开场段子????:
下面我们用平行坐标系图来直观呈现全国各省市高考难度（4维度，分别是一本录取率，211录取率，985录取率，清北录取率）。
平行坐标系图是一种适合呈现高维数据（3维以上）的图表形式。顾名思义，它使用一组平行的坐标系来表达数据，每个样本点表达为坐标系中的一条折线。
先上数据：
（数据来源：统计局，教育部，各省教育厅，国金证券研究所。西藏一本录取率为估计数值。年份为）
再上图片：
然后上视频：
最后上代码：
importnumpyasnpimportpandasaspdimportshapelyfromshapelyimportgeometryasgeoimportmatplotlib.pyplotaspltimportmatplotlib.animationasanimationimportimageioimportosfromPILimportImageplt.rcParams['font.family']='sans-serif'plt.rcParams['font.sans-serif']=['SimHei']plt.rcParams['axes.unicode_minus']=Falseplt.rcParams['animation.writer']='html'plt.rcParams['animation.embed_limit']=100defrgba_to_rgb(img_rgba):img_rgb=Image.new("RGB",img_rgba.size,(255,255,255))img_rgb.paste(img_rgba,mask=img_rgba.split()[3])returnimg_rgbdefhtml_to_gif(html_file,gif_file,duration=0.5):path=html_file.replace(".html","_frames")images=[os.path.join(path,x)forxinsorted(os.listdir(path))]frames=[imageio.imread(x)forxinimages]ifframes[0].shape[-1]==4:frames=[np.array(rgba_to_rgb(Image.fromarray(x)))forxinframes]imageio.mimsave(gif_file,frames,'gif',duration=duration)returngif_filecmap=['#2E91E5','#1CA71C','#DA16FF','#B68100','#EB663B','#00A08B','#FC0080','#6C7C32','#862A16','#62','#DA60CA','#0D2A63']*100df=pd.read_excel("./data/各省市高考录取率数据.xlsx")defparallel_coordinates_dance(df,title="中国各省市高考难度",filename=None,figsize=(8,5),dpi=144,duration=0.5,anotate_points=["江西","上海","河南","山东","西藏","北京","天津"],line_cols=["一本录取率","211录取率","985录取率","清北录取率"],name_col="省份",text_col="高考模式"):fig,ax=plt.subplots(figsize=figsize,dpi=dpi)defplot_frame(i):ax.clear()ax.axis("off")n=len(line_cols)k=i//n+1m=i%ndfvalues=df[line_cols].copy()values=dfvalues.values/dfvalues.values.max(axis=0)dfvalues=pd.DataFrame(values,columns=line_cols)dfvalues.index=df[name_col]dfdata=dfvalues.iloc[:k,:].copy()ax.set_xlim(-(len(line_cols)-1)/20,21*(len(line_cols)-1)/20)ax.set_ylim(-0.1,1.1)#绘制坐标系forj,colinenumerate(line_cols):y=np.array([0,1.05])x=[j,j]line=plt.Line2D(x,y,color="gray",linestyle="-",linewidth=2.5,alpha=0.5)ax.add_artist(line)ax.annotate(col,xy=(j,0),xycoords="data",xytext=(-25,-10),fontsize=10,fontweight="bold",color="black",textcoords="offsetpoints",zorder=1)#绘制平均参考线y=dfvalues.iloc[-1,:].values.tolist()x=list(range(0,len(line_cols)))line=plt.Line2D(x,y,color="gray",linestyle="--",linewidth=3.5,alpha=0.7)ax.add_artist(line)forj,(px,py)inenumerate(zip(x,y)):s=str(df[line_cols].iloc[-1,j])+"%"ax.annotate(s,xy=(px,py),xycoords="data",xytext=(-17,5),fontsize=10,fontweight="bold",color="black",textcoords="offsetpoints",zorder=3)s=dfvalues.index[-1]ln=geo.LineString(np.array(list(zip(x,y))))pt=np.array(ln.interpolate(0.5,normalized=True))px,py=ptax.annotate(s,xy=(px,py),xycoords="data",xytext=(-5,-5),fontsize=12,fontweight="bold",color="black",textcoords="offsetpoints",zorder=4)#绘制最高参考值y=[1.0]*len(line_cols)x=list(range(0,len(line_cols)))arr_s=df[line_cols].max().to_list()line=plt.Line2D(x,y,color="gray",linestyle="--",linewidth=3.5,alpha=0.7)ax.add_artist(line)forr,(px,py)inenumerate(zip(x,y)):s=str(arr_s[r])+"%"ax.annotate(s,xy=(px,py),xycoords="data",xytext=(-17,5),fontsize=10,fontweight="bold",color="black",textcoords="offsetpoints",zorder=3)s="最高参考线"ln=geo.LineString(np.array(list(zip(x,y))))pt=np.array(ln.interpolate(0.5,normalized=True))px,py=ptax.annotate(s,xy=(px,py),xycoords="data",xytext=(-25,-5),fontsize=12,fontweight="bold",color="black",textcoords="offsetpoints",zorder=4)#绘制之前数据forjinrange(k-1):y=dfdata.iloc[j,:].values.tolist()x=list(range(0,len(line_cols)))s=dfdata.index[j]ifsinanotate_points:line=plt.Line2D(x,y,color=cmap[j],linestyle="-",linewidth=2.5,alpha=0.3)ax.add_artist(line)ln=geo.LineString(np.array(list(zip(x,y))))np.random.seed(j)pt=np.array(ln.interpolate(np.random.rand(),normalized=True))px,py=ptax.annotate(s,xy=(px,py),xycoords="data",xytext=(-25,-5),fontsize=12,fontweight="bold",color=cmap[j],textcoords="offsetpoints",zorder=4)#添加标题和排名序号text=df[name_col].iloc[k-1]+":"+df[text_col].iloc[k-1]ax.set_title(title,color="black",fontsize=12)ax.text(0.50,0.50,text,va="center",ha="center",color=cmap[k-1],size=50,alpha=0.5,transform=ax.transAxes,zorder=6)#添加动画j=k-1y=dfdata.iloc[j,:].values.tolist()x=list(range(0,len(line_cols)))s=dfdata.index[j]ax.scatter(x[0:m],y[0:m],s=50,c=cmap[j])ax.scatter(x[m:m+1],y[m:m+1],s=100,c=cmap[j])line=plt.Line2D(x[0:m+1],y[0:m+1],color=cmap[j],linestyle="-",linewidth=3.5,alpha=0.7)ax.add_artist(line)forr,(px,py)inenumerate(zip(x[0:m+1],y[0:m+1])):s=str(df[line_cols].iloc[j,r])+"%"fs=10ifr!=melse20ax.annotate(s,xy=(px,py),xycoords="data",xytext=(-17,8),fontsize=fs,fontweight="bold",color=cmap[j],textcoords="offsetpoints",zorder=5)my_animation=animation.FuncAnimation(fig,plot_frame,frames=range(0,len(line_cols)*(len(df)-1)),interval=int(duration*1000))iffilenameisNone:try:fromIPython.displayimportHTMLHTML(my_animation.to_jshtml())returnHTML(my_animation.to_jshtml())exceptImportError:passelse:my_animation.save(filename)returnfilenamehtml_file="中国各省市高考难度.html"parallel_coordinates_dance(df,filename=html_file)gif_file=html_file.replace(".html",".gif")html_to_gif(html_file,gif_file,duration=0.8)
主要思路是使用 plt.Line2D对象来构建平行坐标系图。
收工。????
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