[USER (data scientist)]: Thank you. Now, I need to know the potential impact of the updated odds on the later rounds of the tournament, specifically the quarterfinals, semifinals, and finals. Please provide a dataframe with Round, Original Odds, Updated Odds, and Potential Impact Metrics (Changes in Match Favorites, Shifts in Tournament Dynamics). The computation for match favorites is based on the assumption that the player with the lower odds is more likely to win the match. And don't consider the impact over time.

[YOU (AI assistant)]: To analyze the potential impact of the updated odds on the later rounds of the tournament (quarterfinals, semifinals, and finals), and provide a dataframe with Round, Original Odds, Updated Odds, and Potential Impact Metrics, you can use the following code:
'''
import pandas as pd
import numpy as np
import pickle

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

# Filter the affected matches for quarterfinals, semifinals, and finals
later_rounds = atp_tennis[atp_tennis['Round'].isin(['Quarterfinals', 'Semifinals', 'Finals'])]
# Merge the original odds and updated odds dataframes on Match ID
later_rounds_odds = later_rounds.merge(updated_odds_df[['Match ID', 'Updated Odd_1', 'Updated Odd_2']], left_index=True, right_on='Match ID')
# Calculate the original and updated match favorites
later_rounds_odds['Original Favorite'] = later_rounds_odds.apply(lambda row: row['Player_1'] if row['Odd_1'] < row['Odd_2'] else row['Player_2'], axis=1)
later_rounds_odds['Updated Favorite'] = later_rounds_odds.apply(lambda row: row['Player_1'] if row['Updated Odd_1'] < row['Updated Odd_2'] else row['Player_2'], axis=1)
# Calculate the changes in match favorites and shifts in tournament dynamics
later_rounds_odds['Change in Favorite'] = later_rounds_odds['Original Favorite'] != later_rounds_odds['Updated Favorite']
later_rounds_odds['Shift in Tournament Dynamics'] = later_rounds_odds['Change in Favorite'].sum()
# Create a dataframe with the required columns
impact_df = later_rounds_odds[['Match ID', 'Round', 'Original Odd_1', 'Original Odd_2', 'Updated Odd_1', 'Updated Odd_2', 'Original Favorite', 'Updated Favorite', 'Change in Favorite', 'Shift in Tournament Dynamics']]
# Display the resulting dataframe
print(impact_df)
'''

[USER (data scientist)]: Woops, it reports the error: ['Original Odd_1', 'Original Odd_2'] not in index.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import numpy as np
import pickle

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

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

print(impact_df)

# save data
pickle.dump(impact_df,open("./pred_result/impact_df.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Here is the correct code:
'''
import pandas as pd
import numpy as np
import pickle

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

# YOUR SOLUTION BEGIN:
