👀 Views: 99 💬 Answers: 1 📅 Created: 2025-06-13

pandas dataframe merge Python

I've searched everywhere and can't find a clear answer. I'm working on a project and hit a roadblock... I'm following best practices but I've searched everywhere and can't find a clear answer... I'm trying to merge two DataFrames using `pd.merge()` in Pandas 1.5.1, but I'm working with a `KeyError` because the column names I'm using for merging don't match exactly between the two DataFrames. Here's what I have: ```python import pandas as pd df1 = pd.DataFrame({ 'user_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'] }) df2 = pd.DataFrame({ 'id': [1, 2, 4], 'age': [25, 30, 22] }) # Attempting to merge on different column names merged_df = pd.merge(df1, df2, left_on='user_id', right_on='id') ``` When I run this, I get the following behavior: ``` KeyError: 'user_id' ``` I've checked the DataFrames, and the columns do exist, but I suspect the scenario arises from some extra spaces or hidden characters in the column names. I've also tried using `df.columns` to debug, and it looks fine. Here’s what I’ve done: 1. Ensured that both DataFrames are not empty before merging. 2. Checked for whitespace using `df.columns.str.strip()` but didn’t find any. 3. Tried renaming the columns directly: ```python # Renaming columns before merge df2.rename(columns={'id': 'user_id'}, inplace=True) merged_df = pd.merge(df1, df2, on='user_id') ``` This did work, but I'm curious if there’s a more efficient way to handle this scenario without needing to rename columns beforehand. Ideally, I’d like to avoid modifying the original DataFrames if possible. Any suggestions or best practices for handling such cases? Any ideas what could be causing this? My development environment is macOS. How would you solve this? I recently upgraded to Python stable. Thanks, I really appreciate it! I'm using Python 3.11 in this project. Am I missing something obvious?