python hypothesis django

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Hypothesis for Django users ¶

Hypothesis offers a number of features specific for Django testing, available in the hypothesis[django] extra . This is tested against each supported series with mainstream or extended support - if you’re still getting security patches, you can test with Hypothesis.

Using it is quite straightforward: All you need to do is subclass hypothesis.extra.django.TestCase or hypothesis.extra.django.TransactionTestCase and you can use @given as normal, and the transactions will be per example rather than per test function as they would be if you used @given with a normal django test suite (this is important because your test function will be called multiple times and you don’t want them to interfere with each other). Test cases on these classes that do not use @given will be run as normal.

I strongly recommend not using TransactionTestCase unless you really have to. Because Hypothesis runs this in a loop the performance problems it normally has are significantly exacerbated and your tests will be really slow. If you are using TransactionTestCase , you may need to use @settings(suppress_health_check=[HealthCheck.too_slow]) to avoid errors due to slow example generation .

Having set up a test class, you can now pass @given a strategy for Django models:

For example, using the trivial django project I have for testing:

Hypothesis has just created this with whatever the relevant type of data is.

Obviously the customer’s age is implausible, which is only possible because we have not used (eg) MinValueValidator to set the valid range for this field (or used a PositiveSmallIntegerField , which would only need a maximum value validator).

If you do have validators attached, Hypothesis will only generate examples that pass validation. Sometimes that will mean that we fail a HealthCheck because of the filtering, so let’s explicitly pass a strategy to skip validation at the strategy level:

Inference from validators will be much more powerful when issue #1116 is implemented, but there will always be some edge cases that require you to pass an explicit strategy.

Tips and tricks ¶

Custom field types ¶.

If you have a custom Django field type you can register it with Hypothesis’s model deriving functionality by registering a default strategy for it:

Note that this mapping is on exact type. Subtypes will not inherit it.

Generating child models ¶

For the moment there’s no explicit support in hypothesis-django for generating dependent models. i.e. a Company model will generate no Shops. However if you want to generate some dependent models as well, you can emulate this by using the flatmap function as follows:

Lets unpack what this is doing:

The way flatmap works is that we draw a value from the original strategy, then apply a function to it which gives us a new strategy. We then draw a value from that strategy. So in this case we’re first drawing a company, and then we’re drawing a list of shops belonging to that company: The just strategy is a strategy such that drawing it always produces the individual value, so models(Shop, company=just(company)) is a strategy that generates a Shop belonging to the original company.

So the following code would give us a list of shops all belonging to the same company:

The only difference from this and the above is that we want the company, not the shops. This is where the inner map comes in. We build the list of shops and then throw it away, instead returning the company we started for. This works because the models that Hypothesis generates are saved in the database, so we’re essentially running the inner strategy purely for the side effect of creating those children in the database.

Using default field values ¶

Hypothesis ignores field defaults and always tries to generate values, even if it doesn’t know how to. You can tell it to use the default value for a field instead of generating one by passing fieldname=default_value to models() :

Django Packages

Packages ›› hypothesis, repo activity last fetched: 17 hours, 35 minutes ago   fetch latest data.

Documentation

http://hypothesis.readthedocs.io/en/latest/django.html

https://github.com/HypothesisWorks/hypothesis-python

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Django Testing on Steroid: pytest + Hypothesis

Generate hundred of test with few lines of code, bojan miletic.

Django TDD Test Libraries (pytest/nose/...) Testing

The talk should hopefully provided value to all listeners, regardless of their knowledge level, but preferably you have some knowledge of pytest test parametrization

We'll use a simple Django project, setup initial tests using pytest with some parallelization in the opening part and afterwards start extending them with Hypothesis. We'll go over the details, how you can use them to detect edge cases, extend test coverage and if time allows it how you can use them to test django models.

Type: Talk (30 mins); Python level: Beginner; Domain level: Beginner

Softerrific

I've starting programming in Python over a decade ago.

Since than I've been CEO and Co-Founder of two companies and worked in almost any roles inside of company(marketing, sales, frontend, backend, ux ..)

I love learning stuff and spreading Python love. For more info you can check my linkedIn profile https://www.linkedin.com/in/boyan-miletic/

Registration

EuroPython Society (EPS) Ramnebacken 45 424 38 Agnesberg Sweden

EPS website

Test faster, fix more

Hypothesis for Python

This is our current primary focus and the only currently production ready implementation of the Hypothesis design.

It features:

• A full implementation of property based testing for Python, including stateful testing .
• An extensive library of data generators and tools for writing your own.
• Compatible with py.test, unittest, nose and Django testing, and probably many others besides.
• Supports CPython and PyPy 3.8 and later (older versions are EoL upstream).
• Open source under the Mozilla Public License 2.0

To use Hypothesis for Python, simply add the hypothesis package to your requirements, or pip install hypothesis directly.

The code is available on GitHub and documentation is available on readthedocs .

Hypothesis for Java

Hypothesis for Java is currently a feasibility prototype only and is not ready for production use. We are looking for initial customers to help fund getting it off the ground.

As a prototype it currently features:

• Enough of the core Hypothesis model to be useful.
• Good JUnit integration.
• A small library of data generators.

The end goal is for Hypothesis for Java to have feature parity with Hypothesis for Python, and to take advantage of the JVM’s excellent concurrency support to provide parallel testing of your code, but it’s not there yet.

The current prototype is released under the AGPL3 (this is not the intended license for the full version, which will most likely be Apache licensed) and is available on GitHub .

Email us at [email protected] if you want to know more about Hypothesis for Java or want to discuss being an early customer of it.

Using Hypothesis to test Django Rest Framework APIs

Why is there a need for hypothesis in testing django applications?

By yvsssantosh

in python ,  django ,  hypothesis ,  testing ,  django rest framework ,  drf ,  polls api

What is hypothesis?

Hypothesis is family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.

It is in short THE testing tool. As quoted by the author, “The purpose of hypothesis is to drag the world kicking and screaming into a new and terrifying age of high quality software”

How to use hypothesis?

Hypothesis integrates into your normal testing workflow. Getting started is as simple as installing a library and writing some code using it - no new services to run, no new test runners to learn.

We can install it by,

The main thing about hypothesis is Strategy . A strategy is a recipe for describing the sort of data you want to generate. Rather than having to hand-write generators for the data needed, we can just compose the ones hypothesis provides us with, to get the data in the required format

Ex: If we need a list of floats which are definitely a number and not infinite, we can use the strategy

As well as it is easier to write, the resultant data will ususally have a distribution that is much better at finding edge cases than most of the heavily tuned maual implementations

Once we understand data generation for tests, the main entrypoint to Hypothesis is the @given decorator. It takes a function with some arguments as input & turns it into a normal test function.

This helps us to realize that hypothesis is not itself a test runner, but it runs alongside our testing framework and all it does is to expose a function of the appropriate name which the test runner picks up.

A simple example illustrating @given decorator (taken from hypothesis docs),

In the function above we are trying to encode something and then decode it to get the same value back. We find a bug immediately,

Hypothesis correctly points out that this code is simply wrong if called on an empty string.

If we wanted to make sure this example was always checked we could add it in explicitly by using the @example decorator i.e.

This ensures to show what kinds of inputs are valid or to ensure that particular edge cases such as "" are tested everytime. Note that both @example and @given support keyword arguments as well as positional i.e.

Once hypothesis finds an error with respect to a test after multiple test runs, it will continue to fail with the same example everytime. This is because Hypothesis has a local test database where it saves all the examples which failed. When we rerun the test, it will first try the previous failure. This is important because, even if at heart hypothesis is random testing, it is repeatable random testing, i.e. a bug will never go away by chance, because further tests will run only, if the previous failure no longer failed.

Using Hypothesis with Django Rest Framework

Now, taking the above example as a sample, lets test it out on a DRF Application. I’ll be using my previous Polls API clone from https://github.com/yvsssantosh/django-polls-rest

Navigate to the file tests.py in polls directory, and lets understand the file part by part.

• The first few lines indicate basic imports which are required for tests. The major imports being,

@given decorator is used because it is the entry point for hypothesis testing @settings decorator is used to modify the way tests are to be implmeneted. More on this will be explained below. We will be importing from TestCase from hypothesis, as it helps all the decorators to work, according to our methods

from_model is used to generate random data according to the given model

Custom imports include importing API views. Then we define our class TestPoll (Note that we are inheriting from hypothesis.django.extra.TestCase ). After this we have the initial setup which sets up APIRequestFactory, APIClient which are helpful in making requests, and authenticating the user respectively.

Once we are done with initial setup, we explore the main part where we’re gonna test our application. The major advantage with hypothesis is that, with just a few decorators, we can simplify our tests which improves readibility as well as thoroughness of our tests.

A deadline is the timeframe (in ms), for max which the test is allowed to run. Default value is 200ms . But with that default value, our tests threw an error DeadlineExceeded . We can test it by removing that parameter in the @settings decorator.

max_examples is used to define the number of iterations we want to test our application randomly. In our case, the test is run with 10 different random test cases. This is all done with the help of @given decorator and from_model method.

The from_model method is really helpful to generate random data with respect to a model. For example, if we want to generate a random instance of User model, we’d just have to do add it to the given decorator, and expect it as a paramater in the following method.

Note that in the response body, for question parameter, we are passing st.text() which again, randomly generates a string and then the request is posted.

We can test our application the way we used to, i.e.,

Ultimately hypothesis provides readability, repeatability, reporting and simplification for randomized tests, and it provides a large library of generators to make it easier to write them. It is also really helpful in generating random use cases which even the human mind can’t think of sometimes.

Thank you for reading the Agiliq blog. This article was written by yvsssantosh on Jan 19, 2019 in python ,  django ,  hypothesis ,  testing ,  django rest framework ,  drf ,  polls api .

You can subscribe ⚛ to our blog .

We love building amazing apps for web and mobile for our clients. If you are looking for development help, contact us today ✉ .

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hypothesis 6.106.1

pip install hypothesis Copy PIP instructions

Released: Jul 12, 2024

A library for property-based testing

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License: Mozilla Public License 2.0 (MPL 2.0) (MPL-2.0)

Author: David R. MacIver and Zac Hatfield-Dodds

Tags python, testing, fuzzing, property-based-testing

Requires: Python >=3.8

Provides-Extra: all , cli , codemods , crosshair , dateutil , django , dpcontracts , ghostwriter , lark , numpy , pandas , pytest , pytz , redis , zoneinfo

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• 5 - Production/Stable
• OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
• Microsoft :: Windows
• Python :: 3
• Python :: 3 :: Only
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Python :: 3.12
• Python :: Implementation :: CPython
• Python :: Implementation :: PyPy
• Education :: Testing
• Software Development :: Testing

Project description

Hypothesis is an advanced testing library for Python. It lets you write tests which are parametrized by a source of examples, and then generates simple and comprehensible examples that make your tests fail. This lets you find more bugs in your code with less work.

Hypothesis is extremely practical and advances the state of the art of unit testing by some way. It’s easy to use, stable, and powerful. If you’re not using Hypothesis to test your project then you’re missing out.

Quick Start/Installation

If you just want to get started:

Links of interest

The main Hypothesis site is at hypothesis.works , and contains a lot of good introductory and explanatory material.

Extensive documentation and examples of usage are available at readthedocs .

If you want to talk to people about using Hypothesis, we have both an IRC channel and a mailing list .

If you want to receive occasional updates about Hypothesis, including useful tips and tricks, there’s a TinyLetter mailing list to sign up for them .

If you want to contribute to Hypothesis, instructions are here .

If you want to hear from people who are already using Hypothesis, some of them have written about it .

If you want to create a downstream package of Hypothesis, please read these guidelines for packagers .

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Pytest With Eric

How to Use Hypothesis and Pytest for Robust Property-Based Testing in Python

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Hypothesis is a powerful, flexible, and easy to use library for property-based testing.

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Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.

This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck , but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.

Hypothesis for Python is the original implementation, and the only one that is currently fully production ready and actively maintained.

Hypothesis for Other Languages

The core ideas of Hypothesis are language agnostic and in principle it is suitable for any language. We are interested in developing and supporting implementations for a wide variety of languages, but currently lack the resources to do so, so our porting efforts are mostly prototypes.

The two prototype implementations of Hypothesis for other languages are:

• Hypothesis for Ruby is a reasonable start on a port of Hypothesis to Ruby.
• Hypothesis for Java is a prototype written some time ago. It's far from feature complete and is not under active development, but was intended to prove the viability of the concept.

Additionally there is a port of the core engine of Hypothesis, Conjecture, to Rust. It is not feature complete but in the long run we are hoping to move much of the existing functionality to Rust and rebuild Hypothesis for Python on top of it, greatly lowering the porting effort to other languages.

Any or all of these could be turned into full fledged implementations with relatively little effort (no more than a few months of full time work), but as well as the initial work this would require someone prepared to provide or fund ongoing maintenance efforts for them in order to be viable.

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Table of Contents

Testing your python code with hypothesis, installing & using hypothesis, a quick example, understanding hypothesis, using hypothesis strategies, filtering and mapping strategies, composing strategies, constraints & satisfiability, writing reusable strategies with functions.

• @composite: Declarative Strategies
• @example: Explicitly Testing Certain Values

Hypothesis Example: Roman Numeral Converter

I can think of a several Python packages that greatly improved the quality of the software I write. Two of them are pytest and hypothesis . The former adds an ergonomic framework for writing tests and fixtures and a feature-rich test runner. The latter adds property-based testing that can ferret out all but the most stubborn bugs using clever algorithms, and that’s the package we’ll explore in this course.

In an ordinary test you interface with the code you want to test by generating one or more inputs to test against, and then you validate that it returns the right answer. But that, then, raises a tantalizing question: what about all the inputs you didn’t test? Your code coverage tool may well report 100% test coverage, but that does not, ipso facto , mean the code is bug-free.

One of the defining features of Hypothesis is its ability to generate test cases automatically in a manner that is:

Repeated invocations of your tests result in reproducible outcomes, even though Hypothesis does use randomness to generate the data.

You are given a detailed answer that explains how your test failed and why it failed. Hypothesis makes it clear how you, the human, can reproduce the invariant that caused your test to fail.

You can refine its strategies and tell it where or what it should or should not search for. At no point are you compelled to modify your code to suit the whims of Hypothesis if it generates nonsensical data.

So let’s look at how Hypothesis can help you discover errors in your code.

You can install hypothesis by typing pip install hypothesis . It has few dependencies of its own, and should install and run everywhere.

Hypothesis plugs into pytest and unittest by default, so you don’t have to do anything to make it work with it. In addition, Hypothesis comes with a CLI tool you can invoke with hypothesis . But more on that in a bit.

I will use pytest throughout to demonstrate Hypothesis, but it works equally well with the builtin unittest module.

Before I delve into the details of Hypothesis, let’s start with a simple example: a naive CSV writer and reader. A topic that seems simple enough: how hard is it to separate fields of data with a comma and then read it back in later?

But of course CSV is frighteningly hard to get right. The US and UK use '.' as a decimal separator, but in large parts of the world they use ',' which of course results in immediate failure. So then you start quoting things, and now you need a state machine that can distinguish quoted from unquoted; and what about nested quotes, etc.

The naive CSV reader and writer is an excellent stand-in for any number of complex projects where the requirements outwardly seem simple but there lurks a large number of edge cases that you must take into account.

Here the writer simply string quotes each field before joining them together with ',' . The reader does the opposite: it assumes each field is quoted after it is split by the comma.

