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I'm trying to iterate over the rows of a DataFrame that contains some int64s and some floats. iterrows() seems to be turning my ints into floats, which breaks everything I want to do downstream:

>>> import pandas as pd
>>> df = pd.DataFrame([[10000000000000001, 1.5], [10000000000000002, 2.5]], columns=['id', 'prc'])
>>> [id for id in df.id]
[10000000000000001, 10000000000000002]
>>> [r['id'] for (idx,r) in df.iterrows()]
[10000000000000000.0, 10000000000000002.0]

Iterating directly over df.id is fine. But through iterrows(), I get different values. Is there a way to iterate over the rows in such a way that I can still index by column name and get all the correct values?

Here's the relevant part of the docs:

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames) [...] To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster as iterrows.

Example for your data:

>>> df = pd.DataFrame([[10000000000000001, 1.5], [10000000000000002, 2.5]], columns=['id', 'prc'])
>>> [t[1] for t in df.itertuples()]
>>> [10000000000000001, 10000000000000002]

If possible you're better off avoiding iteration. Check if you can vectorize your work first.

If vectorization is impossible, you probably want DataFrame.itertuples. That will return an iterable of (named)tuples where the first element is the index label.

In [2]: list(df.itertuples())
Out[2]:
[Pandas(Index=0, id=10000000000000001, prc=1.5),
 Pandas(Index=1, id=10000000000000002, prc=2.5)]

iterrows returns a Series for each row. Since series are backed by numpy arrays, whose elements must all share a single type, your ints were cast as floats.