I have two dataframes
df1
IMPACT Rank
HIGH 1
MODERATE 2
LOW 3
MODIFIER 4
df2['Annotation']
Annotation
A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|MODERATE|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||
A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|HIGH|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||
A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|LOW|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||
There are multiple annotation in separated by ,
(comma), I want to consider only one annotation from the dataframe based on Rank in the df1
. My expected outputwill be:
df['RANKED']
RANKED
A|missense_variant|MODERATE|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999)
A|missense_variant|HIGH|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999)
A|missense_variant|LOW|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999)
I tried following code to generate the output: but did not give me the expected result
d = df1.set_index('IMPACT')['Rank'].to_dict()
max1 = df1['Rank'].max()+1
def f(x):
d1 = {y: d.get(y, max1) for y in x for y in x.split(',')}
return min(d1, key=d1.get)
df2['RANKED'] = df2['Annotation'].apply(f)
Any help appreciated..
df2['RANKED'] = df2['Annotation'].str.split(',')
df2 = df2.explode(column='RANKED')
df2['IMPACT'] = df["RANKED"].str.findall(r"|".join(df1['IMPACT'])).apply("".join)
df_merge = df2.merge(df1, how='left', on='IMPACT')
df_final = df_merge.loc[df_merge.groupby(['Annotation'])['Rank'].idxmin().sort_values()].drop(columns=['Annotation', 'IMPACT'])
First you define your dataframes
df1 = pd.DataFrame({'IMPACT':['HIGH', 'MODERATE', 'LOW', 'MODIFIER'], 'Rank':[1,2,3,4]})
df2 = pd.DataFrame({
'Annotation':[
'A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|MODERATE|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||',
'A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|HIGH|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||',
'A|intron_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000341290|protein_coding||2/4||||||||||-1||HGNC|HGNC:28208||||,A|missense_variant|LOW|PERM1|ENSG00000187642|Transcript|ENST00000433179|protein_coding|1/3||||72|72|24|E/D|gaG/gaT|||-1||HGNC|HGNC:28208|YES|CCDS76083.1|deleterious(0)|probably_damaging(0.999),A|upstream_gene_variant|MODIFIER|PERM1|ENSG00000187642|Transcript|ENST00000479361|retained_intron|||||||||||4317|-1||HGNC|HGNC:28208||||']
})
Now here is the tricky part. You should create a column with the list of the split by comma string of the original Annotation
column. Then you explode this column so you can have the objective values repeated for each original string.
df2['RANKED'] = df2['Annotation'].str.split(',')
df2 = df2.explode(column='RANKED')
Next, you extract the IMPACT word from each RANKED
column.
df2['IMPACT'] = df2["RANKED"].str.findall(r"|".join(df1['IMPACT'])).apply("".join)
Then, you merge df1
and df2
to get the rank of each RANKED
.
df_merge = df2.merge(df1, how='left', on='IMPACT')
Finally, this is the easy part where you discard everything you do not want in the final dataframe. This can be done via groupby.
df_final = df_merge.loc[df_merge.groupby(['Annotation'])['Rank'].idxmin().sort_values()].drop(columns=['Annotation', 'IMPACT'])
RANKED Rank
A|missense_variant|MODERATE|PERM1|ENSG00000187... 2
A|missense_variant|HIGH|PERM1|ENSG00000187642|... 1
A|missense_variant|LOW|PERM1|ENSG00000187642|T... 3
OR by dropping duplicates
df_final = df_merge.sort_values(['Annotation', 'Rank'], ascending=[False,True]).drop_duplicates(subset=['Annotation']).drop(columns=['Annotation', 'IMPACT'])
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