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If you implement this tip, you will soon notice that your Notebook start to look less cluttered and more organised. In addition, using functions will make you less prone to silly copy paste mistakes.

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ered from whatever it was, I went back to eating junk food and consuming large amounts of alcohol. I had to have a nap before going out on Saturday night. Even with a nap I’d start falling asleep at the nightclub while my friends danced the night away. Nothing made sense.

If we just need a simple function, lambda is a good choice, since it can be treated as a simpler way to define a function. Therefore, we can give it a name and use it like a normal function.

Unit testing was not covered in this post as it deserves its own section. If you would like learn about unit testing for Data Science, this PyData talk may be a good starting point.

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.

It’s not uncommon to be tweaking parts of your code anywhere in your Notebook when experimenting. However, once you are done experimenting, clean it up, restart the kernel and ensure cells in your Notebook ran top-down once such that you won’t have out of order cells like this:

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:

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).

◼️ Simple data visualisations in Python that you will find useful ◼️ 6 simple tips for prettier and customised plots in Seaborn (Python) ◼️ Exploratory text analysis in Python ◼️️ 5 tips for pandas users ◼️️ 5 tips for data aggregation in pandas ◼️️ Writing 5 common SQL queries in pandas ◼️️ Writing advanced SQL queries in pandas

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:

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.

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.

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:

◼️ Simple data visualisations in Python that you will find useful ◼️ 6 simple tips for prettier and customised plots in Seaborn (Python) ◼️ Exploratory text analysis in Python ◼️️ 5 tips for pandas users ◼️️ 5 tips for data aggregation in pandas ◼️️ Writing 5 common SQL queries in pandas ◼️️ Writing advanced SQL queries in pandas

Because of its simplicity, the lambda functions can make our Python code more elegant in some using scenarios. This post will demonstrate 5 common uses of lambda functions in Python and explain them with interesting examples.