[USER (data scientist)]: How about a density plot? Please generate the code with the output in [plot, plot] type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from decision_company import read_csv_file, create_figure, set_plot_ylabel, set_plot_xlabel, set_plot_title, show_plots, save_plot, draw_lineplot, plot_kde, add_legend, draw_countplot

# Load the dataset 
atp_tennis = read_csv_file('atp_tennis.csv')

create_figure((12, 6))  

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/updated_odds_change_for_player_1.png')
show_plots()

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
<code2>
[GENERATE YOUR CODE]
</code2>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/updated_odds_change_for_player_2.png')
show_plots()

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! You can use seaborn's kdeplot to create density plots. Here's the code. These plots show the distribution of original and updated odds for both players. What else would you like to check out? 

# MY SOLUTION BEGIN:
