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 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 draw_city_geo(data):
geo = Geo("全国妹子分布城市", "data about beauty", title_color="#fff",
title_pos="center", width=1200,
height=600, background_color='#404a59')
attr, value = geo.cast(data)
geo.add("", attr, value, visual_range=[10, 2500], visual_text_color="#fff",
symbol_size=15, is_visualmap=True)
return geo
def my_personal():
data=[('东莞',10),('深圳',20)]
geo = Geo("全国主要城市空气质量", "data from pm2.5", 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, 200], maptype='china', visual_text_color="#fff",
symbol_size=10, is_visualmap=True)
geo.render("test.html") # 生成html文件
def test_geo_with_noexist_city():
data = [
("海门", 9), ("鄂尔多斯", 12), ("招远", 12),
("舟山", 12), ("齐齐哈尔", 14), ("伦敦", 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,
effect_scale=5)
geo.render()
def test_geo_china_effectscatter():
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,
effect_scale=5)
assert '"type": "effectScatter"' in geo._repr_html_()
def test_geo_shantou_city():
data = [
('澄海区', 30), ('南澳县', 40), ('龙湖区', 50), ('金平区', 60)
]
geo = Geo("汕头市地图示例", **style.init_style)
attr, value = geo.cast(data)
geo.add("", attr, value, maptype="汕头", is_visualmap=True,
tooltip_formatter='{b}', is_legend_show=False,
label_emphasis_textsize=15, label_emphasis_pos='right')
geo.render()
def demo2():
from pyecharts import Geo
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", title_color="#fff", title_pos="center",
width=1200, height=600, background_color='#404a59')
attr, value = geo.cast(data)
geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5)
geo.show_config()
geo.render()
def test_geo_guangdong_province():
data = [
('汕头市', 50), ('汕尾市', 60), ('揭阳市', 35),
('阳江市', 44), ('肇庆市', 72)
]
geo = Geo("广东城市空气质量", "data from pm2.5", **style.init_style)
attr, value = geo.cast(data)
geo.add("", attr, value, maptype='广东', type="effectScatter",
is_random=True, effect_scale=5, is_legend_show=False)
geo.render()
def demo1():
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)]
geo = Geo("全国主要城市空气质量", "data from pm2.5", 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, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True)
geo.show_config()
geo.render()
def test_geo_user_define_coords():
coords = {
"0": [0.572430556, 19.246],
"1": [0.479039352, 1.863],
"2": [0.754143519, -20.579]
}
geo = Geo(**style.init_style)
geo.add("", ["0", "1", "2"], [6, 5.8, 6.2], is_visualmap=True,
geo_cities_coords=coords, maptype="world")
geo.render()
def test_geo_china_scatter():
geo = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
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')
html_content = geo._repr_html_()
assert '"type": "scatter"' in html_content
assert '"type": "heatmap"' not in html_content
assert '"type": "effectScatter"' not in html_content
def test_geo_china_heatmap():
geo = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
attr, value = geo.cast(cities)
geo.add(
"",
attr,
value,
type="heatmap",
is_visualmap=True,
visual_range=[0, 300],
visual_text_color="#fff",
)
assert '"type": "heatmap"' in geo._repr_html_()
def test_geo_guangdong_province():
data = [("汕头市", 50), ("汕尾市", 60), ("揭阳市", 35), ("阳江市", 44), ("肇庆市", 72)]
geo = Geo("广东城市空气质量", "data from pm2.5", **style.init_style)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
maptype="广东",
type="effectScatter",
is_random=True,
effect_scale=5,
is_legend_show=False,
)
geo.render()
