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人口的流動性是造成新型冠狀傳染病毒在全國范圍內擴散的重要驅動因素，很多用於評估傳染病毒的感染速率和擴散規模的預測模型均基於人流數據展開，此次因病毒傳染而封城和春節期間取消春節活動等措施更是說明了限制人口流動是抑制病毒傳播的重要途徑。
春節前的人口大遷徙無疑加速了本次疫情的時空傳播速率，而春節後全國范圍內的人口回流仍然會對疫情的防控帶來巨大的挑戰。可以說，深刻認識春節後人口回流的遷徙特征和規律，將對此次疫情的防控起到非常積極的作用。數據時間為2019-11-30和2020-02-20，分別是疫情前和疫情開始第一個春節。
點此下載數據集
① 疫情前各省人口流動情況——柱形圖/地圖；廣東湖北流動最大的前五個省份飛行圖(依據廣東是人口流動最大省份、湖北是第一個封城的省份)；
② 疫情後各省人口流動情況——柱形圖/地圖；廣東湖北流動最大的前五個省份飛行圖(依據廣東是人口流動最大省份、湖北是第一個封城的省份)；
③ 疫情前後湖北各城市人口流動情況；
import pandas as pd
from pyecharts.charts import *
import pyecharts.options as opts
df1 = pd.read_csv('/XXXXXX/2019-11-30.csv')
df1_1 = df1.groupby(['startProvince']).realIdx.sum().round(1)
df1_2 = df1.groupby(['endProvince']).realIdx.sum().round(1)
df1_3 = pd.merge(df1_1,df1_2,how='left',left_index=True,right_index=True).sort_values('realIdx_x',ascending=False)
df2_1 = df1.groupby(['startProvince','endProvince']).realIdx.sum().round(1).reset_index()
df2_1['r'] = df2_1.groupby(['startProvince'])['realIdx'].rank(method='first',ascending=False)
ls1_city = list(df2_1.query("startProvince in ['廣東'] & startProvince!=endProvince & \
realIdx>0 & r<=6").set_index(['startProvince','endProvince']).index)
ls2_city = list(df2_1.query("startProvince in ['湖北'] & startProvince!=endProvince & \
realIdx>0 & r<=6").set_index(['startProvince','endProvince']).index)
df2 = pd.read_csv('/XXXXXX/2020-02-20.csv')
df3_1 = df2.groupby(['startProvince']).realIdx.sum().round(1)
df3_2 = df2.groupby(['endProvince']).realIdx.sum().round(1)
df3_3 = pd.merge(df3_1,df3_2,how='left',left_index=True,right_index=True).sort_values('realIdx_x',ascending=False)
df4_1 = df2.groupby(['startProvince','endProvince']).realIdx.sum().round(1).reset_index()
df4_1['r'] = df4_1.groupby(['startProvince'])['realIdx'].rank(method='first',ascending=False)
ls3_city = list(df4_1.query("startProvince in ['廣東'] & startProvince!=endProvince & \
realIdx>0 & r<=6").set_index(['startProvince','endProvince']).index)
ls4_city = list(df4_1.query("startProvince in ['湖北'] & startProvince!=endProvince & \
realIdx>0 & r<=6").set_index(['startProvince','endProvince']).index)
hb1 = df1[df1.startProvince=='湖北'].groupby(['startCity']).realIdx.sum()
hb2 = df2[df2.startProvince=='湖北'].groupby(['startCity']).realIdx.sum()
hb = pd.merge(hb1,hb2,left_index=True,right_index=True)
hb['cha'] = hb.realIdx_x-hb.realIdx_y
hb.sort_values('cha',ascending=False,inplace=True)
p1 = Geo()
p1.add_schema(maptype='china',is_roam=False)
p1.add('出發地',list(df1_1.to_dict().items()),type_='effectScatter',symbol_size=5,
label_opts=opts.LabelOpts(is_show=False))
p1.add('目的地',list(df1_2.to_dict().items()),type_='heatmap',
is_large=True,blur_size=16,point_size=16) #當圖表數據量大時可設置為True、光暈大小、點的大小
p1.add('從廣東出發的top5',ls1_city,type_='lines',symbol_size=5,label_opts=opts.LabelOpts(is_show=False))
p1.add('從湖北出發的top5',ls2_city,type_='lines',symbol_size=5,label_opts=opts.LabelOpts(is_show=False))
p1.set_global_opts(title_opts=opts.TitleOpts('疫情前'),
visualmap_opts=opts.VisualMapOpts(range_text=['realIdx分組'],is_piecewise=True,
pieces=[{
'min':180,'color':'#080177'},
{
'min':93,'max':180,'color':'#1203B3'},
{
'min':52,'max':93,'color':'#2106fa'},
{
'min':36,'max':52,'color':'#7C69FD'},
{
'min':0,'max':36,'color':'#D4CDFE'}]))
p2 = Bar()
p2.add_xaxis(list(df1_3.to_dict()['realIdx_x'].keys()))
p2.add_yaxis('出發地',list(df1_3.to_dict()['realIdx_x'].values()),label_opts=opts.LabelOpts(is_show=False))
p2.add_yaxis('目的地',list(df1_3.to_dict()['realIdx_y'].values()),label_opts=opts.LabelOpts(is_show=False))
p2.set_global_opts(title_opts=opts.TitleOpts('2019-11-30各省份人口流動指數'),
legend_opts=opts.LegendOpts(pos_right='right'),datazoom_opts=opts.DataZoomOpts())
p3 = Bar()
p3.add_xaxis(list(df3_3.to_dict()['realIdx_x'].keys()))
p3.add_yaxis('出發地',list(df3_3.to_dict()['realIdx_x'].values()),label_opts=opts.LabelOpts(is_show=False))
p3.add_yaxis('目的地',list(df3_3.to_dict()['realIdx_y'].values()),label_opts=opts.LabelOpts(is_show=False))
p3.set_global_opts(title_opts=opts.TitleOpts('2020-02-20各省份人口流動指數'),
legend_opts=opts.LegendOpts(pos_right='right'),datazoom_opts=opts.DataZoomOpts())
p4 = Geo()
p4.add_schema(maptype='china',is_roam=False)
p4.add('出發地',list(df3_1.to_dict().items()),type_='effectScatter',symbol_size=5,
label_opts=opts.LabelOpts(is_show=False))
p4.add('目的地',list(df3_2.to_dict().items()),type_='heatmap',
is_large=True,blur_size=16,point_size=16) #當圖表數據量大時可設置為True、光暈大小、點的大小
p4.add('從廣東出發的top5',ls3_city,type_='lines',symbol_size=5,label_opts=opts.LabelOpts(is_show=False))
p4.add('從湖北出發的top5',ls4_city,type_='lines',symbol_size=5,label_opts=opts.LabelOpts(is_show=False))
p4.set_global_opts(title_opts=opts.TitleOpts('疫情後'),
visualmap_opts=opts.VisualMapOpts(range_text=['realIdx分組'],is_piecewise=True,
pieces=[{
'min':45,'color':'#080177'},
{
'min':20,'max':45,'color':'#1203B3'},
{
'min':8,'max':20,'color':'#2106fa'},
{
'min':4,'max':8,'color':'#7C69FD'},
{
'min':0,'max':4,'color':'#D4CDFE'}]))
b = Bar()
b.add_xaxis(list(hb.index))
b.add_yaxis('疫情前',list(hb.realIdx_x.round(1)),label_opts=opts.LabelOpts(is_show=False))
b.add_yaxis('疫情後',list(hb.realIdx_y.round(1)),label_opts=opts.LabelOpts(is_show=False))
b.extend_axis(yaxis=opts.AxisOpts(name='cha',position='right'))
b.extend_axis(yaxis=opts.AxisOpts(name='realIdx',position='left'))
l = Line()
l.add_xaxis(list(hb.index))
l.add_yaxis('cha',list(hb.cha.round(1)),yaxis_index=1,label_opts=opts.LabelOpts(is_show=False))
b.overlap(l)
b.set_global_opts(title_opts=opts.TitleOpts('疫情前後，湖北各城市人口流動情況',pos_left='center'),
legend_opts=opts.LegendOpts(pos_top='10%',pos_right='20%'))
p = Page(layout=Page.DraggablePageLayout)
p.add(p2,p1,p3,p4,b)
p.render('hh.html')
p.render_notebook()
p.save_resize_html(source='hh.html',
cfg_file='/xxxxxx/chart_config.json',
dest='/xxxxxx/chart.html')
''' 市.json:可從原數據源下載 '''
a = pd.read_json('/XXXXXX/市.json',orient='index')
ls = []
for i in range(len(a.loc['features',0])):
m = a.loc['features',0][i]['attributes']
ls.append((m['省'],m['省代碼'],m['市'],m['市代碼']))
pd.DataFrame(ls).head()
謝謝大家
