import pandas as pd
import numpy as np
#import scipy.stats as stats 
from scipy.stats.distributions import chi2 as chi2dist

#df = 31
df = 11

chi_1 = np.empty(df)
chi_2 = np.empty(df)
chi_3 = np.empty(df)
chi_4 = np.empty(df)
chi_5 = np.empty(df)
chi_6 = np.empty(df)

# repeat r times:
for j in  range(df):
  chi_1[j] = chi2dist.ppf(0.01, df=j)
  j=j+1
  
for j in  range(df):
  chi_2[j] = chi2dist.ppf(0.05, df=j)
  j=j+1
  
for j in  range(df):
  chi_3[j] = chi2dist.ppf(0.1, df=j)
  j=j+1
  
for j in  range(df):
  chi_4[j] = chi2dist.ppf(0.9, df=j)
  j=j+1
  
for j in  range(df):
  chi_5[j] = chi2dist.ppf(0.95, df=j)
  j=j+1

for j in  range(df):
  chi_6[j] = chi2dist.ppf(0.99, df=j)
  j=j+1

chi_square = pd.DataFrame({'p=0.99':chi_1,'p=0.95':chi_2,'p=0.9':chi_3,'p=0.1':chi_4,'p=0.05':chi_5,'p=0.01':chi_6})
chi_sq = chi_square.dropna(how='all') # to drop if all values in the row are NaN
round(chi_sq, 3)
print("Chi-square Distribution Table with 'p=0.99, ..., p=0.01' : ", f'\n{round(chi_sq, 3)}\n')