[Python] How to draw vertical dashed lines at the last non-NaN value for each sublist in plot?

I have a flat_list of values that I split into sublists of equal size D for a set of patients. Each patient's data is plotted over time (days), and I want to draw a vertical dashed line in the same color as the patient's curve at the last non-NaN x-position of each sublist. The dashed lines should extend from a global y_min - offset to y_max + offset.

Here's a minimal example:

import matplotlib.pyplot as plt
import numpy as np

def plot_app_efficiency(flat_list, D, P):
if len(flat_list) % D != 0:
raise ValueError("The length of the list must be a multiple of D.")

# Split data for each patient
patient_data = {}
for idx, p_id in enumerate(P):
start_idx = idx * D
end_idx = (idx + 1) * D
patient_data[p_id] = flat_list[start_idx:end_idx]

# X-axis: days from 1 to D
days = list(range(1, D + 1))

# Create plot
fig, ax = plt.subplots(figsize=(10, 6))

# Calculate global y_min and y_max across all patients
all_values = np.concatenate([np.array(values) for values in patient_data.values()])
y_min = np.nanmin(all_values)
y_max = np.nanmax(all_values)

for patient, values in patient_data.items():
values = np.array(values)
line, = ax.plot(days, values, label=f'Patient {patient}', marker='o')

# Find the last valid (non-NaN) value
valid_indices = np.where(~np.isnan(values))[0]
if len(valid_indices) > 0:
last_valid_index = valid_indices[-1]
x_val = days[last_valid_index]


# Axis labels and title
ax.set_xlabel('Days (D)')
ax.set_ylabel('Value')
ax.set_title('Values per patient over days')

# Remove grid
ax.grid(False)

# Horizontal legend below the plot
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15),
fancybox=True, shadow=False, ncol=len(P))

plt.tight_layout()
plt.show()

#Example Call
a= [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
b=40
c=[1,2,3]
plot_app_efficiency(a, b, c)


This is what it should look like (sorry for the bad drawing):

Picture

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