Python is a popular general-purpose language, but it's increasingly favored for statistics, data analysis, and data science. If you have a basic knowledge of statistics, how can you apply that to ...

In December 2019 my InfoWorld colleague Sharon Machlis wrote an article called “How to merge data in R using R merge, dplyr, or data.table.” Sharon is a whiz at R programming, and analytics in general ...

What if the tools you already use could do more than you ever imagined? Picture this: you’re working on a massive dataset in Excel, trying to make sense of endless rows and columns. It’s slow, ...

What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...

Arrays in Python give you a huge amount of flexibility for storing, organizing, and accessing data. This is crucial, not least because of Python’s popularity for use in data science. But what ...

Reticulate is a handy way to combine Python and R code. From the reticulate help page suggests that reticulate allows for: "Calling Python from R in a variety of ways including R Markdown, sourcing ...

Data visualization is not just an art form but a crucial tool in the modern data analyst's arsenal, offering a compelling way to present, explore, and understand large datasets. In the context of ...

Why is your Python app so slow? Find out by using Python’s built-in profiler to locate bottlenecks in your Python code Python may not be the fastest language around, but it is often fast enough. And ...