Pandas compare each row with all rows in data frame and save results in list for each row

Question

I try compare each row with all rows in pandas DF through fuzzywuzzy.fuzzy.partial_ratio() >= 85 and write results in list for each row.

in: df = pd.DataFrame( {'id':[1, 2, 3, 4, 5, 6], 'name':['dog', 'cat', 'mad cat', 'good dog', 'bad dog', 'chicken']})

use pandas function with fuzzywuzzy library get result:

out: 
    id  name     match_id_list
    1   dog      [4, 5]
    2   cat      [3, ]
    3   mad cat  [2, ]
    4   good dog [1, 5]
    5   bad dog  [1, 4]
    6   chicken  []

But I don't understand how get this.

Answer 1

The first step would be to find the indices that match the condition for a given name. Since partial_ratio only takes strings, we apply it to the dataframe:

name = 'dog'
df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)

We can then use enumerate and list comprehension to generate the list of true indices in the boolean array:

matches = df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)
[i for i, x in enumerate(matches) if x]

Let's put all this inside a function:

def func(name):
    matches = df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)
    return [i for i, x in enumerate(matches) if x]

We can now apply the function to the entire dataframe:

df.apply(lambda row: func(row['name']), axis=1)