Pandas DataFrame not maintaining column order after merge operation with suffixes
After trying multiple solutions online, I still can't figure this out... I'm sure I'm missing something obvious here, but I am merging two DataFrames using `pd.merge()` with the `how='outer'` option, but I am noticing that the order of the columns in the resulting DataFrame is not what I expect. I want to maintain the original order of columns from the left DataFrame after the merge. Here's what I have tried: ```python import pandas as pd # Sample DataFrames left_df = pd.DataFrame({ 'A': [1, 2], 'B': [3, 4], 'C': [5, 6] }) right_df = pd.DataFrame({ 'B': [7, 8], 'C': [9, 10], 'D': [11, 12] }) # Merging DataFrames result = pd.merge(left_df, right_df, on='B', how='outer', suffixes=('_left', '_right')) print(result) ``` When I run the above code, I expect the resulting DataFrame to maintain the order of columns as in `left_df`, but instead, I get: ``` A B C C_right D 0 1 3 5 NaN NaN 1 2 4 6 NaN NaN 2 NaN 7 NaN 9.0 11.0 3 NaN 8 NaN 10.0 12.0 ``` As you can see, the columns `C_right` and `D` from `right_df` appear at the end, disrupting my intended order. I tried using the `sort=False` parameter in the merge function, but it didn't solve the scenario of order for the columns. Is there a way to force the merged DataFrame to keep the column order of `left_df`? Any suggestions or best practices would be greatly appreciated. This is part of a larger CLI tool I'm building. Any ideas what could be causing this? How would you solve this?