在图的原点开始 Y 刻度，并在 matplotlib pyplot 中轴结束的地方结束刻度

Question

我希望轴刻度在轴开始和结束的地方开始和结束

我有多个列需要一次绘制。在 for 循环中，我无法为每一列定义 y 刻度，因为它们具有不同的值范围。我想要一个代码，如图所示为每列的情节进行轴设置

这是我正在使用的代码的 sn-p

import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import openpyxl
import os
from PIL import Image
import io

x = np.random.randint( 10, 100, size = (50, 4) )


y = np.random.randint( 1, 20, size = (50, 4) )


z = np.concatenate((x,y), axis = 1)

df = pd.DataFrame( z, columns=list('ABCDEFGH') )

columns=list('ABCDEFGH')
cat = ['Cat1','Cat2','Cat3']

cat_list = random.choices(cat,k = 50)

df["Cat"] = cat_list 

barWidth = 0.25

# loop over multiple colums to plot as bar graph
for i in columns[0:6]:
    print(i)
    # The bars classidied according to 
    bars0 = [ df[df['Cat'] == 'Cat1' ][i].mean(),
               
              df[df['Cat'] == 'Cat1' ][i].mean(),
              
              df[ df['Cat'] == 'Cat1' ][i].mean(),
              
              df[ df['Cat'] == 'Cat1' ][i].mean(),
              
              df[ df['Cat'] == 'Cat1' ][i].mean(),
              
              df[ df['Cat'] == 'Cat1' ][i].mean() ]
    # bars 0 represents all the Cat 1 participants 
             
    yerr0 =  [
             
              df[df['Cat'] == 'Cat1' ][i].std(),
               
              df[df['Cat'] == 'Cat1' ][i].std(),
              
              df[ df['Cat'] == 'Cat1' ][i].std(),
              
              df[ df['Cat'] == 'Cat1' ][i].std(),
              
              df[ df['Cat'] == 'Cat1' ][i].std(),
              
              df[ df['Cat'] == 'Cat1' ][i].std() ]          
              
            
    
    
    bars1 = [  df[df['Cat'] == 'Cat2' ][i].mean(),
               
              df[df['Cat'] == 'Cat2' ][i].mean(),
              
              df[ df['Cat'] == 'Cat2' ][i].mean(),
              
              df[ df['Cat'] == 'Cat2' ][i].mean(),
              
              df[ df['Cat'] == 'Cat2' ][i].mean(),
              
              df[ df['Cat'] == 'Cat2' ][i].mean() ]
    # bars 1 represents all the Cat 2 participants 
             
    yerr1 = [ 
             
               df[df['Cat'] == 'Cat2' ][i].std(),
               
              df[df['Cat'] == 'Cat2' ][i].std(),
              
              df[ df['Cat'] == 'Cat2' ][i].std(),
              
              df[ df['Cat'] == 'Cat2' ][i].std(),
              
              df[ df['Cat'] == 'Cat2' ][i].std(),
              df[ df['Cat'] == 'Cat2' ][i].std() ]         
                     
             
            
    
    
    bars8 = [ df[df['Cat'] == 'Cat3' ][i].mean(),
               
              df[df['Cat'] == 'Cat3' ][i].mean(),
              
              df[ df['Cat'] == 'Cat3' ][i].mean(),
              
              df[ df['Cat'] == 'Cat3' ][i].mean(),
              
              df[ df['Cat'] == 'Cat3' ][i].mean(),
              
              df[ df['Cat'] == 'Cat1' ][i].mean()]
    # bars 8 represents all the Cat 3 participants 
             
    yerr8 =  [
             
                df[df['Cat'] == 'Cat3' ][i].std(),
               
              df[df['Cat'] == 'Cat3' ][i].std(),
              
              df[ df['Cat'] == 'Cat3' ][i].std(),
              
              df[ df['Cat'] == 'Cat3' ][i].std(),
              
              df[ df['Cat'] == 'Cat3' ][i].std(),
              df[ df['Cat'] == 'Cat3' ][i].std() ]        
    # standard deciation for the y error bar           
    
    r1 = np.arange(len(bars1))
    r2 = [x + barWidth for x in r1]
    r3 = [x + barWidth for x in r2]
   
    
    fig, ax = plt.subplots()
    right_side = ax.spines["right"]

    right_side.set_visible(False)
    
    top_side = ax.spines["top"]

    top_side.set_visible(False)
    
    # Cat1
    plt.bar(r1 ,bars0,color='r', width=barWidth, edgecolor='white',
            label='Cat1',yerr=yerr0)
    
    # Cat2
    plt.bar(r2,bars1,color='g', width=barWidth, edgecolor='white',
            label='Cat2', yerr=yerr1)
    
    # Cat3
    plt.bar(r3,bars8, color='b', width=barWidth,
            edgecolor='white', label='Cat3', yerr=yerr8)
            
    
    
    plt.xlabel('columns', fontdict={'fontname': 'Arial', 'fontsize': 16,'fontweight':'bold'})
    
    plt.ylabel('AVG '+ columns[columns.index(i)] ,fontdict={'fontname': 'Arial', 'fontsize': 16})
    
    
    
    plt.title(columns[columns.index(i)] ,fontdict={'fontname': 'Arial', 'fontsize': 24})
    
    plt.xticks([r + barWidth for r in range(len(bars1))],
               [ 'A', 'B',
                'C', 'D', 'E', 'F'],fontsize=12)
    
    

当我单独定义 plt.yticks() 时，我能够实现右侧的图表。我无法弄清楚如何循环执行此操作

Answer 1

只需在循环末尾添加plt.ylim(top=plt.yticks()[0][-1])。这利用了自动生成的 yticks 还包含下一个更高的 ticks 的事实，即使它不可见。例如，

    print(plt.ylim())
    print(plt.yticks()[0])

在你的循环中给出

(0.0, 16.5362767162174)
[ 0.  2.  4.  6.  8. 10. 12. 14. 16. 18.]

因此，即使 y 轴变为 16.53...，最大 ytick（不可见，在轴外）已经存在。所以我们所要做的就是将 ylim 的顶部设置为这个最大值，以稍微延长 y 轴。

当然，这会导致轴略微延伸，因此绘图中的数据可能有点长。但是根据示例，它看起来还可以。它也对应于您在问题中显示的内容。此外，为了使这个更优化，需要在重新创建自动 yticks 方面付出更多努力，这可能不值得。