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IntroductionPure Language Processing is an interdisciplinary concept that uses the fundamentals of computational linguistics and Synthetic Intelligence to understand how human languages interact with technology.
To apply NLP to real-world scenarios, it is necessary to have a thorough grasp of various terminology and ideas. Among which some of the important concepts are Half-of-Speech (POS) Tagging, Statistical Language Modeling, Syntactic, Semantic, and Sentiment Evaluation, Normalization, Tokenization, Dependency Parsing, and Constituency Parsing.
We will examine the principles of Dependency Parsing in this article in order to have a better understanding of how it is applied in Natural Language Processing.
Dependency ParsingThe term Dependency Parsing (DP) refers to the process of examining the dependencies between the phrases of a sentence in order to determine its grammatical structure. A sentence is divided into many sections based mostly on this. The process is based on the assumption that there is a direct relationship between each linguistic unit in a sentence. These hyperlinks are called dependencies.
Consider the following statement: “I prefer the morning flight through Denver.”
The diagram below explains the sentence’s dependence structure:
IMAGE – 1
In a written dependency structure, the relationships between each linguistic unit, or phrase, in the sentence are expressed by directed arcs. The root of the tree “prefer” varies the pinnacle of the preceding sentence, as labelled within the illustration.
A dependence tag indicates the relationship between two phrases. For example, the word “flight” changes the meaning of the noun “Denver.” As a result, you may identify a dependence from
This distinguishes the scenario for dependency between the two phrases, where one serves as the pinnacle and the other as the dependent. Currently, the Common Dependency V2 taxonomy consists of 37 common syntactic relationships, as shown in the table below:
Dependency Tag Description
acl clausal modifier of a noun (adnominal clause)
acl:relcl relative clause modifier
advcl adverbial clause modifier
advmod adverbial modifier
advmod:emph emphasizing phrase, intensifier
advmod:lmod
amod adjectival modifier
appos appositional modifier
aux auxiliary
aux:move passive auxiliary
case case-marking
cc coordinating conjunction
cc:preconj preconjunct
ccomp clausal complement
clf classifier
compound compound
compound:lvc gentle verb building
compound:prt phrasal verb particle
compound:redup reduplicated compounds
compound:svc serial verb compounds
conj conjunct
cop copula
csubj clausal topic
csubj:move clausal passive topic
dep unspecified dependency
det determiner
det:numgov рrоnоminаl quаntifier gоverning the саse оf the nоun
det:nummod рrоnоminаl quаntifier agreeing with the саse оf the nоun
det:poss possessive determiner
discourse discourse ingredient
dislocated dislocated parts
expl expletive
expl:impers impersonal expletive
expl:move reflexive pronoun utilized in reflexive passive
expl:pv reflexive clitic with an inherently reflexive verb
mounted mounted multiword expression
flat flat multiword expression
flat:overseas overseas phrases
flat:title names
goeswith goes with
iobj oblique object
checklist checklist
mark marker
nmod nominal modifier
nmod:poss possessive nominal modifier
nmod:tmod temporal modifier
nsubj nominal topic
nsubj:move passive nominal topic
nummod numeric modifier
nummod:gov numeriс mоdifier gоverning the саse оf the nоun
obj object
obl indirect nominal
obl:agent agent modifier
obl:arg indirect argument
obl:lmod locative modifier
obl:tmod temporal modifier
orphan orphan
parataxis parataxis
punct punctuation
reparandum overridden disfluency
root root
vocative vocative
xcomp open clausal complement
Dependency Parsing using NLTKThe Pure Language Toolkit (NLTK) package deal will be used for Dependency Parsing, which is a set of libraries and codes used during statistical Pure Language Processing (NLP) of human language.
We may use NLTK to do dependency parsing in one of several ways:
1. Probabilistic, projective dependency parser: These parsers predict new sentences by using human language data acquired from hand-parsed sentences. They’re known to make mistakes and work with a limited collection of coaching information.
2. Stanford parser: It is a Java-based pure language parser. You would want the Stanford CoreNLP parser to perform dependency parsing. The parser supports a number of languages, including English, Chinese, German, and Arabic.
Here’s how you should use the parser:
from nltk.parse.stanford import StanfordDependencyParser path_jar = ‘path_to/stanford-parser-full-2014-08-27/stanford-parser.jar’ path_models_jar = ‘path_to/stanford-parser-full-2014-08-27/stanford-parser-3.4.1-models.jar’ dep_parser = StanfordDependencyParser( path_to_jar = path_jar, path_to_models_jar = path_models_jar ) consequence = dep_parser.raw_parse(‘I shot an elephant in my sleep’) dependency = consequence.subsequent() checklist(dependency.triples()) The following is the output of the above program: [ ((u’shot’, u’VBD’), u’nsubj’, (u’I’, u’PRP’)), ((u’shot’, u’VBD’), u’dobj’, (u’elephant’, u’NN’)), ((u’elephant’, u’NN’), u’det’, (u’an’, u’DT’)), ((u’shot’, u’VBD’), u’prep’, (u’in’, u’IN’)), ((u’in’, u’IN’), u’pobj’, (u’sleep’, u’NN’)), ((u’sleep’, u’NN’), u’poss’, (u’my’, u’PRP$’)) Constituency ParsingConstituency Parsing is based on context-free grammars. Constituency Context-free grammars are used to parse text. Right here the parse tree includes sentences that have been broken down into sub-phrases, each of which belongs to a different grammar class. A terminal node is a linguistic unit or phrase that has a mother or father node and a part-of-speech tag.
Fоr exаmрle, “A cat” and “a box beneath the bed”, are noun phrases, while “write a letter” and “drive a car” are verb phrases.
Consider the following example sentence: “I shot an elephant in my pajamas.” The constituency parse tree is shown graphically as follows:
IMAGE – 2
The parse tree on the left represents catching an elephant carrying pyjamas, while the parse tree on the right represents capturing an elephant in his pyjamas.
The entire sentence is broken down into sub-phases till we’ve got terminal phrases remaining. VP stands for verb phrases, whereas NP
The Stanford parser will also be used to do constituency parsing. It begins by parsing a phrase using the constituency parser and then transforms the constituency parse tree into a dependency tree.
In case your main objective is to interrupt a sentence into sub-phrases, it is ideal to implement constituency parsing. However, dependency parsing is the best method for discovering the dependencies between phrases in a sentence.
Let’s look at an example to see what the difference is:
A dependency parse links words together based on their connections. Each vertex in the tree corresponds to a word, child nodes to words that are reliant on the parent, and edges to relationships. The dependency parse for “John sees Bill” is as follows:
You should choose the parser type that is most closely related to your objective. If you’re looking for sub-phrases inside a sentence, you’re definitely interested in the constituency parse. If you’re interested in the connection between words, you’re probably interested in the dependency parse.
ConclusionOne of the finest examples is the growth of natural language processing (NLP), with smart chatbots prepared to change the world of customer service and beyond.
In summary, human language is awe-inspiringly complex and diverse.
In addition to assisting in the resolution of linguistic ambiguity, NLP is significant because it offers a helpful mathematical foundation for a variety of downstream applications such as voice recognition and text analytics.
In order to understand NLP, it’s important to have a good understanding of the basics, Dependency Parsing is one of them.
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Lacona – Mac Launcher With Natural Language Support
What was once a neat concept, the launcher is now a dime a dozen, especially in a Mac world. There are tons of alternatives to launchers, both free and paid, and everybody seems to have settled in with their favorite choice. Mac OS X even comes with its own launcher – Spotlight. So, if you are a developer, creating yet another Mac launcher might not be the best thing to do, unless you have something better than other launchers up on your sleeve, like Lacona does.
Lacona is a brand new free launcher for Mac, fresh from the Kickstarter campaign. It’s still in Beta stage with many kinks to iron out, but the app is already fully functional. If you are still looking for alternatives or willing to try out something new, you should try Lacona. You’ll find a few pleasant surprises.
Installation and Basic UsageThere’s nothing special in the installation process. You download the zipped file from the official website, unzip it, and open the app. The first time you use the app it will ask you to move it to the “Applications” folder.
The first thing that you need to do before using Lacona is to open the app’s “Preferences” from the menubar.
There are a couple of things you need to do. First, open the “General Settings” and make sure that the “Launch at Login” option is checked. Second, set the keyboard shortcuts that you want to use to summon Lacona. The default shortcut is “Option + Space,” but you can use other combinations.
There are other things that you can customize from the Preferences such as the “Application Directories,” but unless you know what you are doing, it’s better to leave it alone.
You can add custom “Search Engines” if you want to.
Speak Out NaturallyOne of the drawbacks of most launchers is the lack of support for natural language. You need to use a particular set of commands instead of your natural way of speaking. While this might be good to avoid mistakes, sometimes you can do something faster, more efficiently, and less machine-like if you use natural language.
Being still in the beta stage, this natural language support is not perfect yet. There are many commands that Lacona still doesn’t understand. But the app will help you get around by giving you suggestions while you are typing.
Other Special ThingsAnother surprise that I found while taking Lacona for a ride is its ability to do multiple things at once. For example, by typing “send email to A and B and C about Subject” will create an email addressed to those three people with that particular subject. You can also open multiple apps with this command.
This ability is still limited to do things with one app. For example, you can’t create a reminder and email the reminder using one command. But hopefully this feature will be added in a future version.
I’m starting to like using Lacona to create quick reminders without having to open any apps. For example something like the following..
You can find out more examples of what Lacona can do on its website.
Should You Use Lacona?There are things that Lacona should be able to do before it can replace the default launcher on your Mac. I found out that it can’t do a quick calculation or find a quick word definition without opening the Dictionary. But nobody says you can’t use both. Set up different shortcut keys to summon Lacona and then your default launcher, and you’ll have the best of both worlds.
Jeffry Thurana
Jeffry Thurana is a creative writer living in Indonesia. He helps other writers and freelancers to earn more from their crafts. He’s on a quest of learning the art of storytelling, believing that how you tell a story is as important as the story itself. He is also an architect and a designer, and loves traveling and playing classical guitar.
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Regular Expressions In Python With Examples
Introduction to Python Regular Expressions
Regular expressions, commonly referred to as regex, are dynamic tools used for the manipulation and pattern matching of textual data.
They provide a concise and flexible way to search, extract, and manipulate strings based on specific patterns.
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Regular Expression SyntaxRegular expressions in Python are represented as strings and combine normal characters and special symbols called metacharacters. These metacharacters have special meanings and are used to define the patterns to be matched.
Regular expressions can be combined through concatenation (AB) to form new expressions. When strings p and q match A and B, respectively, the concatenation pq matches AB. Considerations like precedence, boundaries, and group references impact this behavior. These principles simplify constructing complex expressions from simpler ones.
Pattern MatchingPattern matching is the core functionality of regular expressions. It involves searching for specific patterns or sequences of characters within a given text. Regular expressions enable you to define complex patterns using a combination of characters and metacharacters to match against a target string.
MetacharactersMetacharacters are special symbols in regular expressions that have predefined meanings. They allow you to specify rules and constraints for pattern matching. Some commonly used metacharacters include:
‘.’ (dot): Matches single characters except for a new line.
‘^’ (caret): Matches the start of a string.
‘$’ (dollar sign): Matches the string’s end.
‘*’ (asterisk): Matches occurrences of the preceding group or character.
‘+’ (plus): Matches one or more occurrences of the preceding group or character.
‘?’ (question mark): Matches zero or one occurrence of the preceding group or character.
‘[‘ and ‘]’ (square brackets): Defines a character class, matching any single character within the brackets.
‘/’ (backslash): Escapes metacharacters to treat them as literal characters.
These are just a few examples of metacharacters commonly used in regular expressions. Understanding their usage and combinations will allow you to build powerful and precise patterns for pattern matching in Python using regular expressions.
By leveraging regular expressions and their metacharacters, you can perform various tasks such as validating input, extracting specific information from a text, replacing patterns, and much more. Regular expressions are widely used in text processing, data validation, web scraping, and other textual data applications.
Creating Python RegEx PatternsDetailed explanation to create a regex pattern:
Literal CharactersIn regular expressions, literal characters refer to specific characters matched exactly as they appear. For example, the pattern “cat” will only match the sequence of letters “cat” in the given text. You can use literal characters to create precise matches in your regular expressions.
Example: To match the word “hello” in a text, you can use the regular expression pattern “hello”.
Character ClassesCharacter classes in regular expressions allow you to define a set of characters that can match a single character in the given text. They are enclosed within [ ] square brackets. For example, the pattern “[aeiou]” matches any vowel character. Character classes provide flexibility in pattern matching by allowing you to specify a range of characters or exclude specific characters from matching.
Example: The regex pattern “[0-9]” matches any digit character.
QuantifiersQuantifiers in regular expressions control the number of times a character or a group of characters can occur in the given text. They specify how many repetitions or ranges are allowed. For instance, the quantifier “+” indicates that the preceding character or group must appear once or twice.
Example: The regular expression pattern “a+” matches one or more occurrences of the letter “a” in the text.
AnchorsAnchors in regular expressions are used to match positions rather than characters. They allow you to specify where a pattern should start or end in the text. The caret symbol “^” is used as the start anchor, and the dollar symbol “$” is used as the end anchor.
Example: The regular expression pattern “^Hello” matches the word “Hello” only if it appears at the beginning of a line.
Alternation Grouping and CapturingGrouping in regular expressions are denoted by enclosing a pattern within parentheses “( )”. It allows you to create logical units and apply quantifiers or alternations. Capturing groups extract and remember parts of the matched text for later use.
Example: The regular expression pattern “(ab)+” matches one or more occurrences of the sequence “ab” and captures it as a group.
RegEx Functions and Methods in PythonHere are the RegEx functions and methods, including examples:
re.match()This function attempts to match the pattern at the beginning of the string. It returns a match object if the pattern is found or None otherwise. It’s like knocking on the door of a house to see if it matches a specific blueprint.
Example
import re pattern = r"apple" text = "I love apples" match_object = re.match(pattern, text) if match_object: print("Match found!") else: print("No match found!")Output:
re.search()This function searches the entire string for a match to the pattern. It returns a match object if the pattern is found or None otherwise. It’s like searching for a hidden treasure in a room.
Example
import re pattern = r"apple" text = "I love apples" match_object = re.search(pattern, text) if match_object: print("Match found!") else: print("No match found!")Output:
re.find all()The purpose of this function is to provide a list of non-overlapping matches for a given pattern within the string. It’s like gathering a particular item’s instances in a collection.
Example
import re pattern = r"apple" text = "I love apples. Apples are delicious." matches = re.findall(pattern, text) print(matches)Output:
re.finditer()This function provides an iterator that produces match objects for non-overlapping instances of the pattern within the string. It’s like having a spotlight that illuminates each occurrence of a specific item.
Example
import re pattern = r"apple" text = "I love apples. Apples are delicious." match_iterator = re.finditer(pattern, text) for match_object in match_iterator: print(match_object)Output:
re.subn()This function replaces all occurrences of the pattern in the string with a specified replacement string. It returns a tuple containing the modified string and the number of replacements made. It’s like performing a substitution in a text document and counting the changes.
Example
import re pattern = r"apple" replacement = "orange" text = "I love apples. Apples are delicious." modified_text, replacements = re.subn(pattern, replacement, text) print(modified_text) print(replacements)Output
re.split()This method splits the string by the pattern occurrences and returns a list of substrings. It’s like cutting a cake along the defined pattern to get separate pieces.
Example
import re pattern = r"s+" # Matches one or more whitespace characters text = "Hello World! How are you?" substrings = re.split(pattern, text) print(substrings)Output:
re.purge()This function clears the regular expression cache. It removes all cached patterns, making the module forget all compiled regex patterns. It’s like erasing the memory of previously used patterns.
Example
import re pattern = r"apple" text = "I love apples" re.match(pattern, text) re.search(pattern, text) re.purge() # Clearing the regex cache # Attempting to match after purging the cache match_object = re.match(pattern, text) if match_object: print("Match found!") else: print("No match found!")Output:
re.escape(pattern)This function returns a string where all non-alphanumeric characters in the pattern are escaped with a backslash. It ensures that special characters are treated as literal characters. It’s like putting a protective shield on the pattern to avoid any special interpretation.
Example
import re pattern = r"(apple)" text = "I love apples" escaped_pattern = re.escape(pattern) match_object = re.search(escaped_pattern, text) if match_object: print("Match found!") else: print("No match found!")Output:
re.fullmatch()This function attempts to match the pattern against the entire string. It returns a match object if the pattern fully matches the string or None otherwise. It’s like ensuring that the pattern perfectly fits the whole puzzle.
Example
import re pattern = r"apple" text = "apple" match_object = re.fullmatch(pattern, text) if match_object: print("Full match found!") else: print("No full match found!")Output:
This function compiles a regular expression pattern into a regex object, which can be used for matching and searching operations. It’s like creating a custom tool for performing specific regex operations.
Example
import re pattern = r"apple" text = "I love apples" match_object = regex.search(text) if match_object: print("Match found!") else: print("No match found!")Output:
These examples glimpse various RegEx functions and methods’ functionalities and unique word usage. Experimenting with different patterns and texts will further enhance your understanding of regular expressions.
Python RegEx ModifiersHere are some commonly used modifiers in regex:
Case Insensitivity: In regular expressions, the “case insensitivity” modifier allows you to match patterns without distinguishing between uppercase and lowercase letters. It’s denoted by the letter ‘i’ and can be added to the end of the regular expression pattern using the syntax “/pattern/i”. For example, the pattern “/hello/i” would match “hello,” “Hello,” “HELLO,” and any other combination of case variations.
Multiline Mode: The “multiline mode” modifier, represented by the letter ‘m’, alters the behavior of the caret (^) and dollar sign ($) anchors within a regular expression. When enabled using the “/pattern/m” syntax, the caret and dollar sign will match the start and end of each line rather than just the start and end of the entire input string. This is particularly useful when working with multiline text, allowing you to perform matches on individual lines instead of the entire block.
Dot All Mode: The “dot all mode” modifier, denoted by the letter ‘s’, affects the behavior of the dot (.) metacharacter in regular expressions. By default, the dot matches any character except a new line. However, when the dot all mode is enabled using the “/pattern/s” syntax, the dot will match any character, including newline characters. This is useful when you want to match across multiple lines, such as when parsing a text block.
These modifiers enhance the flexibility and functionality of regular expressions, allowing you to create more powerful and precise pattern matches for text processing and manipulation.
Python Regex Metacharacters and EscapingLet’s explore the world of special metacharacters and the art of escaping, allowing you to wield these powerful tools with confidence and finesse.
Special MetacharactersHere, we’ll cover some of the most commonly encountered special metacharacters and their functionalities, including.
The Dot (.)
Usage: The dot metacharacter matches any character except a new line.
Example: The regular expression “c.t” matches “cat,” “cut,” and “cot” but not “cnnt.”
The Caret (^)
Usage: The caret metacharacter denotes the start of a line or the negation of a character class.
Example: The regular expression “^hello” matches “hello” when it appears at the start of a line.
The Dollar Sign ($)
Usage: The dollar sign metacharacter represents the end of a line or string.
Example: The regular expression “world$” matches “world” when it appears at the end of a line.
Usage: The pipe metacharacter signifies alternation or logical OR.
Escaping MetacharactersEscaping metacharacters is the art of rendering their literal interpretation instead of their special meaning. This section explores how to escape metacharacters to treat them as ordinary characters. Some commonly used metacharacters that require escaping include
The Backslash ()
Usage: The backslash metacharacter is used to escape itself or other metacharacters, turning them into literal characters.
The Square Brackets ([])
Usage: Square brackets enclose character classes in regular expressions. To match a literal square bracket, escape it with a backslash.
Example: To match the string “[hello]”, use the regular expression “hello”.
The Asterisk (*)
Usage: The asterisk metacharacter denotes zero or more occurrences of the preceding character or group.
Example: To match the string “2*2=4”, escape the asterisk: “2*2=4”.
Character Classes and Character Sets Predefined Character ClassesThis section will delve into predefined character classes, which are pre-built sets of characters that represent common patterns. These classes allow us to match specific types of characters concisely and efficiently. Let’s explore some of the unique word usages associated with predefined character classes:
#1 Digits and Numerics
The ‘d’ shorthand represents the predefined character class for digits, which matches any numeric digit from 0 to 9.
Conversely, the ‘D’ shorthand negates the predefined character class and matches any character that is not a digit.
#2 Word Boundaries
The ‘b’ metacharacter represents a predefined character class that matches word boundaries, indicating the start or end of a word.
Conversely, the ‘B’ metacharacter negates the predefined character class and matches any position that is not a word boundary.
Custom Character SetsLet’s discover some unique word usages related to custom character sets:
#1 Ranges
By specifying a range within square brackets, such as ‘[a-z]’, we can create a custom character set that matches any lowercase letter from ‘a’ to ‘z’.
Negation can also be applied to custom character sets. For instance, ‘[^a-z]’ matches any character without a lowercase letter.
#2 Character Escapes
We can use backslashes to escape special characters within custom character sets. For example, ‘[]’ matches a left or right square bracket.
Negation can be combined with character escapes. ‘[^]’ matches any character, not a square bracket.
NegationNegation is a powerful tool that allows us to match characters without a specific pattern. Let’s explore some unique word usages associated with negation:
#1 Negating Predefined Character Classes
We can negate a predefined character class by using a caret (^) as the first character inside square brackets. For instance, ‘[^0-9]’ matches any character without a digit.
#2 Negating Custom Character Sets
Similarly, negation can be applied to custom character sets. ‘[^aeiou]’ matches any character that is not a vowel.
The caret (^) is placed immediately after the opening square bracket to negate a custom character set with character escapes. For example, ‘^[^]’ matches any string that does not contain square brackets.
Quantifiers and Grouping in Python RegexQuantifiers and grouping are essential concepts in regular expressions. They allow you to manipulate patterns and specify the number of occurrences or repetitions of certain elements. Understanding these concepts allows you to create more precise and flexible patterns for matching and capturing information.
#1 Greedy vs Non-Greedy MatchingGreedy matching is the default behavior of quantifiers in regular expressions. A quantifier will match as much as possible while allowing the overall pattern to match. On the other hand, non-greedy matching, also known as lazy or minimal matching, matches as little as possible. It ensures that the overall pattern still matches with the smallest possible substring.
For example, consider the pattern: /a.+b/ and the string: “aababcab”. In greedy matching, the pattern would match the entire string “aababcab” because the quantifier “+” matches as much as possible. However, in non-greedy matching, the pattern would match only “aab” because the quantifier “+” matches as little as possible while still allowing the overall pattern to match.
#2 Quantifiers: *, +, ?, {}, etcQuantifiers are symbols in regular expressions that specify the number of occurrences or repetitions of the preceding element. Here are some commonly used quantifiers:
* (asterisk): Matches 0 or more occurrences of the preceding element; for example, /ab*c/ would match “ac”, “abc”, “abbc”, etc.
+(plus): Matches 1 or more occurrences of the preceding element. For example, /ab+c/ would match “abc”, “abbc”, “abbbc”, etc., but not “ac”.
? (question mark): Matches 0 or 1 occurrence of the preceding element. For example, /ab?c/ would match “ac” or “abc”, but not “abbc”.
{n} (curly braces): Matches exactly n occurrences of the preceding element. For example, /ab{3}c/ would match “abbbc”.
{n,m} (curly braces with two values): Matches between n and m occurrences of the preceding element. For example, /ab{2,4}c/ would match “abbc”, “abbbc”, or “abbbbc”, but not “ac” or “abc”.
#3 Grouping and CapturingGrouping in regular expressions is denoted by parentheses (). It allows you to treat multiple elements as a single unit, enabling you to apply quantifiers or modifiers to the group as a whole. Additionally, grouping facilitates capturing specific parts of a match.
For example, consider the pattern: /(ab)+c/. The parentheses create a group, and the “+” quantifier applies to the group as a whole. This pattern would match “abc”, “ababc”, “abababc”, etc.
Grouping also enables capturing. Using parentheses, you can capture and refer to the matched substring later. For example, consider the pattern: /(ab)+c/. In this pattern, the group (ab) is captured. If the string “ababc” matches this pattern, you can access the captured group and retrieve “ab” from the match.
Capturing is useful when extracting specific information from a match, such as dates, phone numbers, or email addresses from a larger text.
Anchors and Word Boundaries Start and End AnchorsStart anchors and end anchors are special characters or constructs that denote the beginning and end of a line or string of text. They are typically used in regular expressions or search patterns to match specific patterns at the start or end of a line.
Advantages
Infallible: The start anchor ensures that the pattern matches only if it appears at the beginning of the line, making it an infallible tool for precise matching.
Pioneering: The end anchor acts as a pioneering force, signaling the endpoint of a line and marking the boundary for further analysis or processing.
Word BoundariesWord boundaries are markers that define the edges of words in a text. They identify the separation between words and non-word characters, such as spaces, punctuation marks, or line breaks.
Advantages
Delimitation: Word boundaries serve as effective delimiters, allowing us to segment text into individual words for linguistic analysis or natural language processing tasks.
Demarcate: By demarcating the boundaries between words, these markers enable accurate tokenization, enhancing the efficiency of language processing algorithms.
Lookahead and Lookbehind in Python RegexThese constructs allow you to check for patterns that occur ahead or behind a particular position in the text without including them in the match itself. Let’s dive deeper into lookahead and look behind, along with their positive and negative variations.
#1 Positive LookaheadRegex: a(?=b)
Text: “abc”
The lookahead asserts that the letter ‘a’ must be followed by ‘b’. In this case, the regex matches the ‘a in “abc” because it’s followed by ‘b’.
#2 Negative LookaheadNegative lookahead is denoted by the syntax (?!…). It asserts that a given pattern must not occur immediately ahead of the current position. Let’s understand this with an example:
Regex: a(?!b)
Text: “acd”
The negative lookahead asserts that the letter ‘a’ must not be followed by ‘b’. The regex matches the ‘a’ in “acd” because no ‘b’ follows it.
#3 Positive LookbehindPositive lookbehind is denoted by the syntax (?<=…). It asserts that a given pattern must occur immediately before the current position. Let’s see an example:
Regex: (?<=a)b
Text: “xab”
The positive lookbehind asserts that the letter ‘b’ must be preceded by ‘a’. In this case, the regex matches the ‘b’ in “xab” because it is preceded by ‘a’.
#4 Negative LookbehindNegative lookbehind is denoted by the syntax (?<!…). It asserts that a pattern must not occur immediately before the current position. Consider the example:
Regex: (?<!a)b
Text: “xcb”
The negative lookbehind asserts that the letter ‘b’ must not be preceded by ‘a’. In this case, the regex matches the ‘b’ in “xcb” because there is no ‘a’ preceding it.
Flags and ModifiersLet’s explore various flags and modifiers available in the re-module of Python and understand their unique functionalities.
