def main_handler(event, context):
geo = Map(init_opts=opts.InitOpts(page_title="国内疫情地图",
js_host="https://js.yiqin.zw2s.ltd/"))
scf = Client(secret_id="******", secret_key="******", region="ap-shanghai")
data = loads(scf.invoke('guonei'))
data2 = loads(scf.invoke('data'))
updatetime = strftime("%Y-%m-%d %H:%M:%S",
localtime(data2['modifyTime'] / 1000))
zhongdata = []
for a in range(len(data)):
tempdata = []
tempdata.append(data[a]['provinceShortName'])
tempdata.append(data[a]['currentConfirmedCount'])
zhongdata.append(tempdata)
pingjun = data2['currentConfirmedCount'] / len(data)
geo.set_global_opts(title_opts=opts.TitleOpts(
title="国内疫情地图",
subtitle="目前共确诊" + str(data2['currentConfirmedCount']) +
"例，数据来自丁香园，截止时间：" + updatetime),
visualmap_opts=opts.VisualMapOpts(max_=pingjun),
legend_opts=opts.LegendOpts(is_show=False))
geo.add("确诊", zhongdata, maptype="china")
return {
"isBase64Encoded": False,
"statusCode": 200,
"headers": {
'Content-Type': 'text/html; charset=utf-8'
},
"body": geo.render_embed()
}
def create_map():
province_list = [
'北京', '天津', '上海', '重庆', '河北', '河南', '云南', '辽宁', '黑龙江', '湖南', '安徽',
'山东', '新疆', '江苏', '浙江', '江西', '湖北', '广西', '甘肃', '山西', '内蒙古', '陕西',
'吉林', '福建', '贵州', '广东', '青海', '西藏', '四川', '宁夏', '海南', '台湾', '香港', '澳门'
]
value_list = []
max_value = 0
for i, province in enumerate(province_list):
province_counts = Patents.select().where(
Patents.proposer_address**('%' + province + '%')).count()
if max_value < province_counts:
max_value = province_counts
value_list.append(province_counts)
map = Map("专利省份分布地图", width=1200, height=600)
map.add('',
province_list,
value_list,
maptype='china',
is_visualmap=True,
is_label_show=True,
visual_text_color='#000',
visual_range=[0, max_value])
return map
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 visulize(
self,
city_name,
school_counts,
):
if (self.FlagOfProcess):
realcity = []
# 处理城市名，只保留两字或三字名称
for citys in city_name:
if "黑龙江" in citys:
a = "黑龙江"
elif "内蒙古" in citys:
a = "内蒙古"
else:
a = citys[4:6]
realcity.append(a)
# print(realcity)
# 将数据处理成列表
list1 = [[realcity[i], school_counts[i]]
for i in range(len(realcity))]
# print(list1)
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2020高校分布"),
visualmap_opts=opts.VisualMapOpts(max_=40) # 最大数据范围
)
map_1.add("2020高校分布", list1, maptype="china")
# map_1.add_js_funcs("window.confirm()")
map_1.render('SchoolMap.html')
print("绘制成功，请在同级目录下查看 SchoolMap.html 文件！")
else:
print("出现错误，停止绘制图表！")
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="CBIC：全球各国现存确诊_至4月7号",subtitle="数据来源Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)"),
legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,\
pieces=pieces_list))
return c
def confirmed_maps():
map_chart = Map()
map_chart.add(
'疫情地圖',
[list(i) for i in zip(city_confirmed.keys(), city_confirmed.values())],
maptype='台湾',
is_map_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False))
map_chart.set_global_opts(title_opts=opts.TitleOpts(title='台灣'),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=[{
"min": 1,
"max": 50,
"label": "1-50人",
"color": "#FFE6BE"
}, {
"min": 51,
"max": 100,
"label": "51-100人",
"color": "#FFB769"
}, {
"min": 101,
"max": 300,
"label": "101-300人",
"color": "#FF8F66"
}, {
"min": 301,
"max": 1000,
"label": "301-1000人",
"color": "#ED514E"
}]))
map_chart.render('confirmed_map.html')
def map_type():
global wid, hei
school_type_local = analysis_data.school_rank()
c = Map(init_opts=opts.InitOpts(width=wid, height=hei))
arr_drop = ['理工类', '综合类', '其他', '民族类', '农林类']
for key, value in school_type_local.items():
if key not in arr_drop:
c.add(key, list(value.iteritems()), "china")
c.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
max_=10
# is_piecewise=True,
# pieces=[
# {"min": 8, "max": 20, "color": 'black'},
# {"min": 5, "max": 7,"color": '#DC143C'},
# {"value": 4,'color':'#00008B'},
# {"value": 3,'color':'green'},
# {"min": 2, "max": 2},
# {"value": 1, "label": '1 一枝独秀','color':'#FFF8DC'},
# ]
),
