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In my column of the data frame i have version numbers like 6.3.5, 1.8, 5.10.0 saved as objects and thus likely as Strings. I want to remove the dots with nothing so i get 635, 18, 5100. My code idea was this:

for row in dataset.ver:
    row.replace(".","",inplace=True)
The thing is it works if I don't set inplace to True, but we want to overwrite it and safe it.
                Please show a sample of your dataframe, it helps very much to come up with a solution.  Furthermore it's best to use vectorized operations, not a for loop like you're maybe used to in pure python. A vectorized solution in pandas would be for example be like this: dataset['ver'].replace("[.]","", inplace=True, regex=True).
– Sander van den Oord
                Dec 5, 2018 at 14:42
You're iterating through the elements within the DataFrame, in which case I'm assuming it's type str (or being coerced to str when you replace).  str.replace doesn't have an argument for inplace=....
You should be doing this instead:
dataset['ver'] = dataset['ver'].str.replace('.', '')
Sander van den Oord in the comments is quite correct to point out:
dataset['ver'].replace("[.]","", inplace=True, regex=True)
This is the way we do operations on a column in Pandas because in general, Pandas tries to optimize over for loops. The Pandas developers consider for loops the among least desirable pattern for row-wise operations in Python (see here.)
        
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