[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 a DataFrame that removes rows with missing values in 'Win_Loss_Ratio_1' and 'Win_Loss_Ratio_2' from a subset of the ATP tennis dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle

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

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE 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: 
'''
import pandas as pd
import pickle

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

# YOUR SOLUTION BEGIN:
