I have the dictionary specifying the value the row should take if the conditions are met. The dictionary can be restructured if needed (or reshape it if you prefer while preserving the information in it)

dict_map = {
"Yes" : {'Sex':'F', 'Pregnant': 'Y'},
"No" : {'Sex':'F', 'Pregnant': 'N'},
"N/A" : {'Sex': 'M'},
}

And the dataframe and list

df = pd.DataFrame(
{'ID': ['AB01', 'AB02', 'AB03', 'AB04', 'AB05','AB06'],'Sex': ["F","M","M",np.nan,"F","F"],'Pregnant': ['Y', 'N',np.nan, 'Y', '', 'N']})cols_of_interest = ["Sex","Pregnant"]

How do I achieve the following result?

Transform your dict_map into a Series of query string usable by query method:

sr = pd.Series({k1: ' & '.join([f"{k2} == '{v}'" for k2, v in d.items()])for k1, d in dict_map.items()})for v, q in sr.iteritems():df.loc[df.query(q).index, 'Status'] = v

Output:

>>> dfID  Sex Pregnant Status
0  AB01    F        Y    Yes
1  AB02    M        N    N/A
2  AB03    M      NaN    N/A
3  AB04  NaN        Y    NaN
4  AB05    F             NaN
5  AB06    F        N     No>>> sr
Yes    Sex == 'F' & Pregnant == 'Y'
No     Sex == 'F' & Pregnant == 'N'
N/A                      Sex == 'M'
Name: Status, dtype: object

Update

I have scenario in which the dictionary is like {'Yes': {'Sex': ('F', 'Female')}, 'No': {'Sex': 'M', 'Pregnant': 'N\A'}} how do I convert this into a series like above format? In english, Yes if sex is F or Female and No if sex is M and pregnant is N\A

Use isin when the value is a tuple:

dict_map2 = {'Yes': {'Sex': ('F', 'Female')},'No': {'Sex': 'M', 'Pregnant': 'N\A'}}sr = pd.Series({k1: ' & '.join((f"{k2} == '{v}'" if isinstance(v, str) else f"{k2}.isin({v})")for k2, v in d.items())for k1, d in dict_map2.items()})
print(sr)# Output:
Yes         Sex.isin(('F', 'Female'))
No     Sex == 'M' & Pregnant == 'N\A'
dtype: object

The loop still the same.