FLAG/MODIFIER USAGE
re.IGNORECASE The re.IGNORECASE flag allows case-insensitive matching. The pattern will interchangeably match uppercase and lowercase letters when this flag is used. For example, when searching for the pattern “apple” with re.IGNORECASE, it will match “apple,” “Apple,” “APPLE,” and so on.
re.MULTILINE The re.MULTILINE flag enables multiline matching. By default, regular expressions consider the input text as a single line. However, when using re.MULTILINE, the ^ and $ anchors will match the beginning and end of each line within the input text rather than the entire string.
re.DOTALL The re.DOTALL flag allows the dot (.) character to match any character, including newline characters (n). Generally, the dot matches every character except newline. With re.DOTALL, the dot will also match newline characters, providing a convenient way to match across multiple lines.
re.VERBOSE
re.ASCII The re.ASCII flag restricts the interpretation of certain character classes to ASCII-only characters. It ensures that non-ASCII characters are not treated as special characters within character classes, such as w, W, b, and B. This flag can be useful when working with text that contains only ASCII characters.
re.DEBUG The re.DEBUG flag enables debug output during the compilation and matching of regular expressions. It provides detailed information about how the regular expression engine interprets and executes the pattern. This flag is particularly helpful for troubleshooting complex regular expressions.
re.LOCALE The re.LOCALE flag enables localized matching based on the current locale settings. It affects the behavior of character classes, such as w and b, to match locale-specific word characters and word boundaries. This flag ensures that the regular expression adapts to the language-specific rules defined by the locale.
re.NOFLAG The re.NOFLAG signifies the absence of any flag. When no flag is specified, the regular expression pattern matches in the default mode, which is case-sensitive, single-line matching, and without any special interpretation for character classes.
Examples and Use CasesIn this learning content, we will explore several practical examples and use cases where Python regex expressions can be applied effectively.
Example 1: Validating Email AddressesCode:
Output:
Explanation:
In this example, we define a function validate_email that takes an email address as input and uses a regex pattern to determine if the email is valid or not. The pattern r’^[w.-][email protected][w.-]+.w+$’ matches email addresses that consist of one or more word characters (w), dots (.), or hyphens (-), followed by the at symbol @, and then one or more word characters, dots, or hyphens again. Finally, it requires a dot (.) followed by one or more word characters at the end. If the email matches the pattern, the function returns True; otherwise, it returns False.
Example 2: Extracting URLs from TextCode:
import re def extract_urls(text): return re.findall(pattern, text) urls = extract_urls(text) print(urls)Output:
Explanation:
Example 3: Parsing Log FilesCode:
import re def parse_log_file(log_file): pattern = r'(d{4}-d{2}-d{2} d{2}:d{2}:d{2}) [(w+)] (.+)' with open(log_file, 'r') as file: for line in file: match = re.search(pattern, line) if match: timestamp = match.group(1) log_level = match.group(2) message = match.group(3) print(f'Timestamp: {timestamp}, Level: {log_level}, Message: {message}') log_file = 'app.log' parse_log_file(log_file)Output:
Explanation:
Example 4: Data Extraction and CleaningCode:
import re def clean_data(data): pattern = r'[W_]+' return re.sub(pattern, ' ', data) text = 'This is some text! It contains punctuation, numbers (123), and _underscores_.' cleaned_text = clean_data(text) print(cleaned_text)Output:
Explanation:
In this example, we define a function clean_data that takes a string of data as input and removes any non-alphanumeric characters using a regex pattern. The pattern r'[W_]+’ matches one or more non-alphanumeric characters or underscores. The chúng tôi function substitutes matches of the pattern with a space character, effectively removing them from the string.
These examples demonstrate just a few of the many practical use cases for Python regex expressions. You can apply regex to various scenarios, allowing you to search, validate, extract, and manipulate text data with precision and efficiency.
Recommended ArticlesWe hope that this EDUCBA information on “Python Regex” was beneficial to you. You can view EDUCBA’s recommended articles for more information.
How Queue Works In Rust With Examples?
Definition on Rust Queue
Rust queue is a data structure that is used to store the elements, queue in Rust works in an FIO manner that means first in first out. This standard queue is available inside the rust collection library, and the queue is a linear data structure. Queue provides us with several operations that can be performed on it to made manipulation on it. We can add any number of elements inside it, all the implementation is based on the vector data structure in Rust. In rust, we have multiple varieties of a queue which can be used per the chúng tôi next section will cover the queue data structure in rust in detail for better understanding and its implementation while programming for better usage.
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A linear data structure is used to store and manipulate data elements. Here is a detailed syntax for implementing it in programming.
In the above syntax, we create a queue using the ‘Queue’ keyword as the variable type. We can specify the size of the queue and give it a custom name. This is a beginner-friendly syntax example for better understanding. We shall examine its internal operations in more detail in the section that follows.
e.g. :
In this way, we can create it.
How Queue works in Rust?As we know, the queue is a linear data structure used to store elements. It is accessible as a collection in the standard library of the Rust computer language. But queue works the same way as in another programming language. In Rust, the queue follows the principle of FIFO (first in, first out). As a result, the queue will take out the first item that was put in, followed by the subsequent items in the order of their addition. For instance, we can take the example of a ticketing system, the person who comes first will get the ticket first, and out from the queue, it works in the same way.
Also, we have one more example, which is email queue processing, while drafting an email to multiple persons, it will follow the first email id mentioned, and so on. In this section, we will discuss the various types and methods available in rust, Let’s get started for more information, see below;
We have several types of a queue available in rust which is mentioned below;
Now let’s explore the different operations that we can perform on the queue in Rust, allowing us to manipulate it effectively. We have below mentioned different methods available in Rust for queue see below;
1) peek: The peek method allows us to retrieve the next element in the queue without removing it.
2) add: In Rust, we use the add method to add new element to the queue object. In Rust, we can also refer to this method as push or enqueue.
3) remove: This method removes elements from the queue. But as we already know, that queue works in a FIFO manner, so it always removes the oldest element from the queue. In Rust, we can also refer to this method as pop or dequeue.
Now we will see the following steps to use the queue inside the program in rust see below;
1) To use a queue inside our program, we must first include its dependency inside it. for this, we can add the below-mentioned dependency inside our chúng tôi file in rust, see below;
queues = "1.0.2"2) After using this, we have to include or import this dependency in our file to use it, mentioned below the line of code inside the file. This is the official documentation of rust see below;
extern crate queues; use queues::*;3) After this, you can create the queue object and assign it value inside your project. To create the queue object, follow the below line of code:
Example1) In this example, we are trying to add the element inside the queue by using the add() method in the queue. Also, remember one point this example will run in a fully configured environment only. It will not go running by using any rust online compiler because we added dependency inside it. So first try to set up the configuration, then run it.
Code:
#[macro_use] extern crate queues; use queues::*; fn main() { println!("Demo pragrma to show queue in rust !!"); demoqueue.add(200); demoqueue.add(300); demoqueue.add(400); demoqueue.add(500); demoqueue.add(600); println!(" value inside the queue is {}", demoqueue ); }Output:
ConclusionWe can store the elements inside by using a queue in rust. Programmers use this data structure to store and manipulate data using the various operations discussed in the tutorial. To utilize these functionalities in programming, programmers need to add the external library to the dependency file. Without doing so, the program will not compile or function properly.
Recommended ArticlesWe hope that this EDUCBA information on “Rust Queue” was beneficial to you. You can view EDUCBA’s recommended articles for more information.
How Json Works In Redshift With Examples?
Definition on Redshift JSON
Redshift JSON has limited support while working with the JSON documents in redshift, basically, there are three types of options available in redshift to load the data into table. First option is we can convert the JSON file into relational model before loading data into the redshift, to load the data using this options we need to create the relational target database. Second option is load all the JSON documents in redshift table and query those documents using JSON functions, there are multiple JSON function available in redshift to query the data of JSON documents.
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Syntax
Below is the syntax of JSON in redshift are as follows.
2) Select json_function (name_of_json_column,) group by, order by
Parameter description syntax of redshift JSON.
1) JSON function – This is the function which was we have using with JSON data to retrieve from JSON column. There are multiple JSON function available in redshift to query the JSON data. We can retrieve the JSON column data using JSON function in redshift.
2) Select – Select command is used with JSON function to retrieve data from table by using the clauses and conditional operator.
3) Name of column – This is the name of JSON data column which was we have using with JSON function to retrieve data from table.
4) Value of json column – This is nothing but the column value which was we have using to segregate the JSON document data in redshift. We can segregate the data from table column as per value which was we have used in our query.
5) Where condition – We can retrieve JSON document from column by using where condition in redshift.
6) Order by condition – We can retrieve JSON document from column by using order by condition in redshift.
7) Group by condition – We can retrieve JSON document from column by using group by condition in redshift.
How JSON works in Redshift?There are multiple options available to load the JSON documents in redshift. After loading the data we can retrieve the JSON data by using following JSON functions.
6) Json extract array element text (JSON_EXTRACT_ARRAY_ELEMENT_TEXT) function.
If we want to store the small number of key-value pairs then JSON document is best suited for the same. Using JSON format we can save the storage space of storing the data.
We can store multiple key value pair in a single column by using JSON format, we cannot stored multiple key-value pair in other format.
To use the JSON function on integer datatype values or the data which was not in JSON format. We can apply JSON function only on JSON type of document.
Below example shows that we can apply JSON function only on JSON type of columns.
Code:
Select json_extract_path_text (stud_name, 'A') as key2 from redshift_json where stud_id = 101;
In above example, we have applied JSON function on stud_name column and trying to retrieve key-value pair as “A”, But it will showing error as invalid JSON object which was we have used in our query.
Also, it will showing the parsing error of query.
We cannot use the integer datatype column with JSON function in redshift, we need to use only JSON type of data.
Below example shows that we cannot use the integer datatype of column with JSON function in redshift.
Select json_extract_path_text (stud_id) from redshift_json where stud_id = 101;
In above example, we have used column name as stud_id with JSON function, stud_id datatype as integer. So it will issues the error like integer does not exist, which was not found any matching function or arguments.
We can use copy command to load the data from JSON file to redshift table. We can also use the JSON files which was stores in the S3 bucket.
We can also copy JSON file fields automatically by using option as auto or we need to specify the path of JSON file.
ExamplesBelow is the example of JSON in redshift are as follows.
1) Querying JSON fields using IS_VALID_JSON functionThe below example shows querying JSON fields using IS_VALID_JSON function are as follows. This function is validates the JSON string.
In below example, we have used JSON column to validate the JSON data from function. We have not found any invalid JSON data in JSON column.
Code:
Select stud_id, json, is_valid_json (json) from redshift_json order by stud_id;
2) Querying JSON fields using is_valid_json_array function
Below example shows querying JSON fields using is_valid_json_array function are as follows. This function validates the JSON array string.
