将多个空列添加到熊猫 DataFrame

I'd concat using a DataFrame:

In [23]:
df = pd.DataFrame(columns=['A'])
df


Out[23]:
Empty DataFrame
Columns: [A]
Index: []


In [24]:
pd.concat([df,pd.DataFrame(columns=list('BCD'))])


Out[24]:
Empty DataFrame
Columns: [A, B, C, D]
Index: []

So by passing a list containing your original df, and a new one with the columns you wish to add, this will return a new df with the additional columns.

Caveat: See the discussion of performance in the other answers and/or the comment discussions. reindex may be preferable where performance is critical.

You could use df.reindex to add new columns:

In [18]: df = pd.DataFrame(np.random.randint(10, size=(5,1)), columns=['A'])


In [19]: df
Out[19]:
A
0  4
1  7
2  0
3  7
4  6


In [20]: df.reindex(columns=list('ABCD'))
Out[20]:
A   B   C   D
0  4 NaN NaN NaN
1  7 NaN NaN NaN
2  0 NaN NaN NaN
3  7 NaN NaN NaN
4  6 NaN NaN NaN

reindex will return a new DataFrame, with columns appearing in the order they are listed:

In [31]: df.reindex(columns=list('DCBA'))
Out[31]:
D   C   B  A
0 NaN NaN NaN  4
1 NaN NaN NaN  7
2 NaN NaN NaN  0
3 NaN NaN NaN  7
4 NaN NaN NaN  6

The reindex method as a fill_value parameter as well:

In [22]: df.reindex(columns=list('ABCD'), fill_value=0)
Out[22]:
A  B  C  D
0  4  0  0  0
1  7  0  0  0
2  0  0  0  0
3  7  0  0  0
4  6  0  0  0

If you don't want to rewrite the name of the old columns, then you can use reindex:

df.reindex(columns=[*df.columns.tolist(), 'new_column1', 'new_column2'], fill_value=0)

Full example:

In [1]: df = pd.DataFrame(np.random.randint(10, size=(3,1)), columns=['A'])


In [1]: df
Out[1]:
A
0  4
1  7
2  0


In [2]: df.reindex(columns=[*df.columns.tolist(), 'col1', 'col2'], fill_value=0)
Out[2]:


A  col1  col2
0  1     0     0
1  2     0     0

And, if you already have a list with the column names, :

In [3]: my_cols_list=['col1','col2']


In [4]: df.reindex(columns=[*df.columns.tolist(), *my_cols_list], fill_value=0)
Out[4]:
A  col1  col2
0  1     0     0
1  2     0     0

Why not just use loop:

for newcol in ['B','C','D']:
df[newcol]=np.nan

Just to add to the list of funny ways:

columns_add = ['a', 'b', 'c']
df = df.assign(**dict(zip(columns_add, [0] * len(columns_add)))

I'd use

df["B"], df["C"], df["D"] = None, None, None

or

df["B"], df["C"], df["D"] = ["None" for a in range(3)]

Summary of alternative solutions:

columns_add = ['a', 'b', 'c']

1. for loop:

   for newcol in columns_add:
   df[newcol]= None
   

2. dict method:

   df.assign(**dict([(_,None) for _ in columns_add]))
   

3. tuple assignment:

   df['a'], df['b'], df['c'] = None, None, None
   

You can make use of Pandas broadcasting:

df = pd.DataFrame({'A': [1, 1, 1]})


df[['B', 'C']] = 2, 3
# df[['B', 'C']] = [2, 3]

Result:

   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3

To add empty columns:

df[['B', 'C', 'D']] = 3 * [np.nan]

Result:

   A   B   C   D
0  1 NaN NaN NaN
1  1 NaN NaN NaN
2  1 NaN NaN NaN