How to apply .replace() inside a column in a pandas data frame to clean data
EDITED to include a working example. 

I have made an example with string type numeric data. I have also include a poorly formatted string number 1,002,*8320. 

The code below will convert the numbers in the format of digits and digits with commas to floats, and convert all other strings to NaN. I have used a regular expression to replace commas with blanks.

The NaN must be used over "" since the comparison operator will not work on strings 

import pandas as pd
import numpy as np
import re

# example data frame
df2 = pd.DataFrame([['1', '2000000000'],
                    ['2', '1,002,*8320'],
                    ['3', '1,000,000']],
                   columns = ['idx','product_ranking'])
# Remove Commas
df2['product_ranking'] = df2['product_ranking'].map(lambda x: re.sub('[,]*' , '', x))

# Remove Convert strings that are numeric into floats.
df2['product_ranking'] = df2['product_ranking'].map(lambda x: float(x) if x.isnumeric() else np.nan)

#Comparison is working
df2['product_ranking'] &gt; 100000000