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Introduction The question mark makes the previous token in the regular expression optional. For example: colou?r is complementary to both colour and colour. A quantifier is what the question mark is known as. You may make multiple tokens optional by combining numerous tokens in parentheses and adding the question mark after the final set of parentheses. Like Nov(ember)? matches between Nov and Nov. Using many question marks, you may create a regular expression matching a wide range of options. Feb(ruary)? 23(rd)? Matches February 23rd, February 23, Feb 23rd and Feb 23. Curly braces can also be used to make something ... Read More
Introduction Statisticians skillfully mesh probability distributions with relevant data sources, thereby lending (or disavowing) plausibility to wide-ranging, though pertinent, hypotheses regarding variable complexities within those databases. In this realm, the Tukey Lambada distribution distinguishes itself via distinct features. With its versatility, the Tukey distribution efficiently models diverse datasets showcasing varied shapes, tails, and degrees of asymmetry. As we dive into Python implementation, it's crucial to understand the Tukey-Lambda distribution's fundamental traits first. Understanding the Tukey-Lambda Distribution In 1960s, John W. Tukey developed the Tukey-Lambda distribution – a statistical constant probability distribution. Flexible enough to accommodate multiple shape variances, this distribution ... Read More
In Python, when dealing with matrices of uneven row lengths, the efficiency in locating each column's minimum values becomes paramount; a variety of approaches each boasting its own strengths and suitability for different scenarios exist to tackle this task. We are going to delve into several methods within this article: from basic nested loops all the way up to advanced tools such as NumPy and Pandas. Ultimately, you will grasp a comprehensive understanding of two crucial skills: mastering the manipulation of uneven-sized matrices and extracting valuable information from them. Method 1: Using Nested Loops This method, utilizing nested loops, iterates ... Read More
Classes that encompass a collection of characters are known as regular expression classes. One of these classes, d, which matches any decimal digit, will be used. Learning how to split data may be valuable. Data arrives in various kinds and sizes, and it's sometimes not as clean as we'd like. You frequently wish to divide a string by more than one delimiter to make it easier to deal with. The built-in regular expression library re is the easiest way to split a string. The library has a.split() function that works similarly to the above example. This approach stands out since ... Read More
We must first understand some regular expression fundamentals as we will use them. There are various ways to declare patterns in regular expressions, which might make them appear complex but are pretty simple. Regular expressions are patterns that can be used to match strings that adhere to that pattern. You need to read the following article to learn how regular expressions operate. You may commonly extract dates from a given text when learning to code. If you are automating a Python script and need to extract specific numerical figures from a CSV file, if you are a data scientist and ... Read More
You can use the fromtimestamp() function from the datetime module to get a date from a UNIX timestamp. This function takes the timestamp as input and returns the datetime object corresponding to the timestamp. Exmaple import datetime timestamp = datetime.datetime.fromtimestamp(1500000000) print(timestamp.strftime('%Y-%m-%d %H:%M:%S'))OutputThis will give the output −2017-07-14 08:10:00
Given with an input by the user and the task is to check whether the given input is an integer or a string. Integer can be any combination of digits between 0 -9 and string can be any combination excluding 0 – 9. Example Input-: 123 Output-: 123 is an integer Input-: Tutorials Point Output-: Tutorials Point is a string Approach used below is as follows − Input the data. Apply isdigit() function that checks whether a given input is numeric character or not. This function takes single argument as an integer and also returns ... Read More
List in python allows duplicate entries, that is we can have the same value twice in a list. That is useful in most cases, but sometimes there is a need to remove the duplicate elements to perform certain operations. In this article we will focus on how we can take up the unique values removing duplicates from a list of integers and finding their product. It has a wide use case scenario and we will try and discuss all possible ways to generate the output. Method 1: Set Implementation Set in python is unordered data collection that is iterable, mutable ... Read More
In this article, we will have input as a list of tuples and our goal will be to print the frequency of unique tuples but it will be order irrespective. Order irrespective means that a tuple (1, 2, 3) and (1, 3, 2) will be treated as same even though their order is different. For example, consider the following − Input [(1, 2, 3), (2, 1, 3), (4, 5, 6), (1, 2, 3), (3, 2, 1)] Output 2 Explanation The tuples at index 0, 1, 3 and 4 are same and hence frequency count increases by ... Read More
Dictionaries in python are widely used, to store data in key-value pairs. Many times, we get stuck in finding the product of elements in the tuple received as value in the dictionary. This mostly arises in situations working with data manipulation or analysis. Through this article, we will code and understand various ways to unpack the dictionary and calculate the product of tuple elements at each index. Input {'a': (1, 3, 5, 7), 'b': (2, 4, 6, 8), 'c': (2, 3, 5, 7)} Output (4, 36, 150, 392) Method 1: Using Tuple Unpacking and zip() Function ... Read More