Overview, types, and differences of regular expressions in data analytics
Data analytics deals with data sets, and whenever the data set is text-based, data scientists use regular expressions or Regex to ease the process. Regex has been extensively used. In 1950, Stephen Cole Kleene, an American mathematician, introduced the usage of the regular expression. Thereafter, in order to write different regular expressions, the syntax was used. The widely famous were Perl and POSIX syntax.
Importance of regular expressions
Regular expressions are used for specifying any particular pattern. Usually, it denotes the usage of formal languages and grammar. It is useful when the user attempts to search distinct text lines within a pattern. It conducts the search on a solo line and does not consider patterns that might begin on one line and end on a different line. Every character within the regular expression contains a special character known as the metacharacter (Schatten, Ševa & Đurić, 2015). The metacharacter has a special meaning as well. For instance, within the regex a., “a” is a literal character and “.” is regarded as the metacharacter. The usage of both literal and metacharacters can help to classify the text of a pattern. Moreover, Regex allows users to define their individual search criteria based on their needs. Hence, a user can form their own language and interpret the text strings by identifying patterns. Regex is a powerful method because it can help to remove irrelevant text within the data set. It ensures proper threading of emails.
Differences between the two types of regular expressions
A regular expression can be categorized into BRE or Basic Regular Expression and ERE or Extended Regular Expression. The syntax BRE is considered the default in sed. In the case of basic syntax (BRE), the characters do not signify any particular or special meaning. Only when the prefix “\” is used, a special meaning can be identified. On the other hand, in the case of ERE, all the characters are considered special ones, and characters involving the backslash “\” is an exception. Hence, the reverse of BRE is ERE. Data scientists usually prefer using the basic syntax (Murthy, Deepak, & Deshpande, 2012). BRE is also applicable to the utilities that support all regular expressions while ERE is supported by special utilities.
Ways in which regular expression aid data manipulation
The Python regular expressions can manipulate data, and the operations can be handled by using Python’s re module. Anyone can implement regular expression by specifying the pattern string. Thereafter, it can be compiled into a regular object of expression. Using the object to conduct a search on a pattern is the next step. Lastly, one can extract a similar pattern. Pattern strings are used to express Python expressions (Liu, Ai, & Xu, 2017). A simple pattern string mainly includes numbers, spaces, and letters. The pattern expresses mostly a particular search query. Following that, the pattern string can be processed and converted into an object. Python later uses the object to conduct the search. It uses the compile () method given in the re module. Different patterns can also be produced using pattern strings sequentially.