Lego

To use scikit-lego you'll need to install it first;

pip install scikit-lego

You can now use it in the pipeline.

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

from sklego.preprocessing import PatsyTransformer

import matplotlib.pylab as plt

X = (df_clean
    .head(2000)
    .loc[lambda d: d['n_born'] > 2000]
    .assign(num_date = lambda d: date_to_num(d['date'])))
y = X['n_born']

pipe = Pipeline([
    ("patsy", PatsyTransformer("(cc(yday, df=12) + wday + num_date)**2")),
    ("scale", StandardScaler()),
    ("model", LinearRegression())
])

np.mean(np.abs(pipe.fit(X, y).predict(X) - y))

Feel free to check out the documentation for more information about the patsy component.