How to implement guide with merging two dataframes on datetime index in pandas
After trying multiple solutions online, I still can't figure this out. I'm trying to merge two DataFrames on a DateTime index, but I'm working with unexpected behavior where the merge results in a lot of NaN values in the resulting DataFrame. I'm using Pandas version 1.3.0. My first DataFrame, `df1`, looks like this: ```python import pandas as pd date_rng = pd.date_range(start='2021-01-01', end='2021-01-10', freq='D') df1 = pd.DataFrame(date_rng, columns=['date']) df1['data1'] = range(10) df1.set_index('date', inplace=True) ``` And my second DataFrame, `df2`, is created as follows: ```python date_rng2 = pd.date_range(start='2021-01-05', end='2021-01-15', freq='D') df2 = pd.DataFrame(date_rng2, columns=['date']) df2['data2'] = range(10, 20) df2.set_index('date', inplace=True) ``` When I attempt to merge them using: ```python result = df1.merge(df2, how='outer', left_index=True, right_index=True) ``` I get a lot of NaN values for the `data2` column where the dates from `df2` exceed those in `df1`. I was expecting the merge to fill those gaps with NaNs only for the dates that don’t match. However, it seems like it’s also introducing NaNs for the overlapping dates. Is there a specific reason for this behavior? What’s the best way to handle this merge to avoid unexpected NaNs? I’ve also tried using `how='inner'` but that doesn’t guide to retain all the data I need. Any tips on how to proceed would be greatly appreciated! For context: I'm using Python on Linux. Any feedback is welcome!