[USER (data scientist)]: How about a density plot? Please generate and display KDE plots comparing the distribution of original and updated odds for Player 1 and Player 2 in the ATP tennis dataset.
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,

# please import the necessary private functions from decision_company first

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

create_figure((12, 6)) 

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE 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>
[COMPLETE 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? 
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

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

create_figure((12, 6)) 

# YOUR SOLUTION BEGIN:
