Pandas DataFrame Iteration with .iterrows() Causes Unexpected Modification of Original Data
I've encountered a strange issue with I'm experiencing an unexpected behavior when using the `.iterrows()` method on a Pandas DataFrame. While trying to iterate through rows to perform calculations and create a new column based on those calculations, I noticed that the original DataFrame seems to be modified unexpectedly. Here's the code I'm using: ```python import pandas as pd data = { 'A': [1, 2, 3], 'B': [4, 5, 6] } df = pd.DataFrame(data) for index, row in df.iterrows(): df.at[index, 'C'] = row['A'] + row['B'] print(df) ``` After running this code, I expected the DataFrame `df` to have a new column `C` with the sum of columns `A` and `B`. However, while `C` is correctly populated, I noticed that the values in column `A` are being modified unexpectedly. Here’s the output I get: ``` A B C 0 5 4 5 1 7 5 7 2 9 6 9 ``` It seems like the values in column `A` are being altered rather than just being read during iteration. I have tried using `.copy()` to create a copy of the row data, but it didn't resolve the scenario. I also checked if there's any other part of the code modifying `df` before this block, but there isn't. Is this a known behavior when using `.iterrows()`? How can I iterate through DataFrame rows without inadvertently modifying the original DataFrame? I'm using Pandas version 1.4.2. Any insights or solutions would be greatly appreciated! I'm using Python 3.11 in this project. Any pointers in the right direction?