Python

Running a Python script could be as easy as opening your IDE or text editor and clicking the run button; however, if the script has to be executed daily or weekly, you don’t want to waste time repeating these steps over and over again. Instead, you could automate your scripts by scheduling jobs that run […]

PDF is probably one of the most common file types that we can find on our computers. We use PDFs for our resumes, reports, invoices, you name it! A common way to create a PDF file is by saving a Word file as .pdf, but we can also create a PDF file using Python. The pros?

Do you know what most office jobs have in common? In most of them, you have to send emails regularly. It doesn’t matter if you’re a programmer, data scientist, or engineer, chances are you have to read and respond to emails on a daily basis. This activity doesn’t add any value to our professions, yet

Think of the most boring task you’ve ever done at work. Chances are, you can automate it with Python. You only need to open your favorite text editor and start writing code. However, you might struggle to automate some tasks more than others and end up wasting more time. What most people don’t know, however,

According to Forbes, data scientists and analysts spend 60% of their time on cleaning and organizing data. This shows how important data cleaning and data wrangling are in a project. Data cleaning consists in cleaning the data we’ll use, so we don’t have incorrect, corrupted, duplicate, or incomplete data. Real-world data isn’t as clean as

One common task data analysts have to deal with when collecting and cleaning data is working with strings. This involves formatting as well as joining strings (also known as string concatenation). Joining strings in Python   is as simple as using the plus operator +. You’ve probably used the code below hundreds of times to join