A naive roundtrip pytest proves the code “works”:

And evidently so:

And for a lot of code that’s where the testing would begin and end. A couple of lines of code to test a couple of functions that outwardly behave in a manner that anybody can read and understand. Now let’s look at what a Hypothesis test would look like, and what happens when we run it:

At first blush there’s nothing here that you couldn’t divine the intent of, even if you don’t know Hypothesis. I’m asking for the argument fields to have a list ranging from one element of generated text up to ten. Aside from that, the test operates in exactly the same manner as before.

Now watch what happens when I run the test:

Hypothesis quickly found an example that broke our code. As it turns out, a list of [','] breaks our code. We get two fields back after round-tripping the code through our CSV writer and reader — uncovering our first bug.

In a nutshell, this is what Hypothesis does. But let’s look at it in detail.

Simply put, Hypothesis generates data using a number of configurable strategies . Strategies range from simple to complex. A simple strategy may generate bools; another integers. You can combine strategies to make larger ones, such as lists or dicts that match certain patterns or structures you want to test. You can clamp their outputs based on certain constraints, like only positive integers or strings of a certain length. You can also write your own strategies if you have particularly complex requirements.

Strategies are the gateway to property-based testing and are a fundamental part of how Hypothesis works. You can find a detailed list of all the strategies in the Strategies reference of their documentation or in the hypothesis.strategies module.

The best way to get a feel for what each strategy does in practice is to import them from the hypothesis.strategies module and call the example() method on an instance:

You may have noticed that the floats example included inf in the list. By default, all strategies will – where feasible – attempt to test all legal (but possibly obscure) forms of values you can generate of that type. That is particularly important as corner cases like inf or NaN are legal floating-point values but, I imagine, not something you’d ordinarily test against yourself.

And that’s one pillar of how Hypothesis tries to find bugs in your code: by testing edge cases that you would likely miss yourself. If you ask it for a text() strategy you’re as likely to be given Western characters as you are a mishmash of unicode and escape-encoded garbage. Understanding why Hypothesis generates the examples it does is a useful way to think about how your code may interact data it has no control over.

Now if it were simply generating text or numbers from an inexhaustible source of numbers or strings, it wouldn’t catch as many errors as it actually does . The reason for that is that each test you write is subjected to a battery of examples drawn from the strategies you’ve designed. If a test case fails, it’s put aside and tested again but with a reduced subset of inputs, if possible. In Hypothesis it’s known as shrinking the search space to try and find the smallest possible result that will cause your code to fail. So instead of a 10,000-length string, if it can find one that’s only 3 or 4, it will try to show that to you instead.

You can tell Hypothesis to filter or map the examples it draws to further reduce them if the strategy does not meet your requirements:

Here I ask for integers where the number is greater than 0 and is evenly divisible by 8. Hypothesis will then attempt to generate examples that meets the constraints you have imposed on it.

You can also map , which works in much the same way as filter. Here I’m asking for lowercase ASCII and then uppercasing them:

Having said that, using either when you don’t have to (I could have asked for uppercase ASCII characters to begin with) is likely to result in slower strategies.

A third option, flatmap , lets you build strategies from strategies; but that deserves closer scrutiny, so I’ll talk about it later.

You can tell Hypothesis to pick one of a number of strategies by composing strategies with | or st.one_of() :

An essential feature when you have to draw from multiple sources of examples for a single data point.

When you ask Hypothesis to draw an example it takes into account the constraints you may have imposed on it: only positive integers; only lists of numbers that add up to exactly 100; any filter() calls you may have applied; and so on. Those are constraints. You’re taking something that was once unbounded (with respect to the strategy you’re drawing an example from, that is) and introducing additional limitations that constrain the possible range of values it can give you.

But consider what happens if I pass filters that will yield nothing at all:

At some point Hypothesis will give up and declare it cannot find anything that satisfies that strategy and its constraints.

Hypothesis gives up after a while if it’s not able to draw an example. Usually that indicates an invariant in the constraints you’ve placed that makes it hard or impossible to draw examples from. In the example above, I asked for numbers that are simultaneously below zero and greater than zero, which is an impossible request.

As you can see, the strategies are simple functions, and they behave as such. You can therefore refactor each strategy into reusable patterns:

The benefit of this approach is that if you discover edge cases that Hypothesis does not account for, you can update the pattern in one place and observe its effects on your code. It’s functional and composable.

One caveat of this approach is that you cannot draw examples and expect Hypothesis to behave correctly. So I don’t recommend you call example() on a strategy only to pass it into another strategy.

For that, you want the @composite decorator.

@composite : Declarative Strategies

If the previous approach is unabashedly functional in nature, this approach is imperative.

The @composite decorator lets you write imperative Python code instead. If you cannot easily structure your strategy with the built-in ones, or if you require more granular control over the values it emits, you should consider the @composite strategy.

Instead of returning a compound strategy object like you would above, you instead draw examples using a special function you’re given access to in the decorated function.

This example draws two randomized names and returns them as a tuple:

Note that the @composite decorator passes in a special draw callable that you must use to draw samples from. You cannot – well, you can , but you shouldn’t – use the example() method on the strategy object you get back. Doing so will break Hypothesis’s ability to synthesize test cases properly.

Because the code is imperative you’re free to modify the drawn examples to your liking. But what if you’re given an example you don’t like or one that breaks a known invariant you don’t wish to test for? For that you can use the assume() function to state the assumptions that Hypothesis must meet if you try to draw an example from generate_full_name .

Let’s say that first_name and last_name must not be equal:

Like the assert statement in Python, the assume() function teaches Hypothesis what is, and is not, a valid example. You use this to great effect to generate complex compound strategies.

I recommend you observe the following rules of thumb if you write imperative strategies with @composite :

If you want to draw a succession of examples to initialize, say, a list or a custom object with values that meet certain criteria you should use filter , where possible, and assume to teach Hypothesis why the value(s) you drew and subsequently discarded weren’t any good.

The example above uses assume() to teach Hypothesis that first_name and last_name must not be equal.

If you can put your functional strategies in separate functions, you should. It encourages code re-use and if your strategies are failing (or not generating the sort of examples you’d expect) you can inspect each strategy in turn. Large nested strategies are harder to untangle and harder still to reason about.

If you can express your requirements with filter and map or the builtin constraints (like min_size or max_size ), you should. Imperative strategies that use assume may take more time to converge on a valid example.

@example : Explicitly Testing Certain Values

Occasionally you’ll come across a handful of cases that either fails or used to fail, and you want to ensure that Hypothesis does not forget to test them, or to indicate to yourself or your fellow developers that certain values are known to cause issues and should be tested explicitly.

The @example decorator does just that:

You can add as many as you like.

Let’s say I wanted to write a simple converter to and from Roman numerals.

Here I’m collecting Roman numerals into numerals , one at a time, by looping over SYMBOLS of valid numerals, subtracting the value of the symbol from number , until the while loop’s condition ( number >= 1 ) is False .

The test is also simple and serves as a smoke test. I generate a random integer and convert it to Roman numerals with to_roman . When it’s all said and done I turn the string of numerals into a set and check that all members of the set are legal Roman numerals.

Now if I run pytest on it seems to hang . But thanks to Hypothesis’s debug mode I can inspect why:

Ah. Instead of testing with tiny numbers like a human would ordinarily do, it used a fantastically large one… which is altogether slow.

OK, so there’s at least one gotcha; it’s not really a bug , but it’s something to think about: limiting the maximum value. I’m only going to limit the test, but it would be reasonable to limit it in the code also.

Changing the max_value to something sensible, like st.integers(max_value=5000) and the test now fails with another error:

It seems our code’s not able to handle the number 0! Which… is correct. The Romans didn’t really use the number zero as we would today; that invention came later, so they had a bunch of workarounds to deal with the absence of something. But that’s neither here nor there in our example. Let’s instead set min_value=1 also, as there is no support for negative numbers either:

OK… not bad. We’ve proven that given a random assortment of numbers between our defined range of values that, indeed, we get something resembling Roman numerals.

One of the hardest things about Hypothesis is framing questions to your testable code in a way that tests its properties but without you, the developer, knowing the answer (necessarily) beforehand. So one simple way to test that there’s at least something semi-coherent coming out of our to_roman function is to check that it can generate the very numerals we defined in SYMBOLS from before:

Here I’m sampling from a tuple of the SYMBOLS from earlier. The sampling algorithm’ll decide what values it wants to give us, all we care about is that we are given examples like ("I", 1) or ("V", 5) to compare against.

So let’s run pytest again:

Oops. The Roman numeral V is equal to 5 and yet we get five IIIII ? A closer examination reveals that, indeed, the code only yields sequences of I equal to the number we pass it. There’s a logic error in our code.

In the example above I loop over the elements in the SYMBOLS dictionary but as it’s ordered the first element is always I . And as the smallest representable value is 1, we end up with just that answer. It’s technically correct as you can count with just I but it’s not very useful.

Fixing it is easy though:

Rerunning the test yields a pass. Now we know that, at the very least, our to_roman function is capable of mapping numbers that are equal to any symbol in SYMBOLS .

Now the litmus test is taking the numeral we’re given and making sense of it. So let’s write a function that converts a Roman numeral back into decimal:

Like to_roman we walk through each character, get the numeral’s numeric value, and add it to the running total. The test is a simple roundtrip test as to_roman has an inverse function from_roman (and vice versa) such that :

Invertible functions are easier to test because you can compare the output of one against the input of another and check if it yields the original value. But not every function has an inverse, though.

Running the test yields a pass:

So now we’re in a pretty good place. But there’s a slight oversight in our Roman numeral converters, though: they don’t respect the subtraction rule for some of the numerals. For instance VI is 6; but IV is 4. The value XI is 11; and IX is 9. Only some (sigh) numerals exhibit this property.

So let’s write another test. This time it’ll fail as we’ve yet to write the modified code. Luckily we know the subtractive numerals we must accommodate:

Pretty simple test. Check that certain numerals yield the value, and that the values yield the right numeral.

With an extensive test suite we should feel fairly confident making changes to the code. If we break something, one of our preexisting tests will fail.

The rules around which numerals are subtractive is rather subjective. The SUBTRACTIVE_SYMBOLS dictionary holds the most common ones. So all we need to do is read ahead of the numerals list to see if there exists a two-digit numeral that has a prescribed value and then we use that instead of the usual value.

The to_roman change is simple. A union of the two numeral symbol dictionaries is all it takes . The code already understands how to turn numbers into numerals — we just added a few more.

This method requires Python 3.9 or later. Read how to merge dictionaries

If done right, running the tests should yield a pass:

And that’s it. We now have useful tests and a functional Roman numeral converter that converts to and from with ease. But one thing we didn’t do is create a strategy that generates Roman numerals using st.text() . A custom composite strategy to generate both valid and invalid Roman numerals to test the ruggedness of our converter is left as an exercise to you.

In the next part of this course we’ll look at more advanced testing strategies.

Unlike a tool like faker that generates realistic-looking test data for fixtures or demos, Hypothesis is a property-based tester . It uses heuristics and clever algorithms to find inputs that break your code.

Testing a function that does not have an inverse to compare the result against – like our Roman numeral converter that works both ways – you often have to approach your code as though it were a black box where you relinquish control of the inputs and outputs. That is harder, but makes for less brittle code.

It’s perfectly fine to mix and match tests. Hypothesis is useful for flushing out invariants you would never think of. Combine it with known inputs and outputs to jump start your testing for the first 80%, and augment it with Hypothesis to catch the remaining 20%.

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How To Use Python To Test SEO Theories (And Why You Should)

Learn how to test your SEO theories using Python. Discover the steps required to pre-test search engine rank factors and validate implementation sitewide.

