#=======================导入数据============================
df = pd.read_excel('某副省级城市长护险试点数据.xlsx')
df = df[df.disable_date.apply(lambda x:len(str(x))>4)]#删除建档时间为空
df.disable_date = pd.to_datetime(df.disable_date)#建档时间提取为时间对象
df['year'] = df.disable_date.apply(lambda x:x.year)
df = df[df.level.notna()]#保留所有失能存在失能等级且建档的样本
df.level = df.level.apply(lambda x:x[-1])#提取失能等级的级数
df2 = df.drop_duplicates('id')
df2.dtime = df2.dtime.apply(lambda x:x if pd.isna(x) else pd.to_datetime(x))
#=======================相关函数============================
def f(df,dtrg,i):#函数注释：计算在档总人数
t = dtrg[i+1]
dt = df[df['disable_date']<=t]
dtime = dt.dtime
dtime2 = dtime[~dtime.isna()]
return len(dt)-(dtime2<t).sum()
def f_new(df,dtrg,i):#函数注释：计算新建档人数
t0 = dtrg[i]
t1 = dtrg[i+1]
dt= df.disable_date
return ((dt>=t0)&(dt<t1)).sum()
def f_dd(df,dtrg,i):#函数注释：计算死亡人数
t0 = dtrg[i]
t1 = dtrg[i+1]
dt= df.dtime
dt = dt[~dt.isna()]
return ((dt>=t0)&(dt<t1)).sum()
dtrg = pd.date_range(start = pd.to_datetime('2015-12-31'),end = pd.to_datetime('2019-12-31'),freq='M') #按照时间区间生成月份列表
idx = [i for i in range(len(dtrg)-1)]#生成index列表
n = [f(df2,dtrg,i) for i in idx]
n_new = [f_new(df2,dtrg,i) for i in idx]
n_dd = [f_dd(df2,dtrg,i) for i in idx]
sr = pd.Series(dtrg)
sr2 = sr.apply(lambda x:str(x.year)+'-'+str(x.month))
#==================================================================
fig = plt.figure(figsize=(20,6),dpi=800)
ax = fig.add_subplot(2,2,1)#对应第一个子图
ax.bar([i for i in idx],n_new,edgecolor=sns.color_palette()[1],facecolor= sns.color_palette()[0])
ax.set_xticks([i for i in idx])
ax.set_xticklabels(sr2.tolist()[1:],fontsize=7)
ax.vlines(18.5,0,3000,colors='black',linestyle='--',lw=1)#对应长护险制度的启动时间
ax.hlines(500,0,48,colors='black',linestyle='--',lw=1)#500为实际月度新增的估计结果
plt.xticks(rotation=45)
plt.xlim(-1,49)
custom_legend = [Patch(edgecolor=sns.color_palette()[1],facecolor= sns.color_palette()[0], label='试点前后政府部门面临的失能群体增量')]
plt.legend(handles=custom_legend,loc='upper right',fontsize=12)
#————————————————————————————————————————————————————————————————————————————————————————————————————————————
ax = fig.add_subplot(2,2,3)#对应第三个子图，均为理论推测数值
ax.bar([i for i in idx],n_new,edgecolor=sns.color_palette()[0],facecolor=sns.color_palette()[3])
ax.set_xticks([i for i in idx]) # 设置刻度标签位置
ax.set_xticklabels(sr2.tolist()[1:],fontsize=7)
ax.vlines(18.5,0,3000,colors='black',linestyle='--',lw=1)
plt.xticks(rotation=45)
a = 19
ax.bar([i for i in idx[:a]],[500 for i in idx[:a]],edgecolor=sns.color_palette()[0],facecolor=sns.color_palette()[3])
v = n_new[a:]
a2 = 32
ax.bar([i for i in idx[a:a2]],[500 for i in idx[a:a2]],edgecolor=sns.color_palette()[1],facecolor=sns.color_palette()[0])
ax.bar([i for i in idx[a2:]],[i for i in n_new[a2:]],edgecolor=sns.color_palette()[1],facecolor=sns.color_palette()[0])
# plt.title('试点前后实际发生的失能群体增量')
custom_legend = [Patch(edgecolor=sns.color_palette()[0],facecolor=sns.color_palette()[3], label='过去发生的失能新增（理论推测）'),
Patch(edgecolor=sns.color_palette()[1],facecolor=sns.color_palette()[0], label='长护险试点后发生的失能新增（理论推测）')]
plt.legend(handles=custom_legend,loc='upper right',fontsize=12)
#===================================================================================================================================
dtrg2 = pd.date_range(start = pd.to_datetime('2017-8-31'),end = pd.to_datetime('2020-8-31'),freq='M')#按照时间区间生成月份列表
idx2 = [i for i in range(len(dtrg2)-1)]
n2 = [f(df2,dtrg2,i) for i in idx2]
n_new2 = [f_new(df2,dtrg2,i) for i in idx2]
n_dd2 = [f_dd(df2,dtrg2,i) for i in idx2]
sr = pd.Series(dtrg2)
sr3 = sr.apply(lambda x:str(x.year)+'-'+str(x.month))
#==================================================================================================
ax = fig.add_subplot(2,2,2)#对应第二个子图
ax.bar([i for i in idx2],n2,edgecolor=sns.color_palette()[1],facecolor= sns.color_palette()[2])
ax.set_xticks([i for i in idx2]) # 设置刻度标签位置
ax.set_xticklabels(sr3.tolist()[1:],fontsize=7)
plt.xticks(rotation=45)
plt.ylim(0,20000)
custom_legend = [Patch(edgecolor=sns.color_palette()[1],facecolor= sns.color_palette()[2], label='长护险试点后政策口径的失能群体总量')]
plt.legend(handles=custom_legend,loc='upper right',fontsize=12)
#==================================================================================================
ax = fig.add_subplot(2,2,4)#对应第四个子图
sns.lineplot(x=idx2,y=[n_dd2[i]/n2[i] for i in idx2],marker='o')
ax.set_xticks([i for i in idx2]) # 设置刻度标签位置
ax.set_xticklabels(sr3.tolist()[1:],fontsize=7)
plt.xticks(rotation=45)
custom_legend = [Patch(edgecolor=sns.color_palette()[0],facecolor= sns.color_palette()[0], label='长护险试点后失能群体的平均死亡率变化')]
plt.legend(handles=custom_legend,loc='upper right',fontsize=12)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.2)
plt.show()
