def load_file():
with open("去哪儿景点.csv", "r", encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
cityList = []
#根据规则读取文件中的每一行文字
for line in reader:
if len(line[0].split('·')) > 1:
city = City()
city.cityName = line[0].split('·')[1]
city.hotValue = float(line[5].split(' ')[1])
foundRepeatCity = 0
#获取城市名字cityName和热度信息hotValue
#针对城市名字cityName做组合操作，也就是针对其做热点求和
for originCity in cityList:
if originCity.cityName == city.cityName:
originCity.hotValue = originCity.hotValue + city.hotValue
foundRepeatCity = 1
break
#过滤掉城市中没有在echart 备案信息
if foundRepeatCity == 0 and Geo().get_coordinate(
name=city.cityName) != None and int(
city.hotValue) != 0:
cityList.append(city)
# 生成输出信息，用来传入到echart中
outputGeoData = []
for city in cityList:
if Geo().get_coordinate(name=city.cityName) != None and int(
city.hotValue) != 0:
outputGeoDataRecord = (city.cityName, int(city.hotValue) * 5)
outputGeoData.append(outputGeoDataRecord)
print(outputGeoData)
return outputGeoData
def load_file():
#打开csv文件
with open("qunar.csv", "r") as csvfile:
reader = csv.reader(csvfile)
#开始绘图
cityList = []
#根据规则读取文件中的文字
for line in reader:
if len(line[0].split('·')) > 1:
city = City()
city.cityName = line[0].split('·')[1]
city.hotValue = float(line[5].split(' ')[1])
foundRepeatCity = 0
#获取城市名字和热度信息
#针对城市名字做联合操作，也就是针对其做热点求和
for originCity in cityList:
if originCity.cityName == city.cityName:
originCity.hotValue = originCity.hotValue + city.hotValue
foundRepeatCity = 1
break
if foundRepeatCity == 0 and Geo().get_coordinate(
name=city.cityName) != None and int(
city.hotValue) != 0:
cityList.append(city)
#生成输出信息，传入图标
outputGeoData = []
for city in cityList:
if Geo().get_coordinate(name=city.cityName) != None and round(
city.hotValue) != 0:
outputGeoDataRecord = (city.cityName,
int(city.hotValue) * 1.25)
outputGeoData.append(outputGeoDataRecord)
return outputGeoData
def geo_scatter(df) -> Geo:
if df.empty is False:
index = df.max() / df.mean() / 50
geo = (
Geo()
.add_schema(maptype="china")
)
for item in df.index:
l = [[item, df.loc[item]]]
geo.add(series_name=item,
data_pair=l,
symbol_size=df.loc[item] / df.mean() / index,
)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(axis_pointer_type='cross'),
# visualmap_opts=opts.VisualMapOpts(),
# title_opts=opts.TitleOpts(title="Geo-基本示例"),
)
else:
geo = (
Geo()
.add_schema(maptype="china")
)
return geo
def load_file():
# 打开csv文件
with open('result.csv', 'r', encoding='UTF-8') as csvfile:
reader = csv.reader(csvfile)
cityList = []
# 根据规则读取文件中的每一行文字
for line in reader:
if len(line[0].split('·')) > 1:
city = City()
city.cityName = line[0].split('·')[1]
city.hotValue = float(line[5].split(' ')[1])
foundRepeatCity = 0
# 获取城市名字cityName和热度信息hotValue
# 针对城市名字cityName做聚合操作，也就是针对其做热点求和
for originCity in cityList:
if originCity.cityName == city.cityName:
originCity.hotValue = originCity.hotValue + city.hotValue
foundRepeatCity += 1
break
# 过滤城市列表中 没有在echart 备案过的城市
if foundRepeatCity == 0 and Geo().get_coordinate(
name=city.cityName) is not None and int(
city.hotValue) != 0:
cityList.append(city)
# 生成输出信息，用来传入到echarts中
outputGeoData = []
for city in cityList:
if Geo().get_coordinate(name=city.cityName) is not None and int(
city.hotValue) != 0:
outputGeoDataRecord = (city.cityName, int(city.hotValue) * 5)
outputGeoData.append(outputGeoDataRecord)
print(outputGeoData)
return outputGeoData
def drawGeoMap(self, aStuData):
sno, sname = aStuData['info'].split(SPLIT_CHAR)
sdata = aStuData['data']
try:
geo = Geo()
def add_data(date, location):
# print(location)
try:
longitude, latitude, address = location
except:
longitude, latitude, address = DEFAULT_Location
geo.add_coordinate(date, longitude, latitude)
geo.add(date, [(date, address)], type_=ChartType.EFFECT_SCATTER)
geo.add_schema(maptype="china")
# add data
for date, data in sdata.items():
add_data(date, data)
title = f'{sno} {sname} 位置动态'
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(legend_opts=opts.LegendOpts(orient='vertical', pos_left='left', pos_top='10%'), title_opts=opts.TitleOpts(title=title))
html_path = ospath_join(self.mapsDir, f'{sno}_{sname}.html')
geo.render(html_path)
except Exception as e:
log.error(f'地图生成失败: {sno} {sname} - 可能是包含了文件名不可用的特殊字符', exc_info=True)
self._signal.emit(f'错误：{sno} {sname} 地图生成失败！可能原因：工号或提交人姓名中包含文件名不可用的特殊字符，如：*。请修改后重试！')
def base_info():
geo = Geo()
geo.add_schema(maptype="河南") # 加入自定义的点，格式为
# 地图形式 type_=ChartType.HEATMAP
geo.add("geo", data, type_=ChartType.EFFECT_SCATTER)
# is_show：是否显示经纬度
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=10),
title_opts=opts.TitleOpts(title="Geo-河南省地图"))
return geo
def draw_map(data_pair, data_coordinate) -> Geo: # ->常常出现在python函数定义的函数名后面，为函数添加元数据,描述函数的返回类型，从而方便开发人员使用。
g = Geo()
# 选择要显示的地图
g.add_schema(maptype="重庆", center=[106.531638,29.561081], itemstyle_opts=opts.ItemStyleOpts(color="#575D57"))
# 使用add_coordinate(name, lng, lat)添加坐标点和坐标名称
for item in data_coordinate:
g.add_coordinate(item['name'], item['lng'], item['lat'])
# 将数据添加到定义的地图上
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=5) # type_=ChartType.HEATMAP
# 设置样式
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\
.set_global_opts(visualmap_opts=opts.VisualMapOpts(min_=60, max_ = 200, is_piecewise=True),title_opts=opts.TitleOpts(title="重庆房源分布图"))
return g
def base_info():
print(data)
geo = Geo()
geo.add_schema(maptype="china") # 加入自定义的点，格式为
# geo.add_coordinate("自定义点", 80.39770014211535,39.90779994986951)
# 地图形式 type_=ChartType.HEATMAP
geo.add("geo", data, type_=ChartType.EFFECT_SCATTER)
# is_show：是否显示经纬度
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo-基本示例"))
return geo
def create_city_map(df):
df1 = df[df["country"] == "中国"]
df1 = df1.copy()
df1["city"] = df1["city"].str.replace("市", "")
df_num = df1.groupby("city")["city"].agg(
count="count").reset_index().sort_values(by="count", ascending=False)
df_city = df_num["city"].values.tolist()
df_count = df_num["count"].values.tolist()
# 初始化配置
geo = Geo(init_opts=opts.InitOpts(width="800px", height="400px"))
# 中国地图
geo.add("", [list(z) for z in zip(df_city, df_count)], "china")
# 设置全局配置项，标题、工具箱(下载图片)、颜色图例
geo.set_global_opts(
title_opts=opts.TitleOpts(title="抖音大V城市分布情况",
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(is_piecewise=True))
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map.render("抖音大V城市分布情况.html")
def load_file():
with open("qunar.csv", "r") as csvfile:
reader = csv.reader(csvfile)
city_list = []
# 逐行读取
for line in reader:
if len(line[0].split('·')) > 1:
city = City()
city.city_name = line[0].split('·')[1]
city.hot_value = float(line[5].split(' ')[1])
found_repeat_city = 0
# 按城市热点求和
for origin_city in city_list:
if origin_city.city_name == city.city_name:
origin_city.hot_value += city.hot_value
found_repeat_city = 1
break
# 过滤掉城市没有在echart备案的信息
if found_repeat_city == 0 \
and Geo().get_coordinate(name=city.city_name) is not None \
and int(city.hot_value) != 0:
city_list.append(city)
# 生成输入信息，用来传入到echart中
output_geo_data = []
for city in city_list:
output_geo_data_record = (city.city_name, int(city.hot_value * 5))
output_geo_data.append(output_geo_data_record)
print(output_geo_data)
return output_geo_data
def map_985_211():
global wid, hei
df = pd.read_csv('data/211_985.csv') #前39所为985
df_985 = df.iloc[:39]
df_211 = df.iloc[39:]
df_985_num = df_985.groupby('所在地').count().iloc[:, 0]
df_211_num = df_211.groupby('所在地').count().iloc[:, 0]
c = (
Geo(init_opts=opts.InitOpts(width=wid, height=hei)).add_schema(
maptype="china").add("985",
list(df_985_num.iteritems()),
type_=ChartType.HEATMAP).add(
"211",
list(df_211_num.iteritems()),
type_=ChartType.HEATMAP).
