Dealing with near duplicates using NLP

I have a dataframe like as shown below

ID,Name,year,output
1,Test Level,2021,1
2,Test Lvele,2022,1
2,dummy Inc,2022,1
2,dummy Pvt Inc,2022,1
3,dasho Ltd,2022,1
4,dasho PVT Ltd,2021,0
5,delphi Ltd,2021,1
6,delphi pvt ltd,2021,1

df = pd.read_clipboard(sep=',')

My objective is

a) To replace near duplicate strings using a common string.

For example - let's pick couple of strings from Name column. We have dummy Inc and dummy Pvt Inc. These both have to be replaced as dummy

I manually prepared a mapping df map_df like as below (but can't do this for big data)

  Name,correct_name
  Test Level,Test
  Test Lvele,Test
  dummy Inc,dummy
  dummy Pvt Inc,dummy
  dasho Ltd,dasho
  dasho PVT Ltd,dasho
  delphi Ltd,delphi
  delphi pvt ltd,delphi

So, I tried the below

map_df = map_df.set_index(Name)
df['Name'] = df['Name'].map(map_df) # but this doesn't work and throws error

Is creating mapping table the only way or is there any NLP based approach?

I expect my output to be like as below

ID,Name,year,output
1,Test,2021,1
2,Test,2022,1
2,dummy,2022,1
2,dummy,2022,1
3,dasho,2022,1
4,dasho,2021,0
5,delphi,2021,1
6,delphi,2021,1