CODEWITHSUNDEEP
pythonpandaslist-comprehension
TH14
TH14

Reputation: 622

Pandas: Extract acronym from substrings of one column and match it to another column with a condition

I am trying to match the names in two columns in the same dataframe, I want to create a function to return True if the name in one column is an acronym of the other even if they contain the same acronym substring.

pd.DataFrame([['Global Workers Company gwc', 'gwc'], ['YTU', 'your team united']] , columns=['Name1','Name2'])

Desired Output:

         Name1                      Name2               Match
0   Global Workers Company gwc           gwc            True
1   YTU                             your team united    True

I have creating a lambda function to only get the acronym but haven't been able to do so

t = 'Global Workers Company gwc'
[x[0] for x in t.split()]

['G', 'W', 'C', 'g']

"".join(word[0][0] for word in test1.Name2.str.split()).upper()

Upvotes: 1

Views: 466

Answers (2)

Prayson W. Daniel
Prayson W. Daniel

Reputation: 15588

I will use a mapper. We will have a lookup dictionary that will transform data to the same type that we can check for equality.

import pandas as pd

#data
df = pd.DataFrame([['Global Workers Company', 'gwc'], ['YTU', 'your team united']] , columns=['Name1','Name2'])


# create a mapper
mapper = {'gwc':'Global Workers Company',
          'YTU': 'your team united'}

def replacer(value, mapper=mapper):
     '''Takes in value and finds its map, 
        if not found return original value
     '''
    return mapper.get(value, value)

# create column checker and assign the equality 
df.assign(
    checker = lambda column: column['Name1'].map(replacer) == column['Name2'].map(replacer)
)

print(df)

Upvotes: 0

Shubham Sharma
Shubham Sharma

Reputation: 71687

You can use Dataframe.apply function along with axis=1 parameter to apply a custom func on the dataframe. Then you can use regular expressions to compare the acronym with the corresponding larger name or phrase.

Try this:

import re

def func(x):
    s1 = x["Name1"]
    s2 = x["Name2"]

    acronym = s1 if len(s1) < len(s2) else s2
    fullform = s2 if len(s1) < len(s2) else s1

    fmtstr = ""
    for a in acronym:
        fmtstr += (r"\b" + a + r".*?\b")

    if re.search(fmtstr, fullform, flags=re.IGNORECASE):
        return True
    else:
        return False


df["Match"] = df.apply(func, axis=1)
print(df)

Output:

                        Name1             Name2  Match
0  Global Workers Company gwc               gwc   True
1                         YTU  your team united   True

Upvotes: 2

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