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How to perform str.strip in dataframe and save it with inplace=true?


How to perform str.strip in dataframe and save it with inplace=true?

By : Ayoub Rima
Date : October 24 2020, 06:10 AM
this one helps. String methods (the attributes of the .str attribute on a series) will only ever return a new Series, you can't use these for in-place changes. Your only option is to assign it back to the same column:
code :
data['Name'] = data['Name'].str.strip().str[13:]
data['Name'].replace(r'(?s)\A\s*(.{,13}).*(?<!\s)\s*\Z', r'\1', regex=True, inplace=True)


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Pandas manipulating a DataFrame inplace vs not inplace (inplace=True vs False)

Pandas manipulating a DataFrame inplace vs not inplace (inplace=True vs False)


By : Manoj S
Date : March 29 2020, 07:55 AM
hope this fix your issue In general, there is no difference between inplace=True and returning an explicit copy - in both cases, a copy is created. It just so happens that, in the first case, the data in the copy is copied back into the original df object, so reassignment is not necessary.
Furthermore, note that as of v0.21, df.sort is deprecated, use sort_values instead.
pandas.DataFrame: difference between inplace = True and assigning the same variable?

pandas.DataFrame: difference between inplace = True and assigning the same variable?


By : Shean
Date : March 29 2020, 07:55 AM
will help you You need copy:
code :
df_aux = df.dropna(axis = 1).copy()
How to apply pandas.DataFrame.replace on selected columns with inplace = True?

How to apply pandas.DataFrame.replace on selected columns with inplace = True?


By : user1370097
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further When you select the columns for replacement with df[col_list], a slice (a copy) of your dataframe is created. The copy is updated, but never written back into the original dataframe.
You should either replace one column at a time or use nested dictionary mapping:
code :
df.replace(to_replace={'col1' : {99 : 0}, 'col2' : {99 : 0}}, 
           inplace=True)
d = {col : {99:0} for col in col_list}
Pandas DataFrame replace does not work with inplace=True

Pandas DataFrame replace does not work with inplace=True


By : M. Sajidashraf
Date : March 29 2020, 07:55 AM
it should still fix some issue 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:
code :
dataset['ver'] = dataset['ver'].str.replace('.', '')
Pandas dataframe: set_index with inplace=True returns a NoneType, why?

Pandas dataframe: set_index with inplace=True returns a NoneType, why?


By : Fizzs
Date : March 29 2020, 07:55 AM
Hope that helps Ok, now I understand, thanks for the comments!
So inplace=True should return None and make the change in the original object. It seemed that on listing the dataframe again, no changes were present.
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