Pandas iterrows change the type of columns to float. How to cast it back to original type?

Question

Pandas iterrows change the type of columns. According to this github issue, it is an intended behavior.

Any idea of a pythonic and elegant way of casting it back to the original type? Note that I have multiple column types.

minimal example

df = pd.DataFrame([range(5), range(5)])
df.iloc[:,1] = df.iloc[:,1].astype('float')
for row in df.iterrows():
    print row

Results with

(0, 0    0.0
1    1.0
2    2.0
3    3.0
4    4.0
Name: 0, dtype: float64)
(1, 0    0.0
1    1.0
2    2.0
3    3.0
4    4.0
Name: 1, dtype: float64)

Note that df.dtypes returns the types of columns, however, I couldn't think of an elegant way of using it to cast the row back to that type.

Answer 1

Try using df.itertuples instead:

df = pd.DataFrame([range(5), range(5)], columns=list('abcde'))
df.iloc[:,1] = df.iloc[:,1].astype('float')

for row in df.itertuples():
    print(row)

Pandas(Index=0, a=0, b=1.0, c=2, d=3, e=4)
Pandas(Index=1, a=0, b=1.0, c=2, d=3, e=4)

Answer 2

You can do this iteration keeping the data type in the following way:

import pandas

df = pandas.DataFrame({'ints': list(range(5)), 'floats': [float(i) for i in range(5)]})
print(df)
for idx in df.index:
    print(f'Integer number: {df.loc[idx,"ints"]}')
    print(f'Float number: {df.loc[idx,"floats"]}')

The output (in Python 3.8.5) is

   ints  floats
0     0     0.0
1     1     1.0
2     2     2.0
3     3     3.0
4     4     4.0
Integer number: 0
Float number: 0.0
Integer number: 1
Float number: 1.0
Integer number: 2
Float number: 2.0
Integer number: 3
Float number: 3.0
Integer number: 4
Float number: 4.0