以下是可以解决您问题的代码：

import pandas as pd
import numpy as np

def login_table( id_password):
    global id_name_verified
    id_name_verified.drop(columns="Verified",inplace=True)
    password = pd.DataFrame(id_password)
    password.columns = ["Id","Password"]
    id_name_verified =id_name_verified.merge(password,on=['Id'])

id_name_verified = pd.DataFrame([[1,"JohnDoe",True],[2,"AnnFranklin",False]],columns=["Id","Login","Verified"])
id_password = np.array([[1,987340123],187031122]],np.int32)
login_table( id_password)
print(id_name_verified)

我所做的更改是，我在函数“ login_table”中声明了“ id_name_verified”为全局变量。完成此操作后，您无需再次将“ id_name_verified”传递给该函数，因为通过使用关键字“ global”，我们已经将“ id_name_verified”的访问权授予了函数“ login_table”。因此删除了“ id_name_verified”作为函数的参数之一。

这是通过TestDome上所有4个测试的代码：

import pandas as pd
import numpy as np

def login_table(id_name_verified,id_password):
    """
    :param id_name_verified: (DataFrame) DataFrame with columns: Id,Login,Verified.   
    :param id_password: (numpy.array) Two-dimensional NumPy array where each element
                        is an array that contains: Id and Password
    :returns: (None) The function should modify id_name_verified DataFrame in-place. 
              It should not return anything.
    """   
    id_name_verified.drop(columns = 'Verified',inplace = True)
    password_df = pd.DataFrame(id_password)
    pass_col = password_df[1]
    id_name_verified['Password'] = pass_col

id_name_verified = pd.DataFrame([[1,np.int32)
login_table(id_name_verified,id_password)
print(id_name_verified)

您删除“已验证”列，然后仅在id_password之外创建一个新数据框，获取包含密码的列并将其添加到id_name_verified数据框。

import pandas as pd
import numpy as np 

def login_table(id_name_verified,id_password):
    # in place makes sure that changes are made in a current dataframe 
    id_name_verified.drop('Verified',1,inplace= True) 
    # id_password[:,1] -> : selects all rows and 1 selects only second column
    id_name_verified['Password'] = id_password[:,1]
    pass


id_name_verified = pd.DataFrame([[1,id_password)
print(id_name_verified)

import pandas as pd
import numpy as np

def login_table(id_name_verified,id_password):
    id_name_verified.drop(inplace=True,columns="Verified") # drop verified column inplace
    id_name_verified.insert(loc=2,column="Password",value=np.nan) # insert password column inplace
    for row in range(len(id_password)): 
        # apply the password on the rows that match using at
        id = id_password[row][0]
        pw = id_password[row][1]
        id_name_verified.at[row,"Password"] = pw

id_name_verified = pd.DataFrame([[1,id_password)
print(id_name_verified)