How to create a table for lasso regression?

Question

I use Lasso and a linear regression for feature selection. I want a table that includes the coefficients but also the significance of a variable. I first used SKlearn, but SKlearn does not create these kind of tables. Thus, I tried to create them myself.

Is the way how I calculated the different values for the lasso regression table correct?

    import pandas as pd
    import numpy as np
    from sklearn.linear_model import Lasso
    from sklearn.preprocessing import StandardScaler
    from scipy.stats import t

    X = data[['actual_total_load_MW', 'DA_total_load_MW', 'DA_onshore_wind_MW', 'DA_offshore_wind_MW', 'DA_solar_MW', 'actual_offshore_wind_MW', 'actual_onshore_wind_MW', 'actual_solar_MW', 'DA_price']]
    y = data['imbalance_MW']

# Standardize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

# Lasso feature selection
lasso = Lasso(alpha=0.1)  # Adjust the alpha value as per your preference
lasso.fit(X_std, y)

# Get coefficient information
coefficients = lasso.coef_
intercept = lasso.intercept_

# Unstandardized coefficients
B = coefficients / scaler.scale_  # Unstandardized coefficients

# Standard deviations (STD)
STD = np.std(X, 0) * coefficients / scaler.scale_

# Standardized coefficients (Beta)
Beta = coefficients

# Compute t-values
n_samples = X.shape[0]
dof = n_samples - np.count_nonzero(coefficients) - 1  # Degrees of freedom
t_values = coefficients / (np.std(X_std, 0) / np.sqrt(n_samples))

# Compute p-values
p_values = 2 * (1 - t.cdf(np.abs(t_values), df=dof))  # Assuming a two-tailed test

# Compute 95% Confidence Intervals (CI)
SE = np.std(X, 0) * np.std(X_std, 0) / np.sqrt(n_samples)  # Standard Errors
ci_low = B - 1.96 * SE  # 95% CI Lower Limit
ci_high = B + 1.96 * SE  # 95% CI Upper Limit

# Create the coefficient table
table = pd.DataFrame({'Estimate': B, 'SE': SE, '95% CI (LL)': ci_low, '95% CI (UL)': ci_high, 'p-value': p_values},
                     index=X.columns)

# Print the coefficient table
print(table)

Thank you in advance!