scenarios while merging DataFrames with NaN values in Pandas resulting in unexpected duplicates
After trying multiple solutions online, I still can't figure this out. I'm sure I'm missing something obvious here, but I'm working with an scenario while trying to merge two Pandas DataFrames that contain NaN values in the key columns. I want to perform a left join, but instead of getting the expected results, I'm ending up with duplicate rows in the output DataFrame due to the presence of NaN in the merge keys. Here's a simplified version of my code: ```python import pandas as pd df1 = pd.DataFrame({ 'id': [1, 2, 3, None], 'value': ['A', 'B', 'C', 'D'] }) df2 = pd.DataFrame({ 'id': [1, 2, None], 'description': ['Desc1', 'Desc2', 'Desc3'] }) merged = pd.merge(df1, df2, on='id', how='left') print(merged) ``` When I run this code, I expect to see a DataFrame with the `id` column containing 1, 2, and NaN, but instead, I get this output: ``` id value description 0 1.0 A Desc1 1 2.0 B Desc2 2 NaN D NaN 3 NaN NaN Desc3 ``` It seems that the merge is treating the NaN values as distinct, leading to the unexpected duplicate rows. I have tried using the `dropna()` function before merging, but that strips out too much data: ```python merged = pd.merge(df1.dropna(subset=['id']), df2.dropna(subset=['id']), on='id', how='left') ``` This doesn't yield the results I need because I want to keep the rows from `df1` that have NaN `id` values with their corresponding rows from `df2`. Is there a way to handle this situation properly while still achieving the desired output without ending up with duplicates? I'm using Pandas version 1.3.3. Any insights on how to resolve this scenario would be greatly appreciated! What am I doing wrong? I'm coming from a different tech stack and learning Python. Could this be a known issue?