Pandas DataFrame: How to Efficiently Merge Multiple DataFrames with Different Column Names?
I'm working on a personal project and I'm currently working with several DataFrames in pandas that I need to merge. However, the DataFrames have different column names and types which is causing issues when I try to use `pd.merge()`. For example, I have the following two DataFrames: ```python import pandas as pd df1 = pd.DataFrame({ 'id': [1, 2, 3], 'value_a': [10, 20, 30] }) df2 = pd.DataFrame({ 'identifier': [1, 2, 4], 'value_b': [100, 200, 400] }) ``` I want to merge these two DataFrames on the `id` column from `df1` and the `identifier` column from `df2`. When I try to do this with the following code: ```python merged_df = pd.merge(df1, df2, left_on='id', right_on='identifier') ``` I get the output I expect, but the merged DataFrame looks like this: ```python id value_a identifier value_b 0 1 10 1 100 1 2 20 2 200 ``` While this is correct, I would prefer to drop the `identifier` column after the merge to simplify the DataFrame. I attempted to do this using: ```python merged_df.drop(columns=['identifier'], inplace=True) ``` However, I'm concerned about the performance implications of using `inplace=True` on large DataFrames. Are there more efficient ways to handle merging and dropping columns, especially when working with larger datasets? Additionally, is there a best practice for renaming columns before merging to avoid this kind of issue? Would it be better to rename before the merge as follows: ```python df2.rename(columns={'identifier': 'id'}, inplace=True) merged_df = pd.merge(df1, df2, on='id') ``` I'm looking for guidance on best practices for merging DataFrames with different column names while considering performance and readability. Any insights would be greatly appreciated!