⼤数据可视化案例⼆：数据可视化地图
Echart：
ECharts，⼀个纯 Javascript 的图表库，可以流畅的运⾏在 PC 和移动设备上，兼容当前绝⼤部分浏览器（IE8/9/10/11，Chrome，Firefox，Safari 等），底层依赖轻量级的 Canvas 类库 ZRender，提供直观，⽣动，可交互，可⾼度个性化定制的数据可视化图表。
ECharts 提供了常规的折线图，柱状图，散点图，饼图，K线图，⽤于统计的盒形图，⽤于地理数据可视化的地图，热⼒图，线图，⽤于关系数据可视化的关系图，treemap，多维数据可视化的平⾏坐标，还有⽤于 BI 的漏⽃图，仪表盘，并且⽀持图与图之间的混搭。
在本次内容中，使⽤Pyechats来实现新冠肺炎疫情地图的绘制。
第⼀步：获取实时的新冠肺炎数据
import requests
from lxml import etree
import re
import json
class Get_data():
#获取数据
def get_data(self):
response = ("voice.baidu/act/newpneumonia/newpneumonia/")
with open('', 'w') as file:
file.)
#提取更新时间
def get_time(self):
with open('','r') as file:
text = ad()
#正则表达式，返回的是列表，提取最新更新时间
北京疫情最新数据time = re.findall('"mapLastUpdatedTime":"(.*?)"', text)[0]
return time
#解析数据
def parse_data(self):
with open('', 'r') as file:
text = ad()
html = etree.HTML(text)
result = html.xpath('//script[@type="application/json"]/text()')
result = result[0]
result = json.loads(result)
#转换成字符串
result = json.dumps(result['component'][0]['caseList'])
with open('data.json', 'w') as file:
file.write(result)
print('数据已写⼊json⽂件。。。')
第⼆步：绘制地图
pyecharts的地图官⽅源码：
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (
Map()
.add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（连续型）"),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
)
效果：
第⼆步：数据可视化地图
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
import os
class Draw_map():
#判断是否存在存放地图的⽂件夹，没有的话创建⽂件夹
def __init__(self):
if not ists('./map/china'):
os.makedirs('./map/china')
#将RGB转换为绘制地图需要的⼗六进制的表达形式
def get_colour(self,a,b,c):
result = '#' + ''.join(map((lambda x: "%02x" % x), (a,b,c)))
return result.upper()
#绘制每个城市的地图
def to_map_city(self,area, variate,province,update_time):
#显⽰标识栏的颜⾊分层表⽰
pieces = [
{"max": 99999999, "min": 10000, "label": "≥10000", "color": _colour(102, 2, 8)},
{"max": 9999, "min": 1000, "label": "1000-9999", "color": _colour(140, 13, 13)},
{"max": 999, "min": 500, "label": "500-999", "color": _colour(204, 41, 41)},
{"max": 499, "min": 100, "label": "100-499", "color": _colour(255, 123, 105)},
{"max": 99, "min": 50, "label": "50-99", "color": _colour(255, 170, 133)},
{"max": 49, "min": 10, "label": "10-49", "color": _colour(255,202,179)},
{"max": 9, "min": 1, "label": "1-9", "color": _colour(255,228,217)},
{"max": 0, "min": 0, "label": "0", "color": _colour(255,255,255)},
]
#绘制地图
c = (
# 设置地图⼤⼩
Map(init_opts=opts.InitOpts(width = '1000px', height='880px'))
.add("累计确诊⼈数", [list(z) for z in zip(area, variate)], province, is_map_symbol_show=False)
# 设置全局变量 is_piecewise设置数据是否连续，split_number设置为分段数，pices可⾃定义数据分段
# is_show设置是否显⽰图例
.set_global_opts(
title_opts=opts.TitleOpts(title="%s地区疫情地图分布"%(province), subtitle = '截⽌%s %s省疫情分布情况'%(update_time,province), pos_left = "center", pos_top = "10px"), legend_opts=opts.LegendOpts(is_show = False),
visualmap_opts=opts.VisualMapOpts(max_=200,is_piecewise=True,
pieces=pieces,
),
)
.render("./map/china/{}疫情地图.html".format(province))
)
# 绘制全国的地图
def to_map_china(self, area,variate,update_time):
pieces = [{"max": 999999, "min": 1001, "label": ">10000", "color": "#8A0808"},
{"max": 9999, "min": 1000, "label": "1000-9999", "color": "#B40404"},
{"max": 999, "min": 100, "label": "100-999", "color": "#DF0101"},
{"max": 99, "min": 10, "label": "10-99", "color": "#F78181"},
{"max": 9, "min": 1, "label": "1-9", "color": "#F5A9A9"},
{"max": 0, "min": 0, "label": "0", "color": "#FFFFFF"},
]
c = (
# 设置地图⼤⼩
Map(init_opts=opts.InitOpts(width='1000px', height='880px'))
.add("累计确诊⼈数", [list(z) for z in zip(area, variate)], "china", is_map_symbol_show=False)
.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情地图分布", subtitle='截⽌%s 中国疫情分布情况'%(update_time), pos_left="center", pos_top="10px"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True,
pieces=pieces,
),
)
.render("./map/中国疫情地图.html")
)
第三步：
使⽤数据来绘制地图：
import json
import map_draw
import data_get
with open('data.json','r') as file:
data = ad()
data = json.loads(data)
map = map_draw.Draw_map()
datas = data_get.Get_data()
<_data()
update_time = _time()
datas.parse_data()
#中国疫情地图数据
def china_map():
area = []
confirmed = []
for each in data:
area.append(each['area'])
confirmed.append(each['confirmed'])
<_map_china(area,confirmed,update_time)
#省份疫情地图数据
def province_map():
for each in data:
city = []
confirmeds = []
province = each['area']
for each_city in each['subList']:
city.append(each_city['city']+"市")
confirmeds.append(each_city['confirmed'])
<_map_city(city,confirmeds,province,update_time) if province == '上海' or '北京' or '天津' or '重庆' or '⾹港':
for each_city in each['subList']:
city.append(each_city['city'])
confirmeds.append(each_city['confirmed'])
<_map_city(city,confirmeds,province,update_time)
效果：
全国：
内蒙古⾃治区：
本次内容参考⾃：
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