[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

atp_tennis = pd.read_csv("atp_tennis.csv")

plt.figure(figsize=(12, 6)) 

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

plt.savefig('./updated_odds_change_for_player_1.png')
plt.show()

plt.figure(figsize=(12, 6)) 

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

plt.savefig('./updated_odds_change_for_player_2.png')
plt.show() 

---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

atp_tennis = pd.read_csv("atp_tennis.csv")

plt.figure(figsize=(12, 6)) 

# YOUR SOLUTION BEGIN:
