Why does my for loop with a Pandas DataFrame produce incorrect results after filtering in Python 3.11?
I'm prototyping a solution and I'm having a hard time understanding I keep running into I'm sure I'm missing something obvious here, but I'm trying to iterate over a Pandas DataFrame to perform some calculations after filtering for specific conditions, but I keep getting incorrect results... I have a DataFrame with columns 'A', 'B', and 'C', and I want to iterate over it after filtering out rows where column 'A' is less than 10. Here's the code I'm using: ```python import pandas as pd data = { 'A': [5, 15, 3, 25], 'B': [2, 3, 6, 8], 'C': [1, 4, 0, 10] } df = pd.DataFrame(data) # Filtering the DataFrame filtered_df = df[df['A'] >= 10] # Iterating over the filtered DataFrame for index, row in filtered_df.iterrows(): print(f"Row {index}: A = {row['A']}, B = {row['B']}, C = {row['C']}") # Perform some calculations df['D'] = row['B'] * 2 # This line seems to cause issues ``` When I run this code, I expect to see only the rows where 'A' is 15 and 25, but instead, I'm getting an behavior: `SettingWithCopyWarning: Trying to set a value on a copy of a slice from a DataFrame`. I understand that it's a warning about possibly modifying a copy instead of the original DataFrame, but I'm unsure how to properly handle this situation. Also, the output doesn't seem to reflect the modifications I intend to make. I've tried using `.copy()` on the filtered DataFrame but it didn't resolve the scenario. Any suggestions on how to correctly implement this iteration and ensure my calculations are accurate? Also, how should I modify the DataFrame while iterating without causing warnings? Any ideas what could be causing this? My development environment is Ubuntu 20.04. Has anyone dealt with something similar? I'm working in a Linux environment.