我有两个csv文件，其结构如下：
Fil1.csv：
66054,14.7065,42.1115
66054,14.7085,42.106
66054,14.7268,42.0937
66054,14.6739,42.125
66054,14.7268,42.0937
66100,14.116,42.3301
66100,14.1405,42.3392
88067,16.431,38.7287
88068,16.5339,38.6899
88068,16.5499,38.685
88068,16.5419,38.6875
87076,16.4795,39.7905
87076,16.4743,39.8161
87100,16.2531,39.2989
87100,16.2944,39.2674
87100,16.3039,39.2709
87052,16.43,39.3449
87053,16.3399,39.3101
87054,16.3171,39.1784
file2.csv：
ABC,66100
"CDF",65125
"123",65125
1234,64100
0123,75025
lmn,85025
abc,88046
"Random",88068
"Raond2",87100
"Raondm3",87100
Raondom4,87054
现在我要做的是为row2[1]中的每个file2.csv，在row1[0]中找到它的第一个匹配项，然后从中提取row1[1]和row1[2]与row2[0]和row2[1]一起插入的行，并将其写入另一个csv文件。这是我为此编写的代码：
updated_list = []
with open("file1.csv","r") as in_file1, open("file2.csv", "r") as in_file2, open("file3.csv", "w", newline='') as out_file:
reader1 = csv.reader(in_file1)
reader2 = csv.reader(in_file2)
writer_final = csv.writer(out_file)
for row2 in reader2: #reader2 is for file2
for row1 in reader1:#reader1 is for file1
if str(row2[1].strip()) == str(row1[0].strip()):
print("Found match for {}".format(row2[1]))
updated_list.append([row2[0],row2[1],row1[1],row1[2]])
break
else:
continue
writer_final.writerows(updated_geo_list)
上面的代码能够匹配row2[1]中的某些file2.csv，但对于许多row1[0]，即使存在，也无法与file1.csv中的87100匹配。例如，在上述示例数据中，尽管87054包含两个值，但是代码无法将file2.csv的{{1}}和file1.csv匹配到file1.csv。我虽然这些字符串中可能会有一些多余的空格，所以我也使用了split()，但是它仍然无法正常工作。为什么没有进行匹配？
答案 0 :(得分：0)
在运行代码并将print语句放在几个地方后，观察到仅对file2.csv的第一个值进行了比较，即“ ABC，66100”。其余代码只是跳过。
那是因为csv.reader返回了一个作为迭代器的阅读器对象。
因此，一旦您迭代了整个对象，它就会变为空。
解决方法是，您需要将阅读器另存为列表，以便反复进行迭代。
修改线
reader1 = csv.reader(in_file1)
到
reader1 = list(csv.reader(in_file1))
应该给您想要的结果。
import csv
updated_list = []
with open("file1.csv","r") as in_file1, open("file2.csv", "r") as in_file2, open("file3.csv", "w", newline='') as out_file:
reader2 = csv.reader(in_file2)
reader1 = list(csv.reader(in_file1))
writer_final = csv.writer(out_file)
for row2 in reader2: #reader2 is for file2
for row1 in reader1: #reader1 is for file1
if str(row2[1].strip()) == str(row1[0].strip()):
updated_list.append([row2[0],row2[1],row1[1],row1[2]])
break
writer_final.writerows(updated_list)
cat file3.csv
ABC,66100,14.116,42.3301
Random,88068,16.5339,38.6899
Raond2,87100,16.2531,39.2989
Raondm3,87100,16.2531,39.2989
Raondom4,87054,16.3171,39.1784
注意
如果文件很大，将阅读器转换为列表可能会有害，因为这可能会影响内存。更好的选择是使用openpyxl或将数据加载到pandas数据框中并在那里进行操作。
答案 1 :(得分：0)
正如我的评论中提到的那样：文件对象是流，一旦经过某个点，您将无法再看到它-您需要将文件放入内存中，以将一个文件的所有行与另一个文件进行比较。
此代码将较小的文件读入内存并逐行处理较大的文件。
匹配文件较小的所有行的第一个匹配行请求者数据，然后将较小文件的行从内存中删除，因此与以后的文件不匹配：
创建文件：
with open("f1.txt","w") as f:
f.write("""66054,14.7065,42.1115
66054,14.7085,42.106
66054,14.7268,42.0937
66054,14.6739,42.125
66054,14.7268,42.0937
66100,14.116,42.3301
66100,14.1405,42.3392
88067,16.431,38.7287
88068,16.5339,38.6899
88068,16.5499,38.685
88068,16.5419,38.6875
87076,16.4795,39.7905
87076,16.4743,39.8161
87100,16.2531,39.2989
87100,16.2944,39.2674
87100,16.3039,39.2709
87052,16.43,39.3449
87053,16.3399,39.3101
87054,16.3171,39.1784""")
with open ("f2.txt","w") as f:
f.write("""ABC,66100
"CDF",65125
"123",65125
1234,64100
0123,75025
lmn,85025
abc,88046
"Random",88068
"Raond2",87100
"Raondm3",87100
Raondom4,87054""")
程序
import csv
d2 ={}
# smaller file: load in memory
with open("f2.txt") as f:
cr = csv.reader(f)
for row in cr:
# store under same key as list of rows to keep same order and
# allow multiple rows with same row[1] value
k = d2.setdefault(row[1],[])
k.append(row)
# process larger file
with open("f1.txt") as f, open("f3.txt","w",newline="") as nf:
cr = csv.reader(f)
writer = csv.writer(nf)
for row in cr:
if d2.get(row[0],[]):
for sl in d2.get(row[0]):
writer.writerow( (sl + [row[1],row[2]]) )
# remove from d2 so no reappearing rows will be written
del d2[row[0]]
with open("f3.txt") as f:
print(f.read())
输出：
ABC,66100,14.116,42.3301
Random,88068,16.5339,38.6899
Raond2,87100,16.2531,39.2989
Raondm3,87100,16.2531,39.2989
Raondom4,87054,16.3171,39.1784
只有来自file2的东西在file1中完全匹配。