25个Matplotlib图的汇编，在数据分析和可视化中最有用。此列表允许您使用Python的Matplotlib和Seaborn库选择要显示的可视化对象。
1.关联
散点图
带边界的气泡图
带线性回归最佳拟合线的散点图
抖动图
计数图
边缘直方图
边缘箱形图
相关图
矩阵图
2.偏差
发散型条形图
发散型文本
发散型包点图
带标记的发散型棒棒糖图
面积图
3.排序
有序条形图
棒棒糖图
包点图
坡度图
哑铃图
4.分布
连续变量的直方图
类型变量的直方图
密度图
直方密度线图
Joy Plot
分布式包点图
包点+箱形图
Dot + Box Plot
小提琴图
人口金字塔
分类图
5.组成
华夫饼图
饼图
树形图
条形图
6.变化
时间序列图
带波峰波谷标记的时序图
自相关和部分自相关图
交叉相关图
时间序列分解图
多个时间序列
使用辅助Y轴来绘制不同范围的图形
带有误差带的时间序列
堆积面积图
未堆积的面积图
日历热力图
季节图
7.分组
树状图
簇状图
安德鲁斯曲线
平行坐标
#!pipinstallbrewer2mplimportnumpyasnpimportpandasaspdimportmatplotlibasmplimportmatplotlib.pyplotaspltimportseabornassnsimportwarnings;warnings.filterwarnings(action='once')large=22;med=16;small=12params={'axes.titlesize':large,'legend.fontsize':med,'figure.figsize':(16,10),'axes.labelsize':med,'axes.titlesize':med,'xtick.labelsize':med,'ytick.labelsize':med,'figure.titlesize':large}plt.rcParams.update(params)plt.style.use('seaborn-whitegrid')sns.set_style("white")%matplotlibinline#Versionprint(mpl.__version__)#>3.0.0print(sns.__version__)#>0.9.0
1. 散点图
Scatteplot是用于研究两个变量之间关系的经典和基本图。如果数据中有多个组，则可能需要以不同颜色可视化每个组。在Matplotlib，你可以方便地使用。
#Importdatasetmidwest=pd.read_csv("/selva86/datasets/master/midwest_filter.csv")#PrepareData#Createasmanycolorsasthereareuniquemidwest['category']categories=np.unique(midwest['category'])colors=[plt.cm.tab10(i/float(len(categories)-1))foriinrange(len(categories))]#DrawPlotforEachCategoryplt.figure(figsize=(16,10),dpi=80,facecolor='w',edgecolor='k')fori,categoryinenumerate(categories):plt.scatter('area','poptotal',data=midwest.loc[midwest.category==category,:],s=20,c=colors[i],label=str(category))#Decorationsplt.gca().set(xlim=(0.0,0.1),ylim=(0,90000),xlabel='Area',ylabel='Population')plt.xticks(fontsize=12);plt.yticks(fontsize=12)plt.title("ScatterplotofMidwestAreavsPopulation",fontsize=22)plt.legend(fontsize=12)plt.show()
2. 带边界的气泡图
有时，您希望在边界内显示一组点以强调其重要性。在此示例中，您将从应该被环绕的数据帧中获取记录，并将其传递给下面的代码中描述的记录。encircle()
frommatplotlibimportpatchesfromscipy.spatialimportConvexHullimportwarnings;warnings.simplefilter('ignore')sns.set_style("white")#Step1:PrepareDatamidwest=pd.read_csv("/selva86/datasets/master/midwest_filter.csv")#Asmanycolorsasthereareuniquemidwest['category']categories=np.unique(midwest['category'])colors=[plt.cm.tab10(i/float(len(categories)-1))foriinrange(len(categories))]#Step2:DrawScatterplotwithuniquecolorforeachcategoryfig=plt.figure(figsize=(16,10),dpi=80,facecolor='w',edgecolor='k')fori,categoryinenumerate(categories):plt.scatter('area','poptotal',data=midwest.loc[midwest.category==category,:],s='dot_size',c=colors[i],label=str(category),edgecolors='black',linewidths=.5)#Step3:Encircling#/questions/44575681/how-do-i-encircle-different-data-sets-in-scatter-plotdefencircle(x,y,ax=None,**kw):ifnotax:ax=plt.gca()p=np.c_[x,y]hull=ConvexHull(p)poly=plt.Polygon(p[hull.vertices,:],**kw)ax.add_patch(poly)#Selectdatatobeencircledmidwest_encircle_data=midwest.loc[midwest.state=='IN',:]#Drawpolygonsurroundingverticesencircle(midwest_encircle_data.area,midwest_encircle_data.poptotal,ec="k",fc="gold",alpha=0.1)encircle(midwest_encircle_data.area,midwest_encircle_data.poptotal,ec="firebrick",fc="none",linewidth=1.5)#Step4:Decorationsplt.gca().set(xlim=(0.0,0.1),ylim=(0,90000),xlabel='Area',ylabel='Population')plt.xticks(fontsize=12);plt.yticks(fontsize=12)plt.title("BubblePlotwithEncircling",fontsize=22)plt.legend(fontsize=12)plt.show()
3. 带线性回归最佳拟合线的散点图
如果你想了解两个变量如何相互改变，那么最合适的线就是要走的路。下图显示了数据中各组之间最佳拟合线的差异。要禁用分组并仅为整个数据集绘制一条最佳拟合线，请从下面的调用中删除该参数。
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")df_select=df.loc[df.cyl.isin([4,8]),:]#Plotsns.set_style("white")gridobj=sns.lmplot(x="displ",y="hwy",hue="cyl",data=df_select,height=7,aspect=1.6,robust=True,palette='tab10',scatter_kws=dict(s=60,linewidths=.7,edgecolors='black'))#Decorationsgridobj.set(xlim=(0.5,7.5),ylim=(0,50))plt.title("Scatterplotwithlineofbestfitgroupedbynumberofcylinders",fontsize=20)
每个回归线都在自己的列中
或者，您可以在其自己的列中显示每个组的最佳拟合线。你可以通过在里面设置参数来实现这一点。
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")df_select=df.loc[df.cyl.isin([4,8]),:]#Eachlineinitsowncolumnsns.set_style("white")gridobj=sns.lmplot(x="displ",y="hwy",data=df_select,height=7,robust=True,palette='Set1',col="cyl",scatter_kws=dict(s=60,linewidths=.7,edgecolors='black'))#Decorationsgridobj.set(xlim=(0.5,7.5),ylim=(0,50))plt.show()
4. 抖动图
通常，多个数据点具有完全相同的X和Y值。结果，多个点相互绘制并隐藏。为避免这种情况，请稍微抖动点，以便您可以直观地看到它们。这很方便使用
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")#DrawStripplotfig,ax=plt.subplots(figsize=(16,10),dpi=80)sns.stripplot(df.cty,df.hwy,jitter=0.25,size=8,ax=ax,linewidth=.5)#Decorationsplt.title('Usejitteredplotstoavoidoverlappingofpoints',fontsize=22)plt.show()
5. 计数图
避免点重叠问题的另一个选择是增加点的大小，这取决于该点中有多少点。因此，点的大小越大，周围的点的集中度就越大。
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")df_counts=df.groupby(['hwy','cty']).size().reset_index(name='counts')#DrawStripplotfig,ax=plt.subplots(figsize=(16,10),dpi=80)sns.stripplot(df_counts.cty,df_counts.hwy,size=df_counts.counts*2,ax=ax)#Decorationsplt.title('CountsPlot-Sizeofcircleisbiggerasmorepointsoverlap',fontsize=22)plt.show()
6. 边缘直方图
边缘直方图具有沿X和Y轴变量的直方图。这用于可视化X和Y之间的关系以及单独的X和Y的单变量分布。该图如果经常用于探索性数据分析（EDA）。
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")#CreateFigandgridspecfig=plt.figure(figsize=(16,10),dpi=80)grid=plt.GridSpec(4,4,hspace=0.5,wspace=0.2)#Definetheaxesax_main=fig.add_subplot(grid[:-1,:-1])ax_right=fig.add_subplot(grid[:-1,-1],xticklabels=[],yticklabels=[])ax_bottom=fig.add_subplot(grid[-1,0:-1],xticklabels=[],yticklabels=[])#Scatterplotonmainaxax_main.scatter('displ','hwy',s=df.cty*4,c=df.manufacturer.astype('category').cat.codes,alpha=.9,data=df,cmap="tab10",edgecolors='gray',linewidths=.5)#histogramontherightax_bottom.hist(df.displ,40,histtype='stepfilled',orientation='vertical',color='deeppink')ax_bottom.invert_yaxis()#histograminthebottomax_right.hist(df.hwy,40,histtype='stepfilled',orientation='horizontal',color='deeppink')#Decorationsax_main.set(title='ScatterplotwithHistogramsdisplvshwy',xlabel='displ',ylabel='hwy')ax_main.title.set_fontsize(20)foritemin([ax_main.xaxis.label,ax_main.yaxis.label]+ax_main.get_xticklabels()+ax_main.get_yticklabels()):item.set_fontsize(14)xlabels=ax_main.get_xticks().tolist()ax_main.set_xticklabels(xlabels)plt.show()
7.边缘箱形图
边缘箱图与边缘直方图具有相似的用途。然而，箱线图有助于精确定位X和Y的中位数，第25和第75百分位数。
#ImportDatadf=pd.read_csv("/selva86/datasets/master/mpg_ggplot2.csv")#CreateFigandgridspecfig=plt.figure(figsize=(16,10),dpi=80)grid=plt.GridSpec(4,4,hspace=0.5,wspace=0.2)#Definetheaxesax_main=fig.add_subplot(grid[:-1,:-1])ax_right=fig.add_subplot(grid[:-1,-1],xticklabels=[],yticklabels=[])ax_bottom=fig.add_subplot(grid[-1,0:-1],xticklabels=[],yticklabels=[])#Scatterplotonmainaxax_main.scatter('displ','hwy',s=df.cty*5,c=df.manufacturer.astype('category').cat.codes,alpha=.9,data=df,cmap="Set1",edgecolors='black',linewidths=.5)#Addagraphineachpartsns.boxplot(df.hwy,ax=ax_right,orient="v")sns.boxplot(df.displ,ax=ax_bottom,orient="h")#Decorations------------------#Removexaxisnamefortheboxplotax_bottom.set(xlabel='')ax_right.set(ylabel='')#MainTitle,XlabelandYLabelax_main.set(title='ScatterplotwithHistogramsdisplvshwy',xlabel='displ',ylabel='hwy')#Setfontsizeofdifferentcomponentsax_main.title.set_fontsize(20)foritemin([ax_main.xaxis.label,ax_main.yaxis.label]+ax_main.get_xticklabels()+ax_main.get_yticklabels()):item.set_fontsize(14)plt.show()
8. 相关图
Correlogram用于直观地查看给定数据帧（或2D数组）中所有可能的数值变量对之间的相关度量。
#ImportDatasetdf=pd.read_csv("/selva86/datasets/raw/master/mtcars.csv")#Plotplt.figure(figsize=(12,10),dpi=80)sns.heatmap(df.corr(),xticklabels=df.corr().columns,yticklabels=df.corr().columns,cmap='RdYlGn',center=0,annot=True)#Decorationsplt.title('Correlogramofmtcars',fontsize=22)plt.xticks(fontsize=12)plt.yticks(fontsize=12)plt.show()
9. 矩阵图
成对图是探索性分析中的最爱，以理解所有可能的数字变量对之间的关系。它是双变量分析的必备工具。
#LoadDatasetdf=sns.load_dataset('iris')#Plotplt.figure(figsize=(10,8),dpi=80)sns.pairplot(df,kind="scatter",hue="species",plot_kws=dict(s=80,edgecolor="white",linewidth=2.5))plt.show()
#LoadDatasetdf=sns.load_dataset('iris')#Plotplt.figure(figsize=(10,8),dpi=80)sns.pairplot(df,kind="reg",hue="species")plt.show()
偏差
10. 发散型条形图
如果您想根据单个指标查看项目的变化情况，并可视化此差异的顺序和数量，那么发散条是一个很好的工具。它有助于快速区分数据中组的性能，并且非常直观，并且可以立即传达这一点。
#PrepareDatadf=pd.read_csv("/selva86/datasets/raw/master/mtcars.csv")x=df.loc[:,['mpg']]df['mpg_z']=(x-x.mean())/x.std()df['colors']=['red'ifx<0else'green'forxindf['mpg_z']]df.sort_values('mpg_z',inplace=True)df.reset_index(inplace=True)#Drawplotplt.figure(figsize=(14,10),dpi=80)plt.hlines(y=df.index,xmin=0,xmax=df.mpg_z,color=df.colors,alpha=0.4,linewidth=5)#Decorationsplt.gca().set(ylabel='$Model$',xlabel='$Mileage$')plt.yticks(df.index,df.cars,fontsize=12)plt.title('DivergingBarsofCarMileage',fontdict={'size':20})plt.grid(linestyle='--',alpha=0.5)plt.show()
11. 发散型文本
分散的文本类似于发散条，如果你想以一种漂亮和可呈现的方式显示图表中每个项目的价值，它更喜欢。
