数据可视化设计

本期我们构建一组简单的时间变化图表数据，当然还有我们常用的颜色字典构建。具体如下：

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

test_dict = {'x':[0,5,10,15,20,25,30],'year':['1990','1995','2000','2005','2010','2015','2020']}

artist_04 = pd.DataFrame(test_dict)

color = ("#F5A34D", "#F26F77", "#48AEBA", "#A3BA74","#958298", "#B88357",'#608CB1' )

data = artist_04['x'].to_list()

data_color = dict(zip(data,color))

data_color

颜色字典如下：

{0: '#F5A34D',

5: '#F26F77',

10: '#48AEBA',

15: '#A3BA74',

20: '#958298',

25: '#B88357',

30: '#608CB1'}

详细绘图代码如下：

fig,ax = plt.subplots(figsize=(8,4),dpi=200,facecolor='#FFF7F2',edgecolor='#FFF7F2')

ax.set_facecolor('#FFF7F2')

#绘制中间横线

ax.set_ylim(-.5,1.5)

#绘制具有端点形状的直线

ax.plot([-3,38],[.5,.5],"-o",lw=1.2,color='gray',markerfacecolor="w",mec='gray',ms=5,

markeredgewidth=1.,zorder=1)

#分上下情况绘制点、线混合图形

for x in [0,10,20,30]:

#绘制横线上的散点，颜色不同

ax.scatter(x,.5,s=120,color=data_color[x],zorder=2)

#绘制叠加在颜色散点之上的散点，颜色为白色

ax.scatter(x,.5,s=50,zorder=3,color='white')

#绘制散点和圆柱之间的连接线，端点为圆点

ax.plot([x,x],[.5,.5+.6],"-o",color=data_color[x],lw=.6,mfc="w",ms=5,mew=1.2,zorder=3)

#绘制横置圆柱图

ax.plot([x,x+7.5],[.5+.6,.5+.6],lw=15,color=data_color[x],solid_capstyle='round',zorder=1)

ax.scatter(x,.5+.6,s=80,zorder=3,color='white')

ax.text(x+4,.5+.6,s='Lorem Ipsum',color='white',fontsize=7.5,fontweight='semibold',ha='center',

va='center')

#添加年份

ax.text(x-1.4,.5+.2,s=artist_04.loc[artist_04['x']==x,'year'].values[0],color='#686866',fontsize=12,

fontweight='bold',rotation=90)

#添加描述文字

ax.text(x+.5,.5+.3,'Optionally, the text can bedisplayed\n in anotherpositionxytext.Anarrow\npointingfrom the text totheannotated\npoint xy canthen beaddedbydefining\narrowprops.',

ha='left', va='center',fontsize = 4,color='gray')

for x in [5,15,25]:

#绘制横线上的散点，颜色不同

ax.scatter(x,.5,s=120,color=data_color[x],zorder=2)

#绘制叠加在颜色散点之上的散点，颜色为白色

ax.scatter(x,.5,s=50,zorder=3,color='white')

#绘制散点和圆柱之间的连接线，端点为圆点

ax.plot([x,x],[.5,.5-.6],"-o",color=data_color[x],lw=.6,mfc="w",ms=5,mew=1.2,zorder=3)

#绘制横置圆柱图

ax.plot([x,x+7.5],[.5-.6,.5-.6],lw=15,color=data_color[x],solid_capstyle='round',zorder=1)

ax.scatter(x,.5-.6,s=80,zorder=3,color='white')

ax.text(x+4,.5-.6,s='Lorem Ipsum',color='white',fontsize=7.5,fontweight='semibold',ha='center',

va='center')

#添加描述文字

ax.text(x+.5,.5-.3,'Optionally, the text can bedisplayed\n in anotherpositionxytext.Anarrow\npointingfrom the text totheannotated\npoint xy canthen beaddedbydefining\narrowprops.',

ha='left', va='center',fontsize = 4,color='gray')

#添加年份

ax.text(x-1.4,.5-.4,s=artist_04.loc[artist_04['x']==x,'year'].values[0],color='#686866',fontsize=12,

fontweight='bold',rotation=90)

#添加题目文本

ax.axis('off')

ax.text(.49,1.15,'\nTIMELINE INFOGRAPHICS',transform = ax.transAxes,

ha='center', va='center',fontsize = 20,color='gray',fontweight='light')

ax.text(.92,.00,'\nVisualization by DataCharm',transform = ax.transAxes,

ha='center', va='center',fontsize = 5,color='black')

plt.savefig(r'F:\DataCharm\商业艺术图表仿制\artist_04.png',width=8,height=4,

dpi=900,bbox_inches='tight',facecolor='#FFF7F2')

知识点：

(1)熟悉ax.plot()函数方法，其他参数设置不同对结果也不同。

(2)ax.scatter()绘制散点。

(3)ax.text()文本的灵活添加。

(4)颜色的合理选择。

结果图表如下：