在Python中使用csv模块写入特定单元格

Question

我必须在我的csv文件中为特定单元格写入一个值（比如第8个单元格）。 我可以看到有一个csvwriter.writerow(row)方法来写整行，但我没有看到任何东西写入一个特定的单元格的值。

Answer 1

csv module提供了读写csv文件的工具，但不允许修改特定的单元格就地。

即使您在问题中突出显示的csvwriter.writerow(row)方法也不允许您识别并覆盖特定的行。而是将row参数写入作者的文件对象，实际上它只是将与作者相关联的csv文件附加到一行。

不要被劝阻使用csv module虽然它使用起来很简单，并且提供的原语可以实现您比较容易找到的更高级别的功能。

例如看看下面的CSV文件：

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

字four是第3列（第四列，但一排就是这么的索引是从零开始的列表），这样可以能够容易地更新为包含数字4与以下程序：

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() 

在输出所得：

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

授予创建一些通用函数，允许识别和更新特定的行和列是多一点工作，但没有太多更多，因为在Python中操作csv文件只是操纵一系列列表。

Answer 2

我同意，这很烦人。我完成了csv.DictReader的子类化。这允许基于单元格的查找编辑和转储。我已经张贴在ActiveState公司代码：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

假设你有一个名为mylist.csv csv文件具有以下行：

a, b, c, d 

e, f, g, h 

i, j, k, l 

，如果你想修改“H”，成为“X” ，可以使用此代码，需要将导入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() 

如果要修改于各行的特定列，只需添加for循环迭代。