【Matplotlib】学术论文黑白柱状图绘制

一. 前言

在学术论文中，当涉及黑白打印时 ，彩色的柱状图往往很难有效地区分，因此我们需要绘制黑白柱状图，并通过添加不同的条纹来实现柱状图的区分。

接下来我将以下面这组数据为例，绘制几款比较常见的柱状图。

# 数据代表一年12个月小明和小刚的智商(纯属捏造)
x = [for i in range(1,13)]
xiaoming = [111,124,101,132,127,114,140,135,104,120,101,118]
xiaogang = [132,142,121,101,114,135,126,116,105,131,97,102]

二. 柱形图绘制

2.1 基础绘制

import numpy as np
import matplotlib.pyplot as plt

# 数据代表一年12个月小明和小刚的智商(纯属捏造)
x = [i for i in range(1,13)]
xiaoming = [111,124,101,132,127,114,140,135,104,120,101,118]
xiaogang = [132,142,121,101,114,135,126,116,105,131,97,102]

plt.rcParams['font.family'] = ['Times New Roman']
fig,ax = plt.subplots(1,1,figsize=(7,4.5),dpi=200)
width = 0.35  # 柱子宽度

#字体设置
label_font = {
    
    
    'weight':'bold',
    'size':14,
    'family':'SimHei'
}

rects = ax.bar(x, xiaoming, width ,ec='k',color='white',  #x代表刻度值范围
                lw=.8,hatch='//')

ax.tick_params(axis='x',direction='out',length=5,width=1.5,labelsize=11,bottom=True)#x轴刻度设置
ax.set_xlabel('月份',fontdict=label_font) #x轴名称
#set_xticks(x)和set_xticklabels(x)一般一起使用
ax.set_xticks(x) #设置显示刻度标签的位置,最好与ax.bar的第一个参数一致
ax.set_xticklabels(x)#设置刻度值对应的标签，可以是字符串型

ax.tick_params(axis='y',direction='out',length=5,width=1.5,labelsize=11,bottom=False) #y轴刻度设置
ax.set_ylabel('智商',fontdict=label_font) #y轴名称
ax.set_ylim(ymin = 90,ymax = 150) #y轴刻度范围
ax.set_yticks(np.arange(90,151,10)) #设置显示刻度标签的位置
ax.set_yticklabels(np.arange(90,151,10))#设置刻度值对应的标签，可以是字符串型

ax.axhline(0, color='black',alpha=0.5) #绘制y=0的线
ax.legend(loc='best',markerscale=10,fontsize=12) #设置图例

# 添加数据标签
def autolabel(rects):
    for rect in rects:
        height = rect.get_height()
        if height>0:
            ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
        else:
            ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, -13),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
autolabel(rects)
fig.tight_layout()
#保存图片  
#plt.savefig(r'E:\Data_resourses\DataCharm 公众号\Python\学术图表绘制\barplot05.png',dpi=600,bbox_inches = 'tight')

ax.bar函数中的hatch=参数即可设置柱形图的条纹，包括\,|,/,.,o,+,-,x等。

2.2 两组数据并列绘制

import numpy as np
import matplotlib.pyplot as plt


# 数据代表一年12个月小明和小刚的智商(纯属捏造)
x = [i for i in range(1,13)]
xiaoming = [111,124,101,132,127,114,140,135,104,120,101,118]
xiaogang = [132,142,121,101,114,135,126,116,105,131,97,102]

#plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['font.family'] = ['Times New Roman']
fig,ax = plt.subplots(1,1,figsize=(7,4.5),dpi=200)
width = 0.35  # 柱子宽度

#字体设置
label_font = {
    
    
    'weight':'bold',
    'size':14,
    'family':'SimHei'
}
legend_font = {
    
    
    'weight':'bold',
    'size':10,
    'family':'SimHei'
}

rects1 = ax.bar(x = [i - 0.2 for i in x], height = xiaoming, width = width ,ec='k',color='white',  
                lw=.8, label = "小明", hatch='//')
rects2 = ax.bar(x = [i + 0.2 for i in x], height = xiaogang, width = width ,ec='k',color='white',  
                lw=.8, label = "小刚", hatch='xxxxxx')

ax.tick_params(axis='x',direction='out',length=5,width=1.5,labelsize=11,bottom=True)#x轴刻度设置
ax.set_xlabel('月份',fontdict=label_font) #x轴名称
#set_xticks(x)和set_xticklabels(x)一般一起使用
ax.set_xticks(x) #设置显示刻度标签的位置,最好与ax.bar的第一个参数一致
ax.set_xticklabels(x)#设置刻度值对应的标签，可以是字符串型

ax.tick_params(axis='y',direction='out',length=5,width=1.5,labelsize=11,bottom=False) #y轴刻度设置
ax.set_ylabel('智商',fontdict=label_font) #y轴名称
ax.set_ylim(ymin = 90,ymax = 150) #y轴刻度范围
ax.set_yticks(np.arange(90,151,10)) #设置显示刻度标签的位置
ax.set_yticklabels(np.arange(90,151,10))#设置刻度值对应的标签，可以是字符串型

ax.axhline(0, color='black',alpha=0.5) #绘制y=0的线
ax.legend(loc='best',markerscale=8,fontsize=10,prop=legend_font) #设置图例

# 添加数据标签
def autolabel(rects):
    for rect in rects:
        height = rect.get_height()
        if height>0:
            ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
        else:
            ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, -13),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
#保存图片  
#plt.savefig(r'E:\Data_resourses\DataCharm 公众号\Python\学术图表绘制\barplot05.png',dpi=600,bbox_inches = 'tight')

2.2 两组数据堆叠绘制

import numpy as np
import matplotlib.pyplot as plt


# 数据代表一年12个月小明和小刚的智商(纯属捏造)
x = [i for i in range(1,13)]
xiaoming = [111,124,101,132,127,114,140,135,104,120,101,118]
xiaogang = [132,142,121,101,114,135,126,116,105,131,97,102]

#plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['font.family'] = ['Times New Roman']
fig,ax = plt.subplots(1,1,figsize=(7,4.5),dpi=200)
width = 0.35  # 柱子宽度

#字体设置
label_font = {
    
    
    'weight':'bold',
    'size':14,
    'family':'SimHei'
}
legend_font = {
    
    
    'weight':'bold',
    'size':10,
    'family':'SimHei'
}

rects1 = ax.bar(x, height = xiaoming, width = width ,ec='k',color='white',  
                lw=.8, label = "小明", hatch='//')
rects2 = ax.bar(x, height = xiaogang, width = width ,ec='k',color='white',  
                lw=.8, label = "小刚", hatch='xxxxxx',bottom = xiaoming)

ax.tick_params(axis='x',direction='out',length=5,width=1.5,labelsize=11,bottom=True)#x轴刻度设置
ax.set_xlabel('月份',fontdict=label_font) #x轴名称
#set_xticks(x)和set_xticklabels(x)一般一起使用
ax.set_xticks(x) #设置显示刻度标签的位置,最好与ax.bar的第一个参数一致
ax.set_xticklabels(x)#设置刻度值对应的标签，可以是字符串型

ax.tick_params(axis='y',direction='out',length=5,width=1.5,labelsize=11,bottom=False) #y轴刻度设置
ax.set_ylabel('智商',fontdict=label_font) #y轴名称
ax.set_ylim(ymin = 90,ymax = 300) #y轴刻度范围
ax.set_yticks(np.arange(90,301,50)) #设置显示刻度标签的位置
ax.set_yticklabels(np.arange(90,301,50))#设置刻度值对应的标签，可以是字符串型

ax.axhline(0, color='black',alpha=0.5) #绘制y=0的线
ax.legend(loc='best',markerscale=8,fontsize=10,prop=legend_font) #设置图例

# 添加数据标签
def autolabel(rects1,rects2):
    for i in range(len(rects1)):
        rect1 = rects1[i]
        rect2 = rects2[i]
        height = rect1.get_height()+rect2.get_height()
        if height>0:
            ax.annotate('{}'.format(height),
                    xy=(rect2.get_x() + rect2.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
        else:
            ax.annotate('{}'.format(height),
                    xy=(rect2.get_x() + rect2.get_width() / 2, height),
                    xytext=(0, -13),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
autolabel(rects1,rects2)
fig.tight_layout()
#保存图片  
#plt.savefig(r'E:\Data_resourses\DataCharm 公众号\Python\学术图表绘制\barplot05.png',dpi=600,bbox_inches = 'tight')

在ax.bar函数中设置 bottom参数 ，表示数据从哪个数开始绘制，该参数的长度应该与将绘制数据的长度相同。

在此案例中，我设置第二个柱形图的bottom为第一个柱形图的数据，表示第二个柱形图堆叠在第一个柱形图上绘制。若有第三个柱形图想要堆在第二个柱形图上，则需要将前两个柱形图的值相加，可以用如下表达式：[x + y for x, y in zip(xiaoming, xiaogang)]，第四个、第五个以此类推。