title_opts=opts.TitleOpts(title='各类院校全国分布'),
legend_opts=opts.LegendOpts(
#type_="scroll", pos_left="80%", orient="vertical",
pos_left='150px',
selected_mode='single'))
return c
def create_province_map(df):
# 筛选数据
df = df[df["country"] == "中国"]
df1 = df.copy()
# 数据替换
df1["province"] = df1["province"].str.replace("省", "").str.replace(
"壮族自治区", "").str.replace("维吾尔自治区", "").str.replace("自治区", "")
# 分组计数
df_num = df1.groupby("province")["province"].agg(count="count")
df_province = df_num.index.values.tolist()
df_count = df_num["count"].values.tolist()
# 初始化配置
map = Map(init_opts=opts.InitOpts(width="800px", height="400px"))
# 中国地图
map.add("", [list(z) for z in zip(df_province, df_count)], "china")
# 设置全局配置项，标题、工具箱(下载图片)、颜色图例
map.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V省份分布情况",
pos_left="center",
pos_top="0"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={"saveAsImage": {}}),
# 设置数值范围0-600，is_piecewise标签值连续
visualmap_opts=opts.VisualMapOpts(max_=600, is_piecewise=False))
map.render("抖音大V省份分布情况.html")
def sxcsfb():
friends = bot.friends(update=True)
dd1 = defaultdict(int) # 如果调用字典的key不存在的时候，默认value为0
for hy in friends:
# print(hy.city)
if '\u9fa5' >= hy.city >= '\u4e00': # 如果省份是汉字就保留，否则就舍弃，因为在中国地图上也不会显示
city = hy.city + "市" # 必须要加，不加会名字识别不到
dd1[city] += 1 # 每次进来如果key相同则进行累加
sj = [list(a) for a in zip(dd1.keys(), dd1.values())]
print(sj)
# 地图部分
m2 = Map()
m2.add(series_name="微信好友",
data_pair=sj,
maptype="陕西",
is_map_symbol_show=True)
m2.set_global_opts(title_opts=opts.TitleOpts(
title="陕西好友分布",
subtitle=time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time()))),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
is_calculable=True,
max_=180))
m2.render("./kshwy/map_sxcsfb.html")
webbrowser.open("E:\study\kshwy\map_sxcsfb.html")
def sffb():
friends = bot.friends(update=True)
dy = defaultdict(
lambda: 0) # defaultdict的作用是在于，当字典里的key不存在但被查找时，返回的不是keyError而是一个默认值
for f in friends:
if '\u9fa5' >= f.province >= '\u4e00': # 如果省份是汉字就保留，否则就舍弃，因为在中国地图上也不会显示
dy[f.province] += 1
attr = dy.keys()
value = dy.values()
dx = [list(dx1) for dx1 in zip(attr, value)
] # zip将可迭代的对象打包成一个一个的元组 如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同
print(dx)
# 地图部分
m1 = Map()
m1.add(series_name="微信好友",
data_pair=dx,
maptype="china",
name_map={"key": "value"},
is_map_symbol_show=True)
m1.set_global_opts(
title_opts=opts.TitleOpts(title="小李的微信好友",
subtitle="2020-04-10"), # 设置标题和副标题在左侧
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
max_=250) # 是否分段,以及分段的最大值
)
m1.render('province.html')
webbrowser.open('province.html')
def base_info():
map = Map()
# 注意：这里是"河南' 不能是河南省
map.add("河南省地图", data, "河南")
map.set_global_opts(title_opts=opts.TitleOpts(title="Map-河南省地图"),
visualmap_opts=opts.VisualMapOpts(max_=10))
return map
def map_visualmap_piecewise():
obj_map = Map()
obj_map.add("中国", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
obj_map.set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（分段型）",subtitle ="副标题"),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
)
return obj_map
def display_recent_global_distribution(pic_file, maxCount=200, **kwargs):
"""
display the distribution of recent total numbers of confirmed patients.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 200)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
OverallDf = pd.read_sql_query("""select * from Region_Data""", conn)
OverallDf['updateTime'] = OverallDf['updateTime'].astype('int64')
recentData = OverallDf.groupby('provinceShortName').apply(
lambda t: t[t['updateTime'] == t['updateTime'].max()])
recentData = recentData.groupby('country').agg({
'confirmedCount': 'sum',
'suspectedCount': 'sum',
'updateTime': 'mean'
})
recentData['date'] = pd.to_datetime(recentData['updateTime'] / 1000,
unit='s')
time = recentData[recentData.index == '中国']['updateTime']
data = [[
searchCountryENName(recentData.index[i]),
int(recentData['confirmedCount'][i])
] for i in range(recentData.shape[0])]
map_3 = Map()
map_3.add("{0} worldwide COVID-19 patients distribution".format(
recentData['date'][1].strftime('%Y-%m-%d')),
data,
maptype='world',
is_map_symbol_show=False)
map_3.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, font_size=100))
map_3.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=maxCount),
title_opts=opts.TitleOpts(title=""))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_3.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_3.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def map_world():
obj_map = Map()
obj_map.add("世界", [list(z) for z in zip(Faker.country, Faker.values())], "world")
obj_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
obj_map.set_global_opts(
title_opts=opts.TitleOpts(title="Map-世界地图",subtitle ="副标题"),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
return obj_map
def map_total_cases(csv):
"""
Description: exports an html file with an interactive map showing the latest total cases
registered per country in the planet
in a two-ax chart for countries with p-score > 1
:param owid_data: OWID main coronavirus dataset in a pandas dataframe format,
it can be already processed with clean_data()
:export: HTML file with a pyecharts interactive map
"""
input_df = pd.read_csv(csv)
if input_df.index.name == "date":
input_df = input_df[["location", "total_cases"]].dropna()
else:
input_df = input_df[["date", "location", "total_cases"]].set_index("date").dropna()
map_df = input_df.loc[input_df.groupby('location').total_cases.idxmax()].head(
len(input_df["location"].unique()) - 1)
map_df.reset_index(drop=True, inplace=True)
country = list(map_df["location"])
totalcases = list(map_df["total_cases"])
list1 = [[country[i], totalcases[i]] for i in range(len(country))]
map_1 = Map(init_opts=opts.InitOpts(width="1000px", height="460px"))
map_1.add("Total Confirmed Cases",
list1,
maptype="world",
is_map_symbol_show=False)
map_1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map_1.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=1100000, is_piecewise=True, pieces=[
{"min": 1000000},
{"min": 600000, "max": 999999},
{"min": 200000, "max": 599999},
{"min": 50000, "max": 119999},
{"min": 10000, "max": 49999},
{"max": 9999}, ]),
title_opts=opts.TitleOpts(
title='Casos confirmados de COVID-19',
subtitle="Últimos datos disponibles",
pos_left="center",
padding=0,
item_gap=2,
title_textstyle_opts=opts.TextStyleOpts(color="darkblue",
font_weight="bold",
font_family="Courier New",
font_size=30),
subtitle_textstyle_opts=opts.TextStyleOpts(color="grey",
font_weight="bold",
font_family="Courier New",
font_size=13)),
legend_opts=opts.LegendOpts(is_show=False))
return map_1.render('map_cases.html')
def geo_base(data):
city_list, province_list = data
# 好友全国省份分布图
geo = Geo(init_opts=opts.InitOpts(theme="vintage"))
for city in city_list:
try:
geo.add_schema(maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="gray"))
geo.add("微信好友分布地图", [city],
type_="effectScatter",
symbol_size=10)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="微信好友分布地图"),
)
except:
pass
print("正在制作好友全国分布图")
make_snapshot(driver, geo.render(), "geo.png")
# 广东好友热力图
# geo = Geo(init_opts=opts.InitOpts(theme="vintage"))
# for city in city_list:
# try:
# geo.add_schema(maptype="广东", itemstyle_opts=opts.ItemStyleOpts(color="gray"))
# geo.add("广东好友热力图", [city], type_="heatmap", symbol_size=10)
# geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="热力图"),
# toolbox_opts=opts.ToolboxOpts())
# except:
# pass
#
# print("正在制作好友广东热力图")
# make_snapshot(driver, geo.render(), "heat.png")
# 好友全国地理图
maps = Map()
maps.add("", province_list, "china")
maps.set_global_opts(title_opts=opts.TitleOpts(title="微信好友分布图"),
visualmap_opts=opts.VisualMapOpts())
print("正在制作好友地理图")
make_snapshot(driver, geo.render(), "map.png")
# 词云图
c = (WordCloud().add("",
city_list,
word_size_range=[15, 50],
shape="diamond",
word_gap=10).set_global_opts(
title_opts=opts.TitleOpts(title="diamond")))
print("正在制作好友城市词云图")
make_snapshot(driver, c.render(), "world.png")
def set_map(name, nums):
# 初始化地图
map0 = Map(init_opts=set_option("1200px", "600px"), )
map0.add(name, data_pair=get_data_series(name), maptype="world")
map0.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="COVID_19 WORLD MAP"),
visualmap_opts=opts.VisualMapOpts(max_=nums),
)
return map0
def chart(self, f): # unsupported city map for now
city, frequency = self.clean(f)[1], self.clean(f)[2]
data = [[city[i], frequency[i]] for i in range(len(city))]
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2020年b站会员购商品线下分布"),
visualmap_opts=opts.VisualMapOpts(max_=100) # 最大数据范围
)
map_1.add("2020年b站会员购商品线下分布", data, maptype="china")
map_1.render('vip.html')
def map_cs_level():
global wid, hei
levels = ['A+', 'A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-']
data = analysis_data.get_cs_level_raw()
c = Map(init_opts=opts.InitOpts(width=wid, height=hei))
for i in levels:
c.add(i, data[i], "china")
c.set_global_opts(visualmap_opts=opts.VisualMapOpts(min_=0, max_=5),
legend_opts=opts.LegendOpts(selected_mode='single'),
title_opts=opts.TitleOpts(title="计科学科评估"))
return c
def map_province():
obj_map = Map()
# Faker.guangdong_city数据生成只有这一个省的城市列表
# henan_city是通过修改Faker文件自定义的
obj_map.add("河南", [list(z) for z in zip(Faker.henan_city, Faker.values())], "河南")
obj_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
obj_map.set_global_opts(
title_opts=opts.TitleOpts(title="Map-河南",subtitle ="副标题"),
visualmap_opts=opts.VisualMapOpts(),
)
return obj_map
def render():
world_map = getData()
china_map = getChinaData()
map = Map(
options.InitOpts(bg_color="#87CEFA",
width="100%",
height="900px",
page_title='世界疫情分布'))
for item in maps:
map_data = {}
for item_map in world_map:
name = item_map['name'] # 国家名
count = int(item_map[item]) # 该国家人数
map_data[name] = count
map_data['中国'] = china_map[item]
map.add(
series_name=maps[item], # 名称
data_pair=list(map_data.items()), # 传入数据
maptype='world', # 地图类型
is_map_symbol_show=False, # 不显示标记
name_map=name_map)
map.set_global_opts(
visualmap_opts=options.VisualMapOpts(max_=1100000,
is_piecewise=True,
pieces=[
{
"min": 500000
},
{
"min": 200000,
"max": 499999
},
{
"min": 100000,
"max": 199999
},
{
"min": 50000,
"max": 99999
},
{
"min": 10000,
"max": 49999
},
{
"max": 9999
},
]))
map.set_series_opts(label_opts=options.LabelOpts(is_show=False)) # 不显示国家名
map.add_js_funcs("""document.body.style.backgroundColor="#87CEFA"
""")
map.render('map.html') # 命名并保存
def render_map():
with open("./data/countries.geo.json", "r") as f:
map = st_Map("world", json.loads(f.read()),)
c = Map(init_opts=opts.InitOpts(bg_color="white"))
c.add("Demo", [list(z) for z in zip(Faker.country, Faker.values())], "world")
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.set_global_opts(
title_opts=opts.TitleOpts(title="Map world"),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
st_pyecharts(c, map=map, height=500)
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
def get_map(labels, counts, where, title, size, pieces):
my_map = Map(init_opts=opts.InitOpts(width=size[0], height=size[1]))
my_map.add("新冠数据", [list(z) for z in zip(labels, counts)], where)
my_map.set_series_opts(label_opts=opts.LabelOpts(font_size=8))
my_map.set_global_opts(
title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(pieces=pieces,
is_piecewise=True,
is_show=False),
)
return my_map
def display_recent_overall_distribution(pic_file, maxCount=500, **kwargs):
"""
display the distribution of recent total numbers of nation-wide confirmed
