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 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 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 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 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 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 echarts_cate_draw(locations,
labels,
file_path,
title="地域分布图",
subtitle="location distribute",
point_size=7):
"""
依据分类生成地域分布的echarts散点图的html文件.
:param locations: 样本的省市区, pandas的dataframe类型.
:param labels: 长度必须和locations相等, 代表每个样本所属的分类.
:param file_path: 生成的html文件路径.
:param title: 图表的标题
:param subtitle: 图表的子标题
:param point_size: 每个散点的大小
"""
_base_input_check(locations)
if len(locations) != len(labels):
from .exceptions import CPCAException
raise CPCAException("locations的长度与labels长度必须相等")
from pyecharts import Geo
geo = Geo(title,
subtitle,
title_color="#000000",
title_pos="center",
width=1200,
height=600,
background_color='#fff')
_geo_update(geo)
uniques = set(list(labels))
def _data_add(_geo, _cate_keys, _category):
real_keys = []
for cate_key in _cate_keys:
if latlng.get(cate_key):
real_keys.append(cate_key)
attr = real_keys
value = [1] * len(real_keys)
geo.add(_category,
attr,
value,
symbol_size=point_size,
legend_pos="left",
legend_top="bottom",
geo_normal_color="#fff",
geo_emphasis_color=" #f0f0f5")
for category in uniques:
cate_locations = locations[labels == category]
_data_add(
geo,
zip(cate_locations["省"], cate_locations["市"], cate_locations["区"]),
category)
geo.render(file_path)
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 create_charts():
page = Page()
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)]
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
chart = Geo("全国", "data from", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
is_visualmap=True,
visual_text_color='#000',
is_label_show=True)
page.add(chart)
return page
def __init__(self):
csv_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..'))
csv_path = os.path.join(csv_path, 'Lagou.csv')
self.data = pd.read_csv(csv_path, header=None, encoding='utf-8')
self.geo = Geo('工资分布图(平均值)',
title_color='#000',
title_pos='center',
width=1200,
height=600)
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 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 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 create_charts():
page = Page()
data = get_data()
chart = Geo("python招聘分布", "data from 拉勾网")
attr, value = chart.cast(data)
chart.add("", attr, value, type="heatmap", is_visualmap=True,
visual_range=[0, 180], visual_text_color='white',
is_legend_show=False)
page.add(chart)
page.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 mapMaking(task_id):
client = pymongo.MongoClient("127.0.0.1", 27017)
list = []
dict = {}
list1 = []
try:
db = client.crawlData # 连接test数据库
collection = db.taobao # 访问test数据库中things集合
for m in collection.find({"task_id": task_id}, {
"address": 1,
"_id": 0
}):
if m['address'] in citys:
list.append(m['address'])
except:
pass
finally:
client.close()
# print list
# 生成关系，查找键值
for index in list: # 遍历词语列表
if index in dict:
dict[index] += 1 # 根据字典键访问键值，如果该键在字典中，则其值+1
else:
dict[index] = 1 # 如果键不在字典中，则设置其键值为1
# print dict #{u'\u4e0a\u6d77': 1, u'\u9633\u6c5f': 6, u'\u6210\u90fd': 1, u'\u91d1\u534e': 1}
for item in dict.items(): # 化成列表类型
list1.append(item[0])
list1.append(item[1])
# print len(list1)
list2 = []
for i in range(0, len(list1), 2): # 拼成pycharts能够输入的格式,步长为2
a = (list1[i], list1[i + 1])
list2.append(a)
data = list2
geo = Geo(u"商品在全国售卖主要城市",
u"商品",
title_color="#fff",
title_pos="center",
width=1000,
height=600,
background_color='#404a59')
geo.height = 800
geo.width = 1500
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 60],
maptype='china',
visual_text_color="#fff",
symbol_size=10,
is_visualmap=True)
geo.render("static/maps/map" + task_id + ".html") # 生成html文件
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 show_map_school(begin_year, end_year):
'''
用地图展示各学校的排名，这样可以观察地域与学校排名的关系
'''
datas = deal_ruanke.ranking_ruanke(begin_year, end_year)
university_name = (list)(datas.keys())
ranking_datas = (list)(datas.values())
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=stand_width,
height=500)
for i in range(begin_year, end_year + 1):
data = []
dict = {}
nan = ''
for j in range(0, len(university_name)):
line = []
line.append(university_name[j])
if (ranking_datas[j][i - begin_year] == "nan"):
continue
line.append(ranking_datas[j][i - begin_year])
line_tuple = (tuple)(line)
data.append(line_tuple)
dict[university_name[j]] = ranking_datas[j][i - begin_year]
if (i == 2016):
nan = "(云南大学，青海大学，西藏大学未纳入排名)"
geo = Geo(str(i) + "年度全国大学排名" + nan,
"全国大学排名",
title_color="#000",
title_pos="center",
width=1600,
height=400,
background_color='#DCDCDC')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 600],
maptype='china',
visual_text_color="#000",
symbol_size=20,
is_visualmap=True,
is_roam=False,
visual_top="center",
geo_normal_color="#404a59",
visual_range_text=["high", "low"],
visual_range_color=['#50a3ba', '#faef61', '#d94e5d'],
label_formatter="{c0}",
tooltip_formatter="{b}: [经度(E)，纬度(N)，排名] {c}名")
geo
timeline.add(geo, str(i) + '年')
return timeline
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 geographical_location_GDP_ratio_distribution():
data = pd.read_excel("./data/car_sales_data.xlsx", sheet_name=u"人均GDP", encoding='utf-8')
data.columns = [str(col).split('-')[0] for col in data.columns]
float_cols = [str(year) for year in range(1998, 2018)]
not_replace = [
u'池州市', u'宣城市', u'眉山市', u'达州市', u'庆阳市',
u'广安市', u'贺州市', u'来宾市', u'崇左市', u'临沧市',
u'固原市', u'中卫市', u'丽江市'
]
data['city_name'] = data['city_name'].apply(lambda xx: xx.replace(u'市', '') if xx not in not_replace else xx)
city_none = [u'黔西南布依族苗族自治州', u'乌兰察布', u'巴音郭楞蒙古自治州（库尔勒）']
data = data[~data['city_name'].isin(city_none)]
data[float_cols] = data[float_cols].fillna(0).applymap(lambda xx: round(xx, 2) if xx else 0)
cols_show = ['city_name'] + float_cols
data = data[cols_show]
data = calc_ratio_percent(data)
data[range(1999, 2018)] = data[range(1999, 2018)].applymap(lambda xx: round(xx * 100, 2))
data.to_excel('./car_sales_visualization/GDP_ratio_distribution.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1999, 2018):
dataset = [(city, sales) for city, sales in data[['city_name'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100.0, 100.0],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/GDP_ratio_distribution.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 geographical_location_ratio_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
data = calc_ratio_percent(data)
data[range(1998, 2019)] = data[range(1998, 2019)].applymap(lambda xx: round(xx * 100, 4))
cols_show = ['city'] + range(1998, 2019)
data = data[cols_show]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
data.to_excel('./car_sales_visualization/car_sales_num_ratio.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[['city'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100, 100],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_ratio.html')
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 Cast(self, name, method=None, message=None, max_bin=100):
'''
casts data ,and filters data with stopwords
:param name: colname
:param method: to decide the func returns a dict or tuple(attr,value)
:param message: a message the user gives,if not None, will be adding to stopwords
:param max_bin: the max number of words on wordcloud
:return:
'''
string = "".join(self.GetOneCol(name))
brokewords = map(
str.strip,
open('./config/stopwords/stopwords.txt', "r",
encoding="utf-8").readlines())
if message:
brokewords = itertools.chain(brokewords, message.split(",")[:])
stopwords = "".join(brokewords)
lis = dict(
Counter([
tag.strip() for tag in analyse.extract_tags(string, max_bin)
if tag.strip() not in stopwords
]))
lis = sorted(lis.items(), key=lambda x: x[1], reverse=True)
if method is None:
return Geo.cast(lis)
elif method == "dict":
return {k[0]: k[1] for k in lis}
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')
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 draw_city_geo(data):
geo = Geo("全国妹子所在地分布",
"制作人：afrunk",
title_color="#fff",
title_pos="left",
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 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 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():
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 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 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 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 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 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 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 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")
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 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_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 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_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 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
# 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')
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)
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()
