我得到了可怕的SettingWithCopyWarning，它没有通过使用iloc语法修复。我的DataFrame是由read_sql从ODBC源创建的。根据上面low - tech的建议，以下方法对我来说是有效的:

df.insert(len(df.columns), 'e', pd.Series(np.random.randn(sLength),  index=df.index))

This worked fine to insert the column at the end. I don't know if it is the most efficient, but I don't like warning messages. I think there is a better solution, but I can't find it, and I think it depends on some aspect of the index. Note. That this only works once and will give an error message if trying to overwrite and existing column. Note As above and from 0.16.0 assign is the best solution. See documentation http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html#pandas.DataFrame.assign Works well for data flow type where you don't overwrite your intermediate values.

直接通过NumPy这样做将是最有效的:

df1['e'] = np.random.randn(sLength)

注意我最初(非常老)的建议是使用map(这要慢得多):

df1['e'] = df1['a'].map(lambda x: np.random.random())

向现有数据框架添加新列的简单方法是:

new_cols = ['a' , 'b' , 'c' , 'd']

for col in new_cols:
    df[f'{col}'] = 0 #assiging 0 for the placeholder

print(df.columns)

编辑2017

正如@Alexander在评论中所指出的，目前将Series的值添加为DataFrame的新列的最好方法是使用assign:

df1 = df1.assign(e=pd.Series(np.random.randn(sLength)).values)

编辑2015 有些人报告说用这段代码得到了SettingWithCopyWarning。 但是，该代码仍然可以在当前的pandas版本0.16.1中完美运行。

>>> sLength = len(df1['a'])
>>> df1
          a         b         c         d
6 -0.269221 -0.026476  0.997517  1.294385
8  0.917438  0.847941  0.034235 -0.448948

>>> df1['e'] = pd.Series(np.random.randn(sLength), index=df1.index)
>>> df1
          a         b         c         d         e
6 -0.269221 -0.026476  0.997517  1.294385  1.757167
8  0.917438  0.847941  0.034235 -0.448948  2.228131

>>> pd.version.short_version
'0.16.1'

SettingWithCopyWarning的目的是通知数据帧副本上可能存在的无效赋值。它不一定会说你做错了(它可能会触发假阳性)，但从0.13.0开始，它会让你知道有更多适合相同目的的方法。然后，如果您得到警告，只需遵循它的建议:尝试使用.loc[row_index,col_indexer] = value代替

>>> df1.loc[:,'f'] = pd.Series(np.random.randn(sLength), index=df1.index)
>>> df1
          a         b         c         d         e         f
6 -0.269221 -0.026476  0.997517  1.294385  1.757167 -0.050927
8  0.917438  0.847941  0.034235 -0.448948  2.228131  0.006109
>>> 

事实上，这是目前熊猫文档中描述的更有效的方法

最初的回答:

使用原始的df1索引创建系列:

df1['e'] = pd.Series(np.random.randn(sLength), index=df1.index)

x=pd.DataFrame([1,2,3,4,5])

y=pd.DataFrame([5,4,3,2,1])

z=pd.concat([x,y],axis=1)

你可以像这样通过for循环插入新列:

for label,row in your_dframe.iterrows():
      your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])

示例代码如下:

import pandas as pd

data = {
  "any_of_column_in_your_dframe" : ["ersingulbahar","yagiz","TS"],
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
your_dframe = pd.DataFrame(data)


for label,row in your_dframe.iterrows():
      your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])
      
      
print(your_dframe) 

输出如下:

你也可以这样用:

your_dframe["new_column_length"]=your_dframe["any_of_column_in_your_dframe"].apply(len)