blocks|key|108385|text|如果我理解正确的话，赋值应该填写：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|108386|>>>+import+numpy+as+np
>>>+import+pandas+as+pd
>>>+df+=+pd.DataFrame({"A":+[1,2,3],+"B":+[2,3,4]})
>>>+df
+++A++B
0++1++2
1++2++3
2++3++4
>>>+df["C"]+=+""
>>>+df["D"]+=+np.nan
>>>+df
+++A++B+C+++D
0++1++2+++NaN
1++2++3+++NaN
2++3++4+++NaN|code-block|syntax|javascript|108387|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 I understand correctly, assignment should fill:

<pre><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; df = pd.DataFrame({"A": [1,2,3], "B": [2,3,4]})
&gt;&gt;&gt; df
 A B
0 1 2
1 2 3
2 3 4
&gt;&gt;&gt; df["C"] = ""
&gt;&gt;&gt; df["D"] = np.nan
&gt;&gt;&gt; df
 A B C D
0 1 2 NaN
1 2 3 NaN
2 3 4 NaN
</code></pre>

blocks|key|108482|text|一个更简单的解决方案是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|108483|df+=+df.reindex(columns+=+header_list)++++++++++++++++|code-block|syntax|javascript|108484|其中"header_list“是要显示的标头的列表。|108485|列表中包含的、在数据帧中尚未找到的任何标头都将在下面添加空白单元格。|108486|所以如果|108487|header_list+=+['a','b','c',+'d']|108488|则c和d将被添加为具有空单元格的列|108489|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|W|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|Y|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

an even simpler solution is: 

<pre><code>df = df.reindex(columns = header_list) 
</code></pre>

where "header_list" is a list of the headers you want to appear.

any header included in the list that is not found already in the dataframe will be added with blank cells below.

so if 

<pre><code>header_list = ['a','b','c', 'd']
</code></pre>

then c and d will be added as columns with blank cells

blocks|key|108549|text|我喜欢：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|108550|df['new']+=+pd.Series(dtype='your_required_dtype')|code-block|syntax|javascript|108551|如果您有一个空的数据帧，此解决方案可确保不会添加仅包含NaN的新行。|offset|length|style|CODE|108552|指定dtype并不是必须的，但是如果不指定，更新的Pandas版本会生成一个DeprecationWarning。|108553|entityMap^0|0|0|R|3|0|2|5|12|I|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|V|8|@$I|W|J|X|K|L]|$I|Y|J|Z|K|L]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|P|$]]

I like:
<pre class="lang-py prettyprint-override"><code>df['new'] = pd.Series(dtype='your_required_dtype')
</code></pre>
If you have an empty dataframe, this solution makes sure that no new row containing only <code>NaN</code> is added.
Specifying <code>dtype</code> is not strictly necessary, however newer Pandas versions produce a <code>DeprecationWarning</code> if not specified.

blocks|key|108505|text|如果要从列表中添加列名|type|unstyled|depth|inlineStyleRanges|entityRanges|data|108506|df=pd.DataFrame()
a=['col1','col2','col3','col4']
for+i+in+a:
++++df[i]=np.nan|code-block|syntax|javascript|108507|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 want to add column name from a list

<pre><code>df=pd.DataFrame()
a=['col1','col2','col3','col4']
for i in a:
 df[i]=np.nan
</code></pre>

blocks|key|108431|text|@emunsing的answer对于添加多个列来说真的很酷，但我在Python2.7中却无法让它为我工作。相反，我发现这是可行的：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|108432|mydf+=+mydf.reindex(columns+=+np.append(+mydf.columns.values,+['newcol1','newcol2'])|code-block|syntax|javascript|108433|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/39405540/7391480^0|A|6|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

@emunsing's <a href="https://stackoverflow.com/a/39405540/7391480">answer</a> is really cool for adding multiple columns, but I couldn't get it to work for me in python 2.7. Instead, I found this works:

<pre><code>mydf = mydf.reindex(columns = np.append( mydf.columns.values, ['newcol1','newcol2'])
</code></pre>

blocks|key|325921|text|这也适用于多个列：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|325922|df+=+pd.DataFrame({"A":+[1,2,3],+"B":+[2,3,4]})
>>>+df
+++A++B
0++1++2
1++2++3
2++3++4

df1+=+pd.DataFrame(columns=['C','D','E'])
df+=+df.join(df1,+how="outer")

>>>df
++++A+++B+++C+++D+++E
0+++1+++2+++NaN+NaN+NaN
1+++2+++3+++NaN+NaN+NaN
2+++3+++4+++NaN+NaN+NaN|code-block|syntax|javascript|325923|然后，对列、pd.Series.fillna(),pd.Series.map()等做任何您想做的事情。|offset|length|style|CODE|325924|entityMap^0|0|0|6|Y|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

this will also work for multiple columns:
<pre><code>df = pd.DataFrame({&quot;A&quot;: [1,2,3], &quot;B&quot;: [2,3,4]})
&gt;&gt;&gt; df
 A B
0 1 2
1 2 3
2 3 4

df1 = pd.DataFrame(columns=['C','D','E'])
df = df.join(df1, how=&quot;outer&quot;)

&gt;&gt;&gt;df
 A B C D E
0 1 2 NaN NaN NaN
1 2 3 NaN NaN NaN
2 3 4 NaN NaN NaN
</code></pre>
Then do whatever you want to do with the columns
<code>pd.Series.fillna(),pd.Series.map()</code>
etc.

blocks|key|325818|text|可以使用df.insert(index_to_insert_at,+column_header,+init_value)在特定索引处插入新列。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|325819|cost_tbl.insert(1,+"col_name",+"")+|code-block|syntax|javascript|325820|上面的语句将在第一列之后插入一个空列。|325821|entityMap^0|4|1K|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|N|$]]

One can use <code>df.insert(index_to_insert_at, column_header, init_value)</code> to insert new column at a specific index.

<pre><code>cost_tbl.insert(1, "col_name", "") 
</code></pre>

The above statement would insert an empty Column after the first column.

blocks|key|325775|text|下面的代码解决了“如何将n个空列添加到现有数据帧中”的问题。为了将类似问题的解决方案保存在一个地方，我在这里添加了它。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|325776|方法1(创建64个额外的列，列名从1到64)|325777|m+=+list(range(1,65,1))+
dd=pd.DataFrame(columns=m)
df.join(dd).replace(np.nan,'')+#df+is+the+dataframe+that+already+exists|code-block|syntax|javascript|325778|方法2(创建64个额外的列，列名从1到64)|325779|df.reindex(df.columns.tolist()+%2B+list(range(1,65,1)),+axis=1).replace(np.nan,'')|325780|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

The below code address the question "How do I add n number of empty columns to my existing dataframe". In the interest of keeping solutions to similar problems in one place, I am adding it here.

Approach 1 (to create 64 additional columns with column names from 1-64)

<pre><code>m = list(range(1,65,1)) 
dd=pd.DataFrame(columns=m)
df.join(dd).replace(np.nan,'') #df is the dataframe that already exists
</code></pre>

Approach 2 (to create 64 additional columns with column names from 1-64)

<pre><code>df.reindex(df.columns.tolist() + list(range(1,65,1)), axis=1).replace(np.nan,'')
</code></pre>

blocks|key|325799|text|你可以做到|type|unstyled|depth|inlineStyleRanges|entityRanges|data|325800|df['column']+=+None+#This+works.+This+will+create+a+new+column+with+None+type
df.column+=+None+#This+will+work+only+when+the+column+is+already+present+in+the+dataframe+|code-block|syntax|javascript|325801|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|$]]

You can do 

<pre><code>df['column'] = None #This works. This will create a new column with None type
df.column = None #This will work only when the column is already present in the dataframe 
</code></pre>

blocks|key|325856|text|很抱歉，我一开始没有很好地解释我的答案。还有另一种方法可以将新列添加到现有数据帧中。第一步，创建一个新的空数据框(包含数据框中的所有列，加上您想要添加的新列或几列)。第二步，将df_temp和数据框组合在一起。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|325857|df_temp+=+pd.DataFrame(columns=(df_null.columns.tolist()+%2B+['empty']))
df+=+pd.concat([df_temp,+df])|code-block|syntax|javascript|325858|这可能是最好的解决方案，但这是思考这个问题的另一种方式。|325859|我之所以使用这种方法，是因为我一直收到这样的警告：|325860|:+SettingWithCopyWarning:+
A+value+is+trying+to+be+set+on+a+copy+of+a+slice+from+a+DataFrame.
Try+using+.loc[row_indexer,col_indexer]+=+value+instead

