def creat_lan_charts(data_list):
page = Page()
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
chart = Geo("python", "python-city", **style.init_style)
attr, value = chart.cast(data_list)
chart.add("",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
is_legend_show=False,
symbol_size=15,
is_visualmap=True,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
chart = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
attr, value = chart.cast(data_list)
chart.add("",
attr,
value,
type="heatmap",
is_visualmap=True,
visual_range=[0, 200],
visual_text_color='#fff',
is_legend_show=False)
page.add(chart)
return page
def draw1(self,time = None):
global local
# some city cannot by process by echart
echart_unsupported_city = [
"菏泽市", "襄阳市", "恩施州", "湘西州","阿坝州", "延边州",
"甘孜州", "凉山州", "黔西南州", "黔东南州", "黔南州", "普洱市", "楚雄州", "红河州",
"文山州", "西双版纳州", "大理州", "德宏州", "怒江州", "迪庆州", "昌都市", "山南市",
"林芝市", "临夏州", "甘南州", "海北州", "黄南州", "海南州", "果洛州", "玉树州", "海西州",
"昌吉州", "博州", "克州", "伊犁哈萨克州"]
if time is None and len(df_aqi) > 0:
time = df_aqi['time'][0]
data = []
for index, row in df_aqi.iterrows():
city = row['city']
aqi = row['aqi']
if city=='长沙市':
print(city)
localAQI=aqi
if city in echart_unsupported_city:
continue
data.append( (city, aqi) )
#lcaqi=''.join(localAQI)
#print(lcaqi)
if localAQI<70:
geo = Geo("全国最新主要城市空气质量（AQI) \n 当前城市：长沙 AQI：%s \n \n您在的城市不错,不开心的话，也出去走走吧" % localAQI, "数据来源于环保部网站",
title_color="#fff",
title_pos="center", width=425,
height=730, background_color='#404a59')
else:
geo = Geo("全国最新主要城市空气质量（AQI) \n 当前城市：长沙 AQI：%s \n \n环境很差！！出去走走吧,已经为您规划路线" % localAQI, "数据来源于环保部网站",
title_color="#fff",
title_pos="center", width=425,
height=730, background_color='#404a59')
attr, value = geo.cast(data)
geo.add("", attr, value, visual_range=[0, 150],
maptype='china',visual_text_color="#fff",
symbol_size=10, is_visualmap=True,
label_formatter='{b}', # 指定 label 只显示城市名
tooltip_formatter='{c}', # 格式：经度、纬度、值
label_emphasis_textsize=15, # 指定标签选中高亮时字体大小
label_emphasis_pos='right' # 指定标签选中高亮时字体位置
)
#print(data)
geo.render('aqi.html')
self.webView.load(QUrl("file:///D:/源代码/aqi.html"))
#self.webView.reload()
self.webView.repaint()
self.webView.update()
def generate_heatmap(title):
data = [(city_com['city'][i], city_com['count'][i])
for i in range(0, city_com.shape[0])]
geo = Geo(title,
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
attr_list = []
value_list = []
len = attr.__len__()
for i in range(len):
try:
attr_list.append(attr[i])
value_list.append(value[i])
geo.add("city",
attr_list,
value_list,
visual_range=[0, 200],
maptype='heatmap',
visual_text_color="#000",
symbol_size=10,
is_visualmap=True)
except ValueError as e:
print(e)
attr_list.remove(attr[i])
value_list.remove(value[i])
continue
geo = Geo(title,
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404b59')
geo.add("city",
attr_list,
value_list,
visual_range=[0, 3500],
maptype='china',
border_color='fff',
visual_text_color="#fff",
symbol_size=5,
is_visualmap=True)
# geo.show_config()
geo.render('resources/heat_map.html')
def geo_test(a, b):
attr = pd.Series(unique(a)).values
value = pd.Series(b.groupby(a).count()).values
data = [("上海", 47647), ("北京", 21454), ("南京", 849), ("南充", 15745),
("南通", 16352), ("合肥", 11895), ("广州", 43589), ("延安", 3180),
("成都", 19979), ("杭州", 33143), ("武汉", 6465), ("沧州", 13223),
("深圳", 31281), ("湖州", 22433), ("牡丹江", 20526), ("西安", 30548),
("金华", 22799), ("阜阳", 12004)]
geo = Geo("全国各个城市的门店分布",
"数据来源：香飘飘-饿了么爬虫原始数据",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
attr_x, value_y = geo.cast(data)
geo.add("",
attr_x,
value_y,
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.show_config()
geo.render("E:\\py_data_html\\ele_data_2.html")
geo
def geographical_location_distribution():
data = pd.read_csv(filepath, encoding='gbk')
data['salary'] = data['salary'].apply(lambda xx: re.sub(u'k|K|以上', '', xx))
data['min_salary'] = data['salary'].apply(
lambda xx: float(xx.split('-')[0]) * 1000)
data['max_salary'] = data['salary'].apply(
lambda xx: float(xx.split('-')[1]) * 1000
if len(xx.split('-')) > 1 else float(xx) * 1000)
# print data[data['min_salary'] == data['max_salary']] ## xx以上
dataset = [(city, min_salary)
for city, min_salary in data['city min_salary'.split()].values]
geo = Geo("Python Job Distribution",
"",
title_pos="center",
width=2000,
height=1200,
background_color='#404a59')
attr, value = geo.cast(dataset)
geo.add("",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[0, 300],
effect_scale=5,
symbol_size=5,
visual_text_color="#fff")
geo.render(filedir + 'job_distribution.html')
def heat_map():
data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14),
("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19),
("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23),
("拉萨", 24), ("云浮", 24), ("梅州", 25), ("乌鲁木齐", 40)]
geo = Geo("全国主要城市空气质量热力图",
"data from pm2.5",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add("空气质量热力图",
attr,
value,
visual_range=[0, max(value)],
type='heatmap',
visual_text_color="#fff",
symbol_size=15,
is_label_show=True,
is_visualmap=True)
# geo.show_config()
geo.render(path="./data/空气质量热力图.html")
def area_condition(get_all_data):
get_all_data = get_all_data["work_place"].value_counts()
del get_all_data['香港特别行政区']
del get_all_data['澳门特别行政区']
del get_all_data['庆阳']
del get_all_data['延边']
geo = Geo("城市分布情况",
title_color="#fff",
title_pos="center",
width = 800,
height = 800,
background_color="#404a59")
attr = get_all_data.index
value = get_all_data.values
geo.add("",
attr,
value,
type = "effectScatter",
visual_range=[0,400],
maptype="china",
visual_text_color = "#fff",
geo_normal_color = "#080808",
geo_emphasis_color = "#FFFF00",
symbol_size= 6,
border_color = "#FFFFFF",
effect_scale = 5,
is_visualmap=True,
is_roam = True)
geo.render("C:/WeSite/DataCharts/岗位概况/岗位-城市分布图.html",pixel_ratio=0.5)
geo.render("C:/WeSite/DataCharts/岗位概况/岗位-城市分布图.png",pixel_ratio=0.5)
def test_geo_visualmap_pieces():
data = [
("海门", 9),
("鄂尔多斯", 12),
("招远", 12),
("舟山", 12),
("齐齐哈尔", 14),
("盐城", 15),
]
geo = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
type="effectScatter",
is_random=True,
is_visualmap=True,
is_piecewise=True,
visual_text_color="#fff",
pieces=[
{"min": 0, "max": 13, "label": "0 < x < 13"},
{"min": 14, "max": 16, "label": "14 < x < 16"},
],
effect_scale=5,
)
content = geo._repr_html_()
assert '"max": 13' in content
assert '"label": "14 < x < 16"' in content
def render():
#获取所在城市
cities = []
with io.open('friends.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
city = row.split(',')[3]
if city != '':
cities.append(city)
handle(cities)
data = Counter(cities).most_common()
#print(data)
#绘执地理坐标图
geo = Geo("好友位置分布",
'',
title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 500],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True)
geo.render('好友位置分布.html')
def getView(self):
db = DBUtils()
sql = 'select city from comment'
city = db.selectallInfo(sql)
print(city)
for i in city:
citys.append(i[0])
data = []
for item in self.all_list(citys):
data.append((item, self.all_list(citys)[item]))
style = Style(title_color="#fff",
title_pos="center",
width=950,
height=650,
background_color="#404a59")
geo = Geo("《西红市首富》粉丝人群地理位置", "刘宏伟", **style.init_style)
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[
0,
],
visual_text_color="#fff",
symbol_size=20,
is_visualmap=True,
is_piecewise=True,
visual_split_number=5)
geo.render('../templates/render.html')
def test_geo_china_scatter(patched):
fixture = "geo_options.json"
patched.return_value = "1"
cities = [("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)]
geo = Geo("全国主要城市空气质量", "data from pm2.5")
attr, value = geo.cast(cities)
geo.add(
"",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
is_legend_show=False,
symbol_size=15,
is_visualmap=True,
tooltip_formatter="{b}",
label_emphasis_textsize=15,
label_emphasis_pos="right",
)
actual_options = json.dumps(geo.options,
sort_keys=True,
indent=4,
cls=DefaultJsonEncoder)
expected = get_fixture_content(fixture)
for a, b in zip(actual_options.split("\n"), expected.split("\n")):
eq_(a.strip(), b.strip())
def plot():
for item in all_list(city):
data.append((item, all_list(city)[item]))
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
geo = Geo("《邪不压正》粉丝人群地理位置", "数据来源：量子皮皮马", **style.init_style)
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 20],
visual_text_color="#fff",
symbol_size=20,
is_visualmap=True,
is_piecewise=True,
visual_split_number=4)
geo.render("plot.html")
def create_geo_charts(data, title):
'''地图'''
page = Page()
# 样式
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#c4ccd3')
# 创建地图模型
chart = Geo(title, "", **style.init_style)
# 数据 ['上海', '北京', '广州', '深圳', '苏州'] [5, 40, 10, 15, 5]
attr, value = chart.cast(data)
# 添加数据
chart.add("",
attr,
value,
maptype='china',
is_visualmap=True,
type="effectScatter",
is_legend_show=False,
geo_emphasis_color='c4ccd3',
visual_text_color='#2f4554')
page.add(chart)
return page
def draw_map(comments):
try:
attr = comments['cityName'].fillna("zero_token")
data = Counter(attr).most_common(100)
#data.remove(data[data.index([(i, x) for i, x in (data) if i == 'zero_token'][0])])
geo = Geo("《毒液》观众位置分布",
"数据来源：猫眼电影 - SmartCrane采集",
title_color="#fff",
title_pos="center",
width=1000,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 1000],
maptype='china',
visual_text_color="#fff",
symbol_size=10,
is_visualmap=True)
geo.render("./观众位置分布-地理坐标图.html") # 生成html文件
geo # 直接在notebook中显示
except Exception as e:
print(e)
def draw_timezone_map(df, map_html):
geo = Geo("星巴克时区分布地图",
title_color="#fff",
title_pos="center",
width=1200,
height=500,
background_color='#404a59')
geo_cities_coords = {
df.iloc[i]['Store Name']: [df.iloc[i]['lon'], df.iloc[i]['lat']]
for i in range(len(df))
}
attr = list(df['Store Name'])
value = list(df['timezone_amount'])
geo.add("",
attr,
value,
visual_text_color="#fff",
maptype="world",
visual_range=[0, 2000],
is_visualmap=True,
symbol_size=5,
center=(-121, 38.5),
tooltip_formatter=geo_formatter,
geo_cities_coords=geo_cities_coords)
geo.render(map_html)
def draw_map(df, map_html):
geo = Geo("星巴克店铺分布图",
title_color="#fff",
title_pos="center",
width=1200,
height=500,
background_color='#404a59')
geo_cities_coords = {
df.iloc[i]['Store Name']: [df.iloc[i]['lon'], df.iloc[i]['lat']]
for i in range(len(df))
}
value = list(df['Average Score'])
attr = list(df['Store Name'])
geo.add("",
attr,
value,
visual_range=[0, 10],
visual_text_color="#fff",
visual_split_number=5,
maptype="world",
is_visualmap=True,
is_piecewise=True,
geo_formatter=geo_formatter,
symbol_size=5,
center=(113, 23),
geo_cities_coords=geo_cities_coords)
geo.render(map_html)
def get_map(data):
print(data)
geo = Geo(
"全国必胜客城市分布",
"data from pizzahut",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
try:
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=10,
is_roam=True,
is_visualmap=True,
)
geo.render()
except ValueError as info:
error_city = str(info).split(' ')[-1]
print(error_city)
dict = {k: v for k, v in data}
print((error_city, dict[error_city]))
data.remove((error_city, dict[error_city]))
get_map(data=data)
def mGeoAll(df, minit):
df.dropna(axis=0, how='any', inplace=True)
print(df.head())
attr = list(df['city'])
value = list(df['count'])
geo = Geo(
"全国油站数量分布",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
range_max = max(value)
geo.add(
"",
attr,
value,
visual_range=[0, range_max],
visual_text_color="#fff",
# symbol_size=15,
is_visualmap=True,
)
geo.render('全国油站数量分布.html')
def geo_qgtd(attr_v1: List[Tuple[str, int]], chart_name: str,
v1_name: str) -> geo.Geo:
"""
生成全国地图-数据通道图
:param attr_v1: 主要数据
:param chart_name: 图表名
:param v1_name: 数据一名
"""
style = Style(title_color="#fff",
title_pos="center",
width=900,
height=600,
background_color='#404a59')
# chart = Map(chart_name, **style.init_style)
# chart.add(v1_name, attr, value, maptype='china', is_visualmap=True,
# visual_text_color='#000')
chart = Geo(chart_name, "", **style.init_style)
attr, value = chart.cast(attr_v1)
chart.add(v1_name,
attr,
value,
visual_range=[0, 70000],
visual_text_color="#fff",
is_legend_show=False,
symbol_size=15,
is_visualmap=True,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right',
type='effectScatter')
return chart
def plt_place(place, htmlfile):
city_name, city_list = clean_cities_name(place) # 对城市名进行清理
city_dict = {city_list[i]: 0 for i in range(len(city_list))}
for i in range(len(city_list)):
city_dict[city_list[i]] = city_name.count(city_list[i])
print(city_dict)
# 根据数量排序
sort_dict = sorted(city_dict.items(), key=lambda d: d[1], reverse=True)
city_name = []
city_num = []
for i in range(len(sort_dict)):
city_name.append(sort_dict[i][0])
city_num.append(sort_dict[i][1])
data = list(zip(city_name, city_num))
geo = Geo("评论者位置分布",
"数据来源知乎",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
# 注意修改显示范围
geo.add('城市',
attr,
value,
visual_range=[0, 20],
visual_text_color='#fff',
symbol_size=10,
is_visualmap=True,
is_picewise=False,
visual_split_number=10)
geo.render(htmlfile)
def gen_price_map(year=None, month=None, province='china'):
# 绘制地区温度图
house_db = HouseDatabase('residential')
year_cond = 'year=%d' % year if year is not None else ''
month_cond = ' and month=%d' % month if month is not None else ''
province_cond = " and province='%s'" % province if province not in [
'china', '中国'
] else ''
condition = year_cond + month_cond + province_cond
latest_price = house_db.query_records(condition)
cities_prices = [(item['city'], item['price']) for item in latest_price
if item['city'] not in STOP_CITY]
geo = Geo("",
'',
title_color="#fff",
title_pos="left",
background_color='#404a59') # width=1000, height=600,
cities, prices = geo.cast(cities_prices)
geo.add('%d-%d' % (NOW.year, NOW.month),
cities,
prices,
maptype=province,
type="heatmap",
is_visualmap=True,
visual_range=[numpy.min(prices),
numpy.max(prices)],
visual_text_color="#fff")
geo.render("./out/房价温度图-%s.html" %
condition.replace(' and ', '-').replace('=', '_'))
def render_city(cities):
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
print(data)
# 定义样式
style = Style(title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
# 根据城市数据生成地理坐标图
geo = Geo('《悲伤逆流成河》粉丝位置分布', **style.init_style)
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 600],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True,
visual_split_number=10)
geo.render(d + '/picture/geo.html')
def place2(self):
City = [] # 微信好友所在城市
for city in self.friends[1:]:
# if city['City']=="东城":
# print(city)
# return
City.append(city['City'])
print(City)
Citys = collections.Counter(City) # 每个城市对应的数量
values = [] # weixin2 Map
for city in set(City): # values 每个城市对应的数量
if (city != '' and city.isalpha()): # 除去没有城市的 和 外国城市
values.append((city, Citys[city]))
print(values)
geo = Geo(u"%s 各省微信好友分布" % self.nickName,
u"冀祥",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(values)
print(attr, value)
geo.add("",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
# geo.show_config()
geo.render("%s的好友分部图2.html" % self.nickName)
def echarts_draw(locations, fileName, path="./", title="地域分布图"
, subtitle="location distribute"):
"""
生成地域分布的echarts热力图的html文件.
:param locations: 样本的省市区, pandas的dataframe类型.
:param fileName: 生成的html文件的文件名.
:param path: 生成的html文件的路径.
:param title: 图表的标题
:param subtitle: 图表的子标题
"""
from pyecharts import Geo
_base_input_check(locations)
map_keys = locations["省"] + "," + locations["市"] + "," + locations["区"]
count_map = {}
from .infrastructure import SuperMap
for map_key in map_keys:
if SuperMap.lat_lon_mapper.get(map_key):
count_map[map_key] = count_map.get(map_key, 0) + 1
geo = Geo(title, subtitle, title_color="#fff",
title_pos="center", width=1200,
height=600, background_color='#404a59')
_geo_update(geo)
attr, value = geo.cast(count_map)
geo.add("", attr, value, type="heatmap", is_visualmap=True,
visual_text_color='#fff',
is_piecewise=True, visual_split_number=10)
geo.render(path + fileName)
def draw_map(comments):
try:
attr = comments['cityName'].fillna("zero_token")
data = Counter(attr).most_common(300)
data.remove(data[data.index([(i, x) for i, x in (data)
if i == 'zero_token'][0])])
#print(data)
geo = Geo("《Avengers:Endgame》全国观众地域分布",
"数据来源：猫眼电影",
title_color="#fff",
title_pos="center",
width=1450,
height=725,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 1000],
maptype='china',
visual_text_color="#fff",
symbol_size=10,
is_visualmap=True)
geo.render("./全国观众地域分布地理坐标图.html")
print("全国观众地域分布已完成")
except Exception as e:
print(e)
def visualize(df, name):
# 导入自定义的地点经纬度
geo_cities_coords = {
df.iloc[i]['poi_name']: [df.iloc[i]['lng'], df.iloc[i]['lat']]
for i in range(len(df))
} # 根据文件大小生成字典
attr = list(df['poi_name']) # 字典的每个键值
value = list(df['poi_porb']) # 由于量值的太大，换算以下(散点的颜色就是和这个想关的)
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
# 可视化
geo = Geo(name, **style.init_style)
geo.add("",
attr,
value,
visual_range=[0.2, 1],
symbol_size=10,
visual_text_color="#fff",
is_piecewise=True,
is_visualmap=True,
maptype='北京',
visual_split_number=10,
geo_cities_coords=geo_cities_coords)
return geo
def create_map(data):
geo = Geo('《无名之辈》观众位置分布', '数据来源：猫眼采集',
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
try:
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
visual_range=[0, 1000],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,
)
geo.render("观众位置分布-地理坐标图.html")
except ValueError as info:
error_city = str(info).split(' ')[-1]
del data[error_city]
create_map(data)
data_top10 = Counter(data).most_common(10)
print(data_top10)
bar = Bar('无名之辈观众来源排行TOP10', '数据来源：猫眼', title_pos='center')
attr, value = bar.cast(data_top10)
bar.add('', attr, value, is_visualmap=True, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True, \
is_label_show=True)
bar.render("观众来源排行榜-柱状图.html")
def render():
# 获取所有城市
cities = []
with open('friends.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
city = row.split(',')[4]
if city != '': # 去掉城市名为空的值
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 统计每个城市出现的次数
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
print(data)
# 根据城市数据生成地理坐标图
geo = Geo('好友位置分布', '', title_color='#fff', title_pos='center', width=800, height=500,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add('', attr, value, visual_range=[0, 500],
visual_text_color='#fff', symbol_size=15,
is_visualmap=True, is_piecewise=True)
geo.render('好友位置分布.html')
# 根据城市数据生成柱状图
data_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP20', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('', attr, value, is_visualmap=True, visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
bar.render('好友所在城市TOP20.html')
def heat_map_with_scatter():
# 空气质量评分
indexs = [
'上海', '北京', '合肥', '哈尔滨', '广州', '成都', '无锡', '杭州', '武汉', '深圳', '西安',
'郑州', '重庆', '长沙'
]
values = [
4.07, 1.85, 4.38, 2.21, 3.53, 4.37, 1.38, 4.29, 4.1, 1.31, 3.92, 4.47,
2.40, 3.60
]
geo = Geo("全国主要城市空气质量评分",
"data from pm2.5",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
# 图例类型，有'scatter', 'effectscatter', 'heatmap'可选。
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geo.add("空气质量评分",
indexs,
values,
type="scatter",
is_random=True,
effect_scale=5,
visual_range=[0, 5],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,
is_roam=False)
# geo.show_config()
geo.render(path="./data/空气质量评分-scatter.html")
def ShowNumWithYear(df):
# show bus num in different year at different cities
years = list(set(df['year']))
years.sort()
cities = []
values = []
total_num = 0
geos = [] # store the geo every year
timeline = Timeline(width=1500,height=800,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,\
timeline_play_interval = 800,timeline_left=20,timeline_right=100 , is_timeline_show = False )
for index in range(len(years)):
df_temp = df[df['year'] == years[index]]
cities = cities + list(df_temp['city'])
values = values + list(df_temp['num'])
total_num = sum(values)
geos.append(Geo( str(years[index]) + " , Fist level title" , title_top = "10%" , title_text_size=50 , subtitle = "second level title" , \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("数量", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'柯桥': [120.443 , 30.0822] ,} , \
pieces=[
{"min":0.1, "max": 500 , "label": "0-500"},
{"min": 500, "max": 1000 , "label": "501-1000"},
{"min": 1001, "max": 2000 , "label": "1001-2000"},
{"min":2001, "max": 5000, "label": "2001-5000"},
{"min":5001, "max": 100000, "label": ">5000"}, ] )
geos[index].show_config()
geos[index].render("数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('redult.html')
