Excel数据格式截图：
主要分析cog数据在不同指标下随时间的变化，有2005 2008 2011 2014四个年度。prov是不同省份等等。
代码如下：
# -*- coding: utf-8 -*- """ Created on Fri May 24 09:56:04 2019 @author: YEXIN @Company:华中科技大学 """ import pandas as pd import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt myfont = mpl.font_manager.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') ##文件位置 ExcelFilePath=r'D:\PycharmWorks\YeXinPython\Others\cognitive(1).xlsx' #读取excel数据到DataFrame里面 data = pd.read_excel(ExcelFilePath,sheet_name=0)##目标sheet所在的位置，以0为起始，比如sheet_name = 1代表第2个工作表 #print(data.head()) ###数据筛选 data_2005=data.loc[(data['year']==2005)]##2005年数据 #data_2008=data.loc[(data['year']==2008)]##2008年数据 #data_2011=data.loc[(data['year']==2011)]##2011年数据 #data_2014=data.loc[(data['year']==2014)]##2014年数据 ##指标列表 year_name=['2005','2008','2011','2014'] index_name=['整体','男性','女性','城市','镇','乡','东部','中部','西部'] index = ['obs','Mean','0-9分','10-17分','18-23分','24-30分'] ##通过循环利用index_name,index;year_name生成指标组合，便于标明下面的DF的数据意义，方面后期画图 names=[] names.append(year_name) for i in range(len(index_name)): tmpnamelist = [] for j in range(len(index)): tmpname = index_name[i]+':'+index[j] tmpnamelist.append(tmpname) names.append(tmpnamelist) ###认知能力:整体，男性，女性 数据DF DataFrame_human = pd.DataFrame(index=names[0],columns=names[1]+names[2]+names[3]+['lower 24-man']+['lower 24-women']+['lower 24']) ###认知能力，城市，镇，乡 数据DF DataFrame_town = pd.DataFrame(index=names[0],columns=names[4]+names[5]+names[6]+['lower 24-城市']+['lower 24-镇']+['lower 24-乡']+['lower 24']) ###认知能力 东，中，西 数据DF DataFrame_space = pd.DataFrame(index=names[0],columns=names[7]+names[8]+names[9]+['lower 24-东']+['lower 24-中']+['lower 24-西']+['lower 24']) ####数据处理并存储 ''' 以下筛选性别分布的认知能力 ''' human_human_Sex_num = data.groupby(['year','gender'])['cog'].count() human_Mean_sex = data.groupby(['year','gender'])['cog'].agg([len,np.mean])##不同年度下不同性别的认知能力的均值 human_Sum_cog = data.groupby(['year'])['cog'].agg([len,np.mean])#总的认知能力：男+女 ##0-9分 human_cog_0_9 = data.loc[(data['cog']<10) & (data['cog']>-1)].groupby(['year','gender'])['id'].count() ##10-17分 human_cog_10_17 = data.loc[(data['cog']>9) & (data['cog']<18)].groupby(['year','gender'])['id'].count() ##18-23分 human_cog_18_23 = data.loc[(data['cog']<24) & (data['cog']>17)].groupby(['year','gender'])['id'].count() ##24-30分 human_cog_24_30 = data.loc[(data['cog']<31) & (data['cog']>23)].groupby(['year','gender'])['id'].count() ##小于24分 human_cog_24 = data.loc[(data['cog']<25)].groupby(['year','gender'])['id'].count() ''' 以下筛选城乡镇分布的认知能力 ''' town_resid_num = data.groupby(['year','resid'])['cog'].count() town_Mean_resid = data.groupby(['year','resid'])['cog'].agg([len,np.mean])##不同年度下不同性别的认知能力的均值 town_Sum_cog = data.groupby(['year'])['cog'].agg([len,np.mean])#总的认知能力：男+女 ##0-9分 town_cog_0_9 = data.loc[(data['cog']<10) & (data['cog']>-1)].groupby(['year','resid'])['id'].count() ##10-17分 town_cog_10_17 = data.loc[(data['cog']>9) & (data['cog']<18)].groupby(['year','resid'])['id'].count() ##18-23分 town_cog_18_23 = data.loc[(data['cog']<24) & (data['cog']>17)].groupby(['year','resid'])['id'].count() ##24-30分 town_cog_24_30 = data.loc[(data['cog']<31) & (data['cog']>23)].groupby(['year','resid'])['id'].count() ##小于24分 town_cog_24 = data.loc[(data['cog']<25)].groupby(['year','resid'])['id'].count() ''' 以下筛选空间分布的认知能力 ''' space_prov_num = data.groupby(['year','prov_1'])['cog'].count() space_Mean_prov = data.groupby(['year','prov_1'])['cog'].agg([len,np.mean])##不同年度下不同性别的认知能力的均值 space_Sum_cog = data.groupby(['year'])['cog'].agg([len,np.mean])#总的认知能力：男+女 ##0-9分 space_cog_0_9 = data.loc[(data['cog']<10) & (data['cog']>-1)].groupby(['year','prov_1'])['id'].count() ##10-17分 space_cog_10_17 = data.loc[(data['cog']>9) & (data['cog']<18)].groupby(['year','prov_1'])['id'].count() ##18-23分 space_cog_18_23 = data.loc[(data['cog']<24) & (data['cog']>17)].groupby(['year','prov_1'])['id'].count() ##24-30分 