Python packages that you may not have heard of

The python package ecosystem is a vast forest with many great packages available. If you have been using python professionally you are more than likely to have stumbled across some of the more popular packages such as requests, django and flask. In this post however, I seek to bring your attention to a few packages that I have been using over the last 6 months or so and have found to be quite useful.


Glom is a package that was first released in April 2018, and it has since become my go to solution for accessing nested data structures in python. It has a very simple interface that allows you to declare what you are looking for in some structure and define a few other options around that. For example, if we have the following dictionary:

my_dict = {
    'foo': {
        'foofoo': 1,
        'foobar': 2
    'bar': {
        'barfoo': 3,
        'barbar': 4

and we wanted to find the value for foobar, we could simply write glom(my_dict, 'foo.foobar'). Furthermore, if we wanted to define a default for a KeyError we could pass a keyword argument like glom(my_dict, 'foo.foobar', default=None) which would then return None if there was nothing at the foo.foobar path.

Now glom’s not just a data access package, but also a data formatting package. For example, given a dictionary of animals we might want to get a list of all the different colors that are in our data. So if we had this dictionary:

my_dict = {
    'animals': [
            'name': 'dog',
            'num_legs': 4,
            'color': 'brown'
            'name': 'cat',
            'num_legs': 4,
            'color': 'orange'
            'name': 'frog',
            'num_legs': 4,
            'color': 'green'

we could get our list of colors with the following call to glom glom(my_dict, ('animals', ['color'])) which would result in ['brown', 'orange', 'green'].


Crayons is another package from serial-creator Kenneth Reitz. This package excels at making it easy to color your text in command line applications. I know I’ve certainly found it useful when writing reporting tools or for coloring prompts for dangerous actions. The API itself is well designed, you pass a string to a function named after the color you want the text to be and then it will return the decorated string for you so that when you print it to the console it’s already colored for you. For example:

import crayons

red ="red")
print(f"The end of this string is {red}.")

which prints

The end of this string is red.

Another useful feature that crayons provides is that of turning off the colors when you want to via calling crayons.disable().


Hypothesis is a testing framework that will make you think differently about testing your python code. Rather than writing standard unit tests where you come up with the input for some function or generate random fake input each time, Hypothesis actually inspects your code and figures out what your edge cases are and tries to test against them, furthermore Hypothesis attempts to report back to you the minimal test case that will break your code so you can find and fix bugs faster than ever. One example of Hypothesis power is as follows, say we are trying to write a function to square a number. Our first attempt might be to produce the square via addition:

def square(x):
    total = 0
    for _ in range(x):
        total += x

    return total

and our normal, naive, test case might look like:

def test_square():
    assert square(4) == 16

Notice that this is a bad test case because we forgot to check edge cases. Well, if we rewrite the test case using Hypothesis, we can find those edge cases. Here’s how we would write it:

from hypothesis import given
from hypothesis.strategies import integers

def test_square(x):
    assert square(x) == x * x

In this test case, we are asking Hypothesis to generate us test cases where we are testing an integer up to 100 and checking that it is squared correctly. Upon running this, Hypothesis alerts us:

Falsifying example: test_square(x=-1)

indicating that when x = -1 we fail our test. We can then fix our function and noticed all tests now pass:

def square(x):
    return x * x


Marshmallow is a serialization library that I have become fond of due to it’s similarity to Django REST Framework Serializers. It has a declarative style that allows you to very quickly describe the data you are trying to serialize to and from native objects to standard formats like JSON. Furthermore, Marshmallow allows us to validate our data on load via specifying keyword arguments to the different field types on our schema:

from marshmallow import fields, Schema

class AnimalSchema(Schema):
    name = fields.String(required=True)
    num_legs = fields.Integer(required=True)
    color = fields.String(required=True)

Here we have a schema that represents some animal, similar to our glom example. Now we could try load some JSON into this:

dog = "{\"name\": \"dog\", \"num_legs\": 4}"
animal = AnimalSchema().loads(dog)

However, Marshmallow realises that this data isn’t the correct format since it is missing the color field and therefore raises a ValidationError alerting us to this fact. Note that if we excluded the required=True argument from the color field this would not happen. If we fix the JSON, our call will return to us a native python dictionary ready to be used elsewhere:

dog = "{\"name\": \"dog\", \"num_legs\": 4, \"color\": \"brown\"}"
animal = AnimalSchema().loads(dog)

print(animal)  # {'num_legs': 4, 'name': 'dog', 'color': 'brown'}


Pystache is one of those libraries that is just so simple and gives back so much value so quickly. I first used Pystache when I was writing an email template for an application. It allowed me to worry more about the presentation of the template than getting the data into it due to it’s extremely well designed API. Simply put, Pystache is a templating library that allows you to embed variables into large and often complex strings. For example, if we wanted to populate a html template with a number of variables we could do it like so:

import pystache

template = """



context = dict(
    title="My Post",
    subtitle="An example of Pystache",
    body="Pystache is awesome"


and the result would be:

<h1>My Post</h1>
<h2>An example of Pystache</h2>

Pystache is awesome
Written on February 2, 2019