In below example, we have used JSON column to validate the JSON array value from function. We have not found any JSON array in JSON column.
Code:
3) Querying JSON fields using json_extract_path_text function
Below example shows querying JSON fields using json_extract_path_text function are as follows. This function is extracting the value from the text.
In below example, we have used json column to extract path text data from function.
Code:
Select stud_id, json, json_extract_path_text (json, 'key2') as json_key from redshift_json order by stud_id;
4) Querying JSON fields using json_parse function
Below example shows querying JSON fields using json_parse function are as follows. This function is used to parse the JSON value.
In below example, we have used json column to parse the data from function.
Code:
Select stud_id, json, json_parse (json) as json_key from redshift_json order by stud_id;
ConclusionWe can use multiple JSON function to query data from table columns. Redshift JSON is very useful and important to store the value in key-value pairs. Using JSON we can store multiple column value within a single column. We can also minimize the storage usage using JSON in redshift.
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How Does Url_For Work In Flask With Examples?
Definition of Flask url_for
Flask url_for is defined as a function that enables developers to build and generate URLs on a Flask application. As a best practice, it is the url_for function that is required to be used, as hard coding the URL in templates and view function of the Flask application tends to utilize more time during modification. If we are using hard coding, and in case we would need to change our URL by inserting another element in the URL, we would have to visit each and every template or form in the code library and make the modifications and will lead to overkill. The url_for function is capable of changing this with just a snap of fingers!
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It is time now for us to look into different syntax that is present to url_for function before we even dive into learning the working of url_for. This will help us map the syntaxes to the working methodology so that the learning is more practical and easier to grasp. So, without much further ado, let us get straight into the syntax!
Creating dynamic URL with no key passed:
Note: We need to make sure that the function name doesn’t carry any argument, else it might lead to an error.
Creating a dynamic URL with a key and corresponding value passed:
Redirect to a URL using Flask (assuming we are passing key and value pair):
How does url_for work in Flask?In this section let us go through the working of url_for in Flask, but before that, it is very much required to know about the need for building URLs using the reversing function url_for( ). The concept of reversing function is to use meaningful URLs to help users. If the web application is able to create a meaningful URL that consists of inputs from users, users may remember the inputs used and will enhance the return to the same page again. Not only this there are other pointers that we will discuss below which signifies the importance of using a dynamic URL, keeping in mind the inputs of the user, instead of hard coding the URL.
Developers can change the content of the URL in one shot, and there is no dependency on remembering locations to manually change the hard-coded URLs.
The process of reversing is more descriptive than hard coding.
The special characters and Unicode data are efficiently handled in case of using dynamic URLs.
This is the easy way to avoid unexpected behavior of relative paths in browsers by allocating absolute paths to the generated URLs.
In the case of an application placed outside URL root, the url_for( ) function is capable of handling such scenarios.
Now that we have an understanding of why url_for( ) is so widely appreciated, we would need to understand the types of View responses, as one of these responses relates to the work of url_for( ). The big 3 ways of route logic, an act of mapping the URLs to their specific actions, are namely generating a page template, providing a response, and redirecting the user to a specified location. The working of url_for( ) falls under the category of redirecting.
The method of redirecting accepts a string and this string is nothing but the path that the user is directed to. For the same, the routes are referred to by their names and not by their URL patterns. In the process of creating this input for the redirect function, we use url_for( ). The function url_for( ) takes the name of the view function as an input and creates an output of the provided view. With the change of route URLs, there will be no broken links between pages. Now, when a view is registered with the @app.route decorator, the endpoint name is determined and is ready to be stored with the route registration. This stored route registration is then used to find all routes which link to the registration with the name along with the parameters passed and then execute them to reveal the output.
One important thing to be kept in mind is that, in case we have registered 2 different functions under the same name, we are bound to get an AssertionError and for the same, we can take the help of the endpoint variable and specify the needful. With this, we complete the working of the url_for( ) function in terms of URL routing.
It’s now time for us to look at the implementation of url_for in a Flask application!
ExamplesNow that we have complete knowledge about the implementation of url_for and the working methodology along with a complete view on syntax, in this section, we will try using them in practice so that it is easier to learn them by knowing what the practical output will look like! In the examples, we would look at using test_request_context( ) so that we can realize it on the python shell on what URL the particular command is routed to.
Example #1Creating dynamic URL with no key passed (Run it on console)
from flask import url_for, Flask appFlask = Flask(__name__) @appFlask.route('/home') def home(): return 'We are in Home Page!' with appFlask.test_request_context(): print(url_for('login'))Output:
Example #2Creating a dynamic URL with a key and corresponding value passed
Syntax:
from flask import url_for, Flask appFlask = Flask(__name__) def profile(authorname): return f'{authorname}'s profile' with appFlask.test_request_context(): print(url_for('profile', authorname='EduCBA')) print(url_for('profile', authorname='EduCBAPremium'))Output:
Here, we can easily see the distinction when 2 different values are passed using parameters
Example #3Syntax:
from flask import Flask, redirect, url_for appFlask = Flask(__name__) def accountType(Type): return 'This is a %s account' % Type def userType(name): if name =='premium': return redirect(url_for('accountType',Type = name)) else: return redirect(url_for('accountType',Type = name)) if __name__ == '__main__': appFlask.run(debug = True)Output:
When the type is Premium account type:
When the type is basic account type:
ConclusionHerewith in this article, we have got an essence of how URL routing happens and what dynamic URL can bring to the table. With this, we encourage our readers to experiment with notes in the article and build an exciting Flask application!
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