[USER (data scientist)]: Now, I'd like to categorize players into ranking groups based on their average rank. Can you help me with that? I want to use three groups: top-ranked (0-50), mid-ranked (51-200), and low-ranked (201+). Please generate and display a DataFrame summarizing unique player data in the ATP tennis dataset, including average rank, average win/loss ratio, and total match count for each player.
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(grouped_data)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Alright, let's categorize players into those ranking groups and calculate the average win/loss ratio for each group and surface type. Here's the code for that: 
'''
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:
