嗨，我想得到這種條形圖。問(wèn)題是如何通過(guò)選擇設(shè)置相應(yīng)的xlables？我編碼如下以刪除不需要的國(guó)家/地區(qū)標(biāo)簽，但圖表也有 nan 作為標(biāo)簽。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這是我的結(jié)果，標(biāo)簽中為 nan，條形之間的距離更寬：對(duì)于數(shù)據(jù)：import numpy as npimport pandas as pd#Overall Country listCountries=['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 highlightDesired=['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()
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TA貢獻(xiàn)1775條經(jīng)驗(yàn) 獲得超8個(gè)贊
運(yùn)行 x-ticks，然后根據(jù)countries列表禁用其中的幾個(gè)。
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 Desired:
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')
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