blocks|key|1799023|text|将它们更改为行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1799024|rows+=+zip(list1,+list2,+list3,+list4,+list5)|code-block|syntax|javascript|1799025|然后只需：|1799026|import+csv

with+open(newfilePath,+"w")+as+f:
++++writer+=+csv.writer(f)
++++for+row+in+rows:
++++++++writer.writerow(row)|1799027|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Change them to rows:
<pre><code>rows = zip(list1, list2, list3, list4, list5)
</code></pre>
Then just:
<pre><code>import csv

with open(newfilePath, &quot;w&quot;) as f:
 writer = csv.writer(f)
 for row in rows:
 writer.writerow(row)
</code></pre>

blocks|key|1584119|text|您可以使用izip组合列表，然后对它们进行迭代|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1584120|for+val+in+itertools.izip(l1,l2,l3,l4,l5):
++++writer.writerow(val)|code-block|syntax|javascript|1584121|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.python.org/2/library/itertools.html#itertools.izip^0|5|4|5|4|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

You can use <a href="http://docs.python.org/2/library/itertools.html#itertools.izip" rel="noreferrer"><code>izip</code></a> to combine your lists, and then iterate them

<pre><code>for val in itertools.izip(l1,l2,l3,l4,l5):
 writer.writerow(val)
</code></pre>

blocks|key|1584228|text|以下代码将python列表写入csv中的列|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1584229|import+csv
from+itertools+import+zip_longest
list1+=+['a',+'b',+'c',+'d',+'e']
list2+=+['f',+'g',+'i',+'j']
d+=+[list1,+list2]
export_data+=+zip_longest(*d,+fillvalue+=+'')
with+open('numbers.csv',+'w',+encoding="ISO-8859-1",+newline='')+as+myfile:
++++++wr+=+csv.writer(myfile)
++++++wr.writerow(("List1",+"List2"))
++++++wr.writerows(export_data)
myfile.close()|code-block|syntax|javascript|1584230|输出如下所示|1584231|​|1584232|📷|atomic|offset|length|1584233|1584234|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/07528a8988c2c270fb98aee2e044372e.png|imageAlt^0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|10|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|11|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|12|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|13|8|@]|9|@$N|14|O|15|1|16]]|A|$]]|$1|P|3|J|5|6|7|17|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X|Y|-4]]]]

The following code writes python lists into columns in csv

<pre><code>import csv
from itertools import zip_longest
list1 = ['a', 'b', 'c', 'd', 'e']
list2 = ['f', 'g', 'i', 'j']
d = [list1, list2]
export_data = zip_longest(*d, fillvalue = '')
with open('numbers.csv', 'w', encoding="ISO-8859-1", newline='') as myfile:
 wr = csv.writer(myfile)
 wr.writerow(("List1", "List2"))
 wr.writerows(export_data)
myfile.close()
</code></pre>

The output looks like this 

<a href="https://i.stack.imgur.com/Limy4.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Limy4.png" alt="enter image description here"></a>

blocks|key|1584241|text|import+csv
dic+=+{firstcol,secondcol}+#dictionary
csv+=+open('result.csv',+"w")+
for+key+in+dic.keys():
++++row+="\n"%2B+str(key)+%2B+","+%2B+str(dic[key])+
++++csv.write(row)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1584242|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>import csv
dic = {firstcol,secondcol} #dictionary
csv = open('result.csv', "w") 
for key in dic.keys():
 row ="\n"+ str(key) + "," + str(dic[key]) 
 csv.write(row)
</code></pre>

blocks|key|1799173|text|如果你乐于使用第三方库，你可以用Pandas来实现。好处包括无缝访问专用方法和行/列标签：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1799174|import+pandas+as+pd

list1+=+[1,+2,+3]
list2+=+[4,+5,+6]
list3+=+[7,+8,+9]

df+=+pd.DataFrame(list(zip(*[list1,+list2,+list3]))).add_prefix('Col')

df.to_csv('file.csv',+index=False)

print(df)

+++Col0++Col1++Col2
0+++++1+++++4+++++7
1+++++2+++++5+++++8
2+++++3+++++6+++++9|code-block|syntax|javascript|1799175|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

If you are happy to use a 3rd party library, you can do this with Pandas. The benefits include seamless access to specialized methods and row / column labeling:

<pre><code>import pandas as pd

list1 = [1, 2, 3]
list2 = [4, 5, 6]
list3 = [7, 8, 9]

df = pd.DataFrame(list(zip(*[list1, list2, list3]))).add_prefix('Col')

df.to_csv('file.csv', index=False)

print(df)

 Col0 Col1 Col2
0 1 4 7
1 2 5 8
2 3 6 9
</code></pre>

blocks|key|1584267|text|我不想导入csv以外的任何东西，并且我的所有列表都有相同数量的项目。这里的顶部答案似乎是将列表划分为一行，而不是每列一列。因此，我在这里得到了答案，并得出了以下结论：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1584268|import+csv
list1+=+['a',+'b',+'c',+'d',+'e']
list2+=+['f',+'g',+'i',+'j','k']
with+open('C:/test/numbers.csv',+'wb%2B')+as+myfile:
++++wr+=+csv.writer(myfile)
++++wr.writerow(("list1",+"list2"))
++++rcount+=+0
++++for+row+in+list1:
++++++++wr.writerow((list1[rcount],+list2[rcount]))
++++++++rcount+=+rcount+%2B+1
++++myfile.close()|code-block|syntax|javascript|1584269|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I didn't want to import anything other than csv, and all my lists have the same number of items. The top answer here seems to make the lists into one row each, instead of one column each. Thus I took the answers here and came up with this:

<pre><code>import csv
list1 = ['a', 'b', 'c', 'd', 'e']
list2 = ['f', 'g', 'i', 'j','k']
with open('C:/test/numbers.csv', 'wb+') as myfile:
 wr = csv.writer(myfile)
 wr.writerow(("list1", "list2"))
 rcount = 0
 for row in list1:
 wr.writerow((list1[rcount], list2[rcount]))
 rcount = rcount + 1
 myfile.close()
</code></pre>

blocks|key|1584302|text|我只是想补充一下这一点--因为坦率地说，我用头撞了它一段时间--尽管我对python还很陌生--也许它能帮助其他人。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1584303|+writer.writerow(("ColName1",+"ColName2",+"ColName"))
+++++++++++++++++for+i+in+range(len(first_col_list)):
+++++++++++++++++++++writer.writerow((first_col_list[i],+second_col_list[i],+third_col_list[i]))|code-block|syntax|javascript|1584304|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I just wanted to add to this one- because quite frankly, I banged my head against it for a while - and while very new to python - perhaps it will help someone else out.

<pre><code> writer.writerow(("ColName1", "ColName2", "ColName"))
 for i in range(len(first_col_list)):
 writer.writerow((first_col_list[i], second_col_list[i], third_col_list[i]))
</code></pre>

I have 5 lists, all of the same length, and I'd like to write them to 5 columns in a CSV. So far, I can only write one to a column with this code:

<pre><code>with open('test.csv', 'wb') as f:
 writer = csv.writer(f)
 for val in test_list:
 writer.writerow([val])
</code></pre>

If I add another <code>for</code> loop, it just writes that list to the same column. Anyone know a good way to get five separate columns?

Writing Python lists to columns in csv

我有5个列表，长度都一样，我想把它们写到CSV中的5列中。到目前为止，我只能用下面的代码在一个列中写一个：with open('test.csv', 'wb') as f:    writer = csv.writer(f)    for val in test_list:        writer.writerow...

问将Python列表写入csv中的列
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