如果数据帧和Series对象具有相同的索引，则为pandas。Concat也在这里工作:

import pandas as pd
df
#          a            b           c           d
#0  0.671399     0.101208   -0.181532    0.241273
#1  0.446172    -0.243316    0.051767    1.577318
#2  0.614758     0.075793   -0.451460   -0.012493

e = pd.Series([-0.335485, -1.166658, -0.385571])    
e
#0   -0.335485
#1   -1.166658
#2   -0.385571
#dtype: float64

# here we need to give the series object a name which converts to the new  column name 
# in the result
df = pd.concat([df, e.rename("e")], axis=1)
df

#          a            b           c           d           e
#0  0.671399     0.101208   -0.181532    0.241273   -0.335485
#1  0.446172    -0.243316    0.051767    1.577318   -1.166658
#2  0.614758     0.075793   -0.451460   -0.012493   -0.385571

以防它们没有相同的索引:

e.index = df.index
df = pd.concat([df, e.rename("e")], axis=1)

这是添加新列的简单方法:df['e'] = e

以下是我所做的…但我对熊猫和Python都很陌生，所以不能保证。

df = pd.DataFrame([[1, 2], [3, 4], [5,6]], columns=list('AB'))

newCol = [3,5,7]
newName = 'C'

values = np.insert(df.values,df.shape[1],newCol,axis=1)
header = df.columns.values.tolist()
header.append(newName)

df = pd.DataFrame(values,columns=header)

如果你要添加的列是一个序列变量，那么只需:

df["new_columns_name"]=series_variable_name #this will do it for you

即使要替换现有列，这种方法也很有效。只需输入与要替换的列相同的new_columns_name。它只会用新的系列数据覆盖现有的列数据。

在最近的Pandas版本中，似乎应该使用df.assign:

df1 = df1.assign(e=np.random)

它不会产生SettingWithCopyWarning。

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)