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)]: Hi Tapilot, I need your assistance in analyzing the atp_tennis dataset to answer some specific questions about the top 10 ranked players in the last five years. Who are the top 10 ranked players in the dataset from the last five years? Please provide a list of player names along with their rankings.

[YOU (AI assistant)]: Sure, I can help you with that. First, we need to filter the dataset to only include matches from the last five years. Then, we can find the top 10 ranked players based on their highest ranking during that period. Here's the code to do that :
'''
import pandas as pd
import numpy as np
import pickle

# Load the dataset
atp_tennis = pd.read_csv("atp_tennis.csv")

# Convert the 'Date' column to datetime format
atp_tennis['Date'] = pd.to_datetime(atp_tennis['Date'])

# Find the last date in the dataset
last_date = atp_tennis['Date'].max()

# Filter the dataset to only include matches from the last five years
five_years_ago = last_date - pd.DateOffset(years=5)
recent_matches = atp_tennis[atp_tennis['Date'] >= five_years_ago]

# Find the top 10 ranked players
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)

# 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)]: Great, now that we have the top 10 ranked players. What is the win rate of each of the top 10 players on grass courts in the last five years? Please provide a data table with player names, number of matches played, number of matches won, and win rate percentage.

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(win_rate_table)

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

---END CODE TEMPLATE---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: Sure, I can help you calculate the win rate of each of the top 10 players on grass courts in the last five years. We'll first filter the dataset to only include grass court matches played by the top 10 players in the last five years. Then, we'll calculate the number of matches played, number of matches won, and win rate percentage for each player. Here's the code to do that:
'''
import pandas as pd
import numpy as np

import pickle

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

# YOUR SOLUTION BEGIN:
