例如，参见以下数据：

import pandas as pd
df = pd.DataFrame({"Name": ['Tim', 'Tim', 'John'],
                  "Age": [2, 4, 6]})
print(df)

   Name  Age
0   Tim    2
1   Tim    4
2  John    6

您可以使用此选项保留重复数据：

df = df.loc[df.duplicated(subset='Name', keep=False)]
df

输出将是：

Name  Age
0  Tim    2
1  Tim    4

如果“无”输出很重要：

if (df.empty):
    print ('None')

因为你没有MRE，如果你已经创建了一个测试数据，下面的解决方案适用于：
对于非重复病例：

import pandas as pd

df = pd.DataFrame({'id' :[1,2,3,4],'full_name' : ['ab','cd','ef','gh']})
df.shape

if df[df.duplicated(subset=['full_name'])].shape[0]>0:
    print(df[df.duplicated(subset=['full_name'], keep=False)])
else:
    print("None")

输出：

"None"

对于重复病例：

df = pd.DataFrame({'id' :[1,2,3,4],'full_name' : ['ab','cd','ef','ab']})
df.shape

if df[df.duplicated(subset=['full_name'])].shape[0]>0:
    print(df[df.duplicated(subset=['full_name'],keep=False)])
else:
    print("None")

输出：

id full_name
0   1        ab
3   4        ab

data={'col1':['A','A','B','C'],'col2':[2,3,4,5]}
df=pd.DataFrame(data)
df1=df[df.duplicated(subset='col1',keep=False)]

if df1.shape[0]==0:
    print("Empty")
else:
    print(df1)