My goal is to aggregate a DataFrame with several million rows including Decimal('...') columns without any precision loss. My current implementation works - but there might be more efficient ways to get the job done?!

Original DataFrame (sample)

Aggregated DataFrame (sample)

My current approach

import pandas as pd
import numpy as np
from decimal import Decimal
from uuid import uuid4

# generating some sample DataFrame
df = pd.DataFrame(
    data={
        "game": [1, 1, 1, 1, 2, 2],
        "position": ["a", "a", "a", "b", "a", "b"],
        "amount": [
            Decimal(f"{uuid4().int}"[:18]) * (Decimal(10) ** -18) for _ in range(6)
        ],
    }
)

# aggregating...
agg_df = df.groupby(["game", "position"], as_index=False).agg(
    {
        "amount": "sum",
    }
)

agg_df["amount_a"] = np.where(agg_df["position"] == "a", agg_df["amount"], 0)
agg_df["amount_b"] = np.where(agg_df["position"] == "b", agg_df["amount"], 0)

agg_df.drop(["amount", "position"], axis=1, inplace=True)

agg_df = agg_df.groupby(["game"], as_index=False).agg(
    {
        "amount_a": "sum",
        "amount_b": "sum",
    }
)

As far as i understand Python and Pandas, the biggest slowdown of my approach comes from the Decimal('...') columns in combination with sum. Since i need the precision of 10 ** -18 using the decimal package is my only option, right?

Is there a way in pandas to do the aggregation in less steps?