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Introduction Microsoft Excel is undoubtedly one of the most powerful tools for organizing and analyzing data. The tool is enriched with extensive features that improve the user's data analytics skills. In this article, we will learn how to generate barcodes in MS Excel. Manually creating barcodes and QR codes consumes lots of users' time. By default, the barcode font is not presented in Excel 2016, or Excel 2019. One of the simplest ways to use the barcode is to download the "3 of 9 Barcode" Font from the external website Dafont. Example 1: To generate a Barcode in Excel ... Read More
Excel worksheets can become extensive, containing countless rows and columns of data. In this article, we will explore creative techniques that allow us to swiftly create random numbers through Analysis toolpak. Users can easily develop the nonconsecutive numbers and conduct some experiments on that data. We may generate random numbers using various methods like the Data Analysis option, the built-in RAND() function, and the user-defined function in VBA. Example 1: Creation of Random Numbers through the Data Analysis tab Step 1 The user must open a new worksheet in Excel like this − Step 2 Switch to the "Data" ... Read More
Problem Statement Suppose we have two lists that are defined in two distinct worksheets. One worksheet contains the basic details of the employee like the Employee ID, Employee First Name, and Employee Last Name whereas the other worksheet contains only two columns named Employee First Name and Contact Number. By using the VLOOKUP formula, we intend to get the contact numbers of the employees and append them to the first list. VLOOKUP is one of the most powerful functions to search for a specific value in a row. To combine two lists using the VLOOKUP formula Step 1 Users need ... Read More
In the field of statistics, there is a major role played by probability distributions in modeling and analyzing various random phenomena. Uniform Discrete Distribution is one of them. It is particularly used when dealing with discrete random variables which have equally likely outcomes. Ahead in this article, we will explore Uniform Discrete Distribution in the light of Python programming, using the scipy.stats.randint() function. Scipy is a powerful python library for scientific analysis and calculations. The stats module provides tools for statistical analysis including probability distributions. The randint() function in the scipy.stats module represents the uniform discrete variable, which inherits its ... Read More
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