space_cog_24_30 = data.loc[(data['cog']<31) & (data['cog']>23)].groupby(['year','prov_1'])['id'].count() ##小于24分 space_cog_24 = data.loc[(data['cog']<25)].groupby(['year','prov_1'])['id'].count() for k in range(len(year_name)): #####存储到DataFrame_human DataFrame_human.iloc[k,0]=human_Mean_sex.iat[k*2,0]+human_Mean_sex.iat[k*2+1,0]##插入数据 DataFrame_human.iloc[k,1]=human_Sum_cog.iat[k,1] DataFrame_human.iloc[k,2]=(human_cog_0_9[k*2]+human_cog_0_9[k*2+1])/human_Sum_cog.iat[k,0] DataFrame_human.iloc[k,3]=(human_cog_10_17[k*2]+human_cog_10_17[k*2+1])/human_Sum_cog.iat[k,0] DataFrame_human.iloc[k,4]=(human_cog_18_23[k*2]+human_cog_18_23[k*2+1])/human_Sum_cog.iat[k,0] DataFrame_human.iloc[k,5]=(human_cog_24_30[k*2]+human_cog_24_30[k*2+1])/human_Sum_cog.iat[k,0] DataFrame_human.iloc[k,6]=human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,7]=human_Mean_sex.iat[k*2,1] DataFrame_human.iloc[k,8]=(human_cog_0_9[k*2])/human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,9]=(human_cog_10_17[k*2])/human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,10]=(human_cog_18_23[k*2])/human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,11]=(human_cog_24_30[k*2])/human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,12]=human_Mean_sex.iat[k*2+1,0] DataFrame_human.iloc[k,13]=human_Mean_sex.iat[k*2+1,1] DataFrame_human.iloc[k,14]=(human_cog_0_9[k*2+1])/human_Mean_sex.iat[k*2+1,0] DataFrame_human.iloc[k,15]=(human_cog_10_17[k*2+1])/human_Mean_sex.iat[k*2+1,0] DataFrame_human.iloc[k,16]=(human_cog_18_23[k*2+1])/human_Mean_sex.iat[k*2+1,0] DataFrame_human.iloc[k,17]=(human_cog_24_30[k*2+1])/human_Mean_sex.iat[k*2+1,0] ###小于24的 DataFrame_human.iloc[k,18]=(human_cog_24[k*2])/human_Mean_sex.iat[k*2,0] DataFrame_human.iloc[k,19]=(human_cog_24[k*2+1])/human_Mean_sex.iat[k*2+1,0] DataFrame_human.iloc[k,20]=(human_cog_24[k*2+1]+human_cog_24[k*2])/human_Sum_cog.iat[k,0] ##########存储到DataFrame_town DataFrame_town.iloc[k,0]=town_Mean_resid.iat[k*3,0]##插入数据 DataFrame_town.iloc[k,1]=town_Mean_resid.iat[k*3,1] DataFrame_town.iloc[k,2]=(town_cog_0_9[k*3])/town_Mean_resid.iat[k*3,0] DataFrame_town.iloc[k,3]=(town_cog_10_17[k*3])/town_Mean_resid.iat[k*3,0] DataFrame_town.iloc[k,4]=(town_cog_18_23[k*3])/town_Mean_resid.iat[k*3,0] DataFrame_town.iloc[k,5]=(town_cog_24_30[k*3])/town_Mean_resid.iat[k*3,0] DataFrame_town.iloc[k,6]=town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,7]=town_Mean_resid.iat[k*3+1,1] DataFrame_town.iloc[k,8]=(town_cog_0_9[k*3+1])/town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,9]=(town_cog_10_17[k*3+1])/town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,10]=(town_cog_18_23[k*3+1])/town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,11]=(town_cog_24_30[k*3+1])/town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,12]=town_Mean_resid.iat[k*3+2,0] DataFrame_town.iloc[k,13]=town_Mean_resid.iat[k*3+2,1] DataFrame_town.iloc[k,14]=(town_cog_0_9[k*3+2])/town_Mean_resid.iat[k*3+2,0] DataFrame_town.iloc[k,15]=(town_cog_10_17[k*3+2])/town_Mean_resid.iat[k*3+2,0] DataFrame_town.iloc[k,16]=(town_cog_18_23[k*3+2])/town_Mean_resid.iat[k*3+2,0] DataFrame_town.iloc[k,17]=(town_cog_24_30[k*3+2])/town_Mean_resid.iat[k*3+2,0] ###小于24的 DataFrame_town.iloc[k,18]=(town_cog_24[k*3])/town_Mean_resid.iat[k*3,0] DataFrame_town.iloc[k,19]=(town_cog_24[k*3+1])/town_Mean_resid.iat[k*3+1,0] DataFrame_town.iloc[k,20]=(town_cog_24[k*3+2])/town_Mean_resid.iat[k*3+2,0] DataFrame_town.iloc[k,21]=(town_cog_24[k*3+1]+town_cog_24[k*3]+town_cog_24[k*3+2])/town_Sum_cog.iat[k,0] ##########存储到DataFrame_space DataFrame_space.iloc[k,0]=space_Mean_prov.iat[k*3,0]##插入数据 DataFrame_space.iloc[k,1]=space_Mean_prov.iat[k*3,1] DataFrame_space.iloc[k,2]=(space_cog_0_9[k*3])/space_Mean_prov.iat[k*3,0] DataFrame_space.iloc[k,3]=(space_cog_10_17[k*3])/space_Mean_prov.iat[k*3,0] DataFrame_space.iloc[k,4]=(space_cog_18_23[k*3])/space_Mean_prov.iat[k*3,0] DataFrame_space.iloc[k,5]=(space_cog_24_30[k*3])/space_Mean_prov.iat[k*3,0] DataFrame_space.iloc[k,6]=space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,7]=space_Mean_prov.iat[k*3+1,1] DataFrame_space.iloc[k,8]=(space_cog_0_9[k*3+1])/space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,9]=(space_cog_10_17[k*3+1])/space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,10]=(space_cog_18_23[k*3+1])/space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,11]=(space_cog_24_30[k*3+1])/space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,12]=space_Mean_prov.iat[k*3+2,0] DataFrame_space.iloc[k,13]=space_Mean_prov.iat[k*3+2,1] DataFrame_space.iloc[k,14]=(space_cog_0_9[k*3+2])/space_Mean_prov.iat[k*3+2,0] DataFrame_space.iloc[k,15]=(space_cog_10_17[k*3+2])/space_Mean_prov.iat[k*3+2,0] DataFrame_space.iloc[k,16]=(space_cog_18_23[k*3+2])/space_Mean_prov.iat[k*3+2,0] DataFrame_space.iloc[k,17]=(space_cog_24_30[k*3+2])/space_Mean_prov.iat[k*3+2,0] ###小于24的 DataFrame_space.iloc[k,18]=(space_cog_24[k*3])/space_Mean_prov.iat[k*3,0] DataFrame_space.iloc[k,19]=(space_cog_24[k*3+1])/space_Mean_prov.iat[k*3+1,0] DataFrame_space.iloc[k,20]=(space_cog_24[k*3+2])/space_Mean_prov.iat[k*3+2,0] DataFrame_space.iloc[k,21]=(space_cog_24[k*3+1]+space_cog_24[k*3]+space_cog_24[k*3+2])/space_Sum_cog.iat[k,0] #################################################################################################### ###画图【单个】 #################################################################################################### ##p1,=plt.plot(DataFrame_human['整体:Mean']) ##p2,=plt.plot(DataFrame_human['男性:Mean']) ##p3,=plt.plot(DataFrame_human['女性:Mean']) # #plt.figure(1) #plt.plot(DataFrame_human['整体:Mean']) #plt.plot(DataFrame_human['男性:Mean']) #plt.plot(DataFrame_human['女性:Mean']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.5) #ax.spines['left'].set_linewidth(1.5) #ax.spines['top'].set_linewidth(1.5) #ax.spines['right'].set_linewidth(1.5) # #plt.ylabel('认知能力均值',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11) #plt.title('不同性别的认知能力均值随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) ##plt.legend([p1,p2,p3],['Wohle','man','women'],loc='upper right') #plt.legend((u'整体均值', u'男性均值',u'女性均值'),loc='best',prop=myfont) ##plt.show() ################################################################################ #plt.figure(2) #plt.plot(DataFrame_human['整体:0-9分']) #plt.plot(DataFrame_human['男性:0-9分']) #plt.plot(DataFrame_human['女性:0-9分']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.2) #ax.spines['left'].set_linewidth(1.2) #ax.spines['top'].set_linewidth(1.2) #ax.spines['right'].set_linewidth(1.2) # #plt.ylabel('百分比',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') #plt.title('不同性别的认知能力百分比【0-9分】随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) #plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) ##plt.show() ################################################################################ #plt.figure(3) #plt.plot(DataFrame_human['整体:10-17分']) #plt.plot(DataFrame_human['男性:10-17分']) #plt.plot(DataFrame_human['女性:10-17分']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.2) #ax.spines['left'].set_linewidth(1.2) #ax.spines['top'].set_linewidth(1.2) #ax.spines['right'].set_linewidth(1.2) # #plt.ylabel('百分比',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') #plt.title('不同性别的认知能力百分比【10-17分】随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) #plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) ##plt.show() ################################################################################ #plt.figure(4) #plt.plot(DataFrame_human['整体:18-23分']) #plt.plot(DataFrame_human['男性:18-23分']) #plt.plot(DataFrame_human['女性:18-23分']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.2) #ax.spines['left'].set_linewidth(1.2) #ax.spines['top'].set_linewidth(1.2) #ax.spines['right'].set_linewidth(1.2) # #plt.ylabel('百分比',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') #plt.title('不同性别的认知能力百分比【18-23分】随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) #plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) ##plt.show() ################################################################################ #plt.figure(5) #plt.plot(DataFrame_human['整体:24-30分']) #plt.plot(DataFrame_human['男性:24-30分']) #plt.plot(DataFrame_human['女性:24-30分']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.2) #ax.spines['left'].set_linewidth(1.2) #ax.spines['top'].set_linewidth(1.2) #ax.spines['right'].set_linewidth(1.2) # #plt.ylabel('百分比',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') #plt.title('不同性别的认知能力百分比【24-30分】随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) #plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) ##plt.show() ################################################################################ #plt.figure(6) #plt.plot(DataFrame_human['lower 24']) #plt.plot(DataFrame_human['lower 24-man']) #plt.plot(DataFrame_human['lower 24-women']) # #ax = plt.gca() # gca = 'get current axis' 获取当前坐标 #ax.spines['bottom'].set_linewidth(1.2) #ax.spines['left'].set_linewidth(1.2) #ax.spines['top'].set_linewidth(1.2) #ax.spines['right'].set_linewidth(1.2) # #plt.ylabel('百分比',fontproperties = myfont,fontsize=11) #plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') #plt.title('不同性别的认知能力百分比【低于24分】随年份的变化',fontproperties = myfont,fontsize=13,fontweight='bold') #plt.xticks(fontsize=11) #plt.yticks(fontsize=11) #plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ################################################################################ #######画图【单个】######################### ################################################################################################### ##画图【整合】DataFrame_human ################################################################################################### plt.figure(1) ax1 = plt.subplot(2, 3, 1) # （行，列，活跃区） plt.plot(DataFrame_human['整体:Mean']) plt.plot(DataFrame_human['男性:Mean']) plt.plot(DataFrame_human['女性:Mean']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.5) ax.spines['left'].set_linewidth(1.5) ax.spines['top'].set_linewidth(1.5) ax.spines['right'].set_linewidth(1.5) plt.ylabel('认知能力均值',fontproperties = myfont,fontsize=12) plt.title('不同性别的认知能力均值随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=11) #plt.legend([p1,p2,p3],['Wohle','man','women'],loc='upper right') plt.legend((u'整体均值', u'男性均值',u'女性均值'),loc='best',prop=myfont,fontsize=5) #plt.show() ############################################################################### ax2 = plt.subplot(2, 3, 2) plt.plot(DataFrame_human['整体:0-9分']) plt.plot(DataFrame_human['男性:0-9分']) plt.plot(DataFrame_human['女性:0-9分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('不同性别的认知能力【0-9分】百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax3 = plt.subplot(2, 3, 3) plt.plot(DataFrame_human['整体:10-17分']) plt.plot(DataFrame_human['男性:10-17分']) plt.plot(DataFrame_human['女性:10-17分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.title('不同性别的认知能力【10-17分】百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax4 = plt.subplot(2, 3, 4) plt.plot(DataFrame_human['整体:18-23分']) plt.plot(DataFrame_human['男性:18-23分']) plt.plot(DataFrame_human['女性:18-23分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=12) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('不同性别的认知能力【18-23分】百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=11) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax5 = plt.subplot(2, 3, 5) plt.plot(DataFrame_human['整体:24-30分']) plt.plot(DataFrame_human['男性:24-30分']) plt.plot(DataFrame_human['女性:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('不同性别的认知能力【24-30分】百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax6 = plt.subplot(2, 3, 6) plt.plot(DataFrame_human['lower 24']) plt.plot(DataFrame_human['lower 24-man']) plt.plot(DataFrame_human['lower 24-women']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('不同性别的认知能力【低于24分】百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ######画图【整合】DataFrame_human######################### ################################################################################################### ##画图【整合】DataFrame_town ################################################################################################### plt.figure(2) ax1 = plt.subplot(2, 3, 1) # （行，列，活跃区） plt.plot(DataFrame_town['城市:Mean']) plt.plot(DataFrame_town['镇:Mean']) plt.plot(DataFrame_town['乡:Mean']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.5) ax.spines['left'].set_linewidth(1.5) ax.spines['top'].set_linewidth(1.5) ax.spines['right'].set_linewidth(1.5) plt.ylabel('认知能力均值',fontproperties = myfont,fontsize=12) plt.title('城乡分布的认知能力均值随年份变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=11) #plt.legend([p1,p2,p3],['Wohle','man','women'],loc='upper right') plt.legend((u'城市群体均值', u'镇群体均值',u'乡群体均值'),loc='best',prop=myfont,fontsize=5) #plt.show() ############################################################################### ax2 = plt.subplot(2, 3, 2) plt.plot(DataFrame_town['城市:0-9分']) plt.plot(DataFrame_town['镇:0-9分']) plt.plot(DataFrame_town['乡:0-9分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('认知能力【0-9分】城乡分布百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax3 = plt.subplot(2, 3, 3) plt.plot(DataFrame_town['城市:10-17分']) plt.plot(DataFrame_town['镇:10-17分']) plt.plot(DataFrame_town['乡:10-17分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.title('认知能力【10-17分】城乡分布百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='best',prop=myfont) #plt.show() plt.close() ############################################################################### ax4 = plt.subplot(2, 3, 4) plt.plot(DataFrame_town['城市:18-23分']) plt.plot(DataFrame_town['镇:18-23分']) plt.plot(DataFrame_town['乡:18-23分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=12) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【18-23分】城乡分布百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=11) plt.legend((u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax5 = plt.subplot(2, 3, 5) plt.plot(DataFrame_town['城市:24-30分']) plt.plot(DataFrame_town['镇:24-30分']) plt.plot(DataFrame_town['乡:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【24-30分】城乡分布百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax6 = plt.subplot(2, 3, 6) plt.plot(DataFrame_town['lower 24']) plt.plot(DataFrame_town['lower 24-城市']) plt.plot(DataFrame_town['lower 24-镇']) plt.plot(DataFrame_town['lower 24-乡']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【低于24分】城乡分布百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='best',prop=myfont) #plt.show() plt.close() ############################################################################### ######画图【整合】DataFrame_town######################### ################################################################################################### ##画图【整合】DataFrame_space ################################################################################################### plt.figure(3) ax1 = plt.subplot(2, 3, 1) # （行，列，活跃区） plt.plot(DataFrame_space['东部:Mean']) plt.plot(DataFrame_space['中部:Mean']) plt.plot(DataFrame_space['西部:Mean']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.5) ax.spines['left'].set_linewidth(1.5) ax.spines['top'].set_linewidth(1.5) ax.spines['right'].set_linewidth(1.5) plt.ylabel('认知能力均值',fontproperties = myfont,fontsize=12) plt.title('认知能力均值空间随年份变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=11) #plt.legend([p1,p2,p3],['Wohle','man','women'],loc='upper right') plt.legend((u'东部省份均值', u'中部省份均值',u'西部省份均值'),loc='best',prop=myfont,fontsize=5) #plt.show() ############################################################################### ax2 = plt.subplot(2, 3, 2) plt.plot(DataFrame_space['东部:0-9分']) plt.plot(DataFrame_space['中部:0-9分']) plt.plot(DataFrame_space['西部:0-9分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('认知能力【0-9分】空间百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax3 = plt.subplot(2, 3, 3) plt.plot(DataFrame_space['东部:10-17分']) plt.plot(DataFrame_space['中部:10-17分']) plt.plot(DataFrame_space['西部:10-17分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.title('认知能力【10-17分】空间百分比随年份的变化',fontproperties = myfont,fontsize=9,fontweight='bold') plt.xticks(fontsize=6) plt.yticks(fontsize=6) plt.legend((u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax4 = plt.subplot(2, 3, 4) plt.plot(DataFrame_space['东部:18-23分']) plt.plot(DataFrame_space['中部:18-23分']) plt.plot(DataFrame_space['西部:18-23分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=12) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【18-23分】空间百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=11) plt.legend((u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax5 = plt.subplot(2, 3, 5) plt.plot(DataFrame_space['东部:24-30分']) plt.plot(DataFrame_space['中部:24-30分']) plt.plot(DataFrame_space['西部:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【24-30分】空间百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='best',prop=myfont) #plt.show() ############################################################################### ax6 = plt.subplot(2, 3, 6) plt.plot(DataFrame_space['lower 24']) plt.plot(DataFrame_space['lower 24-东']) plt.plot(DataFrame_space['lower 24-中']) plt.plot(DataFrame_space['lower 24-西']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=9) ax.yaxis.set_label_position("right") plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('认知能力【低于24分】空间百分比随年份的变化',fontproperties = myfont,fontsize=8,fontweight='bold') plt.xticks(fontsize=9) plt.yticks(fontsize=6) plt.legend((u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='best',prop=myfont) #plt.show() plt.close() ############################################################################### ######画图【整合】DataFrame_space######################### ############################各分段随时间变化############################################ ############################DataFrame_human########################################### plt.figure(4) ax1 = plt.subplot(1, 3, 1) plt.plot(DataFrame_human['整体:0-9分']) plt.plot(DataFrame_human['整体:10-17分']) plt.plot(DataFrame_human['整体:18-23分']) plt.plot(DataFrame_human['整体:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=10) plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=10) plt.title('整体认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 2) plt.plot(DataFrame_human['男性:0-9分']) plt.plot(DataFrame_human['男性:10-17分']) plt.plot(DataFrame_human['男性:18-23分']) plt.plot(DataFrame_human['男性:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('男性认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 3) plt.plot(DataFrame_human['女性:0-9分']) plt.plot(DataFrame_human['女性:10-17分']) plt.plot(DataFrame_human['女性:18-23分']) plt.plot(DataFrame_human['女性:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('女性认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() plt.close() ############################各分段随时间变化############################################ ############################DataFrame_town########################################### plt.figure(4) ax1 = plt.subplot(1, 3, 1) plt.plot(DataFrame_town['城市:0-9分']) plt.plot(DataFrame_town['城市:10-17分']) plt.plot(DataFrame_town['城市:18-23分']) plt.plot(DataFrame_town['城市:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=10) plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=10) plt.title('城市-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 2) plt.plot(DataFrame_town['镇:0-9分']) plt.plot(DataFrame_town['镇:10-17分']) plt.plot(DataFrame_town['镇:18-23分']) plt.plot(DataFrame_town['镇:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('镇-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 3) plt.plot(DataFrame_town['乡:0-9分']) plt.plot(DataFrame_town['乡:10-17分']) plt.plot(DataFrame_town['乡:18-23分']) plt.plot(DataFrame_town['乡:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('乡-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() plt.close() ############################各分段随时间变化############################################ ############################DataFrame_space########################################### plt.figure(4) ax1 = plt.subplot(1, 3, 1) plt.plot(DataFrame_space['东部:0-9分']) plt.plot(DataFrame_space['东部:10-17分']) plt.plot(DataFrame_space['东部:18-23分']) plt.plot(DataFrame_space['东部:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比',fontproperties = myfont,fontsize=10) plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=10) plt.title('东部省份-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 2) plt.plot(DataFrame_space['中部:0-9分']) plt.plot(DataFrame_space['中部:10-17分']) plt.plot(DataFrame_space['中部:18-23分']) plt.plot(DataFrame_space['中部:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('中部省份-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) #plt.show() ############################################################################### ax2 = plt.subplot(1, 3, 3) plt.plot(DataFrame_space['西部:0-9分']) plt.plot(DataFrame_space['西部:10-17分']) plt.plot(DataFrame_space['西部:18-23分']) plt.plot(DataFrame_space['西部:24-30分']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.title('西部省份-认知能力百分比随年份的变化',fontproperties = myfont,fontsize=10,fontweight='bold') plt.xlabel('年份',fontproperties = myfont,fontsize=10,weight='heavy') plt.xticks(fontsize=10) plt.yticks(fontsize=6) plt.legend((u'0-9分', u'10-17分',u'18-23分','24-30分'),loc='best',prop=myfont) plt.show() ##############低于24分的整合图#################################### ################################################################ ################################################################ plt.figure(7) ax1 = plt.subplot(1, 3, 1) plt.plot(DataFrame_human['lower 24']) plt.plot(DataFrame_human['lower 24-man']) plt.plot(DataFrame_human['lower 24-women']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.ylabel('百分比(认知能力低于24分)',fontproperties = myfont,fontsize=11) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('性别群体',fontproperties = myfont,fontsize=13,fontweight='bold') plt.xticks(fontsize=11) plt.yticks(fontsize=11) plt.legend((u'占整体百分比', u'占男性群体百分比',u'占女性群体百分比'),prop=myfont,loc='upper left') ################################################################ ax2 = plt.subplot(1, 3, 2) plt.plot(DataFrame_town['lower 24']) plt.plot(DataFrame_town['lower 24-城市']) plt.plot(DataFrame_town['lower 24-镇']) plt.plot(DataFrame_town['lower 24-乡']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('城乡分布',fontproperties = myfont,fontsize=13,fontweight='bold') plt.xticks(fontsize=11) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占城市群体百分比',u'占镇群体百分比',u'占乡群体百分比'),loc='upper left',prop=myfont) ############################################################# ax3 = plt.subplot(1, 3, 3) plt.plot(DataFrame_space['lower 24']) plt.plot(DataFrame_space['lower 24-东']) plt.plot(DataFrame_space['lower 24-中']) plt.plot(DataFrame_space['lower 24-西']) ax = plt.gca() # gca = 'get current axis' 获取当前坐标 ax.spines['bottom'].set_linewidth(1.2) ax.spines['left'].set_linewidth(1.2) ax.spines['top'].set_linewidth(1.2) ax.spines['right'].set_linewidth(1.2) plt.xlabel('年份',fontproperties = myfont,fontsize=11,weight='heavy') plt.title('空间分布',fontproperties = myfont,fontsize=13,fontweight='bold') plt.xticks(fontsize=11) plt.yticks(fontsize=6) plt.legend((u'占整体百分比', u'占东部省份百分比',u'占中部省份百分比',u'占西部省份百分比'),loc='upper left',prop=myfont) plt.show() 部分结果图展示【不能显示】：为防盗用，加水印了。