entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://stackoverflow.com/questions/30926670/pandas-add-multiple-empty-columns-to-dataframe/30926717|blocks|key|c8mkt|text|您需要逐个添加列。|unstyled|depth|inlineStyleRanges|entityRanges|6fraj|for+col+in+new_cols:
++++df[col]+=+0|code-block|syntax|javascript|bnimk|有关其他方法，请参阅here中的答案。|offset|length|d1etr^0|0|0|A|4|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|S|G|@]|H|@]|6|$]]|$A|I|C|J|2|K|F|T|G|@]|H|@]|6|$L|M]]|$A|N|C|O|2|E|F|U|G|@]|H|@$P|V|Q|W|A|X]]|6|$]]|$A|R|C|-4|2|E|F|Y|G|@]|H|@]|6|$]]]]

You need to add the columns one by one.

<pre><code>for col in new_cols:
 df[col] = 0
</code></pre>

Also see the answers in <a href="https://stackoverflow.com/questions/30926670/pandas-add-multiple-empty-columns-to-dataframe/30926717">here</a> for other methods.

entityMap|blocks|key|e2khv|text|尝试在创建列之前遍历列名：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|f3j19|for+col+in+new_cols:
++++df[col]+=+0|code-block|syntax|javascript|ea8uc^0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|I|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|J|9|@]|A|@]|B|$F|G]]|$2|H|4|-4|6|7|8|K|9|@]|A|@]|B|$]]]]

Try looping through the column names before creating the column:

<pre><code>for col in new_cols:
 df[col] = 0
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html|blocks|key|e2qcl|text|按字典使用assign：|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|bvi0n|df+=+pd.DataFrame({
++++'A':+['a','a','a','a','b','b','b','c','d'],
++++'B':+list(range(9))
})
print+(df)
0++a++0
1++a++1
2++a++2
3++a++3
4++b++4
5++b++5
6++b++6
7++c++7
8++d++8

new_cols+=+['new_1',+'new_2',+'new_3']
df+=+df.assign(**dict.fromkeys(new_cols,+0))
print+(df)
+++A++B++new_1++new_2++new_3
0++a++0++++++0++++++0++++++0
1++a++1++++++0++++++0++++++0
2++a++2++++++0++++++0++++++0
3++a++3++++++0++++++0++++++0
4++b++4++++++0++++++0++++++0
5++b++5++++++0++++++0++++++0
6++b++6++++++0++++++0++++++0
7++c++7++++++0++++++0++++++0
8++d++8++++++0++++++0++++++0|code-block|syntax|javascript|7678a^0|5|6|5|6|0|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|S|G|@$H|T|I|U|J|K]]|L|@$H|V|I|W|A|X]]|6|$]]|$A|M|C|N|2|O|F|Y|G|@]|L|@]|6|$P|Q]]|$A|R|C|-4|2|E|F|Z|G|@]|L|@]|6|$]]]]

Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html" rel="nofollow noreferrer"><code>assign</code></a> by dictionary:

<pre><code>df = pd.DataFrame({
 'A': ['a','a','a','a','b','b','b','c','d'],
 'B': list(range(9))
})
print (df)
0 a 0
1 a 1
2 a 2
3 a 3
4 b 4
5 b 5
6 b 6
7 c 7
8 d 8

new_cols = ['new_1', 'new_2', 'new_3']
df = df.assign(**dict.fromkeys(new_cols, 0))
print (df)
 A B new_1 new_2 new_3
0 a 0 0 0 0
1 a 1 0 0 0
2 a 2 0 0 0
3 a 3 0 0 0
4 b 4 0 0 0
5 b 5 0 0 0
6 b 6 0 0 0
7 c 7 0 0 0
8 d 8 0 0 0
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html|blocks|key|3ho13|text|您可以使用assign|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|9hfrr|new_cols+=+['new_1',+'new_2',+'new_3']
values+=+[0,+0,+0]+++#+could+be+anything,+also+pd.Series

df+=+df.assign(**dict(zip(new_cols,+values)|code-block|syntax|javascript|f9ur9^0|5|6|0|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|Q|G|@]|H|@$I|R|J|S|A|T]]|6|$]]|$A|K|C|L|2|M|F|U|G|@]|H|@]|6|$N|O]]|$A|P|C|-4|2|E|F|V|G|@]|H|@]|6|$]]]]

You can use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html" rel="nofollow noreferrer">assign</a>:

<pre><code>new_cols = ['new_1', 'new_2', 'new_3']
values = [0, 0, 0] # could be anything, also pd.Series

df = df.assign(**dict(zip(new_cols, values)
</code></pre>

entityMap|blocks|key|dlgop|text|import+pandas+as+pd

new_cols+=+['new_1',+'new_2',+'new_3']
df+=+pd.DataFrame.from_records([(0,+0,+0)],+columns=new_cols)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|b0qrh|这就是你要找的吗？|unstyled|7ir2m^0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|I|9|@]|A|@]|B|$C|D]]|$2|E|4|F|6|G|8|J|9|@]|A|@]|B|$]]|$2|H|4|-4|6|G|8|K|9|@]|A|@]|B|$]]]]

<pre><code>import pandas as pd

new_cols = ['new_1', 'new_2', 'new_3']
df = pd.DataFrame.from_records([(0, 0, 0)], columns=new_cols)
</code></pre>

Is this what you're looking for ?

entityMap|blocks|key|6gd5n|text|我们可以使用Apply函数遍历数据帧中的列，并将每个元素分配给一个新字段，例如，数据帧中的一个列表具有一个名为keys的列表|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1r3jk|[10,20,30]|code-block|syntax|javascript|5bc58|在你的例子中，由于都是0，我们可以直接将它们赋值为0，而不是遍历。但是如果我们有值，我们可以像下面这样填充它们。|1s0ij|df['new_01']=df['keys'].apply(lambda+x:+x[0])
df['new_02']=df['keys'].apply(lambda+x:+x[1])
df['new_03']=df['keys'].apply(lambda+x:+x[2])|78ssu^0|0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|M|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|N|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|O|9|@]|A|@]|B|$]]|$2|J|4|K|6|E|8|P|9|@]|A|@]|B|$F|G]]|$2|L|4|-4|6|7|8|Q|9|@]|A|@]|B|$]]]]

We can use the Apply function to loop through the columns in the dataframe and assigning each of the element to a new field
for instance for a list in a dataframe with a list named keys

<pre><code>[10,20,30]
</code></pre>

In your case since its all 0 we can directly assign them as 0 instead of looping through. But if we have values we can populate them as below
...

<pre><code>df['new_01']=df['keys'].apply(lambda x: x[0])
df['new_02']=df['keys'].apply(lambda x: x[1])
df['new_03']=df['keys'].apply(lambda x: x[2])
</code></pre>

I have a list with columns to create :
<pre><code>new_cols = ['new_1', 'new_2', 'new_3']
</code></pre>
I want to create these columns in a dataframe and fill them with zero :
<pre><code>df[new_cols] = 0
</code></pre>
Get error :
<pre><code>&quot;['new_1', 'new_2', 'new_3'] not in index&quot;
</code></pre>
which is true but unfortunate as I want to create them...
EDIT : This is a duplicate of this question : <a href="https://stackoverflow.com/questions/30926670">Add multiple empty columns to pandas DataFrame</a> however I keep this one too because the accepted answer here was the simple solution I was looking for, and it was not he accepted answer out there
EDIT 2 : While the accepted answer is the most simple, interesting one-liner solutions were posted below

Pandas Python : how to create multiple columns from a list

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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 我有一个包含要创建的列的列表： new_cols = ['new_1', 'new_2', 'new_3'] 我想在数据帧中创建这些列，并用零填充它们： df[new_cols] = 0 获取错误： "['new_1', 'new_2', 'new_3'] not in index" 这是真的，但不幸的是我想创建它们...

问Pandas Python :如何从列表创建多列
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