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

df = 11

f_1 = np.empty(df)
f_2 = np.empty(df)
f_3 = np.empty(df)
f_4 = np.empty(df)
f_5 = np.empty(df)
f_6 = np.empty(df)
f_7 = np.empty(df)
f_8 = np.empty(df)
f_9 = np.empty(df)
f_10 = np.empty(df)

# repeat r times:
for j in  range(df):
  f_1[j] = fdist.ppf(0.95, 1, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_2[j] = fdist.ppf(0.95, 2, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_3[j] = fdist.ppf(0.95, 3, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_4[j] = fdist.ppf(0.95, 4, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_5[j] = fdist.ppf(0.95, 5, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_6[j] = fdist.ppf(0.95, 6, j)
  j=j+1

# repeat r times:
for j in  range(df):
  f_7[j] = fdist.ppf(0.95, 7, j)
  j=j+1
  
# repeat r times:
for j in  range(df):
  f_8[j] = fdist.ppf(0.95, 8, j)
  j=j+1
    
# repeat r times:
for j in  range(df):
  f_9[j] = fdist.ppf(0.95, 9, j)
  j=j+1
    
# repeat r times:
for j in  range(df):
  f_10[j] = fdist.ppf(0.95, 10, j)
  j=j+1
        
f_dist = pd.DataFrame({'v2=1':f_1,'v2=2':f_2,'v2=3':f_3,'v2=4':f_4,'v2=5':f_5,'v2=6':f_6,'v2=7':f_7,'v2=8':f_8,'v2=9':f_9,'v2=10':f_10})
f = f_dist.dropna(how='all') # to drop if all values in the row are NaN
round(f, 2)
print("F Distribution Table at 5% significancr level with 'v2=1, ..., v2=10' : ", f'\n{round(f, 2)}\n')