Prerequisite: Regular Expression in Python
In this article, we will see how to extract punctuation used in the specified column of the Dataframe using Regex.
Firstly, we are making regular expression that contains all the punctuation: [!”\$%&\'()*+,\-.\/:;=#@?\[\\\]^_`{|}~]* Then we are passing each row of specific column to re.findall() function for extracting the punctuation and then assigning that extracted punctuation to a new column in a Dataframe.
re.findall() function is used to extract all non-overlapping matches of pattern in string, as a list of strings. The string is scanned left-to-right, and matches are returned in the order found.
Syntax: re.findall(regex, string)
Return: All non-overlapping matches of pattern in string, as a list of strings.
Now, Let’s create a Dataframe:
Python3
# import required libraries import pandas as pd import re # creating Dataframe with # name and their comments df = pd.DataFrame({ 'Name' : ['Akash', 'Ashish', 'Ayush', 'Diksha' , 'Radhika'], 'Comments': ['Hey! Akash how r u' , 'Why are you asking this to me?' , 'Today, what we are going to do.' , 'No plans for today why?' , 'Wedding plans, what are you saying?']}, columns = ['Name', 'Comments'] ) # show the Dataframe df |
Output:
Now, Extracting the punctuation from the column comment:
Python3
# define a function for extracting # the punctuations def check_find_punctuations(text): # regular expression containing # all punctuation result = re.findall(r'[!"\$%&\'()*+,\-.\/:;=#@?\[\\\]^_`{|}~]*', text) # form a string string = "".join(result) # list of strings return return list(string) # creating new column name # as a punctuation_used and # applying user defined function # on each rows of Comments column df['punctuation_used'] = df['Comments'].apply( lambda x : check_find_punctuations(x) ) # show the Dataframe df |
Output:
