get ols regression results with for loop for dataframe against a constant

import pandas as pd

import numpy as np

import statsmodels.api as sm

df = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)),

                  columns=[‘Historic_Rate’, ‘Overnight’, ‘1M’, ‘3M’, ‘6M’])
fit_d = {}  # This will hold all of the fit results and summaries

for col in [x for x in df.columns if x != ‘Historic_Rate’]:

    Y = df[‘Historic_Rate’] – df[‘Historic_Rate’].shift(1)

    # Need to remove the NaN for fit

    Y = Y[Y.notnull()]
    X = df[col] – df[col].shift(1)

    X = X[X.notnull()]
    X = sm.add_constant(X)  # Add a constant to the fit
    fit_d[col] = sm.OLS(Y,X).fit()