Pandas, astype(int) applied to float column returns negative numbers

That number is a 32 bit lower limit. Your number is out of the int32 range you are trying to use, so it returns you the limit (notice that 2**32 = 4294967296, divided by 2 2147483648 that is your number).

You should use astype(int64) instead.

Many questions in one.

So your expected dtype...

pd.to_numeric(df_1.barcode, downcast='integer').fillna(0)

pd.to_numeric downcast to integer would give you an integer, however, you have NaNs in your data and pandas needs to use a float64 type to represent NaNs

I ran into the same problem as OP, using

astype(np.int64)

solved mine, see the link here.

I like this solution because it's consistent with my habit of changing the column type of pandas column, maybe someone could check the performance of these solutions.