Pandas: guide with MultiIndex DataFrame Resampling - Unexpected NaNs in Result
I need some guidance on I'm currently working with a MultiIndex DataFrame that contains time series data and trying to resample it to a higher frequency. My DataFrame looks something like this: ```python import pandas as pd import numpy as np dates = pd.date_range('2023-01-01', periods=5, freq='D') idx = pd.MultiIndex.from_product([['A', 'B'], dates], names=['Category', 'Date']) data = np.random.randn(len(idx)) df = pd.DataFrame(data, index=idx, columns=['Value']) print(df) ``` This results in a DataFrame where each category has daily values. When I attempt to resample the data to a weekly frequency using: ```python df_resampled = df.groupby('Category').resample('W').sum() ``` I encounter unexpected `NaN` values in the resulting DataFrame. My expectation is that the resampling should provide sums for the available weeks, but instead, I see `NaN` entries for some of the categories like this: ```plaintext Category Date A 2023-01-01 0.123456 2023-01-08 NaN B 2023-01-01 0.654321 2023-01-08 NaN ``` I've tried ensuring that the data is properly indexed and that there are no gaps in the dates, but the `NaN` values continue. I also looked into using `fillna()` and `ffill()` to handle these gaps, but I'd prefer to understand why this is happening before applying any workarounds. I'm using Pandas version 1.3.5. Any insights on what might be causing this behavior? What would be the recommended way to handle this?