Converting long integers to strings in pandas (to avoid scientific notation)

Question

I want the following records (currently displaying as 3.200000e+18 but actually (hopefully) each a different long integer), created using pd.read_excel(), to be interpreted differently:

ipdb> self.after['class_parent_ref']
class_id
3200000000000515954    3.200000e+18
3200000000000515951             NaN
3200000000000515952             NaN
3200000000000515953             NaN
3200000000000515955    3.200000e+18
3200000000000515956    3.200000e+18
Name: class_parent_ref, dtype: float64

Currently, they seem to 'come out' as scientifically notated strings:

ipdb> self.after['class_parent_ref'].iloc[0]
3.2000000000005161e+18

Worse, though, it's not clear to me that the number has been read correctly from my .xlsx file:

ipdb> self.after['class_parent_ref'].iloc[0] -3.2e+18
516096.0

The number in Excel (the data source) is 3200000000000515952.

This is not about the display, which I know I can change here. It's about keeping the underlying data in the same form it was in when read (so that if/when I write it back to Excel, it'll look the same and so that if I use the data, it'll look like it did in Excel and not Xe+Y). I would definitely accept a string if I could count on it being a string representation of the correct number.

You may notice that the number I want to see is in fact (incidentally) one of the labels. Pandas correctly read those in as strings (perhaps because Excel treated them as strings?) unlike this number which I entered. (Actually though, even when I enter ="3200000000000515952" into the cell in question before redoing the read, I get the same result described above.)

How can I get 3200000000000515952 out of the dataframe? I'm wondering if pandas has a limitation with long integers, but the only thing I've found on it is 1) a little dated, and 2) doesn't look like the same thing I'm facing.

Thank you!

Answer 1

Convert your column values with NaN into 0 then typcast that column as integer to do so.

df[['class_parent_ref']] = df[['class_parent_ref']].fillna(value = 0)
df['class_parent_ref'] = df['class_parent_ref'].astype(int)

Or in reading your file, specify keep_default_na = False for pd.read_excel() and na_filter = False for pd.read_csv()