CODEWITHSUNDEEP
pythonregex
Chan
Chan

Reputation: 4291

How to extract string in pandas with regex?

I have a dataframe:

swimming(4) 1 4 hiking (1 ) 2 2 running ( 12 ) 3 5 fishing( 2 )

|     |  sid  | Hobby (times per month) |
|-----+-------+-------------------------|
|  0  |   3   |      swimming(4)        |
|-----+-------+-------------------------|
|  1  |   4   |      hiking  (1 )       |
|-----+-------+-------------------------|
|  2  |   2   |      running ( 12 )     |
|-----+-------+-------------------------|
|  3  |   5   |      fishing ( 2 )      |

How to extract strings by removing the brackets in the second column as:

|     |  sid  | Hobby (times per month) |
|-----+-------+-------------------------|
|  0  |   3   |        swimming         |
|-----+-------+-------------------------|
|  1  |   4   |        hiking           |
|-----+-------+-------------------------|
|  2  |   2   |        running          |
|-----+-------+-------------------------|
|  3  |   5   |        fishing          |

Upvotes: 1

Views: 123

Answers (3)

Simas Joneliunas
Simas Joneliunas

Reputation: 3118

to implement the regex in pandas you can use pandas.apply():

import re

def remove_brackets(string):
    part = regexp_matcher.findall(string)
    if not part:
        return string
    return part[0]

regexp_matcher = re.compile(r'^([\w]+)[\s]*\([\s]*[\d]*[\s]*\)[\s]*$')
df = pd.DataFrame()
df['string'] = ['swimming(4)', 'swimming(4)', 'swimming(4)']    
df['new_string'] = df['string'].apply(remove_brackets)

Upvotes: 0

Joey Gao
Joey Gao

Reputation: 939

You can use 'str' method to match the string in pandas

df.columns = ['sid','Hobby']
df.Hobby = df.Hobby.str.extract(r'(\w*)')

Upvotes: 1

Aman Chhabra
Aman Chhabra

Reputation: 3894

If you want for example, swimming(4) to be changed to swimming, you can use below regex:

^([\w]+)[\s]*\([\s]*[\d]*[\s]*\)[\s]*$

Demo: https://regex101.com/r/sTO1Q9/1

Test Cases:

swimming(4)
hiking   (1 )
running ( 12 )
fishing( 2 )
hiking(1)

Match:

Match 1
Full match  0-11    `swimming(4)`
Group 1.    0-8 `swimming`
Match 2
Full match  12-25   `hiking   (1 )`
Group 1.    12-18   `hiking`
Match 3
Full match  26-40   `running ( 12 )`
Group 1.    26-33   `running`
Match 4
Full match  41-53   `fishing( 2 )`
Group 1.    41-48   `fishing`
Match 5
Full match  54-64   `hiking(1) `
Group 1.    54-60   `hiking`

Upvotes: 1

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