[USER (data scientist)]: Cool, now let's clean up the data by getting rid of any rows with missing or wonky data in the Win_Loss_Ratio columns. Please generate the code with the output in dataframe type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle
from decision_company import read_csv_file, df_copy, dropna, get_dummies, corr, f_oneway, avg, sem, t_ppf

atp_tennis = read_csv_file('atp_tennis.csv')

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

print(print(surface_data_clean))

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: You got it! Here's the code to clean up the data: 

# MY SOLUTION BEGIN:
