在python中使用csv模块写入特定单元格

Question

我必须在csv文件中写入一个值到一个特定的单元格（比如第8个单元格）。 我可以看到有一个csvwriter.writerow(row)方法可以写入整行，但我没有看到任何东西可以将值写入特定单元格。

Answer 1

csv module提供了读取和写入csv文件的工具，但是不允许特定于修改的单元格就位。

即使您在问题中突出显示的csvwriter.writerow(row)方法也不允许您标识和覆盖特定行。相反，它将row参数写入writer的文件对象，实际上它只是将与writer关联的csv文件附加到一行。

不要被劝阻使用csv module尽管，它使用起来很简单，如果您可以相对容易地实现您所寻找的更高级别的功能，就可以使用原语。

例如，查看以下csv文件：

1,2,3,four,5
1,2,3,four,5
1,2,3,four,5

单词four位于第3列（第四列，但一行只是一个列表，因此索引是基于零的），可以使用以下程序轻松更新此值以包含数字4：

import csv
in_file = open("d:/in.csv", "rb")
reader = csv.reader(in_file)
out_file = open("d:/out.csv", "wb")
writer = csv.writer(out_file)
for row in reader:
    row[3] = 4
    writer.writerow(row)
in_file.close()    
out_file.close()

导致输出：

1,2,3,4,5
1,2,3,4,5
1,2,3,4,5

允许创建一些允许识别和更新特定行和列的泛型函数需要做更多的工作，但并不是更多，因为在Python中操作csv文件只是操作一系列列表。

Answer 2

我同意，这很烦人。我最终得到了csv.DictReader的子类。这允许基于单元格的就地查找编辑和转储。我在activestate上发布了代码：In place csv lookup, manipulation and export

import csv, collections, copy

"""
# CSV TEST FILE 'test.csv'

TBLID,DATETIME,VAL
C1,01:01:2011:00:01:23,5
C2,01:01:2012:00:01:23,8
C3,01:01:2013:00:01:23,4
C4,01:01:2011:01:01:23,9
C5,01:01:2011:02:01:23,1
C6,01:01:2011:03:01:23,5
C7,01:01:2011:00:01:23,6
C8,01:01:2011:00:21:23,8
C9,01:01:2011:12:01:23,1


#usage (saving this cose as CustomDictReader.py)

>>> import CustomDictReader
>>> import pprint
>>> test = CustomDictReader.CSVRW()
>>> success, thedict = test.createCsvDict('TBLID',',',None,'test.csv')
>>> pprint.pprint(dict(thedict))
{'C1': OrderedDict([('TBLID', 'C1'), ('DATETIME', '01:01:2011:00:01:23'), ('VAL', '5')]),
 'C2': OrderedDict([('TBLID', 'C2'), ('DATETIME', '01:01:2012:00:01:23'), ('VAL', '8')]),
 'C3': OrderedDict([('TBLID', 'C3'), ('DATETIME', '01:01:2013:00:01:23'), ('VAL', '4')]),
 'C4': OrderedDict([('TBLID', 'C4'), ('DATETIME', '01:01:2011:01:01:23'), ('VAL', '9')]),
 'C5': OrderedDict([('TBLID', 'C5'), ('DATETIME', '01:01:2011:02:01:23'), ('VAL', '1')]),
 'C6': OrderedDict([('TBLID', 'C6'), ('DATETIME', '01:01:2011:03:01:23'), ('VAL', '5')]),
 'C7': OrderedDict([('TBLID', 'C7'), ('DATETIME', '01:01:2011:00:01:23'), ('VAL', '6')]),
 'C8': OrderedDict([('TBLID', 'C8'), ('DATETIME', '01:01:2011:00:21:23'), ('VAL', '8')]),
 'C9': OrderedDict([('TBLID', 'C9'), ('DATETIME', '01:01:2011:12:01:23'), ('VAL', '1')])}
>>> thedict.keys()
['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']
>>> thedict['C2']['VAL'] = "BOB"
>>> pprint.pprint(dict(thedict))
{'C1': OrderedDict([('TBLID', 'C1'), ('DATETIME', '01:01:2011:00:01:23'), ('VAL', '5')]),
 'C2': OrderedDict([('TBLID', 'C2'), ('DATETIME', '01:01:2012:00:01:23'), ('VAL', 'BOB')]),
 'C3': OrderedDict([('TBLID', 'C3'), ('DATETIME', '01:01:2013:00:01:23'), ('VAL', '4')]),
 'C4': OrderedDict([('TBLID', 'C4'), ('DATETIME', '01:01:2011:01:01:23'), ('VAL', '9')]),
 'C5': OrderedDict([('TBLID', 'C5'), ('DATETIME', '01:01:2011:02:01:23'), ('VAL', '1')]),
 'C6': OrderedDict([('TBLID', 'C6'), ('DATETIME', '01:01:2011:03:01:23'), ('VAL', '5')]),
 'C7': OrderedDict([('TBLID', 'C7'), ('DATETIME', '01:01:2011:00:01:23'), ('VAL', '6')]),
 'C8': OrderedDict([('TBLID', 'C8'), ('DATETIME', '01:01:2011:00:21:23'), ('VAL', '8')]),
 'C9': OrderedDict([('TBLID', 'C9'), ('DATETIME', '01:01:2011:12:01:23'), ('VAL', '1')])}
>>> test.updateCsvDict(thedict)
>>> test.createCsv('wb')
"""

class CustomDictReader(csv.DictReader):
    """
        override the next() function and  use an
        ordered dict in order to preserve writing back
        into the file
    """

    def __init__(self, f, fieldnames = None, restkey = None, restval = None, dialect ="excel", *args, **kwds):
        csv.DictReader.__init__(self, f, fieldnames = None, restkey = None, restval = None, dialect = "excel", *args, **kwds)

    def next(self):
        if self.line_num == 0:
            # Used only for its side effect.
            self.fieldnames
        row = self.reader.next()
        self.line_num = self.reader.line_num

        # unlike the basic reader, we prefer not to return blanks,
        # because we will typically wind up with a dict full of None
        # values
        while row == []:
            row = self.reader.next()
        d = collections.OrderedDict(zip(self.fieldnames, row))

        lf = len(self.fieldnames)
        lr = len(row)
        if lf < lr:
            d[self.restkey] = row[lf:]
        elif lf > lr:
            for key in self.fieldnames[lr:]:
                d[key] = self.restval
        return d

class CSVRW(object):

    def __init__(self):
        self.file_name = ""
        self.csv_delim = ""
        self.csv_dict  = collections.OrderedDict()

    def setCsvFileName(self, name):
        """
            @brief stores csv file name
            @param name- the file name
        """
        self.file_name = name

    def getCsvFileName(self):
        """
            @brief getter
            @return returns the file name
        """
        return self.file_name

    def getCsvDict(self):
        """
            @brief getter
            @return returns a deep copy of the csv as a dictionary
        """
        return copy.deepcopy(self.csv_dict)

    def clearCsvDict(self):
        """
            @brief resets the dictionary
        """
        self.csv_dict = collections.OrderedDict()

    def updateCsvDict(self, newCsvDict):
        """
            creates a deep copy of the dict passed in and
            sets it to the member one
        """
        self.csv_dict = copy.deepcopy(newCsvDict)

    def createCsvDict(self,dictKey, delim, handle = None, name = None, readMode = 'rb', **kwargs):
        """
            @brief create a dict from a csv file where:
                the top level keys are the first line in the dict, overrideable w/ **kwargs
                each row is a dict
                each row can be accessed by the value stored in the column associated w/ dictKey

                that is to say, if you want to index into your csv file based on the contents of the
                third column, pass the name of that col in as 'dictKey'

            @param dictKey  - row key whose value will act as an index
            @param delim    - csv file deliminator
            @param handle   - file handle (leave as None if you wish to pass in a file name)
            @param name     - file name   (leave as None if you wish to pass in a file handle)
            @param readMode - 'r' || 'rb'
            @param **kwargs - additional args allowed by the csv module
            @return bool    - SUCCESS|FAIL
        """
        self.csv_delim = delim
        try:
            if isinstance(handle, file):
                self.setCsvFileName(handle.name)
                reader = CustomDictReader(handle, delim, **kwargs)
            else:
                if None == name:
                    name = self.getCsvFileName()
                else:
                    self.setCsvFileName(name)
                reader = CustomDictReader(open(name, readMode), delim, **kwargs)
            for row in reader:
                self.csv_dict[row[dictKey]] = row
            return True, self.getCsvDict()
        except IOError:
            return False, 'Error opening file'

    def createCsv(self, writeMode, outFileName = None, delim = None):
        """
            @brief create a csv from self.csv_dict
            @param writeMode   - 'w' || 'wb'
            @param outFileName - file name || file handle
            @param delim       - csv deliminator
            @return none
        """
        if None == outFileName:
            outFileName = self.file_name
        if None == delim:
            delim = self.csv_delim
        with open(outFileName, writeMode) as fout:
            for key in self.csv_dict.values():
                fout.write(delim.join(key.keys()) + '\n')
                break
            for key in self.csv_dict.values():
                fout.write(delim.join(key.values()) + '\n')

Answer 3

假设您有一个名为mylist.csv的csv文件，文件行如下：

a, b, c, d

e, f, g, h

i, j, k, l

如果要将“h”修改为“X”，可以使用此代码，需要导入csv模块：

    f = open('mylist.csv', 'r')
    reader = csv.reader(f)
    mylist = list(reader)
    f.close()
    mylist[1][3] = 'X'
    my_new_list = open('mylist.csv', 'w', newline = '')
    csv_writer = csv.writer(my_new_list)
    csv_writer.writerows(mylist)
    my_new_list.close()

如果要修改每行的特定列，只需添加for循环以进行迭代。