()))
def gcv_main():
"""
TODO
"""
# 生成时间轴的图
timeline = Timeline(
init_opts=opts.InitOpts(width="1600px", height="800px"))
commit_log = repo_analyze()
contribute = {}
for item in commit_log.itertuples():
top_commiter = get_topX(contribute, item, 5)
timeline.add(get_commits_chart(time=item[1], \
names=top_commiter['author'].tolist(), \
c_insert=top_commiter['v1'].tolist(), \
c_delete=top_commiter['v2'].tolist(), \
c_add=top_commiter['v0'].tolist()), \
time_point=item[1])
# 1.0.0 版本的 add_schema 暂时没有补上 return self 所以只能这么写着
timeline.add_schema(is_auto_play=True, play_interval=100)
timeline.render("git.html")
def print_bar():
x = ["no PV", "PV"]
timeline1 = Timeline(init_opts=opts.InitOpts(bg_color="black"))
for i in range(len(Grid)):
price_noPV[i] = ('%.2f' % price_noPV[i])
price_opti[i] = ('%.2f' % price_opti[i])
bar = (Bar(init_opts=opts.InitOpts(
bg_color="black")).add_xaxis(x).add_yaxis(
"overall income", [price_noPV[i], price_opti[i]],
color="rgb(30,144,255)",
label_opts=opts.LabelOpts()).reversal_axis().set_series_opts(
label_opts=opts.LabelOpts(position="right")).
set_global_opts(
title_opts=opts.TitleOpts(
title="The overall income(USD) till time {}".format(
time[i]),
title_textstyle_opts=opts.TextStyleOpts(color="white")),
yaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white"), ),
axislabel_opts=opts.LabelOpts()),
xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white")),
axislabel_opts=opts.LabelOpts()),
legend_opts=opts.LegendOpts(
textstyle_opts=opts.TextStyleOpts(color="white"),
pos_left="right",
pos_top="0%"),
))
timeline1.add(bar, "{}年".format(i)).add_schema(play_interval=2000,
is_auto_play=True,
is_timeline_show=False)
timeline1.render("C:/Users/suqi/Desktop/allcode/barnew.html")
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 temp_show(field, name, path):
# 定义时间滚动栏标题
title = TQHBTrans().get_date()
print(title)
# 创建时间线对象
tl = Timeline()
for i in range(len(title)):
print(i)
data = TQHBTrans().get_temp(title[i], field)
# 定义地图标题
map_title = str(title[i])[0:4] + "年" + str(title[i])[4:6] + "月" + str(
title[i])[6:8] + "日全国各省会" + name + "分布图"
# 创建地图对象
map0 = Map()
# 向地图中添加数据
map0.add("", data, "china")
# 创建地图显示方式
map0.set_global_opts(title_opts=opts.TitleOpts(title=map_title, ),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=[{
"max": 40,
"min": 31,
"label": "31-40℃",
"color": "#FF8000"
}, {
"max": 30,
"min": 21,
"label": "21-30℃",
"color": "#FFBB77"
}, {
"max": 20,
"min": 11,
"label": "11-20℃",
"color": "#FFDCB9"
}, {
"max": 10,
"min": 1,
"label": "1-10℃",
"color": "#FFEEDD"
}, {
"max": 0,
"min": -10,
"label": "-10-0℃",
"color": "#C4E1FF"
}, {
"max": -11,
"min": -20,
"label": "-20--11℃",
"color": "#66B3FF"
}, {
"max": -21,
"min": -30,
"label": "-30--21℃",
"color": "#2894FF"
}]))
# 将地图添加到时间线中
tl.add(map0, title[i])
# 生成html文件
tl.render(path)
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 main():
df = pd.read_csv('../source_data.csv',
usecols=['date', 'country', 'confirmed'])
# 获取顺序的日期列表
date_list = list(set(df['date'].to_list()))
date_list.sort()
date_list = date_list[-20:]
tl = Timeline()
tl.add_schema(is_auto_play=False, play_interval=500, is_loop_play=False)
draw_bar(df, date_list, tl)
tl.render_notebook()
tl.render("timeline_bar_with_graphic.html")
def get_visualization(datatype='Confirmed',
increment_update=False,
maptype='china-cities',
save_png=False):
v = Visualization(datatype=datatype,
increment_update=increment_update,
maptype=maptype)
dates = v.dates
dates = dates + dates[-1:] * 3
# 增量更新图片
if increment_update:
for date in dates:
if not os.path.exists(
f"fig/png/COVID-19_{datatype}_{maptype}_{date}.png"):
chart = v.get_day_chart(date=date)
make_snapshot(
snapshot, chart.render(),
f"fig/png/COVID-19_{datatype}_{maptype}_{date}.png")
else:
timeline = Timeline(init_opts=opts.InitOpts(
width="1440px", height="800px", theme=ThemeType.DARK))
for date in dates:
if date <= '2020-01-23':
continue
try:
chart = v.get_day_chart(date=date)
timeline.add(chart, time_point=date[6:])
if save_png:
make_snapshot(
snapshot, chart.render(),
f"fig/png/COVID-19_{datatype}_{maptype}_{date}.png")
except Exception as e:
print('get char error', date, e)
timeline.add_schema(
orient="vertical",
is_auto_play=True,
is_inverse=True,
play_interval=1000, #播放间隔1000ms
pos_left="null",
pos_right="5",
pos_top="20",
pos_bottom="20",
width="60",
label_opts=opts.LabelOpts(is_show=True, color="#fff"),
)
timeline.render(f"fig/html/COVID-19_{maptype}_{datatype}.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 percent(self):
"""
近六年四大富豪财富占比
:return:
"""
attr = ["马云家族", "马化腾", '王健林家族', '许家印']
tl = Timeline()
for i in range(2015, 2020):
pie = (Pie().add(
"胡润排行榜",
[(name, self.percent_search(name, i)[0].to_dict().get(
EnumType.WEALTH)) for name in attr],
rosetype="radius",
radius=["30%", "55%"],
).set_global_opts(
title_opts=opts.TitleOpts("{}年四大富豪财富占比".format(i))))
tl.add(pie, "{}年".format(i))
tl.render("timeline_pie.html")
def hotmap(year):
timeline_1 = Timeline()
for i in range(0, 4):
if i == 0:
a = 'confirmed'
elif i == 1:
a = 'suspected'
elif i == 2:
a = 'dead'
elif i == 3:
a = 'healed'
list_i = listfilter2(i)
map_1 = (
Map().set_global_opts(
title_opts=opts.TitleOpts(title="2020年全国各省疫情"),
visualmap_opts=opts.VisualMapOpts() # 最大数据范围
).add(a, list_i, maptype="china"))
timeline_1.add(map_1, "{}".format(a))
timeline_1.render("province_confirmed.html")
def plot_healDeadPie():
data = pd.read_csv('data\chinaDayData.csv')
df = pd.DataFrame(data)
#数据
date=df.iloc[:, 1].tolist()
tl = Timeline()
for i in range(len(data)):
pie1 = (
Pie()
.add(
"商家A",
[list(z) for z in zip(["死亡","治愈"],df.iloc[i,3:].tolist())],
radius=["30%", "55%"],
)
.set_global_opts(title_opts=opts.TitleOpts("中国{}日死亡率和治愈率比重".format(date[i])))
)
tl.add(pie1, "{}".format(date[i]))
tl.render("visual_html\中国每日死亡人数与治愈人数比重饼图.html")
return pie1
def timeline_map1() -> Timeline:
a = Timeline()
for i in range(2010, 2020):
m = (
Map()
.add("农业女性就业率",zip(list(农业女性就业.index),list(农业女性就业["{}".format(i)])), "world",
is_map_symbol_show=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="{}年农业女性就业情况".format(i), subtitle=""),
visualmap_opts=opts.VisualMapOpts(max_=100, is_piecewise=True),
)
)
a.add(m, "{}".format(i))
a.render("./templates/ditu.html")
with open("./templates/ditu.html", encoding="utf8", mode="r") as f:
sym = "".join(f.readlines())
return render_template('2.html',
the_sym=sym,
)
def render(input_d):
data, time_list, total_num, maxNum, output_name, title1, title2 = input_d
minNum = 1
timeline = Timeline(init_opts=opts.InitOpts(
width="1520px", height="720px", theme=ThemeType.DARK))
for y in time_list:
g = get_year_chart(y, title1, title2)
timeline.add(g, time_point=str(y))
timeline.add_schema(
orient="vertical",
is_auto_play=True,
is_inverse=True,
play_interval=5000,
pos_left="null",
pos_right="5",
pos_top="20",
pos_bottom="20",
width="73",
label_opts=opts.LabelOpts(is_show=True, color="#fff"),
)
timeline.render(output_name)
def timeline(self):
time_list = [str(d) for d in self.day]
timeline = Timeline(init_opts=opts.InitOpts(
width="1600px", height="900px", theme=self.theme))
for y in time_list:
g = self.__get_year_chart(self.data, y)
timeline.add(g, time_point=str(y[5:]))
