Types of Projects The term Python project can be somewhat misleading. While languages like Swift are designed for specific purposes such as generating macOS/iOS apps, components, and frameworks, Python is used in a much more versatile manner. A Python project might range from an analytical solution developed across multiple Jupyter notebooks to a standalone script querying a database API and extracting results to an application or package facilitating the deployment of models....

Most frequently, users tend to undertake common R installation and management tasks from within the R session. Frequently making use of commands, like install.packages, update.packages or old.packages to obtain or update packages or update/verify the existing packages. Those common tasks can also be accomplished via the GUI offered within RStudio, which provides an effortless mechanism for undertaking basic package management tasks. This is approach is usually sufficient for the vast majority of cases; however, there are some examples when working within REPL^[REPL stands for Read Eval Print Loop and is usually delivered in a form of an interactive shell....

Big-O The purpose of this is not to provide yet another primer on the Big-O/$\Omega$/$\Theta$ notation but to share my enduring appreciation for working with R. I will introduce Big-O only briefly to provide context but I would refer all of those who are interested to the linked materials. What is Big-sth notation… When analysing functions, we may be interested in knowing how fast a function grows. For instance, for function \(T(n)=4n^2-2n+2\), after ignoring constants, we would say that \(T(n)\) grows at the order of \(n^2\)....

Background Handling Hive/CSV interaction is a common reality of many analytical and data environments. The question on exporting data from Hive to CSV and other formats is frequently raised on online forums with answers frequently suggesting making use of sed that combined with nifty regular expressions pipes Hive output into a flat CSV files as an exporting solution. Import of large amounts of data is best handled by suitable tools like Apache Flume....

Recently, I cam across an interesting discussion on StackOverflow^[SO discussion on: Fuzzy Join with Partial String Match in R] pertaining to approach to fuzzy matching tables in R. Good answer contributed by one of the most resilient and excellent contributors to whom I owe a lot of thanks for help suggested relying on regular expression, combining this with basic sting removal and transformations like toupper to deterministically match the tables. The solution solved the problem and was accepted....