Working with Excel Defined Names in OpenPyXL: A Deep Dive =========================================================== In this article, we will delve into the world of Excel Defined Names and explore how to use them with OpenPyXL. We’ll discuss what Defined Names are, how they work, and provide an example implementation using OpenPyXL. What are Excel Defined Names? Defined Names in Excel are a way to create a reference to a cell or range of cells that can be used in formulas.

Conditional Sorting in SQL: A Practical Guide When working with data, it’s not uncommon to need to sort a dataset based on specific conditions. This can be particularly useful when you want to prioritize certain items over others or group similar data together. In this article, we’ll explore how to achieve conditional sorting in SQL using various techniques. Introduction to Conditional Sorting Conditional sorting involves selecting rows from a database table where a condition is met, and then sorting the resulting subset of data based on additional criteria.

Universal Apps in iOS: A Step-by-Step Guide Universal apps in iOS allow developers to create a single app that works seamlessly across multiple device sizes and orientations. This guide will walk you through the process of making an iPhone app work on an iPad, exploring the technical aspects and practical considerations involved. Understanding Universal Apps Before we dive into the steps, it’s essential to understand what makes a universal app. In iOS 9 and later, Apple introduced a new feature called Universal Apps, which allows developers to create a single app that can run on multiple devices, including iPhones and iPads.

Vertically Stacking DataFrames: A Comprehensive Guide Introduction DataFrames are a fundamental data structure in the Python data science ecosystem, particularly popularized by the Pandas library. They provide an efficient and convenient way to store, manipulate, and analyze tabular data. However, when working with multiple DataFrames, it’s not uncommon to encounter the question of how to vertically stack them while maintaining different column names. In this article, we’ll delve into the world of DataFrames, explore their structure, and discuss the challenges associated with vertical stacking.

Understanding Pandas DataFrames and GroupBy Operations Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to work with structured data, including tabular data like DataFrames. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL table. In this article, we’ll explore how to use Pandas DataFrames and GroupBy operations to achieve specific results.

Sifting through CSV Files for Time Stamps Introduction CSV (Comma Separated Values) files are a common format for storing and exchanging data. However, when working with time-based data, such as financial transactions or sensor readings, it’s essential to filter out records that fall outside specific date and time ranges. In this article, we’ll explore how to read CSV files, extract time stamps, and calculate gaps between consecutive records using Python. We’ll use the popular Dask library, which provides a efficient way to process large datasets in parallel.

Linear Regression with Ongoing Data in R Introduction In this article, we will explore the concept of linear regression and its application to ongoing data. We will delve into the details of how to perform linear regression using R and demonstrate a practical example of how to use it for prediction. Background Linear regression is a statistical method used to model the relationship between two or more variables. It is widely used in various fields, including finance, economics, medicine, and data science.

Understanding BLE Peripherals and Advertising Packets BLE (Bluetooth Low Energy) peripherals are small devices that use Bluetooth technology to communicate with other devices, such as smartphones. In this article, we’ll explore how BLE peripherals send advertising packets to iOS apps and how these packets can be received when the screen is black. Introduction to BLE Advertising Packets When a BLE peripheral is powered on, it begins broadcasting advertising packets to its vicinity.

Null Value in Where Clause with Two Different Parameters Problem Statement When querying a database, you may encounter the issue of handling null values in conjunction with two different parameters. In this scenario, we’re given a specific example where l_family_id is always returned as a parameter, but l_account and l_product_id each time result in one of the two being null. Our goal is to overcome this limitation so that you don’t get an error when searching for account or product ID.

Faster Function Than Aggregate() in R: A Comparative Analysis The aggregate() function is a powerful tool in R for aggregating data by a specified column or group. However, it can be slow when dealing with large datasets. In this article, we will explore alternative approaches to performing aggregations in R, focusing on the use of the Tidyverse, base functions, and plyr packages. Background The aggregate() function is part of the built-in R package and uses the data.