您可以通过为频率创建一个twinx轴来完成此操作。您可以切换两个y轴，使频率保持在左侧，计数保持在右侧，但无需重新计算计数轴（这里我们使用tick_left()和tick_right()移动刻度，使用set_label_position移动轴标签
然后，您可以使用matplotlib.ticker模块（具体为ticker.MultipleLocator和ticker.LinearLocator）设置刻度。
至于注解，您可以使用patch.get_bbox().get_points()获取条形图所有4个角的x和y位置。这与正确设置水平和垂直对齐沿着，意味着您不需要向注解位置添加任何任意偏移。
最后，您需要关闭孪生轴的栅格，以防止栅格线显示在条形图的顶部（ax2.grid(None)）。
下面是一个工作脚本：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.ticker as ticker

# Some random data
dfWIM = pd.DataFrame({'AXLES': np.random.normal(8, 2, 5000).astype(int)})
ncount = len(dfWIM)

plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')

# Make twin axis
ax2=ax.twinx()

# Switch so count axis is on right, frequency on left
ax2.yaxis.tick_left()
ax.yaxis.tick_right()

# Also switch the labels over
ax.yaxis.set_label_position('right')
ax2.yaxis.set_label_position('left')

ax2.set_ylabel('Frequency [%]')

for p in ax.patches:
    x=p.get_bbox().get_points()[:,0]
    y=p.get_bbox().get_points()[1,1]
    ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y), 
            ha='center', va='bottom') # set the alignment of the text

# Use a LinearLocator to ensure the correct number of ticks
ax.yaxis.set_major_locator(ticker.LinearLocator(11))

# Fix the frequency range to 0-100
ax2.set_ylim(0,100)
ax.set_ylim(0,ncount)

# And use a MultipleLocator to ensure a tick spacing of 10
ax2.yaxis.set_major_locator(ticker.MultipleLocator(10))

# Need to turn the grid on ax2 off, otherwise the gridlines end up on top of the bars
ax2.grid(None)

plt.savefig('snscounter.pdf')

我使用核心matplotlib的条形图让它工作。显然我没有你的数据，但根据你的数据调整它应该是直截了当的。

方法

我使用了matplotlib的双轴，并将数据绘制为第二个Axes对象上的条形图。其余的只是一些小动作，以获得正确的刻度并进行注解。
希望这能帮上忙。

代码

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns

tot = np.random.rand( 1 ) * 100
data = np.random.rand( 1, 12 )
data = data / sum(data,1) * tot

df = pd.DataFrame( data )
palette = sns.husl_palette(9, s=0.7 )

### Left Axis
# Plot nothing here, autmatically scales to second axis.

fig, ax1 = plt.subplots()
ax1.set_ylim( [0,100] )

# Remove grid lines.
ax1.grid( False )
# Set ticks and add percentage sign.
ax1.yaxis.set_ticks( np.arange(0,101,10) )
fmt = '%.0f%%'
yticks = matplotlib.ticker.FormatStrFormatter( fmt )
ax1.yaxis.set_major_formatter( yticks )

### Right Axis
# Plot data as bars.
x = np.arange(0,9,1)
ax2 = ax1.twinx()
rects = ax2.bar( x-0.4, np.asarray(df.loc[0,3:]), width=0.8 )

# Set ticks on x-axis and remove grid lines.
ax2.set_xlim( [-0.5,8.5] )
ax2.xaxis.set_ticks( x )
ax2.xaxis.grid( False )

# Set ticks on y-axis in 10% steps.
ax2.set_ylim( [0,tot] )
ax2.yaxis.set_ticks( np.linspace( 0, tot, 11 ) )

# Add labels and change colors.
for i,r in enumerate(rects):
    h = r.get_height()
    r.set_color( palette[ i % len(palette) ] )
    ax2.text( r.get_x() + r.get_width()/2.0, \
              h + 0.01*tot,                  \
              r'%d%%'%int(100*h/tot), ha = 'center' )

我想你可以先手动设置y大刻度，然后修改每个标签

dfWIM = pd.DataFrame({'AXLES': np.random.randint(3, 10, 1000)})
total = len(dfWIM)*1.
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')

for p in ax.patches:
        ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))

#put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe
ax.yaxis.set_ticks(np.linspace(0, total, 11))

#adjust the ticklabel to the desired format, without changing the position of the ticks. 
_ = ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))