patients in China.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 500)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
cu.execute("""select provinceShortName, confirmedCount
from Region_Data
where updateTime in (select max(updateTime)
from Region_Data r_d
where r_d.country='中国' and r_d.region_id=Region_Data.region_id)
group by Region_Data.region_id;
""")
recentProvinceData = cu.fetchall()
cu.execute("""select max(updateTime) from Region_Data;""")
recentTime = cu.fetchone()
recentTimeObj = dt.datetime.utcfromtimestamp(int(recentTime[0]) / 1000)
# color-plot
list1 = [[recentProvinceData[i][0], recentProvinceData[i][1]]
for i in range(len(recentProvinceData))]
map_1 = Map()
map_1.add("{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d')),
list1,
maptype="china",
is_map_symbol_show=False)
map_1.set_global_opts(title_opts=opts.TitleOpts(
title="{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d'))),
visualmap_opts=opts.VisualMapOpts(max_=maxCount))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_1.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_1.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def map_visualmap(sequence, date) -> Map:
c =Map()
c.add(date, sequence, maptype="china")
c.set_global_opts(title_opts=opts.TitleOpts(title="CBIC：全国现存确诊人数_至3月30号",subtitle="数据来源于丁香园"),visualmap_opts=opts.VisualMapOpts(max_=1400,range_color=["#F8F8FF",'#FF0909']))
# c = (
# Map()
# .add(date, sequence, maptype="china")
# .set_global_opts(
# title_opts=opts.TitleOpts(title="全国疫情动态地图_确诊累计人数_截至至3月14号"),
# visualmap_opts=opts.VisualMapOpts(max_=1400)
# )
# )
return c
def show_map_charts(df):
# 计数，[0:10]取前10
city_num = df['province'].value_counts() # 应该是按省份显示的
# 返回index列表和value列表组成的元组[('北京', 62), ('上海', 50)]，再将元组转换成列表[['北京', 62], ['上海', 50]]
city_list = [
list(z) for z in zip(city_num.index.tolist(), city_num.values.tolist())
]
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add(series_name='评论数量', data_pair=city_list, maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='评论用户国内城市分布'),
visualmap_opts=opts.VisualMapOpts(max_=50),
toolbox_opts=opts.ToolboxOpts())
map1.render('charts/map_chart.html')
def chinaMap(value):
province_distribution = {
'河南': 45.23,
'北京': 37.56,
'河北': 21,
'辽宁': 12,
'江西': 6,
'上海': 20,
'安徽': 10,
'江苏': 16,
'湖南': 9,
'浙江': 13,
'海南': 2,
'广东': 22,
'湖北': 8,
'黑龙江': 11,
'澳门': 1,
'陕西': 11,
'四川': 7,
'内蒙古': 3,
'重庆': 3,
'云南': 6,
'贵州': 2,
'吉林': 3,
'山西': 12,
'山东': 11,
'福建': 4,
'青海': 1,
'天津': 1,
'其他': 1
}
#provice = list(province_distribution.keys())
#values = list(province_distribution.values())
provice = list()
values = list()
for item in value:
values.append(item['total']['confirm'])
provice.append(item['name'])
#print(provice)
# print(values)
map = Map("疫情地图", width=1200, height=600)
map.add("疫情",
provice,
values,
visual_range=[0, 500],
maptype='china',
is_visualmap=True,
visual_text_color='#000',
is_label_show=True)
map.render(path="中国地图.html")
def map_visualmap(sequence, date) -> Map:
c = Map()
c.add(date, sequence, maptype="china")
c.set_global_opts(title_opts=opts.TitleOpts(title="CBIC：全国各省现存确诊_至4月8号",subtitle="数据来源于丁香园"),legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,\
pieces=pieces_list))
# c = (
# Map()
# .add(date, sequence, maptype="china")
# .set_global_opts(
# title_opts=opts.TitleOpts(title="全国疫情动态地图_确诊累计人数_截至至3月14号"),
# visualmap_opts=opts.VisualMapOpts(max_=1400)
# )
# )
return c
def show_map_charts():
city_num = df['city_dealed'].value_counts()
city_num.drop('国外', inplace=True)
city_num.drop('未填写', inplace=True)
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add("", [
list(z) for z in zip(city_num.index.tolist(), city_num.values.tolist())
],
maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='评论者国内城市分布'),
visualmap_opts=opts.VisualMapOpts(max_=50),
toolbox_opts=opts.ToolboxOpts())
map1.render()
