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

df = 10

t_1 = np.empty(df)
t_2 = np.empty(df)
t_3 = np.empty(df)
t_4 = np.empty(df)
t_5 = np.empty(df)

# repeat r times:
for j in  range(df):
  t_1[j] = tdist.ppf(0.9, df=j)
  j=j+1
  
for j in  range(df):
  t_2[j] = tdist.ppf(0.95, df=j)
  j=j+1
  
for j in  range(df):
  t_3[j] = tdist.ppf(0.975, df=j)
  j=j+1
  
for j in  range(df):
  t_4[j] = tdist.ppf(0.99, df=j)
  j=j+1
  
for j in  range(df):
  t_5[j] = tdist.ppf(0.995, df=j)
  j=j+1

t_dist = pd.DataFrame({'p=0.1':t_1,'p=0.05':t_2,'p=0.025':t_3,'p=0.01':t_4,'p=0.005':t_5})
t = t_dist.dropna(how='all') # to drop if all values in the row are NaN
round(t, 3)
print("t Distribution Table with 'p=0.9, ..., p=0.995' : ", f'\n{round(t, 3)}\n')