import pandas as pd

# Define a dictionary containing data
data = {'a': [0,0,0.671399,0.446172,0,0.614758],
    'b': [0,0,0.101208,-0.243316,0,0.075793],
    'c': [0,0,-0.181532,0.051767,0,-0.451460],
    'd': [0,0,0.241273,1.577318,0,-0.012493]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data)

# Declare a list that is to be converted into a column
col_e = [-0.335485,-1.166658,-0.385571,0,0,0]


df['e'] = col_e

# add column 'e'
df['e'] = col_e

# Observe the result
df

如果我们想给df中一个新列的所有行赋一个标量值，例如:10:

df = df.assign(new_col=lambda x:10)  # x is each row passed in to the lambda func

Df现在在所有行中都有值为10的新列'new_col'。

如果你只需要创建一个新的空列，那么最短的解决方案是:

df.loc[:, 'e'] = pd.Series()

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)

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)

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)