Read unique values from txt file with pandas

I have a txt file which is formatted in this way:

 thi is    a junk data    line to be ignored abc xyz dsfgsrj
AFKSDNG-RBI 20200706    MARS        stu    base-1
AFKSDNG-UBI 20200706    JUPITER     uyt    base-2
AFKSDNG-ABI 20200706    MARS        stu    base-1
AFKSDNG-XBI 20200706    JUPITER     uyt    base-2
AFKSDNG-XBI 20200706    MARS        stx    base-1

Please note that I have only raw data in the txt file without any column name in the header indicating the context of each column.

Each column is separated from the other by one or more whitespaces.

So for example if I wanted to count the occurances of 'MARS' it would be 2 and not 3 because the last record has the 4th column different ('stx') from the previous ones.

I need to count all the unique occurences and produce an excel file like the following:

Column 1     Column 2     Column 3   Column 4   Column 5    Column 6 (occurences)
AFKSDNG-RBI   20200706      MARS        stu        base-1     2
AFKSDNG-UBI   20200706      JUPITER     uyt        base-2     2
AFKSDNG-ABI   20200706      MARS        stu        base-1     2
AFKSDNG-XBI   20200706      JUPITER     uyt        base-2     2
AFKSDNG-XBI   20200706      MARS        stx        base-1     1

EVEN BETTER OUTPUT WOULD BE TO REMOVE THE DUPLICATED RECORDS AFTER COUNTING THEM SO:

Column 1     Column 2     Column 3   Column 4   Column 5    Column 6 (occurences)
AFKSDNG-RBI   20200706      MARS        stu        base-1     2
AFKSDNG-UBI   20200706      JUPITER     uyt        base-2     2
AFKSDNG-XBI   20200706      MARS        stx        base-1     1

I tried writing this code in python for reading and producing an Excel:

import pandas as pd

df = pd.read_csv('CD202205.txt', engine='python', sep='\s{3,}', header=None, skiprows=1)
df.to_excel('export.xlsx', index=False, sheet_name='SHEET1')

But I cannot figure out how to count the occurences. I'm new to python and pandas so any help would be highly appreciated.

-------------------------------------UPDATE---------------------------------------

I noticed a little issue if we slightly change the source txt file.
As I stated before the last 'MARS' is different from the previous ones because the 4th column 'stx' is different. In order to be unique it only takes one column from the 3rd, 4th or the 5th one to be different.

EXAMPLE

thi is    a junk data    line to be ignored abc xyz dsfgsrj
AFKSDNG-RBI 20200706    MARS        stu    base-1
AFKSDNG-UBI 20200706    JUPITER     uyt    base-2
AFKSDNG-ABI 20200706    MARS        stu    base-1
AFKSDNG-XBI 20200706    JUPITER     uyt    base-2
AFKSDNG-XBI 20200706    MARS        stx    base-1 // different cuz stx is different
AFKSDNG-XBI 20200706    PLUTO       stu    base-1 // even though here stu and base-1 is like 'MARS' we have 'PLUTO' so this is a new row

In the accepted answer of @jezrael 'PLUTO' is counted with 'MARS'

For count use GroupBy.transform with DataFrame.drop_duplicates:

df = pd.read_csv('CD202205.txt', engine='python', sep='\s{3,}', header=None, skiprows=1)
print (df)
             0         1        2    3       4
0  AFKSDNG-RBI  20200706     MARS  stu  base-1
1  AFKSDNG-UBI  20200706  JUPITER  uyt  base-2
2  AFKSDNG-ABI  20200706     MARS  stu  base-1
3  AFKSDNG-XBI  20200706  JUPITER  uyt  base-2
4  AFKSDNG-XBI  20200706     MARS  stx  base-1

df['new'] = df.groupby([2,3,4])[2].transform('size')

df = df.drop_duplicates([2,3,4])
print (df)
             0         1        2    3       4  new
0  AFKSDNG-RBI  20200706     MARS  stu  base-1    2
1  AFKSDNG-UBI  20200706  JUPITER  uyt  base-2    2
4  AFKSDNG-XBI  20200706     MARS  stx  base-1    1

df.to_excel('export.xlsx', index=False, sheet_name='SHEET1')

If need set columns names:

df = pd.read_csv('CD202205.txt', engine='python', sep='\s{3,}', header=None, skiprows=1)

f = lambda x: f'Column {x+1}'
df = df.rename(columns=f)
print (df)
      Column 1  Column 2 Column 3 Column 4 Column 5
0  AFKSDNG-RBI  20200706     MARS      stu   base-1
1  AFKSDNG-UBI  20200706  JUPITER      uyt   base-2
2  AFKSDNG-ABI  20200706     MARS      stu   base-1
3  AFKSDNG-XBI  20200706  JUPITER      uyt   base-2
4  AFKSDNG-XBI  20200706     MARS      stx   base-1

df['Column 6']=df.groupby(['Column 3','Column 4','Column 5'])['Column 3'].transform('size')

df = df.drop_duplicates(['Column 3','Column 4','Column 5'])
print (df)
      Column 1  Column 2 Column 3 Column 4 Column 5  Column 6
0  AFKSDNG-RBI  20200706     MARS      stu   base-1         2
1  AFKSDNG-UBI  20200706  JUPITER      uyt   base-2         2
4  AFKSDNG-XBI  20200706     MARS      stx   base-1         1

df.to_excel('export.xlsx', index=False, sheet_name='SHEET1')

EDIT: Test with new data:

df['new'] = df.groupby([2,3,4])[2].transform('size')

df = df.drop_duplicates([2,3,4])
print (df)
             0         1        2    3       4  new
0  AFKSDNG-RBI  20200706     MARS  stu  base-1    2
1  AFKSDNG-UBI  20200706  JUPITER  uyt  base-2    2
4  AFKSDNG-XBI  20200706     MARS  stx  base-1    1
5  AFKSDNG-XBI  20200706    PLUTO  stu  base-1    1