学习进度笔记

学习进度笔记05

今天学习了python的数据爬取

import time

import json

import requests

from datetime import datetime

import numpy as np

import matplotlib

import matplotlib.figure

from matplotlib.font_manager import FontProperties

from matplotlib.backends.backend_agg import FigureCanvasAgg

from matplotlib.patches import Polygon

from matplotlib.collections import PatchCollection

from mpl_toolkits.basemap import Basemap

import matplotlib.pyplot as plt

import matplotlib.dates as mdates

plt.rcParams['font.sans-serif'] = ['FangSong']  # 设置默认字体

plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时'-'显示为方块的问题

def catch_daily():

    """抓取每日确诊和死亡数据"""

    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_cn_day_counts&callback=&_=%d'%int(time.time()*1000)

    data = json.loads(requests.get(url=url).json()['data'])

    data.sort(key=lambda x:x['date'])

    date_list = list() # 日期

    confirm_list = list() # 确诊

    suspect_list = list() # 疑似

    dead_list = list() # 死亡

    heal_list = list() # 治愈

    for item in data:

        month, day = item['date'].split('.')

        date_list.append(datetime.strptime('2020-%s-%s'%(month, day), '%Y-%m-%d'))

        confirm_list.append(int(item['confirm']))

        suspect_list.append(int(item['suspect']))

        dead_list.append(int(item['dead']))

        heal_list.append(int(item['heal']))

    return date_list, confirm_list, suspect_list, dead_list, heal_list

def catch_distribution():

    """抓取行政区域确诊分布数据"""

    data = {'西藏':0}

    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_area_counts&callback=&_=%d'%int(time.time()*1000)

    for item in json.loads(requests.get(url=url).json()['data']):

        if item['area'] not in data:

            data.update({item['area']:0})

        data[item['area']] += int(item['confirm'])

    return data

def plot_daily():

    """绘制每日确诊和死亡数据"""

    date_list, confirm_list, suspect_list, dead_list, heal_list = catch_daily() # 获取数据

    plt.figure('2019-nCoV疫情统计图表', facecolor='#f4f4f4', figsize=(10, 8))

    plt.title('2019-nCoV疫情曲线', fontsize=20)

    plt.plot(date_list, confirm_list, label='确诊')

    plt.plot(date_list, suspect_list, label='疑似')

    plt.plot(date_list, dead_list, label='死亡')

    plt.plot(date_list, heal_list, label='治愈')

    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d')) # 格式化时间轴标注

    plt.gcf().autofmt_xdate() # 优化标注（自动倾斜）

    plt.grid(linestyle=':') # 显示网格

    plt.legend(loc='best') # 显示图例

    plt.savefig('2019-nCoV疫情曲线.png') # 保存为文件

    #plt.show()

def plot_distribution():

    """绘制行政区域确诊分布数据"""

    data = catch_distribution()

    font = FontProperties(fname='res/simsun.ttf', size=14)

    lat_min = 0

    lat_max = 60

    lon_min = 70

    lon_max = 140

    handles = [

            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),

]

    labels = [ '1-9人', '10-99人', '100-999人', '>1000人']

    fig = matplotlib.figure.Figure()

    fig.set_size_inches(10, 8) # 设置绘图板尺寸

    axes = fig.add_axes((0.1, 0.12, 0.8, 0.8)) # rect = l,b,w,h

#    m = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)

    m = Basemap(projection='ortho', lat_0=30, lon_0=105, resolution='l', ax=axes)#正射投影

    m.readshapefile('res/china-shapefiles-master/china', 'province', drawbounds=True)

    m.readshapefile('res/china-shapefiles-master/china_nine_dotted_line', 'section', drawbounds=True)

    m.drawcoastlines(color='black') # 洲际线

    m.drawcountries(color='black')  # 国界线

    m.drawparallels(np.arange(lat_min,lat_max,10), labels=[1,0,0,0]) #画经度线

    m.drawmeridians(np.arange(lon_min,lon_max,10), labels=[0,0,0,1]) #画纬度线

    for info, shape in zip(m.province_info, m.province):

        pname = info['OWNER'].strip('x00')

        fcname = info['FCNAME'].strip('x00')

        if pname != fcname: # 不绘制海岛

            continue

        for key in data.keys():

            if key in pname:

                if data[key] == 0:

                    color = '#f0f0f0'

                elif data[key] < 10:

                    color = '#ffaa85'

                elif data[key] <100:

                    color = '#ff7b69'

                elif  data[key] < 1000:

                    color = '#bf2121'

                else:

                    color = '#7f1818'

                break

        poly = Polygon(shape, facecolor=color, edgecolor=color)

        axes.add_patch(poly)

    axes.legend(handles, labels, bbox_to_anchor=(0.5, -0.11), loc='lower center', ncol=4, prop=font)

    axes.set_title("2019-nCoV疫情地图", fontproperties=font)

    FigureCanvasAgg(fig)

    fig.savefig('2019-nCoV疫情地图（正射投影）.png')

if __name__ == '__main__':

    plot_daily()

    plot_distribution()