👀 Views: 98 💬 Answers: 1 📅 Created: 2025-06-10

pandas dataframe merge Python

I'm working through a tutorial and I need some guidance on I'm experiencing unexpected results when trying to merge two DataFrames on multiple keys using the `pd.merge()` function..... I have two DataFrames, `df1` and `df2`, and I want to merge them based on two columns: 'id' and 'date'. However, after the merge, I'm seeing duplicate rows in the resulting DataFrame that I didn't anticipate. My DataFrames look like this: ```python import pandas as pd df1 = pd.DataFrame({ 'id': [1, 2, 3, 1], 'date': ['2023-10-01', '2023-10-01', '2023-10-02', '2023-10-01'], 'value1': [10, 20, 30, 40] }) df2 = pd.DataFrame({ 'id': [1, 2], 'date': ['2023-10-01', '2023-10-01'], 'value2': [100, 200] }) ``` When I perform the merge: ```python result = pd.merge(df1, df2, on=['id', 'date'], how='inner') print(result) ``` I expected the result to have unique rows based on the keys, but instead, I see: ``` id date value1 value2 0 1 2023-10-01 10 100 1 1 2023-10-01 40 100 2 2 2023-10-01 20 200 ``` Can anyone explain why this is happening? I've checked for duplicates in `df1` and `df2`, and they don't exist based on the merge keys. It seems like the merge operation is duplicating rows based on `value1` in `df1`. I'm using Pandas version 1.5.1. Any help would be appreciated! Am I missing something obvious? What would be the recommended way to handle this? Thanks in advance!