def test_geo_shantou_city():
data = [("澄海区", 30), ("南澳县", 40), ("龙湖区", 50), ("金平区", 60)]
geo = Geo("汕头市地图示例", **style.init_style)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
maptype="汕头",
is_visualmap=True,
tooltip_formatter="{b}",
is_legend_show=False,
label_emphasis_textsize=15,
label_emphasis_pos="right",
)
geo.render()
def demo3():
from pyecharts import Geo
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),
("文登", 25), ("上海", 25), ("攀枝花", 25), ("威海", 25), ("承德", 25), ("厦门", 26),
("汕尾", 26), ("潮州", 26), ("丹东", 27), ("太仓", 27), ("曲靖", 27), ("烟台", 28),
("福州", 29), ("瓦房店", 30), ("即墨", 30), ("抚顺", 31), ("玉溪", 31), ("张家口", 31),
("阳泉", 31), ("莱州", 32), ("湖州", 32), ("汕头", 32), ("昆山", 33), ("宁波", 33),
("湛江", 33), ("揭阳", 34), ("荣成", 34), ("连云港", 35), ("葫芦岛", 35), ("常熟", 36),
("东莞", 36), ("河源", 36), ("淮安", 36), ("泰州", 36), ("南宁", 37), ("营口", 37),
("惠州", 37), ("江阴", 37), ("蓬莱", 37), ("韶关", 38), ("嘉峪关", 38), ("广州", 38),
("延安", 38), ("太原", 39), ("清远", 39), ("中山", 39), ("昆明", 39), ("寿光", 40),
("盘锦", 40), ("长治", 41), ("深圳", 41), ("珠海", 42), ("宿迁", 43), ("咸阳", 43),
("铜川", 44), ("平度", 44), ("佛山", 44), ("海口", 44), ("江门", 45), ("章丘", 45),
("肇庆", 46), ("大连", 47), ("临汾", 47), ("吴江", 47), ("石嘴山", 49), ("沈阳", 50),
("苏州", 50), ("茂名", 50), ("嘉兴", 51), ("长春", 51), ("胶州", 52), ("银川", 52),
("张家港", 52), ("三门峡", 53), ("锦州", 54), ("南昌", 54), ("柳州", 54), ("三亚", 54),
("自贡", 56), ("吉林", 56), ("阳江", 57), ("泸州", 57), ("西宁", 57), ("宜宾", 58),
("呼和浩特", 58), ("成都", 58), ("大同", 58), ("镇江", 59), ("桂林", 59), ("张家界", 59),
("宜兴", 59), ("北海", 60), ("西安", 61), ("金坛", 62), ("东营", 62), ("牡丹江", 63),
("遵义", 63), ("绍兴", 63), ("扬州", 64), ("常州", 64), ("潍坊", 65), ("重庆", 66),
("台州", 67), ("南京", 67), ("滨州", 70), ("贵阳", 71), ("无锡", 71), ("本溪", 71),
("克拉玛依", 72), ("渭南", 72), ("马鞍山", 72), ("宝鸡", 72), ("焦作", 75), ("句容", 75),
("北京", 79), ("徐州", 79), ("衡水", 80), ("包头", 80), ("绵阳", 80), ("乌鲁木齐", 84),
("枣庄", 84), ("杭州", 84), ("淄博", 85), ("鞍山", 86), ("溧阳", 86), ("库尔勒", 86),
("安阳", 90), ("开封", 90), ("济南", 92), ("德阳", 93), ("温州", 95), ("九江", 96),
("邯郸", 98), ("临安", 99), ("兰州", 99), ("沧州", 100), ("临沂", 103), ("南充", 104),
("天津", 105), ("富阳", 106), ("泰安", 112), ("诸暨", 112), ("郑州", 113), ("哈尔滨", 114),
("聊城", 116), ("芜湖", 117), ("唐山", 119), ("平顶山", 119), ("邢台", 119), ("德州", 120),
("济宁", 120), ("荆州", 127), ("宜昌", 130), ("义乌", 132), ("丽水", 133), ("洛阳", 134),
("秦皇岛", 136), ("株洲", 143), ("石家庄", 147), ("莱芜", 148), ("常德", 152), ("保定", 153),
("湘潭", 154), ("金华", 157), ("岳阳", 169), ("长沙", 175), ("衢州", 177), ("廊坊", 193),
("菏泽", 194), ("合肥", 229), ("武汉", 273), ("大庆", 279)]
geo = Geo("全国主要城市空气质量", "data from pm2.5", 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, 200], maptype='china', visual_text_color="#fff",
symbol_size=10, is_visualmap=True)
geo.render("全国主要城市空气质量.html") # 生成html文件
def load_data():
lines = []
data = []
with open('haiwang.txt','r',encoding='utf-8') as f:
lines = f.readlines()
for item in lines:
cityinfo = item.split()
print(cityinfo[0],cityinfo[1])
data.append((cityinfo[0],cityinfo[1]))
geo = Geo('海王全国观众分布','data from maoyan',title_color="#fff",title_pos="center",width=1200,height=600,background_color=
"#404a59")
attr,value = geo.cast(data)
geo.add("",attr,value,type="heatmap",
is_random=True, visual_range=[0, 1600],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,border_color="#111",
geo_cities_coords=None)
geo.render('hai_wang_guan_zhong.html')
class PlotGeo:
def __init__(self):
self.data = {{'label': []}, }
self.titles = ['全国销售情势图', 'data from sale']
self.indexes = []
self.geo = Geo()
self.script_list = self.geo.get_js_dependencies()
def plot_geo(self, dat, tit, ind):
self.data = dat
self.titles = tit
self.indexes = ind
self.geo = Geo(self.titles[0], self.titles[1],
title_pos='center', title_color='#fff',
width=1200, height=600, background_color='#404a59')
for label in self.data.keys():
self.geo.add(label, self.indexes, self.data[label],
visual_range=[0, 200], visual_text_color='#fff',
symbol_size=15, is_visualmap=True)
return self.geo.render_embed()
def test_geo_china_scatter():
geo = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
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',
)
html_content = geo._repr_html_()
assert '"type": "scatter"' in html_content
assert '"type": "heatmap"' not in html_content
assert '"type": "effectScatter"' not in html_content
def heat_diagram():
df = read_mongo(db, MONGO_DB, {'_id': 0, 'citys': 1})
i = [i for i in df['city_name']]
j = [j for j in df['nums']]
data = list(zip(i, j))
geo = Geo("全国主要旅游城市",
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, 55000],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.render('旅游景点热力图.html')
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 test_full_example():
data = [("广州", 45), ("漳州", 35), ("A市", 43)]
geo = Geo("全国主要城市空气质量", "data from pm2.5", **style.init_style)
coordinate = geo.get_coordinate("广州")
assert 2 == len(coordinate)
with assert_raises(ValueError):
geo.get_coordinate("A市", raise_exception=True)
attr, value = geo.cast(data)
with assert_raises(ValueError):
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,
)
geo.add_coordinate("A市", 119.3, 26.08)
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,
)
geo.render()
def demo2():
from pyecharts import Geo
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",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
type="effectScatter",
is_random=True,
effect_scale=5)
geo.show_config()
geo.render()
def create_map(df):
# 绘制地图
value = [i for i in df['line']]
attr = [i for i in df['city']]
geo = Geo(
"已开通地铁城市分布情况",
title_pos='center',
title_top='0',
width=800,
height=400,
title_color="#fff",
background_color="#404a59",
)
geo.add("",
attr,
value,
is_visualmap=True,
visual_range=[0, 25],
visual_text_color="#fff",
symbol_size=15)
geo.render("已开通地铁城市分布情况.html")
def guo_report(request):
# select province, difi_re_num from REPORT_REG
ret = """select city, difi_re_num from REPORT_REG a, province_to_city b where a.province=b.province"""
data_list = exc_sql(ret)
attr = [i[0] for i in data_list]
value = [i[1] for i in data_list]
geo = Geo("全国各地用户注册图", width=1200, height=600)
geo.add("各省注册量",
attr,
value,
type="effectScatter",
border_color="#ffffff",
symbol_size=2,
is_label_show=True,
label_text_color="#00FF00",
label_pos="inside",
symbol_color="yellow",
geo_normal_color="#006edd",
geo_emphasis_color="#0000ff")
data = {'data': geo.render_embed()}
return render(request, 'guo_report.html', data)
def showHotMap():
attr, value, geo_cities_coords = getHotMapData()
style = Style(title_color="#fff",
title_pos="center",
width=800,
height=600,
background_color="#132F3D")
geo = Geo('浙江水利厅发文热力图', **style.init_style)
geo.add("",
attr,
value,
visual_range=[0, 8000],
symbol_size=15,
visual_text_color="#fff",
is_piecewise=True,
is_visualmap=True,
maptype='浙江',
geo_cities_coords=geo_cities_coords)
#geo.add("", attr, value, type="effectScatter", is_random=True, maptype = '浙江',effect_scale=4,geo_cities_coords=geo_cities_coords)
#geo.render('浙江水利厅发文热力图.html')
return geo
def test_geo():
geo = Geo("大蒜价格和地区的关系",
"",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr = arealist
value = pricelist
geo.add("",
attr,
value,
visual_range=[0, 15],
visual_split_number=0.5,
visual_text_color="#fff",
symbol_size=16,
is_visualmap=True)
geo.show_config()
geo.render('大蒜1.html')
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("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 drawAll_WithDifferentColor(weizhi, attr, value):
#用不同的颜色表示不同的时区上的店
geo = Geo("星巴克门店在全球不同时区的分布情况",
"正代表东，负代表西，数字代表第几个时区,缺失时区值的店铺没有显示",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
geo.add("",
attr,
value,
visual_range=[-10, 10],
visual_text_color="#fff",
maptype="world",
symbol_size=15,
geo_cities_coords=weizhi,
is_visualmap=True,
is_piecewise=True,
visual_split_number=21)
geo.render("timezone_geo.html")
def geo_picture(data, city_sets):
# 导入相关工具库
# 导入Geo模块进行城市数据分析
from pyecharts import Geo
useless_cities = []
city_infos = []
for i in range(len(data)):
geo = Geo("《流浪地球》全国观影粉丝分布图",
"data from Maoyan",
title_color="#fff",
title_pos="center",
width=1000,
height=750,
background_color='#404a59')
attr, value = geo.cast(data)
try:
geo.add("",
attr,
value,
visual_range=[0, 200],
maptype='china',
type="effectScatter",
visual_text_color="#fff",
symbol_size=10,
is_visualmap=True)
except Exception as e:
# print(e)
# 在异常信息中提取无法被pyecharts识别的城市
city = str(e).split()[-1]
# 将不能识别的城市名称收集到列表中
useless_cities.append(city)
# 剔除无法识别的城市信息
city_sets.pop(city)
city_infos = geo_city_infos(city_sets)
data = city_infos
#生成html文件
geo.render("全国观影粉丝分布图.html")
return geo, city_infos, useless_cities
def demo1():
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)]
geo = Geo("全国主要城市空气质量",
"data from pm2.5",
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, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.show_config()
geo.render()
def create_city(cities):
fc = []
for v in cities:
if v != '' and v != '海淀' and v != '延边':
fc.append(v)
# 统计每个城市出现的次数
data = Counter(fc).most_common(5) # 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=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
print(attr)
print(value)
geo.add('',
attr,
value,
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_top10 = Counter(fc).most_common(10) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP10', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top10)
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TOP10.html')
def geo(self, title=None, data=None):
"""
地图
:param title: 数据可视化的标题(字符串)
:param data: {地名:value} 字典{'宿迁市': 89, '安康市': 46, '佳木斯市': 50,}
:return:地图
"""
data = data
geo = Geo(title, 'data from AQI', title_color='#fff', \
title_pos='center', width=1200, height=600, background_color='#404a59')
attr, value = geo.cast(data) # data is not.0
geo.add('',
attr,
value,
visual_range=[0, 200],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True,
visual_split_number=6)
self.page.add(geo)
self.page.render()
def DrawGeo(city_names):
data = []
for cn in city_names:
temp = qr.get_data(cn)
data.append((cn, temp['pm25']))
geo = Geo('全国部分城市空气质量',
'pm2.5',
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, 200],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
type='effectScatter')
geo.render('weatherGeo.html')
def unicorn_china(data):
data = data.groupby(by='Headquarter').count()
data.sort_values(by='Company', ascending=False, inplace=True)
value = list(data['Company'].values)
