50. Capstone: designing a small analytics tool

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.

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