Removing repetitive/duplicate occurance in excel using python - excel

Question

I am trying to remove the repetitive/duplicate Names which is coming under NAME column. I just want to keep the 1st occurrence from the repetitive/duplicate names by using python script.
This is my input excel:
And need output like this:

Answer 1

This isn't removing duplicates per say you're just filling duplicate keys in one column as blanks, I would handle this as follows :
by creating a mask where you return a true/false boolean if the row is == the row above.
assuming your dataframe is called df
mask = df['NAME'].ne(df['NAME'].shift())
df.loc[~mask,'NAME'] = ''
explanation :
what we are doing above is the following,
first selecting a single column, or in pandas terminology a series, we then apply a .ne (not equal to) which in effect is !=
lets see this in action.
import pandas as pd
import numpy as np
# create data for dataframe
names = ['Rekha', 'Rekha','Jaya','Jaya','Sushma','Nita','Nita','Nita']
defaults = ['','','c-default','','','c-default','','']
classes = ['forth','third','foruth','fifth','fourth','third','fifth','fourth']
now, lets create a dataframe similar to yours.
df = pd.DataFrame({'NAME' : names,
'DEFAULT' : defaults,
'CLASS' : classes,
'AGE' : [np.random.randint(1,5) for len in names],
'GROUP' : [np.random.randint(1,5) for len in names]}) # being lazy with your age and group variables.
so, if we did df['NAME'].ne('Omar') which is the same as [df['NAME'] != 'Omar'] we would get.
0 True
1 True
2 True
3 True
4 True
5 True
6 True
7 True
so, with that out of the way, we want to see if the name in row 1 (remember python is a 0 index language so row 1 is actually the 2nd physical row) is .eq to the row above.
we do this by calling [.shift][2] hyperlinked for more info.
what this basically does is shift the rows by its index with a defined variable number, lets call this n.
if we called df['NAME'].shift(1)
0 NaN
1 Rekha
2 Rekha
3 Jaya
4 Jaya
5 Sushma
6 Nita
7 Nita
we can see here that that Rekha has moved down
so putting that all together,
df['NAME'].ne(df['NAME'].shift())
0 True
1 False
2 True
3 False
4 True
5 True
6 False
7 False
we assign this to a self defined variable called mask you could call this whatever you want.
we then use [.loc][2] which lets you access your dataframe by labels or a boolean array, in this instance an array.
however, we only want to access the booleans which are False so we use a ~ which inverts the logic of our array.
NAME DEFAULT CLASS AGE GROUP
1 Rekha third 1 4
3 Jaya fifth 1 1
6 Nita fifth 1 2
7 Nita fourth 1 4
all we need to do now is change these rows to blanks as your initial requirment, and we are left with.
NAME DEFAULT CLASS AGE GROUP
0 Rekha forth 2 2
1 third 1 4
2 Jaya c-default forth 3 3
3 fifth 1 1
4 Sushma fourth3 1
5 Nita c-default third 4 2
6 fifth 1 2
7 fourth1 4
hope that helps!