尝试从一个字典列表创建一个 Stock 实例列表，然后计算总成本： >>> import fileparse >>> with open('Data/portfolio.csv') as lines: ... portdicts = fileparse.parse_csv(lines, select= ['name','shares','price'], types=[str,int,float]) ... >>> portfolio = [ stock.Stock(d['name'], d['shares'], d['price']) for d in portdicts] >>> portfolio [<stock.Stock object at 0x10c9e2128>, <stock.Stock object at 0x10c9e2048>, <stock.Stock object at 0x10c9e2080>, <stock.Stock object at 0x10c9e25f8>, <stock.Stock object at 0x10c9e2630>, <stock.Stock object at 0x10ca6f748>, <stock.Stock object at 0x10ca6f7b8>] >>> sum([s.cost() for s in portfolio]) 44671.15。写fileparse.py

好的，以下是一个简单的fileparse模块的示例代码，用于解析CSV格式的文件：

import csv
import os


def parse_csv(lines, select=None, types=None, has_headers=True, delimiter=',', silence_errors=False):
    rows = csv.reader(lines, delimiter=delimiter)

    # 读取列头
    headers = []
    if has_headers:
        try:
            headers = next(rows)
        except csv.Error as e:
            if not silence_errors:
                print(f"Error reading headers: {e}")

    # 如果有指定列，则仅保留指定的列
    if select and headers:
        indices = [headers.index(colname) for colname in select]
        headers = select
    else:
        indices = []

    # 如果指定了类型，则将指定列转换为指定类型
    if types:
        types = [str] * len(headers) if not types else types
        try:
            rows = [[func(val) for func, val in zip(types, row)] for row in rows]
        except ValueError as e:
            if not silence_errors:
                print(f"Error parsing row: {e}")
            return None

    # 组合每一行的数据
    records = []
    for row in rows:
        if not row:
            continue
        if indices:
            row = [row[index] for index in indices]
        if has_headers:
            record = dict(zip(headers, row))
        else:
            record = tuple(row)
        records.append(record)

    return records

这个模块可以解析CSV格式的文件，并将每一行的数据组合成一个字典或者元组。接下来可以用这个模块来解析投资组合的CSV文件，然后创建Stock实例列表，并计算总成本。