Split string into columns using regex in pandas DataFrame

Method # 1 :
In this method we will use re.search (pattern, string, flags = 0) . Here, pattern refers to the pattern we want to find. It accepts a string with the following values:

• / w matches alphanumeric characters
• / d matches numbers, which means 0-9
• / s matches whitespace
• / S matches non-whitespace characters
• , matches any character other than the newline character / n
• * matches 0 or more instances of the pattern

# import regex library import pandas as pd import re    # Create a list with all lines movie_data = [ "Name: The_Godfather Year: 1972 Rating: 9.2" , "Name: Bird_Box Year: 2018 Rating: 6.8" , "Name: Fight_Club Year: 1999 Rating: 8.8 " ]    # Create a dictionary with the required columns # Used later to convert to DataFrame movies = { " Name " : [], "Year" : [], " Rating " : []}   < / p> for item in movie_data:      # For name field name_field = re.search ( "Name:. *" , item)      if name_field is not None : name = re.search ( ’w * sw * ’ , name _field.group ()) else : name = None movies [ "Name" ]. append (name.group ())     # Year field year_field = re.search ( "Year:. *" , item) if year_field is not None  : year = re.search ( ’sdddd’ , year_field.group ())   else : year = None movies [ "Year" ]. append (year.group (). strip ())   # For the rating field rating_field = re.search ( " Rating:. * " , item)   if rating_field is not None :  rating = re.search ( ’sd.d ’ , rating_field.group ())   else :    rating - None movies [ "Rating" ]. append (rating.group (). strip ())    # Create DataFrame df = pd.DataFrame (movies) print (df)

Output:

Explanation :

• In the above code, we are using a for loop to iterate over the movie data so that we can work with each movie in turn. We create a movie dictionary that will store all the details of every detail such as rating and title.
• We then find the complete Name field using re.search () . . means any character except / n and * expands it to the end of the line. Assign that to the variable name_field .
• But the data isn’t always straightforward. This may contain surprises. For example, what if there is no Name: field? The script will give an error and break. We anticipate errors from this script and check for " No" .
• We again use the re.search () function to retrieve the last required row from the name_field. For the name, we use / w * to represent the first word, / s to represent the space between them, and / w * for the second word.
• Do the same for the year and rankings and get the final required vocabulary.

Method # 2:
To split a string, we will use Series.str.extract (pat, flags = 0, expand = True) . Here pat refers to the pattern we want to find.

import pandas as pd   dict = { ’movie_data’ : [ ’The Godfather 1972 9.2’ ,   ’Bird Box 2018 6.8’ ,   ’Fight Club 1999 8.8’ ]}    # Convert dictionary to data frame df = pd.DataFrame ( dict )    # Extract name from string df [ ’Name’ ] = df [ ’movie_data’ ]. str . extract ( ’(w * sw *)’ , expand = True )    # Extract year from string df [ ’Year’ ] = df [ ’movie_data’ ].   str . extract ( ’(dddd)’ , expand = True )   # Extract rating from string df [ ’Rating’ ] = df [ ’movie_data’ ]. str . extract ( ’ (dd) ’ , expand = True ) print (df)

Output: