Here’s a simple way to assess if a function has an intuitive name: If you think a colleague who hasn’t seen the function

Another good use for pickles is to save objects like lists, dictionaries and the like. For instance, if we assigned manual selection of categorical features to a list in one Notebook and plan to also use it in another Notebook, we can write the list to a pickle file and load it in other Notebooks. This way if we manually make changes to the list, then we only have to do it in one place. Here’s an example code to write or load a pickle file named categorical.pkl where categorical refers to an object in Python (e.g. list).

One popular use of pickling in Data Science is saving trained models or machine learning pipelines into .pkl files. In the following example, we can see that there are couple of models saved in pickle:

In many ways my whole programming career feels like a long wait for a replacement to C. 20 years ago I though that was C . Over time I learned C was a complex monster that could never be tamed no matter how many thick best practices books I read.

#### Headers # Header 1 ## Header 2 #### Styles *Italic*, **bold**, _underscore_, ~~strikethrough~~ #### Hyperlink [hyperlink](https://www.markdownguide.org/) #### Table | Default | Left-aligned | Center-aligned | Right-aligned | |---------|:-------------|:--------------:|--------------:| | Default | Left | Center | Right | #### Others * Bulleted item 1 * Bulleted subitem 1 * Bulleted item 2 *** 1. Ordered item 1 1.1. Ordered subitem 1 2. Ordered item 2 *** - [ ] Unchecked box - [x] Checked box

I’m sure there are massive hurdles here, especially in the privacy and security space, as well as data stove pipes and cross-app, government, and inter-departmental operation resistance. Still, Covid-19 is bigger than all of us and it requires out-of-the box thinking and tapping into the power of data and technology we already have at our fingertips.

Pickles are not only delicious, but when used in the context of Python, they allow us to save objects as .pkl files. The process of saving the object is referred to as pickling.

You can have more or less number of Notebooks than the example. That is, the exact number of Notebook is not important. The key idea here is to have logical major tasks (it’s up to you how you define this) spread out, so it’s easier to see the overall workflow and find things. For instance, when writing production-ready code for deployment, we just have to refer to 5_final_pipeline.ipynb.

Markdown cells in Notebook allow us to be more expressive in how we document our workflow compared to code comments. We could use headers of different font size, ordered or unordered list of items, tables, mathematical formulas, checkboxes, hyperlinks, images and many more with Markdown. Here’s a one-minute introduction to Markdown on its commonly used syntax:

If we look at this example, we have a couple of Notebooks. From the name, we can see the sequence of Notebooks. For instance, we can guess that 3_base_models.ipynb precedes 4_gbm_model.ipynb while 4_gbm_model.ipynb and 4_rfc_model.ipynb are probably exploring two options in parallel. If we renamed these two to 4A_gbm_model.ipynb and 4B_rfc_model.ipynb, then we can hint an order in the name in a slightly different way as well.

You may have heard of the DRY principle: Don’t Repeat Yourself. If you haven’t heard of this software engineering principle before, it is about “not duplicating a piece of knowledge within a system”. One of my interpretation of this principle in Data Science is to create functions to abstract away the reoccurring tasks to reduce copy pasting. You can even use classes if it makes sense in your case.

Java and C# was in many ways just attempts at rehashing C . They may have kept things more simple, but kind of ended up being too caught up in the Virtual Machine and object-oriented programming hype of the 90s. Not saying Java or C# are bad but not really my cup of tea. A lot of that may have more to do with the community around those languages which favor bloated IDEs and over-engineering.

Go from Google was a welcome departure from the excesses C , D, Java and C#. It took us right back to the starting point, to C. Go reimagined what C could have been if it did not venture down the C path. What we got back was a simple language that fixed many of the issues I always had with C.

While implementing a simple game engine in C and Lua, I came to realize that it was actually less mental overhead to keep these two languages in the head at the same time than C . It brought me some renewed love for C. For all its limitations, C is a fairly simple language that gives you a lot of control.

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sts, some of them genuine gamers themselves, are taking to the video game streaming platform because it represents a new mission field for approaching people with the Gospel. It’s also well suited to fellowshipping — a Christian term for hanging out — with Fortniters and Fall Guys streamers alike. And there are a lot of people hanging out: In 2019, an estimated 2.5 billion gamers worldwide spent around six hours a week playing games, and Covid-19 has significantly increased that number. To Christians looking to evangelize others online, that degree of lifestyle dedication makes Twitch the perfect platform to approach what’s known as an “unreached people group” — a community with no churches or missionaries serving them.

So while being a professional C programmer I always had a look at the alternatives. The first hopeful alternative was D. D looked promising initially but upon closer inspection I decided that D was really just a cleaned up version of what was fundamentally a bad idea. One of the key problems with C its kitchen sink approach to language design. There is just too much in there.