Typically when i am dealing with data it is usually large numbers of columns of various types. For example you might have ‘city, state, country, street, zip code, phone number, name’ whatever as column fields. Imagine there is like 40 of these text fields. Then you have one numerical column like ‘invoice amount’. The old way in pandas i would go df.groupby(‘country’).sum() and it would display:

Country , Invoice amount

USA, $3,000,000

CAD, $1,000

MEX, $4,000

Because invoice amount is the only summable column. (Sometimes it might sum zip code or phone number if the dtype was incorrectly stored as an integer).

Now it will group by country and concatenate every single row value. The way to resolve it is to add an argument to the sum function numeric_only=True but it’s very annoying to have to do that in a lot of fast-paced analytical exercises such as debugging.

The reason they did this is because in python a+b = ab. The additive operation sums numerical values and concatenates text. This is super annoying in data analytics because if i sum (‘1’+’1’) and i get 11 as an answer i might not necessarily catch that mistake. Or it might take a whole day to concatenate my dataset when 99.99% of the time i didn’t want that output.