I have a data frame like this,
df
col1 col2
A 'the value is zero'
B 'this is a cat'
C 'the value is one'
D 'nothing is here'
E 'the colour is blue'
F 'this is dog'
G 'empty sequence'
H 'the colour is red'
I 'the colour is green' 1
Now I want the similar kind of strings as flagged as 1 and others as zero, so the final data frame should look like,
col1 col2 col1
A 'the value is zero' 1
B 'this is a cat' 1
C 'the value is one' 1
D 'nothing is here' 0
E 'the colour is blue' 1
F 'this is dog' 1
G 'empty sequence' 0
H 'the colour is red' 1
I 'the colour is green' 1
The 0 and 1 can be obtained using SequenceMatcher(SequenceMatcher(None, s1, s2).ratio()) function and with some threshold value we can make it to zero or one.
But if I use for loops to find the similarity between each other then it will take longer time to execute. Looking for some pandas shortcuts/pythonic way to do this efficiently.
Similarly to is it possible to do fuzzy match merge with python pandas?,
we can use difflib and check if we find more than 1 similar string (to exclude its own) by looking at the length of the list returned by difflib.get_close_matches:
import difflib
df['col1'] = [(len(difflib.get_close_matches(x, df['col2'], cutoff=0.7))>1)*1
for x in df['col2']]
print(df)
col1 col2
0 1 'the value is zero'
1 1 'this is a cat'
2 1 'the value is one'
3 0 'nothing is here'
4 1 'the colour is blue'
5 1 'this is dog'
6 0 'empty sequence'
7 1 'the colour is red'
8 1 'the colour is green'
One could also be interested in obtaining a similarity matrix setting all values in a pivoted column to 1 if the strings are similar. For this we could proceed similarly as above, but keeping the entire list, exploding it and pivoting the resulting dataframe with pd.crosstab:
df['sim'] = [difflib.get_close_matches(x, df['col2'], cutoff=0.7) for x in df['col2']]
sim_df = df.explode('sim')
pd.crosstab(sim_df.col2, sim_df.sim)
sim empty sequence nothing is here the colour is blue... the value is zero this is a cat this is dog
col2
empty sequence 1 0 0 ... 0 0 0
nothing is here 0 1 0 ... 0 0 0
the colour is blue 0 0 1 ... 0 0 0
the colour is green 0 0 1 ... 0 0 0
the colour is red 0 0 1 ... 0 0 0
the value is one 0 0 0 ... 1 0 0
the value is zero 0 0 0 ... 1 0 0
this is a cat 0 0 0 ... 0 1 1
this is dog 0 0 0 ... 0 1 1