Drop similar text rows of one column in Python - python-3.x

Question

import pandas as pd
from difflib import SequenceMatcher
df = pd.DataFrame({"id":[9,12,13,14],
"text":["Error number 609 at line 10", "Error number 609 at line 22", "Error string 'foo' at line 11", "Error string 'bar' at line 14"]})
Output:
id text
0 9 Error number 609 at line 10
1 12 Error number 609 at line 22
2 13 Error string 'foo' at line 11
3 14 Error string 'bar' at line 14
I want to use difflib.SequenceMatcher to remove similarity score lower than 80 rows and only keep one.
a = "Error number 609 at line 10"
b = "Error number 609 at line 22"
c = "Error string 'foo' at line 11"
d = "Error string 'bar' at line 14"
print(SequenceMatcher(None, a, b).ratio()*100) #92.5925925925926
print(SequenceMatcher(None, b, c).ratio()*100) #60.71428571428571
print(SequenceMatcher(None, c, d).ratio()*100) #86.20689655172413
print(SequenceMatcher(None, a, c).ratio()*100) #64.28571428571429
How can I get expected result as follows in Python? You can use difflib or other python packages. Thank you.
id text
0 9 Error number 609 at line 10
2 13 Error string 'foo' at line 11

Answer 1

You can use:
#cross join with filter onl text column
df = df.assign(a=1).merge(df[['text']].assign(a=1), on='a')
#filter out same columns per rows
df = df[df['text_x'] != df['text_y']]
#sort columns per rows
df[['text_x','text_y']] = pd.DataFrame(np.sort(df[['text_x','text_y']],axis=1), index=df.index)
#remove duplicates
df = df.drop_duplicates(subset=['text_x','text_y'])
#get similarity
df['r'] = df.apply(lambda x: SequenceMatcher(None, x.text_x, x.text_y).ratio(), axis=1)
#filtering
df = df[df['r'] > 0.8].drop(['a','r'], axis=1)
print (df)
id text_x text_y
1 9 Error number 609 at line 10 Error number 609 at line 22
11 13 Error string 'bar' at line 14 Error string 'foo' at line 11