Locate an id in Dataframe using constraint on columns percentile - python-3.x

Question

I am trying to do a Weighted Aged Historical Var based on the below Dataframe. I would like to identify the ID in my dataframe corresponding to the 5% quantile of the 'Weight_Age_Cumul' column (like in the below example i found on internet)
enter image description here
I ve tryied the following line of code but i get the following error message : 'DataFrame' object has no attribute 'idmax'
cac_df_sorted[cac_df_sorted.Weight_Age_Cumul]<=0.05].CAC_Log_returns.idmax()
enter image description here
If you can help me on that it you be great, thank you
full code below if needed :
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from tabulate import tabulate
from scipy.stats import norm
import yfinance as yf
from yahoofinancials import YahooFinancials
import sys
cac_df = yf.download('^FCHI',
start='2020-04-01',
end='2022-05-31',
progress=False,
)
cac_df.head()
cac_df = cac_df.drop(columns=['Open','High','Low','Close','Volume'])
#convertion into retuns
cac_df['Adj Close_-1'] = cac_df['Adj Close'].shift(1)
cac_df['CAC_Log_returns'] = np.log(cac_df['Adj Close']/cac_df['Adj Close_-1'])
cac_df.index = pd.to_datetime(cac_df.index, format = '%Y-%m-%d').strftime('%Y-%m-%d')
#plot CAC returns graph & histogram
cac_df['CAC_Log_returns'].plot(kind='line',figsize=(15,7))
plt.show()
cac_df['CAC_Log_returns'].hist(bins=40,normed=True,histtype='stepfilled',alpha=0.5)
plt.xlabel('Returns')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()
#Historical Var Constant weight & Age Weighted & Vol Weighted
cac_df_sorted = cac_df.copy()
cac_df_sorted.sort_values(by=['Date'],inplace=True,ascending = False)
#Weight for Var Age weighted
lamb = 0.98
n = len(cac_df_sorted['CAC_Log_returns'])
weight_age= []
weight_age = [(lamb**(i-1) * (1-lamb))/(1-lamb**n)for i in range(1, n+1)]
#design of the dataframe
cac_df_sorted['Weight_Age'] = weight_age
cac_df_sorted.sort_values(by=['CAC_Log_returns'],inplace=True,ascending = True)
cac_df_sorted['Weight_Age_Cumul'] = np.cumsum(weight_age)
#Historical Var Constant weight
Var_95_1d_CW = -cac_df_sorted['CAC_Log_returns'].quantile(0.05)
Var_99_1d_CW = -cac_df_sorted['CAC_Log_returns'].quantile(0.01)
#from Var1d to Var10d
mean = np.mean(cac_df['CAC_Log_returns'])
Var_95_10d_CW =(np.sqrt(10)*Var_95_1d_CW)+(mean *(np.sqrt(10)-10))
Var_99_10d_CW = (np.sqrt(10)*Var_99_1d_CW) +(mean *(np.sqrt(10)-10))
print(tabulate([['95%',Var_95_1d_CW,Var_95_10d_CW],['99%',Var_99_1d_CW,Var_99_10d_CW]], headers= ['Confidence Level', 'Value at Risk 1 day Constant Weight','Value at Risk 10 days Constant Weight']))
print(cac_df_sorted)
# Historical Var Age weighted
#Find where cumulative (percentile) hits 0.05 and 0.01
cac_df_sorted[cac_df_sorted['Weight_Age_Cumul']<=0.05].CAC_Log_returns.idmax()