使用python读取/写入/附加到CSV

我正在尝试使用python的CSV模块修改CSV文件。该文件表示股票，并以列的形式列出当天的日期、开盘价、高价、低价、收盘价和成交量。我想做的是通过对现有数据执行代数来创建多个新列。例如，我想在这里创建一个列，显示任意一天开盘价与高价之间的百分比，以及从昨天收盘价到今天收盘价之间的百分比变化，现在考虑添加10个列

有没有一种紧凑的方法可以做到这一点？现在，我正在打开原始文件，并将感兴趣的值读取到列表中。然后使用该列表将修改后的值写入某个临时文件。然后使用for循环写入新文件，并添加每个电子表格中的行。然后将新文件的全部内容写入原始csv，因为我想保留csv ticker.csv的名称

希望我已经把我的问题说清楚了。如果您想要任何澄清或进一步的细节，请不要犹豫

编辑：我已经包含了下面一个函数的代码片段。该函数试图创建一个新列，该列包含从昨天收盘到今天收盘的百分比变化

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

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

---

你也应该退房；你可以使用loadtxt和向量数学，但@lightalchesman是对的，它就是为此而设计的。

唉，至少我不清楚。然而，我想你只需要通过一次就可以把数据读入内存，再通过另一次就可以把它写出来——我怀疑中间文件是否真的有必要。为了提供更清晰的解释，请至少发布代码的概要。@LarryLustig-edit。谢谢你的回复！你可能想考虑使用熊猫图书馆。您可以将csv文件作为数据帧读入，创建一个新系列，并在写入之前将该系列附加到数据帧。