Given some mixed data containing multiple values as a string, let’s see how can we divide the strings using regex and make multiple columns in Pandas DataFrame.
Method #1:
In this method we will use re.search(pattern, string, flags=0). Here pattern refers to the pattern that we want to search. It takes in a string with the following values:
- \w matches alphanumeric characters
- \d matches digits, which means 0-9
- \s matches whitespace characters
- \S matches non-whitespace characters
- . matches any character except the new line character \n
- * matches 0 or more instances of a pattern
# import the regex library import pandas as pd import re # Create a list with all the strings 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":[]} for item in movie_data: # For Name field name_field = re.search("Name: .*",item) if name_field is not None: name = re.search('\w*\s\w*',name_field.group()) else: name = None movies["Name"].append(name.group()) # For Year field year_field = re.search("Year: .*",item) if year_field is not None: year = re.search('\s\d\d\d\d',year_field.group()) else: year = None movies["Year"].append(year.group().strip()) # For rating field rating_field = re.search("Rating: .*",item) if rating_field is not None: rating = re.search('\s\d.\d',rating_field.group()) else: rating - None movies["Rating"].append(rating.group().strip()) # Creating DataFrame df = pd.DataFrame(movies) print(df) |
Output:
Explanation:
- In the code above, we use a for loop to iterate through movie data so we can work with each movie in turn. We create a dictionary, movies, that will hold all the details of each detail, such as the rating and name.
- We then find the entire Name field using the
re.search()function. The . means any character except \n, and * extends it to the end of the line. Assign this to the variable name_field. - But, data isn’t always straightforward. It can contain surprises. For instance, what if there’s no Name: field? The script would throw an error and break. We pre-empt errors from this scenario and check for a not None case.
- Again we use the re.search() function to extract the final required string from the name_field. For the name we use \w* to represent the first word, \s to represent the space in between and \w* for the second word.
- Do the same for year and rating and get the final required dictionary.
Method #2:
To break up the string we will use Series.str.extract(pat, flags=0, expand=True) function. Here pat refers to the pattern that we want to search for.
import pandas as pd dict = {'movie_data':['The Godfather 1972 9.2', 'Bird Box 2018 6.8', 'Fight Club 1999 8.8'] } # Convert the dictionary to a dataframe df = pd.DataFrame(dict) # Extract name from the string df['Name'] = df['movie_data'].str.extract('(\w*\s\w*)', expand=True) # Extract year from the string df['Year'] = df['movie_data'].str.extract('(\d\d\d\d)', expand=True) # Extract rating from the string df['Rating'] = df['movie_data'].str.extract('(\d\.\d)', expand=True) print(df) |
Output:
