用Python抓新型冠状病毒肺炎实时数据，绘制省级疫情分布图
说明: 本文是Python可视化技术结合时下热点进行进行开发、讲解的案例，也是计算机编程课程思政的一个形式。文章遵守CSDN平台规定和国家法规（非新闻资质的网站不允许发布疫情数据），对运行结果图片中的数据（2020年2月4日）打马赛克屏蔽，同时删除提供数据的网站链接以及报文中的数值信息。
文章目录
- 运行结果（2020-2-4日数据，福建省）
- 数据爬取
- 抓包分析
- 数据格式
- 地理数据
- 代码实现
- 其他
运行结果（2020-2-4日数据，福建省）
数据爬取
数据来源于腾讯实时疫情动态
抓包分析
数据格式
具体数值用星号替代
'''
"chinaTotal": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
},
"chinaAdd": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
},
"lastUpdateTime": "2020-02-04 12:43:19",
"areaTree": [{
"name": "中国",
"children": [{
"name": "湖北",
"children": [{
"name": "武汉",
"total": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
},
"today": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
}
}, {
"name": "黄冈",
"total": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
},
"today": {
"confirm": ***,
"suspect": ***,
"dead": ***,
"heal": ***
}
}, {
。。。。。。
'''
地理数据
gadm地图数据（gadm36_CHN_shp.zip）请自行下载，这个数据包括国家、省、市三级行政区域，但是市级数据不准确
代码实现
#%%
import time, json, requests
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.font_manager import FontProperties
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
import jsonpath
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
#%%
# 获取全国疫情报告，来源于腾讯
def catch_world_disease_report():
timestamp = '%d'%int(time.time()*1000)
url_area = ('https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
'&callback=&_=') + timestamp
# 不处理异常了
data_json = json.loads(requests.get(url=url_area).json()['data'])
return data_json
world_data = catch_world_disease_report()
# print(area_data)
#%%
# 福建疫情地区分布(确诊病例)
# get请求，腾讯返回json，如下：
def catch_fj_disease_distribution():
# world_data = catch_world_disease_report()
dic_fj_confirm = dict()
# 解析福建各个城市的数据
dic_fj = jsonpath.jsonpath(world_data,
expr='$.areaTree[0].children[?(@.name=="福建")].children[*]')
for item in dic_fj:
if item['name'] not in dic_fj_confirm:
dic_fj_confirm.update({item['name']:0})
dic_fj_confirm[item['name']] += int(item['total']['confirm'])
return dic_fj_confirm
area_data = catch_fj_disease_distribution()
print(area_data)
#%%
# 绘制福建省确诊分布图
def plot_fj_area_distribution():
area_cfm_data = catch_fj_disease_distribution()
# 标签颜色和文本
legend_handles = [
matplotlib.patches.Patch(color='#7FFFAA', alpha=1, linewidth=0),
matplotlib.patches.Patch(color='#ffaa85', alpha=1, linewidth=0),
matplotlib.patches.Patch(color='#ff7b69', alpha=1, linewidth=0),
matplotlib.patches.Patch(color='#bf2121', alpha=1, linewidth=0),
matplotlib.patches.Patch(color='#7f1818', alpha=1, linewidth=0),
]
legend_labels = ['0人', '1-10人', '10-30人', '30-50人', '>50人']
fig = plt.figure(facecolor='#f4f4f4', figsize=(10, 8))
axes = fig.add_axes((0.1, 0.1, 0.8, 0.8))
axes.set_title('福建省新型冠状病毒疫情地图', fontsize=20)
axes.legend(legend_handles, legend_labels, bbox_to_anchor=(0.5, -0.11), loc='lower center', ncol=5)
# 横轴墨卡托投影
china_map = Basemap(llcrnrlon = 115.8, llcrnrlat = 23.3, urcrnrlon = 120.6, urcrnrlat = 28.4,
resolution = 'i', projection = 'tmerc',
lat_0 = 26, lon_0 = 119,
ax=axes)
# gadm36_CHN_1 省一级；gadm36_CHN_2 市一级；gadm36_CHN_1 县一级
'''
{'GID_0': 'CHN', 'NAME_0': 'China', 'GID_1': 'CHN.4_1',
'NAME_1': 'Fujian', 'NL_NAME_1': '福建',
'GID_2': 'CHN.4.1_1', 'NAME_2': 'Fuzhou', 'VARNAME_2': 'Fúzhōu', 'NL_NAME_2': '福州市',
'TYPE_2': 'Dìjíshì', 'ENGTYPE_2': 'Prefecture City', 'CC_2': '', 'HASC_2': '', 'RINGNUM': 1, 'SHAPENUM': 20}
'''
china_map.readshapefile('res/gadm36_CHN_shp/gadm36_CHN_2', 'states', drawbounds=True)
china_map.drawmapboundary(fill_color='aqua')
china_map.fillcontinents(color='white', lake_color='aqua')
china_map.drawcoastlines()
for info, shape in zip(china_map.states_info, china_map.states):
prov_name = info['NAME_1'].strip()
if prov_name != 'Fujian':
continue
is_reported = False # 若龙岩没有疫情，数据源就取不到其数据，而非显示0
city_name = info['NL_NAME_2'].strip()
city_name = city_name.rstrip('市')
if city_name in area_cfm_data.keys():
is_reported = True
# print('city: ', city_name, ' count:', fujian_data[city_name])
if area_cfm_data[city_name] == 0:
color = '#f0f0f0'
elif area_cfm_data[city_name] <= 10:
color = '#ffaa85'
elif area_cfm_data[city_name] <= 30:
color = '#ff7b69'
elif area_cfm_data[city_name] <= 50:
color = '#bf2121'
else:
color = '#7f1818'
if not is_reported:
area_cfm_data.setdefault(city_name, 0)
color = '#7FFFAA'
poly = Polygon(shape, facecolor=color, edgecolor=color, linewidth=1)
axes.add_patch(poly)
# 绘制各市的确诊数
# 将经纬度转换为笛卡尔坐标
city_loc_list = [china_map(119.30 - 0.4, 26.08),
china_map(118.10 - 0.1, 24.46 + 0.3),
china_map(118.16 - 0.4, 26.65 + 0.5),
china_map(119.52 - 0.5, 26.65 + 0.2), # 宁德
china_map(119.00 - 0.2, 25.44),
china_map(118.58 - 0.5, 24.93 + 0.2), # 泉州
china_map(117.35, 24.52),
china_map(117.01 - 0.5, 25.12 + 0.2),
china_map(117.61 - 0.2, 26.23)]
city_name_list = ['福州\n{0}例'.format(area_cfm_data['福州']),
'厦门\n{0}例'.format(area_cfm_data['厦门']),
'南平\n{0}例'.format(area_cfm_data['南平']),
'宁德\n{0}例'.format(area_cfm_data['宁德']),
'莆田\n{0}例'.format(area_cfm_data['莆田']),
'泉州\n{0}例'.format(area_cfm_data['泉州']),
'漳州\n{0}例'.format(area_cfm_data['漳州']),
'龙岩\n{0}例'.format(area_cfm_data['龙岩']),
'三明\n{0}例'.format(area_cfm_data['三明'])]
for city_loc, city_name in zip(city_loc_list, city_name_list):
# Basemap没有直接写文本的方法，故用plt的
plt.text(city_loc[0], city_loc[1], city_name,fontsize=12,fontweight='bold',
ha='left',va='center',color='black')
#%%
plot_fj_area_distribution()
#%%
其他
通过疾控中心通报网页内容析取数值请参见本人 的 绘制市级地图（福州）那篇文章