I have some existing Pandas code which calculates the mean of some timeseries data on a month period.

 df
.groupby(
    pandas.Grouper(
        key='transaction_date',
        freq='M',
    )
)
.aggregate(
    {
        'transaction_date': 'first',
        'price': 'mean'
    }
)

The resulting data still maintains a large amount of variance. I would like to reduce this variance by applying a rolling mean operation over a period of 6 months.

I have not found a good solution to this.

• I tried replacing groupby with rolling and using a pandas.Grouper. However the rolling API does not work with Grouper objects.
• I tried another code (below), however this failed with an exception.

raise ValueError("window must be an integer 0 or greater")
    ValueError: window must be an integer 0 or greater

    df_slow = (
        df
        .copy()
        .set_index('transaction_date')
        .rolling(
            window=timedelta(days=1),
        )
        .aggregate(
            {
                'price': 'mean'
            }
        )
        .rename(columns={'price': 'mean'})
    )
    
    df_slow['transaction_date'] = (
        df
        .copy()
        .set_index('transaction_date')
        .index
        .to_series()
        .rolling(
            window=timedelta(days=1)
        )
        .min()
    )

• Finally I tried this (below).

    df_slow = (
        df
        .copy()
        .groupby(
            pandas.Grouper(
                key='transaction_date',
                freq='D',
            )
        )
        .aggregate({'price': 'mean'})
    )

    df_slow['price_rolling'] = (
        df        ['price']
        .rolling(
            window=180, # 180 days
        )
        .aggregate('mean')
    )

This works, but I would have thought there is a more straght forward solution to this problem.

Can anyone help me understand what the sensible approach is here?