I have a dataframe as follows df["Annotations"]

missense_variant&splice_region_variant
stop_gained&splice_region_variant
splice_acceptor_variant&coding_sequence_variant&intron_variant
splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&5_prime_UTR_variant&intron_variant
missense_variant&NMD_transcript_variant
frameshift_variant&splice_region_variant
splice_acceptor_variant&intron_variant
splice_acceptor_variant&coding_sequence_variant
stop_lost&3_prime_UTR_variant
missense_variant
splice_region_variant

I want to replace or add a new column with priority of orders. Priority is given as

Type                 Rank
frameshift_variant      1
stop_gained             2
splice_region_variant   3
splice_acceptor_variant 4
splice_donor_variant    5
missense_variant        6
coding_sequence_variant 7

I want to get replace df['Annotations'] or add new column df['Anno_prio'] as:

splice_region_variant
stop_gained
splice_acceptor_variant
splice_acceptor_variant
missense_variant
frameshift_variant
splice_acceptor_variant
splice_acceptor_variant
stop_lost
missense_variant
splice_region_variant

The way I tried was for each term:

df['Annotation']=df['Annotation'].str.replace('missense_variant&splice_region_variant','splice_region_variant')

Are there any other approach to do it using pandas?

process:

1. Split by "&" and use pandas.Series.explode transform each element of a list-like to a row.
2. use map Series to convert the Type to Rank
3. then sort Rank and drop_duplicates with origin index
4. fillna with the first Type in Annotations

anno_map = df_rank.set_index('Type')['Rank']
obj_anno_split = df['Annotations'].str.split('&')
df_anno_map = obj_anno_split.explode().reset_index()
# create a new column rank use map
df_anno_map['rank'] = df_anno_map['Annotations'].map(anno_map)# keep the first rank for every index, by sort and drop_duplicates
df_anno_map = (df_anno_map.dropna().sort_values('rank').drop_duplicates('index', keep='first').set_index('index').sort_index())# assing Anno_prio with index broadcast
df['Anno_prio'] = df_anno_map['Annotations']# fillna with the the split's first item
df['Anno_prio'] = df['Anno_prio'].combine_first(obj_anno_split.str[0])# print(df_anno_map)
# print(df)

result:

print(df_anno_map)Annotations  rank
index                               
0        splice_region_variant   3.0
1                  stop_gained   2.0
2      splice_acceptor_variant   4.0
3      splice_acceptor_variant   4.0
4             missense_variant   6.0
5           frameshift_variant   1.0
6      splice_acceptor_variant   4.0
7      splice_acceptor_variant   4.0
9             missense_variant   6.0
10       splice_region_variant   3.0print(df)Annotations                Anno_prio
0              missense_variant&splice_region_variant    splice_region_variant
1                   stop_gained&splice_region_variant              stop_gained
2   splice_acceptor_variant&coding_sequence_varian...  splice_acceptor_variant
3   splice_donor_variant&splice_acceptor_variant&c...  splice_acceptor_variant
4             missense_variant&NMD_transcript_variant         missense_variant
5            frameshift_variant&splice_region_variant       frameshift_variant
6              splice_acceptor_variant&intron_variant  splice_acceptor_variant
7     splice_acceptor_variant&coding_sequence_variant  splice_acceptor_variant
8                       stop_lost&3_prime_UTR_variant                stop_lost
9                                    missense_variant         missense_variant
10                              splice_region_variant    splice_region_variant