Question

我正在尝试使用python的CSV模块来修改CSV文件。该文件代表一个股票并列出（作为列）日期，开盘价，高价，低价，收盘价和当天的交易量。我想要做的是通过对现有数据执行代数来创建多个新列。例如，我想创建一个列，从开放价格到任何特定日期的高价格，以及从昨天接近今天收盘时的百分比变化（这里没有结束，现在正在考虑要添加的10列）。

有一种紧凑的方法吗？截至目前，我正在打开原始文件并将列表读入感兴趣的值。然后使用该列表在一些临时文件上写入修改后的值。然后使用for循环写入一些新文件并添加每个电子表格中的行。然后将该新文件的全部内容写入原始csv，因为我希望保留csv（ticker.csv）的名称。

希望我已经明确了我的问题。如果您想要任何澄清或进一步的细节，请不要犹豫。

编辑：我已经为下面的一个函数添加了一段代码。该函数旨在创建一个新列，其变化百分比从昨天接近今天收盘。

def add_col_pchange(ticker):
    """
    Add column with percent change in closing price.
    """
    original = open('file1', 'rb')
    reader = csv.reader(original)
    reader.next()
    close = list()
    for row in reader:
        # build list of close values; entries from left to right are reverse chronological
        # index 4 corresponds to "Close" column
        close.append(float(row[4])
    original.close()

    new = open(file2, 'wb')
    writer = csv.writer(new)
    writer.writerow(["Percent Change"])
    pchange = list()
    for i in (0, len(close)-1):
        x = (close[i]-close[i+1])/close[i+1]
        pchange.append(x)
    new.close()

    # open original and new csv's as read, write out to some new file.  
    # later, copy that entire file to original csv in order to maintain 
    # original csv's name and include new data

Answer 1

希望这有帮助

def add_col_pchange(ticker):
    """
    Add column with percent change in closing price.
    """
    # always use with to transparently manage opening/closing files
    with open('ticker.csv', 'rb') as original:
        spam = csv.reader(original)
        headers = spam.next()  # get header row
        # get all of the data at one time, then transpose it using zip
        data = zip(*[row for row in spam])
    # build list of close values; entries from left to right are reverse chronological
    # index 4 corresponds to "Close" column
    close = data[4]  # the 5th column has close values

    # use map to process whole column at one time
    f_pchange = lambda close0, close1: 100 * (float(close0) - float(close1)) / float(close1)
    Ndays = len(close)  # length of table
    pchange = map(f_pchange, close[:-1], close[1:])  # list of percent changes
    pchange = (None,) + tuple(pchange)  # add something for the first or last day
    headers.append("Percent Change")  # add column name to headers
    data.append(pchange)
    data = zip(*data)  # transpose back to rows

    with open('ticker.csv', 'wb') as new:
        spam = csv.writer(new)
        spam.writerow(headers)  # write headers
        for row in data:
            spam.writerow(row)

    # open original and new csv's as read, write out to some new file.  
    # later, copy that entire file to original csv in order to maintain 
    # original csv's name and include new data

你应该看看numpy;您可以使用loadtxt()和矢量数学，但@lightalchemist是正确的，pandas就是为此设计的。

使用python读/写/附加到CSV

1 个答案: