Totting up the cost of vlogging: The beginning

It’s now been a month since I started vlogging. Therefore, it’s a good time to quickly take stock of my costs to-date and evaluate the budget creep. Let’s take a look at my total expenses to date. Basic vlogging items (excl. sewing) Item Cost Description Camera $0 (existing) Pentax K-50 (purchased approx. 5 yrs ago)

Pandas for Productivity Vlog Ep3: Creating time series-based ranges in NumPy

Why this topic? There’s a lot of documentation on using Numpy’s arange function to create ranges of numbers. However, not much is said about creating date- and time- based ranges, which are much more useful for business. What it covers: First, I give a quick review of the NumPy arange function syntax. Then, I walk

Pandas for Productivity Vlog Ep2: Think like a data engineer!

Why this topic? When we join two flat files, or a flat file to a SQL query output in Python, they probably come from 2 different sources. Therefore, we can’t assume that they’re engineered to be combined directly. What it covers: I walk through 2 examples of joining data from completely different sources. In both

Pandas for Productivity vlog Ep1: Relabeling x-axis in Matplotlib

Welcome to the launch of the Pandas for Productivity vlog series! Here, I discuss the peskier data wrangling challenges that you may encounter. For my first episode, I show you how to relabel the x-axis on a stacked area time series chart in Matplotlib. Why this topic? Matplotlib has been inconsistent with plotting time-series data.

Vlogging diary wk2: Creating a first tutorial

Whee! I’ve just created and posted my first tutorial. Furthermore, by and large I achieved my vision for the look and feel. However, I didn’t fully succeed with getting under 5 minutes (it was 6 minutes 45 seconds). For future reference, here’s my journal of the process and experience. Upgrading my lighting Firstly, my new

Plotting charts in Python vs. Excel: A Demo

We previously discussed here that plotting charts in Python is second priority for beginners. Indeed, Python’s matplotlib library is very useful for creating elegant charts from large data sets. However, you need to remember a lot of code to make it work well! Example: COVID-19 public data set We will use the COVID-19 Case Surveillance