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! Please answer me how many grass court matches with top 10 players from the last five years?

A. 137
B. 157
C. 206
D. 88
E. None of above

[YOU (AI assistant)]: 