printf(“%dn”, 42); /* I am running a Linear regression (OLS) and i want my code model to include when Intercept/constant is True and when intercept is False. I don't want to manually indicate it in my code. i want to be able to include it in my code

import pandas as pd
import stats-models.api as sm

`# List of all variables in your dataset
all_variables = variables[‘variable_name_df’].tolist()
missing_variable_coefs = pd.DataFrame(columns=[‘Variable’, ‘Beta’])

if model_type.lower() in [‘stepwise ols (max)’, ‘stepwise ols’,’stepwise fast’,’stepwise baseline’]: # Adjusted to handle case sensitivity
# List of all variables in your dataset
all_variables = variables[‘variable_name_df’].tolist()

# Initialize a set to store variables already added to the DataFrame
added_variables = set()

# Initialize an empty list to store variables not in the model
missing_vars = []

# Loop through each variable
for var in all_variables:
    # Check if the variable is not in the coef_df DataFrame
    if var not in coef_df.index:
        # Append the variable to the list
        missing_vars.append(var)
        
# First, ensure that the variables in missing_vars exist in your DataFrame X
missing_vars = [var for var in missing_vars if var in X.columns]

# Define final_model_vars based on the final model
final_model_vars = [var for var in X.columns if var in coef_df.index]

# Check if the variable type is already in the model
for var_type in name_to_type.values():
    if not any(var in final_model_vars for var in variables[variables['clientdatavariabletypename'] == var_type]['variable_name_df']):
        missing_vars += variables[variables['clientdatavariabletypename'] == var_type]['variable_name_df'].tolist()
        
    
# Ensure that the variables in missing_vars exist in your DataFrame X
missing_vars = [var for var in missing_vars if var in X.columns]


# If no valid variables exist, print a message and exit
if not missing_vars:
    print("None of the missing variables exist in X.")
else:
    # Loop through each missing variable
    for var in missing_vars:
        # Check if the variable type is already in the final model
        var_type = variables.loc[variables['variable_name_df'] == var, 'clientdatavariabletypename'].values[0]
        if any(var in final_model_vars for var in variables[variables['clientdatavariabletypename'] == var_type]['variable_name_df']):
            continue  # Skip fitting this variable type if it's already in the final model
            
        # Add the variable to the variables that made it into the final model
        temp_vars = final_model_vars + [var]
        
        # Ensure that the variables in temp_vars exist in your DataFrame X
        temp_vars = [var for var in temp_vars if var in X.columns]
        
        # Extract the corresponding features
        X_temp = X[temp_vars]
        
        if intercept:
            # Add a constant (intercept) to your variables
            X_temp = sm.add_constant(X_temp)
        else:
            # Do not include the intercept
            pass

        # Fit a linear regression model using the missing variable added to the final model
        missing_model = sm.OLS(y, X_temp).fit()
        
        # Extract the coefficient of the added variable
        beta = missing_model.params[var]
        # Check if the variable is already added to the DataFrame
        if var not in added_variables:
            # Append the variable and its coefficient to the DataFrame
            missing_variable_coefs = missing_variable_coefs.append({'Variable': var, 'Beta': beta}, ignore_index=True)
            # Add the variable to the set of added variables
            added_variables.add(var)
if intercept:
    missing_variable_coefs.loc['intercept'] = [missing_model.params, abs(missing_model.params)]
    

    

when i did this:
missing_model.params, i got this the below ouptut

const -0.068478
ctv_xxx_max100_hrf10_ads70_lag0 0.072673
linear_tv_hispanic_xxx_max100_hrf90_ads90_lag0 0.1403“88
olv_xxx_max100_hrf10_ads90_lag0 0.163421
sale_qty_xxx_lag0 0.771655
twitter_xxx_max100_hrf90_ads90_lag0 -0.130767
does that mean the constant/intercept was included in the model?.

Please i need help to fix this so that my code can have the flexibility the add when intercetp/const is True and when it is False.

Thanks */