查找两个数据帧之间的字符串匹配

Question

I have a DataFrame as below.

DF1:

   A
Any Match
Credit
I need a debit card.
Logging
Awesome

I have another DataFrame as below:

DF2:

          B
I did not find any match.
I want a credit card.
I need a debit card.
I do not know.
I am logging into credit portal.

I need my output as:

              B                           A
     I did not find any match.        Any Match
     I want a credit card.            Credit
     I need a debit card.             I need a debit card.
     I am logging into credit portal. logging,credit

Here if the phrase present in the DF1 is in any of the text present in DF2. Print the o/p as Text and important phrase.

Answer 1

Try Fuzzywuzzy :

import pandas as pd
from fuzzywuzzy import fuzz

matched_entities = []

for row in df1.index:
    name1 = vendor_df.get_value(row,"A")
    for columns in df2.index:
        name2=df2.get_value(columns,"B")
        matched_token=fuzz.partial_ratio(name1,name2)
        if matched_token> 80:
            matched_vendors.append([A,B])

df_partial_ratio = pd.DataFrame(columns=['A', 'B'], data=matched_entities)

In your DB if fuzz.partial_ratio doesn't work try fuzz.ratio or fuzz.token_sort_ratio . These two can be implemented by changing one line of above code by below codes:

matched_token=fuzz.ratio(name1,name2)

OR

matched_token=fuzz.token_sort_ratio(name1,name2)

Answer 2

You could do something like this. First, define a lookup function that matches "normalized" text, eg lowercased:

def lookup(x, values):
    for value in values:
        if value.lower() in x.lower():
            return value

Then apply this function to your DF2:

dfB['A'] = dfB['B'].apply(lambda x: lookup(x, dfA['A']))

Which should give you:

    B                           A
0   I did not find any match.   Any Match
1   I want a credit card.       Credit
2   I need a debit card.        Debit
3   I do not know.              None

Answer 3

try this

df1['B'] = float('nan')

pos = 0
for i in range(len(df1)):
    for j in range(len(df2)):
        if df1['A'][i].lower() in df2['B'][j].lower():
            df1['B'].iloc[pos] = df2['B'][j]
            pos+=1
            break

df1.dropna(axis=0)

output

                     A                          B
0            Any Match  I did not find any match.
1               Credit      I want a credit card.
2  I need a debit card       I need a debit card.