matplotlib条形图，仅具有突出显示值

Question

您好，我想获得这种条形图。问题是如何通过选择设置相应的xlables？

我按照以下代码进行编码，以删除不需要的国家/地区标签，但图形也包含nan作为标签。

countries=['United States','Mexico','Japan','China','Korea,Rep.','Ireland','France','Italy']
new_index=list(df.index)
for i in range(len(new_index)):
    if new_index[i] not in countries :
        new_index[i]=np.nan

这是我的结果，标签中带有nan，条之间的距离更宽：

有关数据：

import numpy as np
import pandas as pd

#Overall Country list
Countries=['United States','Mexico','Japan','China','Korea,Rep.','Ireland','France','Italy','Czech Republic',
 'Austria',
 'Slovak Republic',
 'Slovenia',
 'Germany',
 'Portugal',
 'Hungary',
 'Colombia',
 'New Zealand',
 'Norway',
 'Latvia']

#Countries to highlight
Desired=['United States','Mexico','Japan','China','Korea,Rep.','Ireland','France','Italy']

np.random.seed(0)
Value=np.random.rand(len(Countries))
df = pd.DataFrame({'Countries': Countries,'Value': Value,})
df.sort_values(['Value'],inplace=True)

df.set_index('Countries',drop=True,inplace=True)
ax_1 = df['Value'].plot(kind='bar', title ="graph", figsize=(10, 6), fontsize=12)
ax_1.set_xlabel("Country Name", fontsize=12)
plt.show()

Answer 1

现在遍历x-ticks，然后根据countries列表禁用其中的一些。

import numpy as np
import pandas as pd

#Overall Country list
Countries=['United States','Mexico','Japan','China','Korea,Rep.','Ireland','France','Italy','Czech Republic',
 'Austria',
 'Slovak Republic',
 'Slovenia',
 'Germany',
 'Portugal',
 'Hungary',
 'Colombia',
 'New Zealand',
 'Norway',
 'Latvia']

#Countries to highlight
Desired=['United States','Mexico','Japan','China','Korea,Rep.','Ireland','France','Italy']

np.random.seed(0)
Value=np.random.rand(len(Countries))
df = pd.DataFrame({'Countries': Countries,'Value': Value,})
df.sort_values(['Value'],inplace=True)

df.set_index('Countries',drop=True,inplace=True)
ax_1 = df['Value'].plot(kind='bar', title ="graph", figsize=(10, 6), fontsize=12)
ax_1.set_xlabel("Country Name", fontsize=12)



for ticks in ax_1.xaxis.get_major_ticks():
    if ticks.label1.get_text() not in countries:
        ticks.label1.set_visible(False)
        ax_1.patches[df.index.get_indexer([ticks.label1.get_text()])[0]].set_facecolor('w')
        ax_1.patches[df.index.get_indexer([ticks.label1.get_text()])[0]].set_edgecolor('black')
    else:
        ax_1.patches[df.index.get_indexer([ticks.label1.get_text()])[0]].set_facecolor('r')