Python Utilities

Sometimes your list of dictionaries can be nested like above. How can you get the second price of each apple?

That is when the map_ method of pydash comes in handy. Pydash is the kitchen sink of Python utility libraries for doing “stuff” in a functional way.

Link to pydash.

My previous tips on pydash.

Lazy evaluation is an evaluation strategy that holds the evaluation of an expression until its value is needed. This method avoids repeated evaluation and discards sub-expressions that are not directly linked to the final result of the expression.

To use lazy evaluation in Python, use pydash’s method chaining. pydash’s method chaining also increases the readability of your code when applying multiple methods to a Python object.

Link to pydash.

Play with pydash in this Google Colab notebook.

What is pydash?
pydash is a Python utility library inspired by Lodash in JavaScript. It provides a collection of functions for working with dictionaries, lists, and other data structures more safely and expressively. One of its most useful features is pydash.get(), which lets you access deeply nested dictionary values using dot-notation paths without raising KeyError exceptions when a key is missing.
Conclusion
If you’ve ever found it lengthy to use brackets to access elements in a deeply nested dictionary, if so try using pydash.get(). It replaces chained .get() calls and try/except blocks with clean dot-notation paths, preventing crashes from missing keys when dealing with JSON responses from APIs or configuration files with unpredictable structures.
Withpydash.get, you can use a dot notation with a dictionary like above.

Other useful methods pydash provides.

Link to pydash.
That said, pydash.get() can sometimes be a band-aid for a deeper problem. Using pydash.get() is great for:

quick scripts
messy or unpredictable data
external API responses

But if you are:

building production systems
relying heavily on nested dicts

If you find yourself constantly navigating messy nested dictionaries, it might be time to rethink how you structure your data altogether. In our deep dive on The Hidden Cost of Python Dictionaries (And 3 Safer Alternatives), we look at how NamedTuples, dataclasses, and Pydantic can catch bugs that dictionaries silently let through.

typing is a Python module that allows developers to specify the types of inputs to make sure the input types are correct. To specify an input is of type function, use typing.Callable.

Callable can be used static type checker such as mypy to check if the input is indeed a function.

Do you find it annoying to write an __init__ method every time you want to create a class in Python? If so, try attrs.

attrs allows you to:

declaratively define the attributes of a classget a nice human-readable __repr__ without extra codeturn attributes of an instance into a dictionary

Find other benefits of using attrs here.

Link to my previous tips on Python classes.

If you want to group elements in a list by a key, use itertools.groupby. In the example above, I grouped elements in the list by the first element in each tuple.

Link to my previous tips on itertools.

Normally, to access multiple indices from a list, you need to use list comprehension. To do the same thing with simpler syntax, use operator.itemgetter instead.

Link to other tips on Python’s built-in libraries.

Normally, you cannot filter a list using a list. To filter a list using a list of booleans, use itertools.compress instead.

Link to my previous tips on itertools.

map is a useful method that allows you to apply a function to elements in a list. However, it can’t apply a function with more than one argument to a list.
To apply a function with more than 2 arguments to elements in a list, use itertools.starmap. With starmap, elements in each tuple of the list nums are used as arguments for the function multiply.
Link to my previous tips about itertools.