timeline.add_schema(
orient="vertical",
is_auto_play=True,
is_inverse=True,
play_interval=1000,
pos_left="null",
pos_right="5",
pos_top="20",
pos_bottom="20",
width="60",
)
save = self.save + ".html"
print(save)
timeline.render(save)
def timeline_map2() -> Timeline:
df = pd.read_csv('fenshenggdp.csv')
tl = Timeline()
for i in range(2017, 2014):
map0 = (Map().add(
"各省GDP（亿元）",
zip(list(df.地区), list(df["{}".format(i)])),
"china",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="{}全国各省GDP".format(i),
subtitle=""),
visualmap_opts=opts.VisualMapOpts(min_=900,
max_=90000,
is_piecewise=True),
))
tl.add(map0, "{}".format(i))
tl.render("./templates/2.html")
with open("./templates/2.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
return render_template(
'the_fenshenggdp.html',
the_fenshenggdp=plot_all,
)
def timeline_map1() -> Timeline:
df = pd.read_csv('nengyuanxiaohao.csv')
tl = Timeline()
for i in range(2017, 2014):
map0 = (Map().add(
"碳煤石油天然气消耗量（万吨标准煤）",
zip(list(df.地区), list(df["{}".format(i)])),
"china",
is_map_symbol_show=False).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title="{}国各省碳煤石油天然气消耗量".format(i), subtitle=""),
visualmap_opts=opts.VisualMapOpts(min_=30,
max_=6000,
is_piecewise=True),
))
tl.add(map0, "{}".format(i))
tl.render("./templates/3.html")
with open("./templates/3.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
return render_template(
'5.html',
the_map=plot_all,
)
def industry_percent(self):
"""
近六年排行榜前20行业财富占比
:return:
"""
tl = Timeline()
res = defaultdict(int)
for i in [2015, 2020]:
for result in self.industry_search(i):
result = result.to_dict()
name = result.get(EnumType.NAME_CN)
res[self.industry[self.industry_map.get(name)]] += int(
result.get(EnumType.WEALTH))
pie = (Pie().add(
"胡润排行榜",
list(zip(res.keys(), res.values())),
rosetype="radius",
radius=["30%", "55%"],
center="center").set_global_opts(
title_opts=opts.TitleOpts("{}年行业财富".format(i))))
tl.add(pie, "{}年".format(i))
tl.render("industry_pie.html")
def timeline_map(width='800px', height='500px') -> Timeline:
year = df.columns.values.tolist()[7:]
tl = Timeline(opts.InitOpts(width=width, height=height, theme=ThemeType.INFOGRAPHIC))
for i in year:
map0 = (
Map()
.add(
"生育率", list(zip(list(df.country), list(df["{}".format(i)]))), "world", is_map_symbol_show=False
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="{}年世界生育率".format(i), subtitle="",
subtitle_textstyle_opts=opts.TextStyleOpts(color="red", font_size=18,
font_style="italic")),
# 设置副标题样式，进行图例说明
visualmap_opts=opts.VisualMapOpts(max_=8),
)
)
tl.add(map0, "{}年".format(i))
return tl.render('app/templates/pyecharts/jiaohu/jiaohu_t1.html')
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(name='单位: 英寸'),
yaxis_opts=opts.AxisOpts(name='销量: 台')).set_series_opts(
label_opts=opts.LabelOpts(is_show=True),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
))
pie = (Pie().add(
series_name="销售占比",
data_pair=[["小屏(<=38寸)", classified_data[month][0]],
["中等大小屏幕", classified_data[month][1]],
["大屏(>=65寸)", classified_data[month][2]]],
center=["25%", "35%"],
radius="28%",
).set_series_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="item")))
return bar.overlap(pie)
# 生成时间轴的图
timeline = Timeline(init_opts=opts.InitOpts(width="1400px", height="700px"))
for m in range(3, 11):
timeline.add(get_month_overlap_chart(month=m), time_point=str(m))
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("sellStats.html")
{
'min': 1000,
'max': 5000,
'label': '1000-5000',
'color': '#e57373'
},
{
'min': 1000,
'label': '5000+',
'color': '#c62828'
},
]