#PrepareDatadf=pd.read_csv("/selva86/datasets/raw/master/mtcars.csv")x=df.loc[:,['mpg']]df['mpg_z']=(x-x.mean())/x.std()df['colors']=['red'ifx<0else'green'forxindf['mpg_z']]df.sort_values('mpg_z',inplace=True)df.reset_index(inplace=True)#Drawplotplt.figure(figsize=(14,14),dpi=80)plt.hlines(y=df.index,xmin=0,xmax=df.mpg_z)forx,y,texinzip(df.mpg_z,df.index,df.mpg_z):t=plt.text(x,y,round(tex,2),horizontalalignment='right'ifx<0else'left',verticalalignment='center',fontdict={'color':'red'ifx<0else'green','size':14})#Decorationsplt.yticks(df.index,df.cars,fontsize=12)plt.title('DivergingTextBarsofCarMileage',fontdict={'size':20})plt.grid(linestyle='--',alpha=0.5)plt.xlim(-2.5,2.5)plt.show()
12. 发散型包点图
发散点图也类似于发散条。然而，与发散条相比，条的不存在减少了组之间的对比度和差异。
#PrepareDatadf=pd.read_csv("/selva86/datasets/raw/master/mtcars.csv")x=df.loc[:,['mpg']]df['mpg_z']=(x-x.mean())/x.std()df['colors']=['red'ifx<0else'darkgreen'forxindf['mpg_z']]df.sort_values('mpg_z',inplace=True)df.reset_index(inplace=True)#Drawplotplt.figure(figsize=(14,16),dpi=80)plt.scatter(df.mpg_z,df.index,s=450,alpha=.6,color=df.colors)forx,y,texinzip(df.mpg_z,df.index,df.mpg_z):t=plt.text(x,y,round(tex,1),horizontalalignment='center',verticalalignment='center',fontdict={'color':'white'})#Decorations#Lightenbordersplt.gca().spines["top"].set_alpha(.3)plt.gca().spines["bottom"].set_alpha(.3)plt.gca().spines["right"].set_alpha(.3)plt.gca().spines["left"].set_alpha(.3)plt.yticks(df.index,df.cars)plt.title('DivergingDotplotofCarMileage',fontdict={'size':20})plt.xlabel('$Mileage$')plt.grid(linestyle='--',alpha=0.5)plt.xlim(-2.5,2.5)plt.show()
13. 带标记的发散型棒棒糖图
带标记的棒棒糖通过强调您想要引起注意的任何重要数据点并在图表中适当地给出推理，提供了一种可视化分歧的灵活方式。
#PrepareDatadf=pd.read_csv("/selva86/datasets/raw/master/mtcars.csv")x=df.loc[:,['mpg']]df['mpg_z']=(x-x.mean())/x.std()df['colors']='black'#colorfiatdifferentlydf.loc[df.cars=='FiatX1-9','colors']='darkorange'df.sort_values('mpg_z',inplace=True)df.reset_index(inplace=True)#Drawplotimportmatplotlib.patchesaspatchesplt.figure(figsize=(14,16),dpi=80)plt.hlines(y=df.index,xmin=0,xmax=df.mpg_z,color=df.colors,alpha=0.4,linewidth=1)plt.scatter(df.mpg_z,df.index,color=df.colors,s=[600ifx=='FiatX1-9'else300forxindf.cars],alpha=0.6)plt.yticks(df.index,df.cars)plt.xticks(fontsize=12)#Annotateplt.annotate('MercedesModels',xy=(0.0,11.0),xytext=(1.0,11),xycoords='data',fontsize=15,ha='center',va='center',bbox=dict(boxstyle='square',fc='firebrick'),arrowprops=dict(arrowstyle='-[,widthB=2.0,lengthB=1.5',lw=2.0,color='steelblue'),color='white')#AddPatchesp1=patches.Rectangle((-2.0,-1),width=.3,height=3,alpha=.2,facecolor='red')p2=patches.Rectangle((1.5,27),width=.8,height=5,alpha=.2,facecolor='green')plt.gca().add_patch(p1)plt.gca().add_patch(p2)#Decorateplt.title('DivergingBarsofCarMileage',fontdict={'size':20})plt.grid(linestyle='--',alpha=0.5)plt.show()
14.面积图
通过对轴和线之间的区域进行着色，区域图不仅强调峰值和低谷，而且还强调高点和低点的持续时间。高点持续时间越长，线下面积越大。
importnumpyasnpimportpandasaspd#PrepareDatadf=pd.read_csv("/selva86/datasets/raw/master/economics.csv",parse_dates=['date']).head(100)x=np.arange(df.shape[0])y_returns=(df.psavert.diff().fillna(0)/df.psavert.shift(1)).fillna(0)*100#Plotplt.figure(figsize=(16,10),dpi=80)plt.fill_between(x[1:],y_returns[1:],0,where=y_returns[1:]>=0,facecolor='green',interpolate=True,alpha=0.7)plt.fill_between(x[1:],y_returns[1:],0,where=y_returns[1:]<=0,facecolor='red',interpolate=True,alpha=0.7)#Annotateplt.annotate('Peak1975',xy=(94.0,21.0),xytext=(88.0,28),bbox=dict(boxstyle='square',fc='firebrick'),arrowprops=dict(facecolor='steelblue',shrink=0.05),fontsize=15,color='white')#Decorationsxtickvals=[str(m)[:3].upper()+"-"+str(y)fory,minzip(df.date.dt.year,df.date.dt.month_name())]plt.gca().set_xticks(x[::6])plt.gca().set_xticklabels(xtickvals[::6],rotation=90,fontdict={'horizontalalignment':'center','verticalalignment':'center_baseline'})plt.ylim(-35,35)plt.xlim(1,100)plt.title("MonthEconomicsReturn%",fontsize=22)plt.ylabel('Monthlyreturns%')plt.grid(alpha=0.5)plt.show()
排序
15. 有序条形图
有序条形图有效地传达了项目的排名顺序。但是，在图表上方添加度量标准的值，用户可以从图表本身获取精确信息。
#PrepareDatadf_raw=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")df=df_raw[['cty','manufacturer']].groupby('manufacturer').apply(lambdax:x.mean())df.sort_values('cty',inplace=True)df.reset_index(inplace=True)#Drawplotimportmatplotlib.patchesaspatchesfig,ax=plt.subplots(figsize=(16,10),facecolor='white',dpi=80)ax.vlines(x=df.index,ymin=0,ymax=df.cty,color='firebrick',alpha=0.7,linewidth=20)#AnnotateTextfori,ctyinenumerate(df.cty):ax.text(i,cty+0.5,round(cty,1),horizontalalignment='center')#Title,Label,TicksandYlimax.set_title('BarChartforHighwayMileage',fontdict={'size':22})ax.set(ylabel='MilesPerGallon',ylim=(0,30))plt.xticks(df.index,df.manufacturer.str.upper(),rotation=60,horizontalalignment='right',fontsize=12)#AddpatchestocolortheXaxislabelsp1=patches.Rectangle((.57,-0.005),width=.33,height=.13,alpha=.1,facecolor='green',transform=fig.transFigure)p2=patches.Rectangle((.124,-0.005),width=.446,height=.13,alpha=.1,facecolor='red',transform=fig.transFigure)fig.add_artist(p1)fig.add_artist(p2)plt.show()
16. 棒棒糖图
棒棒糖图表以一种视觉上令人愉悦的方式提供与有序条形图类似的目的。
#PrepareDatadf_raw=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")df=df_raw[['cty','manufacturer']].groupby('manufacturer').apply(lambdax:x.mean())df.sort_values('cty',inplace=True)df.reset_index(inplace=True)#Drawplotfig,ax=plt.subplots(figsize=(16,10),dpi=80)ax.vlines(x=df.index,ymin=0,ymax=df.cty,color='firebrick',alpha=0.7,linewidth=2)ax.scatter(x=df.index,y=df.cty,s=75,color='firebrick',alpha=0.7)#Title,Label,TicksandYlimax.set_title('LollipopChartforHighwayMileage',fontdict={'size':22})ax.set_ylabel('MilesPerGallon')ax.set_xticks(df.index)ax.set_xticklabels(df.manufacturer.str.upper(),rotation=60,fontdict={'horizontalalignment':'right','size':12})ax.set_ylim(0,30)#Annotateforrowindf.itertuples():ax.text(row.Index,row.cty+.5,s=round(row.cty,2),horizontalalignment='center',verticalalignment='bottom',fontsize=14)plt.show()
17. 包点图
点图表传达了项目的排名顺序。由于它沿水平轴对齐，因此您可以更容易地看到点彼此之间的距离。
#PrepareDatadf_raw=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")df=df_raw[['cty','manufacturer']].groupby('manufacturer').apply(lambdax:x.mean())df.sort_values('cty',inplace=True)df.reset_index(inplace=True)#Drawplotfig,ax=plt.subplots(figsize=(16,10),dpi=80)ax.hlines(y=df.index,xmin=11,xmax=26,color='gray',alpha=0.7,linewidth=1,linestyles='dashdot')ax.scatter(y=df.index,x=df.cty,s=75,color='firebrick',alpha=0.7)#Title,Label,TicksandYlimax.set_title('DotPlotforHighwayMileage',fontdict={'size':22})ax.set_xlabel('MilesPerGallon')ax.set_yticks(df.index)ax.set_yticklabels(df.manufacturer.str.title(),fontdict={'horizontalalignment':'right'})ax.set_xlim(10,27)plt.show()
18. 坡度图
斜率图最适合比较给定人/项目的“之前”和“之后”位置。
importmatplotlib.linesasmlines#ImportDatadf=pd.read_csv("/selva86/datasets/master/gdppercap.csv")left_label=[str(c)+','+str(round(y))forc,yinzip(df.continent,df['1952'])]right_label=[str(c)+','+str(round(y))forc,yinzip(df.continent,df['1957'])]klass=['red'if(y1-y2)<0else'green'fory1,y2inzip(df['1952'],df['1957'])]#drawline#/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941defnewline(p1,p2,color='black'):ax=plt.gca()l=mlines.Line2D([p1[0],p2[0]],[p1[1],p2[1]],color='red'ifp1[1]-p2[1]>0else'green',marker='o',markersize=6)ax.add_line(l)returnlfig,ax=plt.subplots(1,1,figsize=(14,14),dpi=80)#VerticalLinesax.vlines(x=1,ymin=500,ymax=13000,color='black',alpha=0.7,linewidth=1,linestyles='dotted')ax.vlines(x=3,ymin=500,ymax=13000,color='black',alpha=0.7,linewidth=1,linestyles='dotted')#Pointsax.scatter(y=df['1952'],x=np.repeat(1,df.shape[0]),s=10,color='black',alpha=0.7)ax.scatter(y=df['1957'],x=np.repeat(3,df.shape[0]),s=10,color='black',alpha=0.7)#LineSegmentsandAnnotationforp1,p2,cinzip(df['1952'],df['1957'],df['continent']):newline([1,p1],[3,p2])ax.text(1-0.05,p1,c+','+str(round(p1)),horizontalalignment='right',verticalalignment='center',fontdict={'size':14})ax.text(3+0.05,p2,c+','+str(round(p2)),horizontalalignment='left',verticalalignment='center',fontdict={'size':14})#'Before'and'After'Annotationsax.text(1-0.05,13000,'BEFORE',horizontalalignment='right',verticalalignment='center',fontdict={'size':18,'weight':700})ax.text(3+0.05,13000,'AFTER',horizontalalignment='left',verticalalignment='center',fontdict={'size':18,'weight':700})#Decorationax.set_title("Slopechart:ComparingGDPPerCapitabetween1952vs1957",fontdict={'size':22})ax.set(xlim=(0,4),ylim=(0,14000),ylabel='MeanGDPPerCapita')ax.set_xticks([1,3])ax.set_xticklabels(["1952","1957"])plt.yticks(np.arange(500,13000,2000),fontsize=12)#Lightenbordersplt.gca().spines["top"].set_alpha(.0)plt.gca().spines["bottom"].set_alpha(.0)plt.gca().spines["right"].set_alpha(.0)plt.gca().spines["left"].set_alpha(.0)plt.show()
19. 哑铃图
哑铃图传达各种项目的“前”和“后”位置以及项目的排序。如果您想要将特定项目/计划对不同对象的影响可视化，那么它非常有用。
importmatplotlib.linesasmlines#ImportDatadf=pd.read_csv("/selva86/datasets/master/health.csv")df.sort_values('pct_',inplace=True)df.reset_index(inplace=True)#Functodrawlinesegmentdefnewline(p1,p2,color='black'):ax=plt.gca()l=mlines.Line2D([p1[0],p2[0]],[p1[1],p2[1]],color='skyblue')ax.add_line(l)returnl#FigureandAxesfig,ax=plt.subplots(1,1,figsize=(14,14),facecolor='#f7f7f7',dpi=80)#VerticalLinesax.vlines(x=.05,ymin=0,ymax=26,color='black',alpha=1,linewidth=1,linestyles='dotted')ax.vlines(x=.10,ymin=0,ymax=26,color='black',alpha=1,linewidth=1,linestyles='dotted')ax.vlines(x=.15,ymin=0,ymax=26,color='black',alpha=1,linewidth=1,linestyles='dotted')ax.vlines(x=.20,ymin=0,ymax=26,color='black',alpha=1,linewidth=1,linestyles='dotted')#Pointsax.scatter(y=df['index'],x=df['pct_'],s=50,color='#0e668b',alpha=0.7)ax.scatter(y=df['index'],x=df['pct_'],s=50,color='#a3c4dc',alpha=0.7)#LineSegmentsfori,p1,p2inzip(df['index'],df['pct_'],df['pct_']):newline([p1,i],[p2,i])#Decorationax.set_facecolor('#f7f7f7')ax.set_title("DumbellChart:PctChange-vs",fontdict={'size':22})ax.set(xlim=(0,.25),ylim=(-1,27),ylabel='MeanGDPPerCapita')ax.set_xticks([.05,.1,.15,.20])ax.set_xticklabels(['5%','15%','20%','25%'])ax.set_xticklabels(['5%','15%','20%','25%'])plt.show()
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20. 连续变量的直方图
直方图显示给定变量的频率分布。下面的表示基于分类变量对频率条进行分组，从而更好地了解连续变量和串联变量。
#ImportDatadf=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")#Preparedatax_var='displ'groupby_var='class'df_agg=df.loc[:,[x_var,groupby_var]].groupby(groupby_var)vals=[df[x_var].values.tolist()fori,dfindf_agg]#Drawplt.figure(figsize=(16,9),dpi=80)colors=[plt.cm.Spectral(i/float(len(vals)-1))foriinrange(len(vals))]n,bins,patches=plt.hist(vals,30,stacked=True,density=False,color=colors[:len(vals)])#Decorationplt.legend({group:colforgroup,colinzip(np.unique(df[groupby_var]).tolist(),colors[:len(vals)])})plt.title(f"StackedHistogramof${x_var}$coloredby${groupby_var}$",fontsize=22)plt.xlabel(x_var)plt.ylabel("Frequency")plt.ylim(0,25)plt.xticks(ticks=bins[::3],labels=[round(b,1)forbinbins[::3]])plt.show()
21. 类型变量的直方图
分类变量的直方图显示该变量的频率分布。通过对条形图进行着色，您可以将分布与表示颜色的另一个分类变量相关联。
#ImportDatadf=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")#Preparedatax_var='manufacturer'groupby_var='class'df_agg=df.loc[:,[x_var,groupby_var]].groupby(groupby_var)vals=[df[x_var].values.tolist()fori,dfindf_agg]#Drawplt.figure(figsize=(16,9),dpi=80)colors=[plt.cm.Spectral(i/float(len(vals)-1))foriinrange(len(vals))]n,bins,patches=plt.hist(vals,df[x_var].unique().__len__(),stacked=True,density=False,color=colors[:len(vals)])#Decorationplt.legend({group:colforgroup,colinzip(np.unique(df[groupby_var]).tolist(),colors[:len(vals)])})plt.title(f"StackedHistogramof${x_var}$coloredby${groupby_var}$",fontsize=22)plt.xlabel(x_var)plt.ylabel("Frequency")plt.ylim(0,40)plt.xticks(ticks=bins,labels=np.unique(df[x_var]).tolist(),rotation=90,horizontalalignment='left')plt.show()
22. 密度图
密度图是一种常用工具，可视化连续变量的分布。通过“响应”变量对它们进行分组，您可以检查X和Y之间的关系。以下情况，如果出于代表性目的来描述城市里程的分布如何随着汽缸数的变化而变化。
#ImportDatadf=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")#DrawPlotplt.figure(figsize=(16,10),dpi=80)sns.kdeplot(df.loc[df['cyl']==4,"cty"],shade=True,color="g",label="Cyl=4",alpha=.7)sns.kdeplot(df.loc[df['cyl']==5,"cty"],shade=True,color="deeppink",label="Cyl=5",alpha=.7)sns.kdeplot(df.loc[df['cyl']==6,"cty"],shade=True,color="dodgerblue",label="Cyl=6",alpha=.7)sns.kdeplot(df.loc[df['cyl']==8,"cty"],shade=True,color="orange",label="Cyl=8",alpha=.7)#Decorationplt.title('DensityPlotofCityMileagebyn_Cylinders',fontsize=22)plt.legend()
