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.
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
- 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.
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.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.