Data cleaning challenge

I have a dataset with a column Highest Level Of Education.
The df.value_counts produces the results below.
Bachelors Degree 639
Diploma 291
O - Level 264
Masters Degree 149
Certificate 126
A - Level 69
PGD 40
Bachelors Degree 28
A-Level 20
O-Level 15
Masters 10
Bachelors 6
diploma 5
certificate 5
Ph.D 4
A- Level 2
Msc Environment 1
BBA 1
O- Level 1
PhD 1
Masters 1
Post Graduate Diploma 1
Name: HIGHEST LEVEL OF EDUCATION, dtype: int64
The data is from excel and you can see that levels are repeated with variations in spellings and naming.
How can use series.map to replace the values.
Here is my dictionary:
mapp={“Bachelor’s Degree”:[“Bachelors Degree”,“Bachelors”,“BBA”,“Bachelors Degree”],
“Ordinary Diploma”:[“diploma”],
“Ordinary Level”:[“O - Level”,“O-Level”,“O- Level”],
“Master’s Degree”:[“Masters Degree”,“Masters”,“Msc Environment”,“Masters”],
“Certificate”:[“certificate”],
“Advanced Level”:[“A - Level”,“A-Level”,“A- Level”],
“Post Graduate Diploma”:[“Post Graduate Diploma”,“PGD”],
“PHD”:[“Ph.D”,“PhD”]
}

using f[‘EDUCATION_LEVEL’]=df[‘EDUCATION_LEVEL’].map(mapp) does not work(i get Nan values).
Can i use series.map in this case?
I cant upload the file since i am a new user.
Thanks

A workaround can be set mapp dictionary with all declinations, like this:
mapp = {
“Bachelors Degree”:“Bachelor’s Degree”,
“Bachelors”:“Bachelor’s Degree”,
“BBA”:“Bachelor’s Degree”,
“Bachelors Degree”:“Bachelor’s Degree”,
“diploma”:“Ordinary Diploma”,
“Diploma”:“Ordinary Diploma”,
“O - Level”:“Ordinary Level”,
“O-Level”:“Ordinary Level”,
“O- Level”:“Ordinary Level”,
“Masters Degree”:“Master’s Degree”,
“Masters”:“Master’s Degree”,
“Msc Environment”:“Master’s Degree”,
“Masters”:“Master’s Degree”,
“certificate”:“Certificate”,
“Certificate”:“Certificate”,
“A - Level”:“Advanced Level”,
“A-Level”:“Advanced Level”,
“A- Level”:“Advanced Level”,
“Post Graduate Diploma”:“Post Graduate Diploma”,
“PGD”:“Post Graduate Diploma”,
“Ph.D”:“PHD”,
“PhD”:“PHD”
}

But my advice is clean in advance the “highest level of education”, to uniform the data