So don’t worry about eating more “superfoods” like kale, tiger nuts, or Indian gooseberries: The key to eating well is p

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.

If you saved these functions in a helpers.py file and imported helpers module (by the way, Python module just means a .py file) in our Notebook with import helpers, you can access the documentation by writing the function name followed by Shift Tab:

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 before could roughly guess what the function does just by looking at its name, then you are on the right track. When documenting these functions, I have adapted a few different styles in a way it made more sense to me. While these examples serve as a working example function for Data Science, I highly encourage you to check out official guides such as below to learn the best practices in naming and documentation conventions, style guides and type hints:

3. If someone cares more about what could be on their résumé more than they do their eulogy, believe they care more about how others see them than how they make others feel.

8. If someone threatens to leave you when they are angry — but promises to stay when they are calm — believe that they are just trying to hurt you more than they actually want to go.

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rn and Aquarius. Saturn moves into Aquarius in mid-December 2020. If your Saturn is in Capricorn this means your Saturn Return is ending. If your Saturn is in Aquarius, this means your return is beginning for real this time. Either way, you will feel this December transit quite literally. If that’s you, I have tips.

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:

2. When someone tells you that they “aren’t looking for a relationship” right now, believe them, because that’s pretty much always how people rationalize not wanting to commit to someone who seems like they should be right.

#### 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

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).

If you have many functions, you could even categorise and put them in separate modules. If you take this approach, you may even want to create a folder containing all the modules. While putting stable code into a module makes sense, I think it is fine to keep experimental functions in your Notebook.

7. If someone goes out of their way to apologize for something that they did in the past, believe that they really are sorry, because at that point, there’s nothing to gain other than their own sanctity of mind.

1. When a friend seems to have something negative to say about everyone they know, believe that they are treating you the same way behind your back — chances are, you’re probably not the outlier.

11. If someone is irrationally judgmental or angry toward other people, believe that those people have something they want very, very badly (and are completely convinced they cannot get).

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.