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My dataset has 2 columns:

1) Gender( that has 1 or 2)

2) Rating1(That has 3 ratings (1,2,3))

rating1: 1,2,3,3,2

gender: 2,1,2,2,1

I want output as:

I have tried this:

nutri.groupby(['Rating1','Gender']).nunique()

but I get an output which looks wrong.

You can do that by

>>> import pandas as pd
>>> m = pd.DataFrame('gender': [1, 2, 2, 1, 1, 2, 1], 'rating': [3, 4, 2, 1, 3, 1, 5])
>>> m.groupby(['rating','gender']).size().to_frame('count').reset_index()
 rating gender count
0 1 1 1
1 1 2 1
2 2 2 1
3 3 1 2
4 4 2 1
5 5 1 1

Hope this is what you want to pull.

Edit:
As mentioned, I did not account for zero values.

You need to do one more step additional to get what you want. Finding the combinations that are missing and then joining it. The one liner solution would be

>>> from itertools import product
>>> m.groupby(['rating', 'gender']).size().to_frame('count').reset_index().merge(
 pd.DataFrame(list(set([i for i in product(*[m.gender, m.rating])])), columns=['gender', 'rating']),
 on=['rating', 'gender'],
 how='right').fillna(value=0)
 rating gender count
0 1 1 1.0
1 1 2 1.0
2 2 2 1.0
3 3 1 2.0
4 4 2 1.0
5 5 1 1.0
6 2 1 0.0
7 4 1 0.0
8 3 2 0.0
9 5 2 0.0

Explanation

Get the original grouped counts as mentioned before the first edit but this time you need to join with the combinations that are missing to get the zero counts. Use itertools.product to get all combinations of gender and rating and right join it with original grouped frame on rating and gender to get merged DataFrame which has numpy.na values if no count is present and then use fillna method to fill it with zero. The only loophole is if a rating say 4 is not there in the original data, there is no combination that follows it.

Hope this helps.