Pandas: Issue with merging DataFrames based on index and multiple columns, resulting in unexpected NaN values
I'm encountering an issue while trying to merge two DataFrames in pandas based on the index and additional columns..... I have two DataFrames, `df1` and `df2`, and I want to merge them based on their indexes as well as a common column named `key`. However, the resulting DataFrame contains unexpected NaN values in several rows after the merge, and I'm unsure why this is happening. Here's a snippet of my code: ```python import pandas as pd # Creating the first DataFrame index1 = pd.date_range('2023-01-01', periods=5) df1 = pd.DataFrame({ 'key': ['A', 'B', 'C', 'D', 'E'], 'value1': [10, 20, 30, 40, 50] }, index=index1) # Creating the second DataFrame index2 = pd.date_range('2023-01-02', periods=5) df2 = pd.DataFrame({ 'key': ['B', 'C', 'D', 'F', 'G'], 'value2': [100, 200, 300, 400, 500] }, index=index2) # Merging based on index and 'key' column merged_df = pd.merge(df1, df2, how='outer', left_index=True, right_index=True, suffixes=('_left', '_right')) print(merged_df) ``` When I execute this code, I see the following output: ``` key_left value1 key_right value2 2023-01-01 A 10 NaN NaN 2023-01-02 B 20 100.0 100.0 2023-01-03 C 30 200.0 200.0 2023-01-04 D 40 300.0 300.0 2023-01-05 E 50 NaN NaN 2023-01-06 NaN NaN F 400.0 2023-01-07 NaN NaN G 500.0 ``` The NaN values appear in the rows where the date indexes don't match, but I expected the merge to handle that based on the 'key' column as well. I tried using both `how='outer'` and `how='inner'`, but the NaNs still persist in the merged DataFrame. Additionally, I have confirmed that the `key` values in `df1` and `df2` do overlap, so I'm puzzled as to why the merge isn't functioning as intended. I'm using pandas version 1.5.3, and I would appreciate any guidance or suggestions on how to resolve this issue. Is there a different approach I should consider for merging these DataFrames correctly without resulting in NaNs? My development environment is Windows 10. Hoping someone can shed some light on this.