Unexplained "drops" in Savgol smoothing with higher polynomial for trends, stock, energy data (all kinds of time series basically!) - smoothing

Question

I have been trying to smooth curves with Savgol (scikit) and, in several of my attempt, raising the polynomial degree resulted in "drops" like the one I show below. This example is from Google trends data, but I had similar problems with stock data and electricity consumption data. Any lead as to why it behaves like it or how to solve it (and be able to raise the polynomial degree) would be highly appreciated.
Image below: "Sample output".
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
from pytrends.request import TrendReq
pytrends = TrendReq(hl='en-US', tz=360)
from scipy.signal import savgol_filter
kw_list = ["Carbon footprint"]
pytrends.build_payload(kw_list, timeframe='2004-12-14 2019-12-25', geo='', gprop='')
da1 = pytrends.interest_over_time()
#(drop last one for Savgol as need odd number, used to have 196 records)
Y3 = da1["Carbon footprint"]
fig = plt.figure(figsize=(18,9))
l = Y3.shape[0]
l = l if l%2 == 1 else l-1
# window = odd number closest to size of data
ax1 = plt.subplot(2,1,1)
ax1 = sns.lineplot(data=Y3, color="navy")
#Savgol with polynomial order = 7 is fine (but misses the initial plateau)
Y3_smooth = savgol_filter(Y3,l, 7)
ax1 = sns.lineplot(x=da1.index.to_pydatetime(),y=Y3_smooth, color="red")
plt.title(f"red = with Savgol, polynomial order = 7, window = {l}", fontsize=18)
ax2 = plt.subplot(2,1,2)
ax2 = sns.lineplot(data=Y3, color="navy")
#Savgol with polynomial order = 9 or more has a weird drop
Y3_smooth = savgol_filter(Y3,l, 10)
ax2 = sns.lineplot(x=da1.index.to_pydatetime(),y=Y3_smooth, color="red")
plt.title(f"red = with Savgol, polynomial order = 10, window = {l}", fontsize=18)
Sample output

Answer 1

If anyone is interested, I found this workaround using a different way to smooth. It works well including in the beginning and end, and allows a fine tuning of the degree of smoothing.
from scipy.ndimage.filters import gaussian_filter1d
def smooth(y, sigma=2):
y_smooth = gaussian_filter1d(y, sigma)
return y_smooth