set_global_opts(
title_opts=opts.TitleOpts(title="985_211全国分布"),
visualmap_opts=opts.VisualMapOpts(max_=5),
# visualmap_opts=opts.VisualMapOpts(is_piecewise=True,pieces=[
# {"min":10, "max": 30},
# {"min": 5, "max": 10},
# {"value": 4},
# {"value": 3},
# {"min": 2,"max": 2},
# {"value": 1, "label": '1 一枝独秀', "color": 'grey'},
# ]
# )
))
return c
def geo_charts(content):
"""绘制世界地图"""
zip_data = content[0]
start_city = content[1]
geo = (Geo().add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48",
border_color="#00000"),
).add(
"",
data_pair=zip_data,
type_=ChartType.EFFECT_SCATTER,
color="white",
).add(
"geo",
data_pair=start_city,
type_=GeoType.LINES,
effect_opts=opts.EffectOpts(symbol=SymbolType.RECT,
symbol_size=6,
color="yellow",
is_show=True),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="Python开发工程师可视化界面")))
return geo
def geo_lines_background() -> Geo:
c = (
Geo()
.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
)
.add(
"",
[("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)],
type_=ChartType.EFFECT_SCATTER,
color="white",
)
.add(
"geo",
[("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆")],
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=SymbolType.ARROW, symbol_size=6, color="blue"
),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="Geo-Lines-background"))
)
return c
def geo_lines() -> Geo:
c = (
Geo()
.add_schema(maptype="china")
.add(
"",
[("北京", 1), ("重庆", 2), ("长沙", 3), ("长春", 4), ("成都", 5), ("大连", 6), ("广州", 7), ("贵阳", 8),
("合肥", 9), ("杭州", 10), ("哈尔滨", 11), ("海口", 12), ("济南", 13), ("昆明", 14), ("柳州", 15), ("绵阳", 16),
("潞西市", 17), ("南宁", 18), ("南京", 19), ("沈阳", 20), ("三亚", 21), ("上海", 22), ("天津", 23), ("徐州", 24),
("义乌", 25), ("郑州", 26), ("张家界", 27)],
type_=ChartType.EFFECT_SCATTER,
color="#ffa022"
)
.add(
"",
[("揭阳", "北京"), ("揭阳", "重庆"), ("揭阳", "长沙"), ("揭阳", "长春"), ("揭阳", "成都"),
("揭阳", "大连"), ("揭阳", "广州"), ("揭阳", "贵阳"), ("揭阳", "合肥"), ("揭阳", "杭州"),
("揭阳", "哈尔滨"), ("揭阳", "海口"), ("揭阳", "济南"), ("揭阳", "昆明"), ("揭阳", "柳州"),
("揭阳", "绵阳"), ("揭阳", "潞西市"), ("揭阳", "南宁"), ("揭阳", "南京"), ("揭阳", "沈阳"),("揭阳", "三亚"),
("揭阳", "上海"), ("揭阳", "天津"), ("揭阳", "徐州"),("揭阳", "义乌"),("揭阳", "郑州"), ("揭阳", "张家界")],
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=symbol_list[0], symbol_size=15, color='#a6c84c', period=6, trail_length=0, is_show=True
# 特效图形的标记。有 RECT', 'DIAMOND', 'ARROW' 可选
),
linestyle_opts=opts.LineStyleOpts(curve=0.2, color='#46bee9', width=1, opacity=0.5),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="揭阳出发国内航线", subtitle="数能工作室制作", pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color='black',font_size=20, font_weight="bold"),
subtitle_textstyle_opts=opts.TextStyleOpts(color='black',font_size=15),item_gap=15))
)
return c
def geo_sichuan(title) -> Geo:
c = (Geo().add_schema(maptype='四川').add(
title, [list(z) for z in zip(Data.sichuan_city, Data.values())],
type_=ChartType.HEATMAP).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=42, is_piecewise=True),
title_opts=opts.TitleOpts(title='四川温度变化情况')))
return c
def city(self):
sql = 'select city, count(1) counts from jobs group by city'
results = self.query(sql)
c = (Geo().add_schema(maptype="china").add(
"城市热力图",
list(results),
type_=ChartType.HEATMAP,
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(), ).render("拉钩城市热力图.html"))
sql = 'select city,counts from (select city, count(1) counts from jobs group by city) a order by counts desc limit 20'
results = self.query(sql)
citys = []
values = []
for row in results:
citys.append(row[0])
values.append(row[1])
c = (Bar().add_xaxis(citys).add_yaxis(
"各城市的招聘数量 Top 20",
values).set_global_opts(xaxis_opts=opts.AxisOpts(
name_rotate=60, name="城市",
axislabel_opts={"rotate": 45})).render("拉钩城市招聘图.html"))
def hotpoint(movie_name):
#读取城市数据
with open(movie_name + '\\' + movie_name + 'dbplaces.txt',
'r+',
encoding='utf-8-sig') as f:
heat_1 = f.read()
for i in heat_1:
if i in string.punctuation:
heat_1 = heat_1.replace(i, "")
heat = heat_1.split()
heat_dic = Counter(heat)
#整理数据
heat_list = []
for place, times in heat_dic.items():
c_word = (place, times)
heat_list.append(c_word)
#出图
heat_map = (Geo().add_schema(maptype="china").add(
"", heat_list).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title=movie_name + " 豆瓣观影热点图",
subtitle=None),
toolbox_opts=opts.ToolboxOpts(is_show=True)))
heat_map.render(movie_name + '\\' + movie_name + 'dbhotpoint.html')
def city_info(self):
dict_city = dict()
for friend in self.friends:
key = friend['City']
if key not in dict_city.keys():
dict_city[key] = 1
else:
dict_city[key] += 1
city = []
# max_num = 0 # 某个城市中最多的人数
for key, value in dict_city.items():
# 其他地区
if len(key) == 0:
continue
# 过滤英文等非市级
if len(key) >= 3:
continue
city.append(tuple((key, value)))
# if value > max_num:
# max_num = value
# 地图标注
# geo = Geo("微信好友城市分布图", "data from WeChat", title_color="#fff", title_pos="center",
# width=1200, height=600, background_color='#404a59')
# attr, value = geo.cast(city)
# geo.add("", attr, value, visual_range=[0, 50], visual_text_color="#fff", symbol_size=15, is_visualmap=True)
# geo.show_config()
geo = (Geo().add_schema(maptype="china").add("人数", ))
geo.render("city.html")
def geo_base() -> Geo:
c = (Geo().add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(
color="#323c48", border_color="#111")).add(
"人/平方千米", zip(list(df.地区), list(df.人口密度))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="2018年36个主要城市的人口密度情况"),
visualmap_opts=opts.VisualMapOpts(max_=2306.6,
min_=18.7,
is_piecewise=True,
pieces=[{
'min':
924,
'max':
2306.593060
}, {
'min':
602,
'max':
923.156006
}, {
'min':
362,
'max':
600.689035
}, {
'min':
18.781760,
'max':
361.142947
}])))
return c
def show_covid():
covid = (Geo().add_schema(maptype="澳大利亚").add_coordinate(
"Sydney", 151.207,
-33.868).add_coordinate("Melbourne", 144.963, -37.814).add_coordinate(
"Brisbane", 153.028,
-27.468).add_coordinate("Perth", 115.861, -31.952).add_coordinate(
"Adelaide", 138.599, -34.929).add_coordinate(
"Cold Coast", 153.431,
-28).add_coordinate("Newcastle", 151.78, -32.93).add(
"geo",
[list(z) for z in zip(geo_city, geo_cov)
]).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
type_="size",
max_=500,
min_=0,
range_text=[
'Highly discussed', 'Rarely discussed'
]),
title_opts=opts.TitleOpts(
title="Twitter Covid Map"),
legend_opts=opts.LegendOpts(is_show=False)))
return covid
def geoHeatMap(self,area=None,data=None,maptype='china',title='热力图示例',subtitle='副标题'):
'''
热力图
param area: 省份的列表 type list 例如 ['吉林', '辽宁', '河北', '河南']
param data: 对应的值 type list 例如 [63,10,2,30]
应用场景 省份或者城市排行榜
# ['黑龙江', '吉林', '辽宁', '河北', '河南', '山东', '山西', '安徽', '江西', '江苏', '浙江', '福建', '台湾', '广东',
# '湖南', '湖北', '海南', '云南', '贵州', '四川', '青海', '甘肃', '陕西', '内蒙古', '新疆', '广西', '宁夏', '西藏',
# '北京','天津','上海','重庆','香港','澳门']
'''
area = area or Faker.provinces
data = data or Faker.values()
c = (
Geo()
.add_schema(maptype=maptype)
.add(
"geo",
[list(z) for z in zip(area, data)],
type_=ChartType.HEATMAP,
)
# .set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title=title,subtitle=subtitle),
)
)
chart_name = f'static/chart{self.index}_heatmap'
make_snapshot(snapshot,c.render(f'{chart_name}.html'),f'{chart_name}.png',is_remove_html=True)
self.index += 1
def geo_getmap() -> Geo:
c = (
Geo().add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48",
border_color="#111"),
).add(
"",
allcases,
type_=ChartType.EFFECT_SCATTER,
# symbol_size="size",
color="white",
).add(
"",
traces,
type_=ChartType.LINES,
# effect_opts=opts.EffectOpts(
# symbol=SymbolType.ARROW, symbol_size=6, color="red"
# ),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="2019-nCoV Trace Map(China)")))
make_snapshot(snapshot, c.render(), "tracemap.png")
def ocean_export_lines():
data = pd.read_csv(r"F:\ftp\erbranch\OEin24hrs\data.csv")
chargeable_weight = []
origin_dest = []
for index, row in data.iterrows():
chargeable_weight.append([row['DEST CITY'], row['CW']])
origin_dest.append([row['ORIGIN CITY'], row['DEST CITY']])
min_data = min([d[1] for d in chargeable_weight])
max_data = max([d[1] for d in chargeable_weight])
c = (
Geo().add_schema(maptype="world", is_roam=False).add_coordinate_json(
json_file=current_app.config['STATIC_PATH'] + r"\world_cities.json"
).add(
"Chargeable Weight",
chargeable_weight,
type_=GeoType.EFFECT_SCATTER,
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).add(
"geo",
origin_dest,
type_=GeoType.LINES,
# is_polyline = True,
# effect_opts=opts.EffectOpts(symbol=SymbolType.DIAMOND, symbol_size=5, color="blue",),
linestyle_opts=opts.LineStyleOpts(curve=0.3,
width=2,
opacity=0.2,
color='Red'),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="TSN Ocean Export LINES in 24 Hrs"),
visualmap_opts=opts.VisualMapOpts(
min_=min_data, max_=max_data)))
return render_template('dashboard/ocean_export_lines.html',
geo=c.dump_options())
def geo_line() -> Bar:
db = getattr(g, "_db", None)
col = db.trafficmodels
users = col.distinct("user")
list_place = []
for user in users:
place = col.find_one({"user": user}, {"place": 1})["place"]
places = get_all_IP(user)
for p in places:
list_place.append([place, p])
c = (Geo().add_schema(maptype="world").add_coordinate_json(
json_file="world_country.json").add(
"交互图",
list_place,
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(symbol=SymbolType.DIAMOND,
symbol_size=6,
color="blue"),
linestyle_opts=opts.LineStyleOpts(
curve=0.2,
opacity=0.2,
)).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="总交互图"),
tooltip_opts=opts.TooltipOpts(is_show=True)))
return c
def _geo_chart() -> Geo:
return (
Geo()
.add_schema(maptype="china", center=[39, 117.7], zoom=9)
.add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
.set_global_opts(visualmap_opts=opts.VisualMapOpts())
)
def geoLineCity(self, data) -> Geo:
# 指定城市航班连接城市图
count = len(data[0])
city_Geoline = (
Geo(init_opts=opts.InitOpts(
width='1000px', height='500px')).add_schema(
maptype="china", zoom=1.1).add_coordinate_json(
self.geo_file).add("",
data[1],
symbol_size=6,
color='#725e82') # 标记点大小与颜色
.add(
"",
data[0],
type_=ChartType.LINES,
linestyle_opts=opts.LineStyleOpts(curve=0.1,
width=1,
color='#1685a9'), # 连线的宽度与颜色
effect_opts=opts.EffectOpts(is_show=False)) # 关闭涟漪效果
.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="{}民航班机航线图".format(data[0][0][0]),
subtitle='{}，{}共有直飞全国{}个城市的航班'.format(
self.today_ymd, data[0][0][0], count))))
return city_Geoline
def workarea_chart(df):
# 统计各地区出现次数, 并转换为元组的形式
data = Counter(df.workarea).most_common()
print(data)
# 生成地理坐标图
# geo = Geo("岗位各地区需求量", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59')
# attr, value = geo.cast(data)
# print(attr)
# print(value)
# 添加数据点
# # type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
# geo.add('', attr, value, type="effectScatter", visual_range=[0, 100], visual_text_color='#fff', symbol_size=5, is_visualmap=True, is_piecewise=True)
# geo.show_config()
# geo.render()
geo = (
Geo()
.add_schema(maptype="china")
.add("", data)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="岗位城市分布图"),
)
)
geo.render("./chart/workarea_chart.html")
def geo_lines() -> Geo:
c = (
Geo(
init_opts=opts.InitOpts(width="1000px",height="700px",theme=ThemeType.PURPLE_PASSION)
)
.add_schema(maptype="china")
.add(
"航班数量",
df2,
type_=ChartType.EFFECT_SCATTER,
#color="#ffa022"
)
.add(
"航线",
df1,
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=symbol_list[0], symbol_size=15, period=6, trail_length=0, is_show=True
),
linestyle_opts=opts.LineStyleOpts(curve=0.2, width=1, opacity=0.5),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="国内航线", #pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(font_size=30, font_weight="bold"),
#subtitle_textstyle_opts=opts.TextStyleOpts(color='black',font_size=15)
))
)
def geo_base() -> Geo:
citys = [
'北京', '天津', '上海', '重庆', '石家庄', '太原', '西安', '济南', '长春', '哈尔滨', '南京',
'杭州', '合肥', '南昌', '福州', '武汉', '长沙', '成都', '贵阳', '昆明', '广州', "郑州", "沈阳",
'海口', '兰州', '西宁', '呼和浩特', '乌鲁木齐', '拉萨', '南宁', '银川'
]
a_list = [(citys[i], [i, i * 2]) for i in range(len(citys))]
# print(a_list)
c = (
Geo(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS)) # 地图主题
.add_schema(
maptype="china",
is_roam=False,
itemstyle_opts=opts.ItemStyleOpts(area_color='#E6E6FA')) # 地图颜色
.add("geo",
[list(z) for z in zip(Faker.provinces, Faker.values())]).add(
"geo", [('南昌', 12), ('北京', 12), ('天津', 12), ('拉萨', 12),
('呼和浩特', 12), ('桂林', 12), ('沈阳', 12), ('乌鲁木齐', 12),
('海口', 12)],
type_="effectScatter",
itemstyle_opts=opts.ItemStyleOpts(
border_color='#8A2BE2')) # 点的颜色
.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
visualmap_opts=opts.VisualMapOpts(series_index=[0][0]),
title_opts=opts.TitleOpts(title="Geo-基本示例"),
))
return c
def visual(self):
with open('price.json', 'r', encoding='utf-8') as f:
data = json.loads(f.read())
for n in data:
price = data[n]['price']
if not price.isdigit(): # 清除价格不规范的数据
continue
else:
self.list_n.append((n, int(price)))
house_map = (
Geo(init_opts=opts.InitOpts(width='1200px', height='500px'))
.add_schema(maptype="沈阳", center=[123.434039, 41.798102], zoom=6)
# 从json文件导入坐标值
.add_coordinate_json(r'position.json')
.add('', self.list_n, symbol_size=10, color='#f20c00')
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局配置项
.set_global_opts
(
visualmap_opts=opts.VisualMapOpts(type_='color', min_=5000, max_=18000),
title_opts=opts.TitleOpts(title='沈阳市楼盘分布图',
subtitle='数据来源：贝壳网(2020-01) 共{}个楼盘'.format(len(self.list_n))),
)
# 设置系列配置项
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
return house_map