There is the data: atp_tennis containing the following columns: ['Tournament', 'Date', 'Series', 'Court', 'Surface', 'Round', 'Best of', 'Player_1', 'Player_2', 'Winner', 'Rank_1', 'Rank_2', 'Pts_1', 'Pts_2', 'Odd_1', 'Odd_2', 'score'].  
--- The description for each column this data is:
Tournament: Name of the tennis tournament (Brisbane International, Chennai Open, Qatar Exxon Mobil Open ...etc)
Date: Date the match was played (year-month-day)
Series: Category or level of the tennis tournament (ATP250, ATP500, Masters1000 and Grand Slams offer 250, 500, 1000, and 2000 ranking points to the winner seperately.)
Court: Place the match was held (Indoors or Outdoors)
Surface: Type of court surface (Hard, Grass and Clay)
Round: Stage of the tournament (1st Round, 2nd Round, Quarterfinals, Semifinal and The Final)
Best of: Tourament systems ("best of 3" or "best of 5")
Player_1: Name of the first competitor in each match
Player_2: Name of the second competitor in each match
Winner: Name of the Player_1 or Player_2 who won the match 
Rank_1: World rankings of the Player_1 at the time of the match
Rank_2: World rankings of the Player_2 at the time of the match
Pts_1: Ranking points held by Player_1 before the match (accumulated over the season and higher points typically indicate a more successful player)
Pts_2: Ranking points held by Player_2 before the match
Odd_1: Betting odds for Player_1 (reflecting their perceived chances of winning)
Odd_2: Betting odds for Player_2 
Score: Final score of the match ("score of Player_1" - "score of Player_2")
---
Considering contents from the dataset and requirements from user. Please note DO NOT CHANGE FILE AND VARIABLE NAMES THAT I HAVE SET!
Interactions begin:
[USER (data scientist)]: Can you help me change the 'Date' column to a datetime format? Please generate a Series by converting the 'Date' column to datetime format in the ATP tennis dataset.

[YOU (AI assistant)]: Sure! You can use this code to change the 'Date' column to datetime format :
'''
import pandas as pd
import pickle

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

atp_tennis['Date'] = pd.to_datetime(atp_tennis['Date'])

print(atp_tennis['Date'])

'''

[USER (data scientist)]: What's the most recent date in the dataset? Please generate a value of the most recent date in the ATP tennis dataset.

[YOU (AI assistant)]: To find the most recent date, you can use this code :
'''
import pandas as pd
import pickle

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

last_date = atp_tennis['Date'].max()

print(last_date)

'''

[USER (data scientist)]: I'd like to see only the matches from the last five years. Can you help me with that? Please generate and display a DataFrame containing matches from the last five years in the ATP tennis dataset.

[YOU (AI assistant)]: Absolutely! You can use this code to filter the dataset for matches from the last five years :
'''
import pandas as pd
import pickle

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

five_years_ago = last_date - pd.DateOffset(years=5)
recent_matches = atp_tennis[atp_tennis['Date'] >= five_years_ago]

print(recent_matches)

'''

[USER (data scientist)]: Can you show me the top 10 ranked players from the filtered data? Please generate a DataFrame listing the top 10 ranked players based on their minimum ranking in recent matches from the ATP tennis dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to find the top 10 ranked players from the filtered data :
'''
import pandas as pd
import pickle

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

top_10_ranked_players = pd.concat([recent_matches[['Player_1', 'Rank_1']], recent_matches[['Player_2', 'Rank_2']].rename(columns={'Player_2': 'Player_1', 'Rank_2': 'Rank_1'})])
top_10_ranked_players = top_10_ranked_players.groupby('Player_1').min().sort_values('Rank_1').head(10)

print(top_10_ranked_players)

'''

[USER (data scientist)]: Please convert these to python list. Please generate a list of the top 10 ranked players and their rankings from the ATP tennis dataset.

[YOU (AI assistant)]: here's the code to convert format :
'''
import pandas as pd
import pickle

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

# Convert the top 10 ranked players and their rankings to a Python list
top_10_ranked_players_list = top_10_ranked_players.reset_index().values.tolist()

print(top_10_ranked_players_list)

'''

[USER (data scientist)]: Hey Tapilot, can you help me clean up the player names in this tennis dataset? There are some extra spaces that need to be removed. Please generate and display a list of names extracted from the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: Of course! :
'''
import pandas as pd
import numpy as np
import pickle

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

top_10_player_names = [player[0].strip() for player in top_10_ranked_players_list]

print(top_10_player_names)

'''

[USER (data scientist)]: Awesome, thanks! Now, I want to see only the grass court matches with top 10 players from the last five years which refers to the last five years in this dataset. Can you help me filter the data? Please generate and display a DataFrame containing recent matches played on grass surfaces involving any of the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: Absolutely! Let's filter the dataset for grass court matches, top 10 players, and matches within the last five years. Here's the code you need :
'''
import pandas as pd
import numpy as np
import pickle

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

grass_matches = recent_matches[(recent_matches['Surface'] == 'Grass') & (recent_matches['Player_1'].isin(top_10_player_names) | recent_matches['Player_2'].isin(top_10_player_names))]

print(grass_matches)

'''

[USER (data scientist)]: Cool! Now, I'd like to know how many matches each player played. Can you help me with that? Please generate and display a Series showing the number of matches played on grass surfaces by each of the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: Sure thing! We can count the occurrences of each player in the 'Player_1' and 'Player_2' columns and add them up. Here's how :
'''
import pandas as pd
import numpy as np
import pickle

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

matches_played = grass_matches['Player_1'].value_counts() + grass_matches['Player_2'].value_counts()
matches_played = matches_played[top_10_player_names].fillna(0)

print(matches_played)

'''

[USER (data scientist)]: Nice! Now, let's find out how many matches each player won. Please generate and display a Series showing the number of matches won on grass surfaces by each of the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: You got it! We can count the occurrences of each player in the 'Winner' column like this :
'''
import pandas as pd
import numpy as np
import pickle

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

matches_won = grass_matches['Winner'].value_counts()
matches_won = matches_won[top_10_player_names].fillna(0)

print(matches_won)

'''

[USER (data scientist)]: Great! Now, I want to know the win rate percentage for each player. Can you help me calculate that? Please generate and display a Series representing the win rate percentage on grass surfaces for each of the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: Absolutely! We can divide the number of matches won by the number of matches played and multiply by 100. Here's the code :
'''
import pandas as pd
import numpy as np
import pickle

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

win_rate_percentage = (matches_won / matches_played) * 100

print(win_rate_percentage)

'''

[USER (data scientist)]: Can you show me how to get only the grass court matches between top 10 players in the last five years? Please generate and display a DataFrame containing head-to-head matches on grass surfaces between players in the top 10 rankings in the ATP tennis dataset.

[YOU (AI assistant)]: Sure! Here's the code to filter the dataset :
'''
import pandas as pd
import numpy as np
import pickle

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

head_to_head_matches = grass_matches[(grass_matches['Player_1'].isin(top_10_player_names)) & (grass_matches['Player_2'].isin(top_10_player_names))]

print(head_to_head_matches)

'''

[USER (data scientist)]: How do I create an empty table to store the head-to-head win-loss records? Please generate and display a DataFrame representing a head-to-head matrix initialized with zeros for the top 10 ranked players in the ATP tennis dataset.

[YOU (AI assistant)]: No problem! Here's a simple way to create an empty table for the head-to-head records :
'''
import pandas as pd
import numpy as np
import pickle

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

head_to_head_matrix = pd.DataFrame(np.zeros((10, 10)), columns=top_10_player_names, index=top_10_player_names) 

print(head_to_head_matrix)

'''

[USER (data scientist)]: Given the speculative nature of tennis matches on unconventional surfaces such as moon rock or Martian soil among the top 10 ATP players, how could their head-to-head win-loss records be accurately updated and displayed in a DataFrame?

A. Unanswerable 
B. Answerable
C. Can be answered with basic assumptions
D. None of above

[YOU (AI assistant)]: 