attr = list(data.index)
# print(value,attr)
map = Geo("大中华区独角兽公司分布", '北京突出', title_color="#fff",
title_pos="center", background_color='#404a59')
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
# 发散圆点图
map.add("", attr, value, type="effectScatter",
is_random=True, effect_scale=5,
visual_range=[0, 80],
visual_text_color="#fff",
symbol_size=5,
is_visualmap=True,
)
map.render(path='./国内独角兽公司城市分布.png', pixel_ration=1)
def render_city(dir, cities):
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 使用Counter类统计出现的次数，并转换为元组列表
data = Counter(cities).most_common()
# print(data)
geo = Geo("电影《影》观众分布",
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, 600],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.render(dir + r"\geo.html")
def plotmap():
geo = Geo("湖南省地质中学G1115班同学本科毕业去向（持续更新）",
"Made by YJT, Module provided by pyecharts",
title_color="#fff",
title_pos="center",
width=1366,
height=768,
page_title ='湖南省地质中学G1115班同学本科毕业去向',
background_color="#404a59")
with open("G1115.txt") as file:
temp = file.readline().split("：")
data = []
while temp != [""]:
data.append(tuple(temp))
temp = file.readline().split("：")
attr, value = geo.cast(data)
geo.add("", attr, value,
type="effectScatter",
is_random=False,
maptype='world',
effect_scale=5,
tooltip_formatter=formatter_for_tooltip,
symbol_size=10,
#geo_normal_color="#F0F8FF",
#geo_emphasis_color="#4169E1",
label_formatter="{b}",
label_text_size=15,
label_emphasis_textsize=15,
label_color=['#FFFFFF']*len(attr),
label_emphasis_textcolor='#FFFFFF',
#tooltip_tragger_on='click',
tooltip_font_size=15,
tooltip_background_color="rgba(50,50,50,0.3)",)
geo.render("G1115.html")
def line3d():
# --------------------地图---------------------------------
a = list(JobMsg.objects.all().values_list('job_address'))
# print(a)
city_names = []
for city_id in a:
try:
city_names.append(JobCitys.objects.get(cityid=city_id[0]).cityname)
except:
pass
ss = {}
for i in city_names:
if city_names.count(i) >= 1:
ss[i] = city_names.count(i)
# print(ss)
hh = []
for i, j in ss.items():
if i not in ['丽江', '达州', '黔西南']:
hh.append((i, j))
# print(hh)
geo = Geo("城市与需求量关系图",
title_color="#fff",
title_pos="center",
width=1000,
height=600,
background_color='#404a59')
attr, value = geo.cast(hh)
geo.add("",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
return geo
def city_distribution_geo(self, cities):
# 统计城市名称,该城市出现的次数
for city in cities:
if city == '':
cities.remove(city)
coun = Counter(cities)
# 处理地图库数据
city_name = self.city_handles(list(coun))
city_dict = dict(coun)
city_num = []
for city in city_name:
city_num.append(city_dict[city])
# print('%s : %s' %(len(city_name), city_name))
# print('%s : %s' %(len(city_num), city_num))
city_max = max(city_num)
geo = Geo('评论者城市分布情况',
"data from maoyan",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
geo.add('',
city_name,
city_num,
type='scatter',
maptype='china',
visual_range=[0, city_max],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True
# is_piecewise=True
# visual_split_number=10
)
geo.render(self.path + r'/city_geo.html')
def _draw_hot1(day, hour, minute, tp):
"""
pycharts热力图
:param day:
:param hour, minute:
:param tp:
:return:
"""
geo = Geo(
"上海市出租车",
"data from 强生出租车",
title_color="#fff",
title_pos="center",
width=500,
height=400,
background_color="#404a59",
)
data = da.hot_deal(day, hour, minute, tp)
for i in data.index:
geo.add_coordinate('%s' % i, data.loc[i, 'lon'], data.loc[i, 'lat'])
list1 = [('%s' % i, data.loc[i, 'num']) for i in data.index]
attr, value = geo.cast(list1)
geo.add(
"",
attr,
value,
maptype=u'上海',
coordinate_region=u'上海',
# type='heatmap',
is_visualmap=True,
visual_text_color="#fff",
visual_range=[1, 10],
symbol_size=2,
is_roam=False)
return geo
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 = dumps_actual_options(geo.options)
expected = get_fixture_content(fixture)
for a, b in zip(actual_options.split("\n"), expected.split("\n")):
eq_(a.strip(), b.strip())
def draw_city_pic(csv_file):
page = Page(csv_file+":城市职位分析")
d = pd.read_csv(csv_file, engine='python', encoding='utf-8') # 读取CSV转为dataframe格式，并丢弃评论为空的的记录
city_info = d['city'].value_counts()
geo = Geo("", "城市职位分布", title_pos="center", width=1200, height=600,
background_color='#404a59', title_color="#fff")
geo.add("", city_info.index, city_info.values, maptype="china",visual_range=[1, 200], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=10, symbol_size=15, is_visualmap=True, is_more_utils=True)
geo.render(csv_file[:-4] + "_城市职位dotmap.html")
page.add_chart(geo)
geo2 = Geo("", "城市职位热力图",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff",)
geo2.add("", city_info.index, city_info.values, type="heatmap", is_visualmap=True, visual_range=[1, 200],visual_text_color='#fff', is_more_utils=True)
geo2.render(csv_file[:-4]+"_城市职位heatmap.html") # 取CSV文件名的前8位数
page.add_chart(geo2)
city_pie = pyecharts.Pie("", "城市职位饼图", title_pos="right", width=1200, height=600)
city_pie.add("", city_info.index, city_info.values, radius=[20, 50], label_text_color=None, is_label_show=True,
legend_orient='vertical', is_more_utils=True, legend_pos='left')
city_pie.render(csv_file[:-4] + "_城市职位饼图.html") # 取CSV文件名的前8位数
page.add_chart(city_pie)
page.render(csv_file[:-4] + "_城市分析汇总.html")
def city_avg_salary():
data = sel_place_avg_salary()
print(data)
geo = Geo("全国招聘薪资水平热力图",
"数据来源:拉勾网",
title_color="#000",
title_pos="center",
width=1120,
height=800,
background_color='#F4F3EF')
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
type="heatmap", #类型热力图
is_label_show=True,
is_visualmap=True,
visual_range=[0, 20],
visual_text_color='#000',
geo_normal_color="#FFFACD",
geo_emphasis_color="#FFEC8B")
return geo
def test_geo_formatter_func():
style = Style(
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
data = [("汕头市", 50), ("汕尾市", 60), ("揭阳市", 35), ("阳江市", 44), ("肇庆市", 72)]
geo = Geo("广东城市空气质量", "data from pm2.5", **style.init_style)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
maptype="广东",
type="effectScatter",
tooltip_formatter=geo_formatter,
is_random=True,
effect_scale=5,
is_legend_show=False,
)
assert "function geo_formatter(" in geo._repr_html_()
def geo_graph(brand):
"""
热力图绘制
:param brand: 汽车品牌
:return:
"""
data = car_city(brand)
geo = Geo(brand,
brand + "distribution",
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, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.render()
def create_map(self, city, value, name="观影地区分布"):
"""Scatter类型地图"""
geo = Geo(name,
"数据来源于猫眼",
title_color="#fff",
title_pos="center",
width=800,
height=500,
background_color='#404a59')
# 添加城市坐标
geo.add_coordinate_json(CITY_JSON_FILE)
geo.add("",
city,
value,
is_label_show=True,
is_roam=True,
visual_range=[0, 40],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
geo.render(MAP_PATH)
print("地理分布图【成功】")
def draw_citys_pic(csv_file):
page = Page(csv_file+":评论城市分析")
info = count_city(csv_file)
geo = Geo("","Ctipsy原创",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff")
while True: # 二次筛选，和pyecharts支持的城市库进行匹配，如果报错则删除该城市对应的统计
try:
attr, val = geo.cast(info)
geo.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=6, symbol_size=15, is_visualmap=True)
except ValueError as e:
e = str(e)
e = e.split("No coordinate is specified for ")[1] # 获取不支持的城市名称
info.pop(e)
else:
break
info = sorted(info.items(), key=lambda x: x[1], reverse=False) # list排序
# print(info)
info = dict(info) #list转dict
# print(info)
attr, val = [], []
for key in info:
attr.append(key)
val.append(info[key])
geo1 = Geo("", "评论城市分布", title_pos="center", width=1200, height=600,
background_color='#404a59', title_color="#fff")
geo1.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=10, symbol_size=15, is_visualmap=True, is_more_utils=True)
#geo1.render(csv_file + "_城市dotmap.html")
page.add_chart(geo1)
geo2 = Geo("", "评论来源热力图",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff",)
geo2.add("", attr, val, type="heatmap", is_visualmap=True, visual_range=[0, 50],visual_text_color='#fff', is_more_utils=True)
#geo2.render(csv_file+"_城市heatmap.html") # 取CSV文件名的前8位数
page.add_chart(geo2)
bar = Bar("", "评论来源排行", title_pos="center", width=1200, height=600 )
bar.add("", attr, val, is_visualmap=True, visual_range=[0, 100], visual_text_color='#fff',mark_point=["average"],mark_line=["average"],
is_more_utils=True, is_label_show=True, is_datazoom_show=True, xaxis_rotate=45)
#bar.render(csv_file+"_城市评论bar.html") # 取CSV文件名的前8位数
page.add_chart(bar)
pie = Pie("", "评论来源饼图", title_pos="right", width=1200, height=600)
pie.add("", attr, val, radius=[20, 50], label_text_color=None, is_label_show=True, legend_orient='vertical', is_more_utils=True, legend_pos='left')
#pie.render(csv_file + "_城市评论Pie.html") # 取CSV文件名的前8位数
page.add_chart(pie)
page.render(csv_file + "_城市评论分析汇总.html")
import datetime
url = "http://www.86pm25.com/paiming.htm"
head = {
'Referer': 'http://www.86pm25.com/city/Dazhou.html',
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36'
}
html = requests.get(url, headers=head)
html.encoding = 'utf-8'
soup = BeautifulSoup(html.text, "lxml")
data1 = soup.find(id="goodtable").find_all(name='a')
data2 = str(soup.find(id='goodtable').find_all(name='td'))
data = re.findall(r'<td>(\d{1,3}.)</td>', data2)
with open("城市.txt", 'r') as f:
CityList = f.read()
city = []
for i in range(0, 367):
if str(data1[i].string) in CityList:
citytuple = (data1[i].string, int(data[i]))
city.append(citytuple)
geo = Geo("全国主要城市空气质量实时监控", "实时：" + str(datetime.datetime.now()), title_color="#fff",
title_pos="center", width='100%',
height=790, background_color='#404a59')
attr, value = geo.cast(city)
geo.add("", attr, value, visual_range=[0, 150], maptype='china', visual_text_color="#fff",
symbol_size=13, is_visualmap=True)
page = Page()
page.add(geo)
page.render("全国主要城市空气质量实时监控.html")
webbrowser.open("全国主要城市空气质量实时监控.html", new=0, autoraise=True)
# 全国城市地图示例
from pyecharts import Geo
data = [
("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15),
("惠州", 37),("江阴", 37),("蓬莱", 37),("韶关", 38),("嘉峪关", 38),("广州", 38),
("张家港", 52),("三门峡", 53),("锦州", 54),("南昌", 54),("柳州", 54),("三亚", 54),
("呼和浩特", 58),("成都", 58),("大同", 58),("镇江", 59),("桂林", 59),("张家界", 59),
("北京", 79),("徐州", 79),("衡水", 80),("包头", 80),("绵阳", 80),("乌鲁木齐", 84),
("菏泽", 194),("合肥", 229),("武汉", 273),("大庆", 279)]
geo = Geo(
"全国部分城市空气质量",
title_color="#fff",
title_pos="center",
width=800,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,
)
geo.render('result.地理坐标系图.全国城市地图示例.html')