See+the+caveats+in+the+documentation:+https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
++df["empty1"],+df["empty2"]+=+[np.nan,+""]|325861|太棒了，我找到了禁用警告的方法|325862|pd.options.mode.chained_assignment+=+None+|325863|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|W|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|6|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

Sorry for I did not explain my answer really well at beginning. There is another way to add an new column to an existing dataframe.
1st step, make a new empty data frame (with all the columns in your data frame, plus a new or few columns you want to add) called df_temp
2nd step, combine the df_temp and your data frame.
<pre><code>df_temp = pd.DataFrame(columns=(df_null.columns.tolist() + ['empty']))
df = pd.concat([df_temp, df])
</code></pre>
It might be the best solution, but it is another way to think about this question.
the reason of I am using this method is because I am get this warning all the time:
<pre><code>: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
 df[&quot;empty1&quot;], df[&quot;empty2&quot;] = [np.nan, &quot;&quot;]
</code></pre>
great I found the way to disable the Warning
<pre><code>pd.options.mode.chained_assignment = None 
</code></pre>

blocks|key|325901|text|我寻找这样的解决方案的原因很简单，就是在多个DFs之间添加空格，这些DFs已经使用pd.concat函数按列连接，然后使用xlsxwriter写入excel。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|325902|df['+']=df.apply(lambda+_:+'',+axis=1)
df_2+=+pd.concat([df,df1],axis=1)++++++++++++++++#worked+but+only+once.+
#+Note:+df+&+df1+have+the+same+rows+which+is+my+index.+
#
df_2['+']=df_2.apply(lambda+_:+'',+axis=1)+++++++#didn't+work+this+time+!!?+++++
df_4+=+pd.concat([df_2,df_3],axis=1)|code-block|syntax|javascript|325903|然后我将第二个lambda调用替换为|325904|df_2['']=''+++++++++++++++++++++++++++++++++#which+appears+to+add+a+blank+column
df_4+=+pd.concat([df_2,df_3],axis=1)|325905|我测试的输出是使用xlsxwriter来执行excel。Jupyter空白列看起来和excel中的一样，虽然没有xlsx格式。不确定为什么第二个Lambda调用不起作用。|325906|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

The reason I was looking for such a solution is simply to add spaces between multiple DFs which have been joined column-wise using the pd.concat function and then written to excel using xlsxwriter.
<pre><code>df[' ']=df.apply(lambda _: '', axis=1)
df_2 = pd.concat([df,df1],axis=1) #worked but only once. 
# Note: df &amp; df1 have the same rows which is my index. 
#
df_2[' ']=df_2.apply(lambda _: '', axis=1) #didn't work this time !!? 
df_4 = pd.concat([df_2,df_3],axis=1)
</code></pre>
I then replaced the second lambda call with
<pre><code>df_2['']='' #which appears to add a blank column
df_4 = pd.concat([df_2,df_3],axis=1)
</code></pre>
The output I tested it on was using xlsxwriter to excel.
Jupyter blank columns look the same as in excel although doesnt have xlsx formatting.
Not sure why the second Lambda call didnt work.

What's the easiest way to add an empty column to a pandas <code>DataFrame</code> object? The best I've stumbled upon is something like

<pre><code>df['foo'] = df.apply(lambda _: '', axis=1)
</code></pre>

Is there a less perverse method?

How to add an empty column to a dataframe?

向pandas DataFrame对象添加空列的最简单方法是什么？我偶然发现的最好的东西是这样的df['foo'] = df.apply(lambda _: '', axis=1)有没有一种不那么反常的方法？

问如何在数据框中添加空列？
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