[USER (data scientist)]: Cool, now let's go through the top 10 players and check their stats in early round, how many matches they played, how many they won, and their winning percentage. Specifically, you can generate a value of the win rate for each top 10 ranked player in specific rounds of grass surface matches in the ATP tennis dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import numpy as np
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

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

print(win_rate)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! You can use nested loops to go through each player and round, then filter your dataset to grab the matches played by that player in that round. Here's the code you'll need:
'''
import pandas as pd
import numpy as np
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

# YOUR SOLUTION BEGIN:
