A realistic small tool ingests raw records, validates/cleans them, aggregates statistics, and formats a report — the same shape whether it analyzes trades, transactions, or gas usage.
python
from dataclasses import dataclass
from collections import defaultdict
@dataclass
class Trade:
pair: str
amount: float
fee: float
def parse_trades(rows: list[dict]) -> list[Trade]:
return [Trade(r["pair"], float(r["amount"]), float(r["fee"])) for r in rows]
def summarize(trades: list[Trade]) -> dict:
volumes = defaultdict(float)
fees = defaultdict(float)
for t in trades:
volumes[t.pair] += t.amount
fees[t.pair] += t.fee
return {"volume": dict(volumes), "fees": dict(fees)}Composing small, tested, single-purpose functions like this — rather than one monolithic script — is what makes real-world Python codebases (blockchain analytics included) maintainable as they grow.