sklearn LinearRegression, why only one coefficient returned by the model?

Question

I'm trying out scikit-learn LinearRegression model on a simple dataset (comes from Andrew NG coursera course, I doesn't really matter, look the plot for reference)

this is my script

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

dataset = np.loadtxt('../mlclass-ex1-008/mlclass-ex1/ex1data1.txt', delimiter=',')
X = dataset[:, 0]
Y = dataset[:, 1]


plt.figure()
plt.ylabel('Profit in $10,000s')
plt.xlabel('Population of City in 10,000s')
plt.grid()
plt.plot(X, Y, 'rx')

model = LinearRegression()
model.fit(X[:, np.newaxis], Y)

plt.plot(X, model.predict(X[:, np.newaxis]), color='blue', linewidth=3)

print('Coefficients: \n', model.coef_)

plt.show()

my question is: I expect to have 2 coefficient for this linear model: the intercept term and the x coefficient, how comes I just get one?

Solution

OOOPS

I didn't notice that the intercept is a separated attribute of the model!

print('Intercept: \n', model.intercept_)

look documentation here

intercept_ : array

Independent term in the linear model.