如何减少使用 seaborn 绘制此猫图所需的时间

如何解决如何减少使用 seaborn 绘制此猫图所需的时间

我试图从 17001 行 × 6 列的 DataFrame 绘制预期寿命 life_expect['CountryName'] 超过 80 岁的国家 life_expect['Value'] 。 处理和绘图所需的时间超过 30 分钟。我做错了什么，如何使代码更快？

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

indicators = pd.read_csv('Indicators.csv')

year = indicators['Year']
hist_indicator = indicators['IndicatorName'].str.contains('Life expectancy at birth')
le = indicators[year & hist_indicator]
x = le[~le['CountryName'].str.contains('OECD')]
x = x[~x['CountryName'].str.contains('income')]
x = x[~x['CountryName'].str.contains('Euro')]
life_expect = x

highest_life_expect = life_expect[life_expect['Value'] > 80]
sns.set_style("darkgrid")
sns.catplot(x = 'CountryName',y = 'Value',hue = 'Year',row='IndicatorName',data = highest_life_expect,palette = 'YlOrBr',kind = 'strip',dodge = True,height = 1.5,aspect=3)
plt.xticks(rotation= 70)

plt.show() 

数据帧示例：

life_expect = pd.DataFrame({'CountryName': {23215: 'arab World',23216: 'arab World',23217: 'arab World',23311: 'Caribbean small states',23312: 'Caribbean small states',23313: 'Caribbean small states',23657: 'East Asia & Pacific (developing only)',23658: 'East Asia & Pacific (developing only)',23659: 'East Asia & Pacific (developing only)',24249: 'Fragile and conflict affected situations'},'CountryCode': {23215: 'ARB',23216: 'ARB',23217: 'ARB',23311: 'CSS',23312: 'CSS',23313: 
'CSS',23657: 'EAP',23658: 'EAP',23659: 'EAP',24249: 'FCS'},'IndicatorName': {23215: 'Life expectancy at birth,female (years)',23216: 'Life expectancy at birth,male (years)',23217: 'Life expectancy at birth,total (years)',23311: 'Life expectancy at birth,23312: 'Life expectancy at birth,23313: 'Life expectancy at birth,23657: 'Life expectancy at birth,23658: 'Life expectancy at birth,23659: 'Life expectancy at birth,24249: 'Life expectancy at birth,female (years)'},'IndicatorCode': {23215: 'SP.DYN.LE00.FE.IN',23216: 'SP.DYN.LE00.MA.IN',23217: 'SP.DYN.LE00.IN',23311: 'SP.DYN.LE00.FE.IN',23312: 'SP.DYN.LE00.MA.IN',23313: 'SP.DYN.LE00.IN',23657: 'SP.DYN.LE00.FE.IN',23658: 'SP.DYN.LE00.MA.IN',23659: 'SP.DYN.LE00.IN',24249: 'SP.DYN.LE00.FE.IN'},'Year': {23215: 1961,23216: 1961,23217: 1961,23311: 1961,23312: 1961,23313: 1961,23657: 1961,23658: 1961,23659: 1961,24249: 1961},'Value': {23215: 48.461242672561596,23216: 46.445430867169,23217: 47.4276465406613,23311: 64.82771091890339,23312: 60.810503390166595,23313: 62.7689979262271,23657: 47.95864452502961,23658: 44.11634167689369,23659: 45.98732426589021,24249: 43.4402244401312}})