python distinguish between '300' and '300.0' for a dataframe column
Some solutions: 

Go through all the files, change the columns names, then save the result in a new folder. Now when you read a file, you can go to the new folder and read it from there.
Wrap the normal file read function in another function that automatically changes the column names, and call that new function when you read a file.
Wrap column selection in a function. Use a try/except block to have the function try to access the given column, and if it fails, use the other form.
Recently I have been developing some code to read a csv file and store key data columns in a dataframe. Afterwards I plan to have some mathematical functions performed on certain columns in the dataframe. 
I've been fairly successful in storing the correct columns in the dataframe. I have been able to have it do whatever maths is necessary such as summations, additions of dataframe columns, averaging etc. 
My problem lies in accessing specific columns once they are stored in the dataframe. I was working with a test file to get everything working and managed this no problem. The problems arise when I open a different csv file, it will store the data in the dataframe, but the accessing the column I want no longer works and it stops at the calculation part. 
From what I can tell the problem lies with how it reads the column name. The column names are all numbers. For example, df['300'], df['301'] etc. When accessing the column df['300'] works fine in the testfile, while the next file requires df['300.0']. If I switch to a different file it may require df['300'] again. All the data was obtained in the same way so I am not certain why some are read as 300 and the others 300.0. 
Short of constantly changing the column labels each time I open a different file, is there anyway to have it automatically distinguish between '300' and '300.0' when opening the file, or force '300.0' = '300'? 
Thanks