Fix resampler chaining issue for compound returns in core.py

[Pranav-20186017]

Bug Fix: Resample Operation in quantstats/_plotting/core.py

Description

This PR fixes a small bug in quantstats/_plotting/core.py (lines 292–298), where resample operations were separated into two statements. That caused a DatetimeIndexResampler object to be stored in returns or benchmark, triggering the error:

Error: numpy operations are not valid with resample. Use .resample(...).sum() instead

What Changed

Before:

if resample:
    returns = returns.resample(resample)
    returns = returns.last() if compound else returns.sum(axis=0)
    if isinstance(benchmark, _pd.Series):
        benchmark = benchmark.resample(resample)
        benchmark = benchmark.last() if compound else benchmark.sum(axis=0)

The returns and benchmark were first assigned to a resampler object and then incorrectly processed. This resulted in an error when attempting NumPy operations on the resampler object.

Updated Implementation:

if resample:
    if compound:
        returns = returns.resample(resample).last()
        if isinstance(benchmark, _pd.Series):
            benchmark = benchmark.resample(resample).last()
    else:
        returns = returns.resample(resample).sum()
        if isinstance(benchmark, _pd.Series):
            benchmark = benchmark.resample(resample).sum()

The resample and aggregation calls are now correctly chained. This approach ensures that returns and benchmark remain valid DataFrame/Series objects after resampling.

Motivation for the Change

• When compound = True, .last() is applied to capture the final value for each resampled period
• When compound = False, .sum() is applied to aggregate the values within each resampled period
• This change avoids assigning resampler objects and prevents runtime errors

Benefits

• Prevents errors caused by attempting NumPy operations on resampler objects
• Restores the correct resampling and aggregation logic for both compound and non-compound cases
• Maintains compatibility with Pandas 2.x, which enforces stricter rules for resamplers
• Adheres to common chaining practices like df.resample(...).sum()

Please review this PR and let me know if you have any feedback or suggestions! I am happy to make further improvements if needed.