23. 直方密度线图
带有直方图的密度曲线将两个图表传达的集体信息汇集在一起，这样您就可以将它们放在一个图形而不是两个图形中。
#ImportDatadf=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")#DrawPlotplt.figure(figsize=(13,10),dpi=80)sns.distplot(df.loc[df['class']=='compact',"cty"],color="dodgerblue",label="Compact",hist_kws={'alpha':.7},kde_kws={'linewidth':3})sns.distplot(df.loc[df['class']=='suv',"cty"],color="orange",label="SUV",hist_kws={'alpha':.7},kde_kws={'linewidth':3})sns.distplot(df.loc[df['class']=='minivan',"cty"],color="g",label="minivan",hist_kws={'alpha':.7},kde_kws={'linewidth':3})plt.ylim(0,0.35)#Decorationplt.title('DensityPlotofCityMileagebyVehicleType',fontsize=22)plt.legend()plt.show()
24. Joy Plot
Joy Plot允许不同组的密度曲线重叠，这是一种可视化相对于彼此的大量组的分布的好方法。它看起来很悦目，并清楚地传达了正确的信息。它可以使用joypy基于的包来轻松构建matplotlib。
#!pipinstalljoypy#ImportDatampg=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")#DrawPlotplt.figure(figsize=(16,10),dpi=80)fig,axes=joypy.joyplot(mpg,column=['hwy','cty'],by="class",ylim='own',figsize=(14,10))#Decorationplt.title('JoyPlotofCityandHighwayMileagebyClass',fontsize=22)plt.show()
25. 分布式点图
分布点图显示按组分割的点的单变量分布。点数越暗，该区域的数据点集中度越高。通过对中位数进行不同着色，组的真实定位立即变得明显。
importmatplotlib.patchesasmpatches#PrepareDatadf_raw=pd.read_csv("/selva86/datasets/raw/master/mpg_ggplot2.csv")cyl_colors={4:'tab:red',5:'tab:green',6:'tab:blue',8:'tab:orange'}df_raw['cyl_color']=df_raw.cyl.map(cyl_colors)#MeanandMediancitymileagebymakedf=df_raw[['cty','manufacturer']].groupby('manufacturer').apply(lambdax:x.mean())df.sort_values('cty',ascending=False,inplace=True)df.reset_index(inplace=True)df_median=df_raw[['cty','manufacturer']].groupby('manufacturer').apply(lambdax:x.median())#Drawhorizontallinesfig,ax=plt.subplots(figsize=(16,10),dpi=80)ax.hlines(y=df.index,xmin=0,xmax=40,color='gray',alpha=0.5,linewidth=.5,linestyles='dashdot')#DrawtheDotsfori,makeinenumerate(df.manufacturer):df_make=df_raw.loc[df_raw.manufacturer==make,:]ax.scatter(y=np.repeat(i,df_make.shape[0]),x='cty',data=df_make,s=75,edgecolors='gray',c='w',alpha=0.5)ax.scatter(y=i,x='cty',data=df_median.loc[df_median.index==make,:],s=75,c='firebrick')#Annotateax.text(33,13,"$red;dots;are;the:median$",fontdict={'size':12},color='firebrick')#Decorationsred_patch=plt.plot([],[],marker="o",ms=10,ls="",mec=None,color='firebrick',label="Median")plt.legend(handles=red_patch)ax.set_title('DistributionofCityMileagebyMake',fontdict={'size':22})ax.set_xlabel('MilesPerGallon(City)',alpha=0.7)ax.set_yticks(df.index)ax.set_yticklabels(df.manufacturer.str.title(),fontdict={'horizontalalignment':'right'},alpha=0.7)ax.set_xlim(1,40)plt.xticks(alpha=0.7)plt.gca().spines["top"].set_visible(False)plt.gca().spines["bottom"].set_visible(False)plt.gca().spines["right"].set_visible(False)plt.gca().spines["left"].set_visible(False)plt.grid(axis='both',alpha=.4,linewidth=.1)plt.show()
本文参考自：
/plots/top-50-matplotlib-visualizations-the-master-plots-python/
