只是想指出@Matthias Fripp回答中的选项2

(2)我并不一定期望DataFrame以这种方式工作，但它确实如此 df[[‘column_new_1’,‘column_new_2’,‘column_new_3]] = pd.DataFrame ([[np。Nan， 'dogs'， 3]]， index=df.index)

熊猫自己的文档中已经有记录了吗 http://pandas.pydata.org/pandas-docs/stable/indexing.html#basics

您可以将列列表传递给[]以按此顺序选择列。 如果数据帧中不包含列，则会引发异常。 也可以通过这种方式设置多个列。 您可能会发现这对于将转换(就地)应用到列的子集很有用。

你可以对列名和值的字典使用赋值。

In [1069]: df.assign(**{'col_new_1': np.nan, 'col2_new_2': 'dogs', 'col3_new_3': 3})
Out[1069]:
   col_1  col_2 col2_new_2  col3_new_3  col_new_1
0      0      4       dogs           3        NaN
1      1      5       dogs           3        NaN
2      2      6       dogs           3        NaN
3      3      7       dogs           3        NaN

concat的用法:

In [128]: df
Out[128]: 
   col_1  col_2
0      0      4
1      1      5
2      2      6
3      3      7

In [129]: pd.concat([df, pd.DataFrame(columns = [ 'column_new_1', 'column_new_2','column_new_3'])])
Out[129]: 
   col_1  col_2 column_new_1 column_new_2 column_new_3
0    0.0    4.0          NaN          NaN          NaN
1    1.0    5.0          NaN          NaN          NaN
2    2.0    6.0          NaN          NaN          NaN
3    3.0    7.0          NaN          NaN          NaN

不太确定你想用它做什么。南,“狗”,3)。也许现在把它们设置为默认值?

In [142]: df1 = pd.concat([df, pd.DataFrame(columns = [ 'column_new_1', 'column_new_2','column_new_3'])])
In [143]: df1[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs', 3]

In [144]: df1
Out[144]: 
   col_1  col_2  column_new_1 column_new_2  column_new_3
0    0.0    4.0           NaN         dogs             3
1    1.0    5.0           NaN         dogs             3
2    2.0    6.0           NaN         dogs             3
3    3.0    7.0           NaN         dogs             3

如果用相同的值添加很多缺失的列(a, b, c，....)，这里是0，我这样做:

    new_cols = ["a", "b", "c" ] 
    df[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=df.index)

这是基于公认答案的第二种变体。

import pandas as pd
df = pd.DataFrame({
 'col_1': [0, 1, 2, 3], 
 'col_2': [4, 5, 6, 7]
 })
df['col_3'],  df['col_4'] =  [df.col_1]*2

>> df
col_1   col_2   col_3   col_4
0      4       0       0
1      5       1       1
2      6       2       2
3      7       3       3

使用.assign()映射字典:

这是在处理多个列时用值分配新列的最具可读性和最动态的方式。

import pandas as pd
import numpy as np

new_cols = ["column_new_1", "column_new_2", "column_new_3"]
new_vals = [np.nan, "dogs", 3]
# Map new columns as keys and new values as values
col_val_mapping = dict(zip(new_cols, new_vals))
# Unpack new column/new value pairs and assign them to the data frame
df = df.assign(**col_val_mapping)

如果你只是想把新列的值初始化为空，因为你不知道这些值会是什么，或者你有很多新列。

import pandas as pd
import numpy as np

new_cols = ["column_new_1", "column_new_2", "column_new_3"]
new_vals = [None for item in new_cols]
# Map new columns as keys and new values as values
col_val_mapping = dict(zip(new_cols, new_vals))
# Unpack new column/new value pairs and assign them to the data frame
df = df.assign(**col_val_mapping)