利用Python绘制全国各省新型冠状病毒疫情变化动态图

• 2020 年 2 月 25 日
• 笔记

题图：Image by enriquelopezgarre from Pixabay

截至2月2日24时，累计报告确诊病例17205例，现有重症病例2296例，累计死亡病例361例，累计治愈出院475例。现有疑似病例21558例。

图表来自百度疫情实时大数据报告

在家闲来无事，就用python绘制了全国各省新型冠状病毒疫情状况动态图表，其地图数据来源于腾讯的疫情实时追踪展示地图：https://github.com/dongli/china-shapefiles

全国各省的疫情实时数据来源于丁香园：https://github.com/BlankerL/DXY-2019-nCoV-Data/blame/master/DXYArea.csv#

先看下具体效果：

具体实现代码

我们使用下载的 china.shp 和 china_nine_dotted_line.shp 两个文件，可以绘制如下所示的带南海地区单独展示的中国地图

import pandas as pd  import numpy as np  import seaborn as sns  import matplotlib.pyplot as plt  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.dates as mdates  import matplotlib as mpl  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    %matplotlib inline

fig = plt.figure(figsize=(12,12))  ax = fig.gca()    #plt.subplots_adjust(left=0.12, right=0.98, top=0.75, bottom=0)  basemap = Basemap(llcrnrlon= 80,llcrnrlat=10,urcrnrlon=150,urcrnrlat=50,projection='poly',lon_0 = 116.65,lat_0 = 40.02,ax = ax)  basemap.readshapefile(shapefile = 'china',name = "province", drawbounds=True)  basemap.readshapefile('china_nine_dotted_line',name ='section', drawbounds=True)  ax.spines['right'].set_color('none')  ax.spines['top'].set_color('none')  ax.spines['left'].set_color('none')  ax.spines['bottom'].set_color('none')    df_mapData = pd.DataFrame(basemap.province_info)  df_mapData['OWNER'] = [x.strip('x00') for x in df_mapData['OWNER']] #省份  df_mapData['FCNAME'] =[x.strip('x00') for x in df_mapData['FCNAME']]    province=np.unique(df_mapData['OWNER'])  color = sns.husl_palette(len(province),h=15/360, l=.65, s=1).as_hex()  colors = dict(zip(province.tolist(),color))      for info, shape in zip(basemap.province_info, basemap.province):          pname = info['OWNER'].strip('x00')          fcname = info['FCNAME'].strip('x00')          if pname != fcname: # 不绘制海岛              continue          color = colors[pname]            poly = Polygon(shape, facecolor=color, edgecolor='k')          ax.add_patch(poly)      ax2= fig.add_axes([0.7, 0.25, 0.15, 0.15])  basemap2 = Basemap(llcrnrlon= 106.55,llcrnrlat=4.61,urcrnrlon=123.58,urcrnrlat=25.45,projection='poly',lon_0 = 116.65,lat_0 = 40.02,ax = ax2)  basemap2.readshapefile(shapefile = 'china',name = "province", drawbounds=True)  basemap2.readshapefile('china_nine_dotted_line',name ='section', drawbounds=True)    for info, shape in zip(basemap2.province_info, basemap2.province):          pname = info['OWNER'].strip('x00')          fcname = info['FCNAME'].strip('x00')          if pname != fcname: # 不绘制海岛              continue          color = colors[pname]          poly = Polygon(shape, facecolor=color, edgecolor='k')          ax2.add_patch(poly)

from datetime import datetime  from matplotlib import cm,colors    df_data=pd.read_csv('DXYArea.csv')  df_data['updateTime']=[datetime.strptime(d, '%Y-%m-%d %H:%M:%S.%f').date() for d in df_data['updateTime']]      df_data['month']=[d.month for d in df_data['updateTime']]  df_data['day']=[d.strftime('%d') for d in df_data['updateTime']]  df_data['date']=[d.strftime('%m-%d') for d in df_data['updateTime']]  df_data=df_data.drop_duplicates(subset = ['month','day','provinceName']).reset_index()    labels = [ '1-9', '10-99', '100-999', '1000-10000','>10000']  n_colors=len(labels)  color=[colors.rgb2hex(x) for x in cm.get_cmap( 'YlOrRd',n_colors)(np.linspace(0, 1, n_colors))]  color_array=[x for x in cm.get_cmap( 'YlOrRd',n_colors)(np.linspace(0, 1, n_colors))]    df_data['lablels']=pd.cut(df_data['province_confirmedCount'], [0,10,100,1000,10000,100000], labels=labels)  df_data['color']=[color[i] for i in df_data['lablels'].values.codes]  df_data=df_data.set_index('provinceName',drop=False)    days=[ '24', '25', '26', '27', '28', '29', '30', '31','01', '02', '03']#np.unique(df_data['day'])    df_day=df_data[df_data['day']==days[7]][['provinceName','province_confirmedCount','day','month','color','date']]

def draw_ChinaMap(Num_day):      ax.clear()        df_day=df_data[df_data['day']==days[Num_day]][['provinceName','province_confirmedCount','day','month','color','date']]        basemap = Basemap(llcrnrlon= 80,llcrnrlat=10,urcrnrlon=150,urcrnrlat=50,projection='poly',lon_0 = 116.65,lat_0 = 40.02,ax = ax)      basemap.readshapefile(shapefile = 'C:/Users/Peter_Zhang/Desktop/Hex_Map/china_shapefiles_master/china',                        name = "province", drawbounds=True)      basemap.readshapefile('C:/Users/Peter_Zhang/Desktop/Hex_Map/china_shapefiles_master/china_nine_dotted_line',                         name ='section', drawbounds=True)      ax.spines['right'].set_color('none')      ax.spines['top'].set_color('none')      ax.spines['left'].set_color('none')      ax.spines['bottom'].set_color('none')          for info, shape in zip(basemap.province_info, basemap.province):              pname = info['OWNER'].strip('x00')              fcname = info['FCNAME'].strip('x00')              if pname != fcname: # 不绘制海岛                  continue                color='white'              if sum(df_day['provinceName']==pname)>0:                  color = df_day.loc[pname,'color']                poly = Polygon(shape, facecolor=color, edgecolor='k')              ax.add_patch(poly)        patches = [ mpatches.Patch(color=color_array[i], label=labels[i]) for i in range(n_colors) ]      # put those patched as legend-handles into the legend      legend=ax.legend(handles=patches, borderaxespad=0,loc="center right",markerscale=1.3,            edgecolor='none',facecolor='none',fontsize=15,title='')        ax.text(0.02,1.07, s='全国各省新型冠状病毒疫情状况', transform=ax.transAxes, size=30, weight='bold',color='k')      ax.text(0.02,1.0, s='全国新型冠状病毒确诊总数为：'+str(df_day['province_confirmedCount'].sum())+';  湖北省新型冠状病毒确诊总数为:'+ str(df_day.loc['湖北省','province_confirmedCount']),          transform=ax.transAxes, size=20,weight='light', color='k')      ax.text(0.05,0.22, s=df_day['date'][0], transform=ax.transAxes, size=70, color='gray',weight='bold',family='Arial')      ax.text(0.02,0.05, s='数据来源：https://github.com/BlankerL/DXY-2019-nCoV-Data/blame/master/DXYArea.csv', transform=ax.transAxes, size=10, color='k')        basemap2 = Basemap(llcrnrlon= 106.55,llcrnrlat=4.61,urcrnrlon=123.58,urcrnrlat=25.45,projection='poly',lon_0 = 116.65,lat_0 = 40.02,ax = ax2)      basemap2.readshapefile(shapefile = 'china',name = "province", drawbounds=True)      basemap2.readshapefile('china_nine_dotted_line',name ='section', drawbounds=True)        for info, shape in zip(basemap2.province_info, basemap2.province):              pname = info['OWNER'].strip('x00')              fcname = info['FCNAME'].strip('x00')              if pname != fcname: # 不绘制海岛                  continue                color='white'              if sum(df_day['provinceName']==pname)>0:                  color = df_day.loc[pname,'color']              poly = Polygon(shape, facecolor=color, edgecolor='k')              ax2.add_patch(poly)    fig = plt.figure(figsize=(12,12))  ax = fig.gca()  ax2= fig.add_axes([0.75, 0.2, 0.15, 0.15])  plt.subplots_adjust(left=0.12, right=0.98, top=0.85, bottom=0.1)  draw_ChinaMap(2)

matplotlib 包和 plotnine 包都可以实现动态数据的可视化演示。

其中，在matplotlib包中，函数FuncAnimation(fig,func,frames,init_func,interval,blit)

是绘制动图的主要函数，其参数如下：

(1) fig 为绘制动图的画布名称；

(2) func为自定义动画函数update()，比如11-4-1的draw_barchart(year)和11-4-2的draw_areachart(Num_Date)；

(3) frames为动画长度，一次循环包含的帧数，在函数运行时，其值会传递给函数update(n)的形参“n”；

(4) init_func为自定义开始帧，即初始化函数，可省略；

(5) interval为更新频率，以ms计算；

(6) blit为选择更新所有点，还是仅更新产生变化的点。应选择True，但mac用户请选择False，否则无法显示。

plotnine 包的 PlotnineAnimation() 函数也可以绘制动态图表，但是对于不断更新的数据绘制动态图表时，动态图表生成速度很慢。

import matplotlib.animation as animation  from IPython.display import HTML  fig = plt.figure(figsize=(12,12))  ax = fig.gca()  ax2= fig.add_axes([0.75, 0.2, 0.15, 0.15])  plt.subplots_adjust(left=0.12, right=0.98, top=0.85, bottom=0.1)  animator = animation.FuncAnimation(fig, draw_ChinaMap, frames=np.arange(0,len(days),1),interval=1000)  HTML(animator.to_jshtml())