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The below column 'Total' from my data frame has integers with extra .0 (1.0,2.0..) & Null values. Before exporting data to csv, when below line of code executes it is throwing the error : ValueError: invalid literal for int() with base 10: ''

df['Total'] = df['Total'].astype('Int64')

How to handle a column which has int & null values. At the same time if i remove above validation, Total output column coming as extra .0 (Ex: 11.0, 199.0, 33.0 etc). I want to export pure int/null to my csv. Any help is highly appreciated.

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It seems there are some non numeric values, you can try to_numeric with errors='coerce' for convert this values to NaNs:

df['Total'] = pd.to_numeric(df['Total'], errors='coerce').astype('Int64')
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Thank you so much for the reply : After above change, now i'm getting below error: raise ValueError('Cannot convert non-finite values (NA or inf) to ' ValueError: Cannot convert non-finite values (NA or inf) to integer
@RK. - I guess problem is some floats values like 32.4, how working df['Total'] = pd.to_numeric(df['Total'], errors='coerce').round().astype('Int64') for convert like 32.4 to 32 by round?
It has all int/null values only. integers like 32.0, 12.0, etc. No value after dot, we can trim it also.
@RK. - What is your pandas version? Because converting to integers is possible only in some 5 last version of pandas, pandas 0.24+

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