数据可视化

最近有很多小伙伴私信我关于双Y轴图的绘制方法？ 这里给出Python-matplotlib绘制方法和R-ggplot2的绘制方法

Python语言这里我们直接就给出数据预览和可视化设计的代码，图中部分代码我们再做详细解释，数据预览如下：

自定义的颜色字典year_color构造代码如下：

代码语言：javascript复制color = ("#51C1C8", "#E96279", "#44A2D6", "#536D84",

"#51C1C8", "#E96279", "#44A2D6", "#536D84")

year = artist_01.index.to_list()

year_color = dict(zip(year,color))

year_color

可视化代码如下：

代码语言：javascript复制plt.style.use('fivethirtyeight')

fig,ax = plt.subplots(figsize=(8,4),dpi=200,facecolor='white',edgecolor='white')

ax.set_facecolor('white')

x = np.arange(0,len(artist_01),1)

y = artist_01['data01'].values

#绘制连接点的线

line = ax.plot(x,y,color='#333333',lw=1.,zorder=2)

#绘制不同散点图

scatter_out = ax.scatter(x,y,s=500,zorder=1,color='white',ec='grey',alpha=.7,lw=.5)

for i in artist_01.index.to_list():

scatter = ax.scatter(x[i],y[i],s=180,zorder=3,ec='k',lw=.4,color=year_color[i])

scatter_in = ax.scatter(x,y,s=30,zorder=3,color="#333333")

#定制化绘制(设置图表风格)

ax.grid(color='gray',lw=.5,alpha=.5) #设置网格

ax.tick_params(left=False,bottom=False,labelbottom=False,labelsize=10,colors='gray')#设置刻度

ax.set_ylim(bottom=-3,top=43)#设置轴范围

ax.set_yticks(np.arange(0, 45, step=5)) #设置刻度标签

ax.set_xticks(np.arange(-.5, 8, step=.5))

#添加横线(修饰)

ax.axhline(y=0,color='#45627C',lw=3)

#添加数字标签

label_text = {"size":13,"color":"k",'weight':'semibold'}

for a,b in zip(x,y):

ax.text(a, b+2.5, '%.0f' % b, ha='center', va= 'bottom',fontdict=label_text,color=year_color[a])

#设置轴脊(spine)

for spine in ['top','bottom','left','right']:

ax.spines[spine].set_color("#FFFFFF") #设置颜色/set_visible()设置显示与否

for i in artist_01.index.to_list()[:3]:

axins.scatter(x[i],y[i],s=80,color=year_color[i],zorder=2)

#添加标题处小图

#添加小散点图：重点掌握

axins = inset_axes(ax, width=.4, height=.4,loc='upper left',

bbox_to_anchor=(0.01, 0.22, 1, 1),

bbox_transform=ax.transAxes,

borderpad=0)

axins.set_ylim(bottom=8,top=35)

axins.set_xlim(left=-.5,right=2.5)

axins.plot(x[:3],y[:3],color='#333333',lw=1.,zorder=1)

for i in artist_01.index.to_list()[:3]:

axins.scatter(x[i],y[i],s=80,color=year_color[i],zorder=2)

axins.axis('off')

#绘制小横线：原理同上

line = inset_axes(ax,width=5.3, height=.4,loc='upper left',

bbox_to_anchor=(-0.015, 0.15, 1, 1),

bbox_transform=ax.transAxes,

borderpad=0)

line.plot([.1,.7],[.1,.1],color='#45627C',lw=2)

line.axis('off')

#添加阴影效果

for i in artist_01.index.to_list():

ax.axvspan(i-.35, i+.35, facecolor='gray',alpha=.1,zorder=0)

#添加双y轴：使用Axes.twinx()方法绘制

second_plot = ax.twinx()

second_plot.set_ylim(bottom=-3,top=43)

second_plot.set_yticks(np.arange(0, 50, step=10))

second_plot.set_xticks(np.arange(-.5, 8, step=.5))

second_plot.tick_params(left=False,bottom=False,labelbottom=False,labelsize=10,colors='k')

second_plot.grid(color="none",zorder=0)

second_plot.set_axisbelow(True)

for spine in ['top','bottom','left','right']:

second_plot.spines[spine].set_visible(False) #("#FFFFFF")

y2 = artist_01['data02'].values

label_text = {"size":28,"color":"white",'weight':'light'}

for x,y2 in zip(np.arange(len(artist_01)).tolist(),artist_01['data02'].to_list()):

second_plot.plot([x,x],[0,y2],lw=20,color=color[x],solid_capstyle='round')

#绘制空心圆

second_plot.scatter(x,0,s=150,c='white',zorder=3)

second_plot.scatter(x,0,s=60,c=color[x],zorder=4)

second_plot.scatter(x,0,s=15,c='white',zorder=5)

# 添加文本信息

label_font = {"size":15,'weight':'bold'}

for i,x,text in zip(artist_01.index.to_list(),np.arange(0,len(artist_01),1),artist_01['year'].values):

ax.text(x, -8,text ,ha='center', va= 'bottom',fontdict=label_font,color=year_color[i],zorder=2)

ax.text(.39,1.2,'\nSecond Y Axes Plot Exercise',transform = ax.transAxes,

ha='center', va='center',fontsize = 20,color='k',fontweight="bold")

ax.text(.02,1.04,'Use the Matplotlib axes.Axes.twinx()',

transform = ax.transAxes,

ha='left', va='center',fontsize = 9,color='#45627C')

ax.text(.91,.02,'\nVisualization by DataCharm',transform = ax.transAxes,

ha='center', va='center',fontsize = 7,color='black')

plt.savefig(r'double_y_axis_plot.png',width=6,height=3,

dpi=900,bbox_inches='tight',facecolor='white')

#ax.set_axisbelow(True)

plt.show()

解释：

1. 添加横线(修饰)

代码语言：javascript复制ax.axhline(y=0,color='#45627C',lw=3)

2. 添加标题处小图

代码语言：javascript复制axins = inset_axes(ax, width=.4, height=.4,loc='upper left',

bbox_to_anchor=(0.01, 0.22, 1, 1),

bbox_transform=ax.transAxes,

borderpad=0)

axins.set_ylim(bottom=8,top=35)

axins.set_xlim(left=-.5,right=2.5)

axins.plot(x[:3],y[:3],color='#333333',lw=1.,zorder=1)

for i in artist_01.index.to_list()[:3]:

axins.scatter(x[i],y[i],s=80,color=year_color[i],zorder=2)

axins.axis('off')

3. 添加双y轴：使用Axes.twinx()方法绘制:重点

代码语言：javascript复制#添加双y轴：使用Axes.twinx()方法绘制

second_plot = ax.twinx()

second_plot.set_ylim(bottom=-3,top=43)

second_plot.set_yticks(np.arange(0, 50, step=10))

second_plot.set_xticks(np.arange(-.5, 8, step=.5))

second_plot.tick_params(left=False,bottom=False,labelbottom=False,labelsize=10,colors='k')

second_plot.grid(color="none",zorder=0)

second_plot.set_axisbelow(True)

for spine in ['top','bottom','left','right']:

second_plot.spines[spine].set_visible(False) #("#FFFFFF")

y2 = artist_01['data02'].values

label_text = {"size":28,"color":"white",'weight':'light'}

for x,y2 in zip(np.arange(len(artist_01)).tolist(),artist_01['data02'].to_list()):

second_plot.plot([x,x],[0,y2],lw=20,color=color[x],solid_capstyle='round')

#绘制空心圆

second_plot.scatter(x,0,s=150,c='white',zorder=3)

second_plot.scatter(x,0,s=60,c=color[x],zorder=4)

second_plot.scatter(x,0,s=15,c='white',zorder=5)

最终的可视化结果如下：

总结：Python-matplotlib 绘制双Y轴的关键就是使用Axes.twinx()方法再次添加一个绘图对象，再把要绘制的对象在此绘图对象上绘制即可,其他和正常的matplotlib语法一样。

R语言在介绍完Python-matplotlib 绘制双Y轴后，我们再次介绍R-ggplot2如何绘制双Y轴，由于绘制上面的可视化结果较为繁琐，这里我们直接生成样例数据进行双Y轴的讲解。主要涉及的知识点就是scale_y_continuous() 或scale_x_continuous()中的sec.axis()属性设置。

构建数据这里我们构建虚拟数据，代码如下：

代码语言：javascript复制data <- data.frame(

day = as.Date("2019-01-01") + 0:99,

temperature = runif(100) + seq(1,100)^2.5 / 10000,

price = runif(100) + seq(100,1)^1.5 / 10

)

head(data)

数据预览如下(部分)：

可视化绘制这里我们直接给出代码，大家不懂的可以参考ggplot2官网，代码如下：

代码语言：javascript复制coeff <- 10

temperatureColor <- "#75B8D1"

priceColor <- "#D175B8"

double_y <-ggplot(data, aes(x=day)) +

geom_line(aes(y=temperature), size=1.5, color=temperatureColor) +

geom_line(aes(y=price / coeff), size=1.5, color=priceColor) +

#设置双轴关键代码

scale_y_continuous(

# first axis name

name = "Temperature (C°)",

# 定制化设置第二个图例属性

sec.axis = sec_axis(trans = ~.*coeff, name="Price ($)")

) +

labs(x="",

title = "R Charts Exercise: Double Y Axis",

subtitle = "processed charts with scale_y/x_continuous:sec_axis()",

caption = "Visualization by DataCharm") +

theme_ft_rc()+

theme(

axis.title.y = element_text(color = temperatureColor, size=13),

axis.title.y.right = element_text(color = priceColor, size=13),

plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",

size = 20, margin = margin(t = 1, b = 12)),

plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),

plot.caption = element_markdown(face = 'bold',size = 12),

)

设置双轴代码：

代码语言：javascript复制 #设置双轴关键代码

scale_y_continuous(

# first axis name

name = "Temperature (C°)",

# 定制化设置第二个图例属性

sec.axis = sec_axis(trans = ~.*coeff, name="Price ($)")

) +

最终可视化代码如下：

总结本期推文我们简单介绍了Python-matplotlib和R-ggplot2 绘制双Y轴的绘制方法，希望可以帮助到有需要的小伙伴。我们基础系列的图表绘制教程还在继续中哦，感谢大家持续支持和关注。