import random as r
    import numpy as np
    import pandas as pd
    import statsmodels as smf

    j = 0
    #Here below i'm just saying that j has to be lower then n*(n-1)*(n-2)*(n-3)*(n-4), which is the amount of combinations that you can find by extracting 5 different coefficients from a n columns database.#

    while j < ((Dataframe.shape[1])*(Dataframe.shape[1]-1)*(Dataframe.shape[1]-2)*(Dataframe.shape[1]-3)*(Dataframe.shape[1]-4)):

  #Below our dependent variable X, which is randomized from the dataframe#

        X = Dataframe.iloc[:, r.sample(range(4,Dataframe.shape[1]),5)]
        #Below our model, a classic OLS with constant#
        sm.add_constant(X , prepend=True )
        Model = smf.OLS(Y, X)
        #Below the results for a single regression#
        Results = Model.fit()

#The Results parameters type is "Pandas.Core.Series.Series",
        therefore I have transformed the values within Results.params into a
        Database#

        Parameters = pd.DataFrame(Results.params).reset_index()
        Parameters.columns = ['names', 'values']

        #Below you will see our database, named as DB_Parameters, right now
        #it's nothing but a repository for all the 47 coefficient names to be in place.#

        DB_Parameters = pd.DataFrame(np.unique(np.append(DB_Parameters, Parameters.iloc[:,0])))
        DB_Parameters.columns = ['names']

        if all((Parameters['names']).isin(Databank.iloc[:,0])):

             #Here i want start to populate the database with the regression parameters#

print(DB_Parameters):
         names params
         const 0.04
         B_1   0.05
         B_2   0.03
         B_3   NaN
         B_4   NaN
         B_5   0.96

const = 0.04
       B_1 = 0.05

const = 0.06
       B_1 = 0.12

name  iteration1      iteration2   .... .....  ....
       const 0.04            0.06
       B_1   0.05            0.015
       B_2   0.03           "Eventually, another value, if extracted"
       B_3   NaN            "Eventually, another value, if extracted"
       ...   ...             NaN