# noinspection PyTypeChecker
map.add('', zip(provices, valuse))
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=pieces,
pos_left='100px'),
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{b}<br/>{c}"),
)
filepath = 'Maps/' + time + '.html'
map.render(path=filepath)
tl.add(map, "{}".format(time))
tl.render("ChinaChart/ChinaMap.html")
from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.faker import Faker
tl = Timeline()
for i in range(2015, 2020):
bar = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(),
label_opts=opts.LabelOpts(position="right")).add_yaxis(
"商家B", Faker.values(), label_opts=opts.LabelOpts(
position="right")).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(
"Timeline-Bar-Reversal (时间: {} 年)".format(i))))
tl.add(bar, "{}年".format(i))
tl.render("timeline_bar_reversal.html")
init_opts=opts.InitOpts(
width='1220px',
height='900px',
))
for it in headers:
tmp = pd_exp_fz_2[['INDIC_KEY', it]]
pie = (
Pie().add(
"资产负债表",
[list(z) for z in zip(tmp['INDIC_KEY'], tmp[it])],
# 是否展示成南丁格尔图，通过半径区分数据大小，有'radius'和'area'两种模式。
# radius：扇区圆心角展现数据的百分比，半径展现数据的大小
# area：所有扇区圆心角相同，仅通过半径展现数据大小
rosetype="radius",
# 饼图的半径，数组的第一项是内半径，第二项是外半径
# 默认设置成百分比，相对于容器高宽中较小的一项的一半
radius=["30%", "60%"],
label_opts=opts.LabelOpts(position="center")
# 提示框组件配置项，参考 `series_options.TooltipOpts`
# tooltip_opts= Union[opts.TooltipOpts, dict, None] = None,
).set_global_opts(
title_opts=opts.TitleOpts("蒙商银行资产结构情况："),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="15%",
pos_left="2%")).set_series_opts(label_opts=opts.LabelOpts(
formatter="{b}: {d}%")))
tl.add(pie, "{}".format(it))
tl.render("timeline_pie.html")
for time in populations.index:
values = populations.loc[time].tolist()
popu_map = Map()
popu_map.add("", [list(z) for z in zip(attr, values)],
maptype="china",
is_map_symbol_show=False)
popu_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
popu_map.set_global_opts(title_opts=opts.TitleOpts(
title="China population by province from 2000 to 2018",
subtitle="Units: 10K"),
visualmap_opts=opts.VisualMapOpts(
max_=12000,
is_piecewise=True,
split_number=12,
range_color=colors),
toolbox_opts=opts.ToolboxOpts())
time_map.add(popu_map, "{}".format(time))
time_map.render("plots/population-map-over-time.html")
#动态展示各省市人口变化
sorted_pops = []
for year in range(2000, 2019):
populations.sort_values(year, axis=1, inplace=True)
sorted_pops.append(
[year, populations.loc[year].tolist(),
populations.columns.tolist()])
import plot_population_by_province
plot = plot_population_by_province.Plot(sorted_pops)
plot.showGif('plots/population-by-province.gif')
#定义每日地图绘制函数
def map_visualmap(sequence, date):
c = Map()
c.add(date,
sequence,
maptype="world",
label_opts=opts.LabelOpts(is_show=False, font_size=8),
is_map_symbol_show=False,
is_roam=True)
c.set_global_opts(title_opts=opts.TitleOpts(title=titleIn,subtitle="数据来源约翰霍普金斯大学JHU CSSE"),
legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,\
pieces=pieces_list))
return c
for i in range(n):
#取每日数据
row = df2.iloc[i, ].tolist()
#将数据转换为二元的列表
sequence_temp = list(zip(attr, row))
#对日期格式化以便显示
time = format(df2.index[i], "%Y-%m-%d")
#创建地图
map_temp = map_visualmap(sequence_temp, time)
#将地图加入时间轴对象
timeLine.add(map_temp, time).add_schema(play_interval=360,
is_auto_play=False,
is_loop_play=False)
# 地图创建完成后，通过render()方法可以将地图渲染为html
timeLine.render(fileNameOut)
from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.faker import Faker
tl = Timeline()
for i in range(2015, 2020):
bar = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("某商店{}年营业额".format(i))))
tl.add(bar, "{}年".format(i))
tl.render("Timeline_multi_axis.html")
