Initial plot not clearing when using dropdown menu to choose new data

I am teaching myself how to use the ipywidgets package by doing analysis on the total covid deaths for each country and want to make an interactive plot with a dropdown menu for Country choice.

My code works almost perfectly. The only thing is that when I chose a new country, the initial plot does not clear and I am left with 2 plots, the correct plot from choice, and the initial plot (as seen in the image below).

Can someone please point me in the right direction as to how I can remove the initial plot?

Here is the code that I am using:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ipywidgets as widgets
import seaborn as sns
# Get dataset
data_url = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/total_deaths_per_million.csv"
df = pd.read_csv(data_url, index_col=0, parse_dates=[0], engine='python')
# na values = 0
df.fillna(0, inplace=True)
df.head()
# add year-week column
df['Year_Week'] = df.index.to_period('W').strftime('%Y-%U')
# keep only last day of week and change to datetime type
df = df.groupby(df['Year_Week']).last('1D')
df.index = pd.to_datetime(df.index + '-0', format='%Y-%U-%w')
# drop columns that aren't a country
df_country = df.drop(['World', 
'Africa', 
'Asia', 
'Europe', 
'European Union', 
'High income', 
'Low income', 
'Lower middle income', 
'North America', 
'South America', 
'Upper middle income'], 
axis=1)
# create function to update plot based on selected country
def update_plot(country):
ax.clear()  # clear existing plot
ax.plot(df.index, df_country[country])  # plot selected country
# set x-axis tick locations and labels
xticks = pd.date_range(start=df_country.index[0].strftime('%Y-01-01'), end=df_country.index[-1], freq='AS')
xticklabels = [x.strftime('%Y') for x in xticks]
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
ax.set_title(f"Total deaths per million ({country})")  # update plot title
ax.set_xlabel("Date")
ax.set_ylabel("Deaths per million")
fig.canvas.draw()  # redraw canvas
# create drop-down menu with country names as options
country_dropdown = widgets.Dropdown(
options=df_country.columns, 
value=df_country.columns[0], 
description='Country'
)
# create plot
fig, ax = plt.subplots()
update_plot(country_dropdown.value) # initial plot
# set up widget interaction
output = widgets.Output()
display(country_dropdown, output)
def on_change(change):
if change['type'] == 'change' and change['name'] == 'value':
with output:
output.clear_output()
update_plot(change['new'])
display(fig)
country_dropdown.observe(on_change)

If you're working in a notebook, make your plot interactive by adding %matplotlib widget at the beginning of the cell.

Then, you don't need to use Output, simply update your fig and re-draw it in the event callback function.

%matplotlib widget

# [...]

# create plot
fig, ax = plt.subplots()
update_plot(country_dropdown.value) # initial plot

# set up widget interaction
display(country_dropdown)

def on_change(change):
    if change['type'] == 'change' and change['name'] == 'value':
        update_plot(change['new'])
        plt.draw()
        
country_dropdown.observe(on_change)

Execute the cell

Change the dropdown value. The figure is updated in the cell output without the initial one