When working on sites with traffic, there is as much to lose as there is to gain from implementing SEO recommendations.

The downside risk of an SEO implementation gone wrong can be mitigated using machine learning models to pre-test search engine rank factors.

Pre-testing aside, split testing is the most reliable way to validate SEO theories before making the call to roll out the implementation sitewide or not.

We will go through the steps required on how you would use Python to test your SEO theories.

Choose Rank Positions

One of the challenges of testing SEO theories is the large sample sizes required to make the test conclusions statistically valid.

Split tests – popularized by Will Critchlow of SearchPilot – favor traffic-based metrics such as clicks, which is fine if your company is enterprise-level or has copious traffic.

If your site doesn’t have that envious luxury, then traffic as an outcome metric is likely to be a relatively rare event, which means your experiments will take too long to run and test.

Instead, consider rank positions. Quite often, for small- to mid-size companies looking to grow, their pages will often rank for target keywords that don’t yet rank high enough to get traffic.

Over the timeframe of your test, for each data point of time, for example day, week or month, there are likely to be multiple rank position data points for multiple keywords. In comparison to using a metric of traffic (which is likely to have much less data per page per date), which reduces the time period required to reach a minimum sample size if using rank position.

Thus, rank position is great for non-enterprise-sized clients looking to conduct SEO split tests who can attain insights much faster.

Google Search Console Is Your Friend

Deciding to use rank positions in Google makes using the data source a straightforward (and conveniently a low-cost) decision in Google Search Console (GSC) , assuming it’s set up.

GSC is a good fit here because it has an API that allows you to extract thousands of data points over time and filter for URL strings.

While the data may not be the gospel truth, it will at least be consistent, which is good enough.

Filling In Missing Data

GSC only reports data for URLs that have pages, so you’ll need to create rows for dates and fill in the missing data.

The Python functions used would be a combination of merge() (think VLOOKUP function in Excel ) used to add missing data rows per URL and filling the data you want to be inputed for those missing dates on those URLs.

For traffic metrics, that’ll be zero, whereas for rank positions, that’ll be either the median (if you’re going to assume the URL was ranking when no impressions were generated) or 100 (to assume it wasn’t ranking).

The code is given here .

Check The Distribution And Select Model

The distribution of any data represents its nature, in terms of where the most popular value (mode) for a given metric, say rank position (in our case the chosen metric) is for a given sample population.

The distribution will also tell us how close the rest of the data points are to the middle (mean or median), i.e., how spread out (or distributed) the rank positions are in the dataset.

This is critical as it will affect the choice of model when evaluating your SEO theory test.

Using Python, this can be done both visually and analytically; visually by executing this code:

The chart above shows that the distribution is positively skewed (think skewer pointing right), meaning most of the keywords rank in the higher-ranked positions (shown towards the left of the red median line). To run this code please make sure to install required libraries via command  pip install pandas plotnine :

Now, we know which test statistic to use to discern whether the SEO theory is worth pursuing. In this case, there is a selection of models appropriate for this type of distribution.

Minimum Sample Size

The selected model can also be used to determine the minimum sample size required.

The required minimum sample size ensures that any observed differences between groups (if any) are real and not random luck.

That is, the difference as a result of your SEO experiment or hypothesis is statistically significant, and the probability of the test correctly reporting the difference is high (known as power).

This would be achieved by simulating a number of random distributions fitting the above pattern for both test and control and taking tests.

When running the code, we see the following:

To break it down, the numbers represent the following using the example below:

(39.333, : proportion of simulation runs or experiments in which significance will be reached, i.e., consistency of reaching significance and robustness.

1.0) : statistical power, the probability the test correctly rejects the null hypothesis, i.e., the experiment is designed in such a way that a difference will be correctly detected at this sample size level.

60000: sample size

The above is interesting and potentially confusing to non-statisticians. On the one hand, it suggests that we’ll need 230,000 data points (made of rank data points during a time period) to have a 92% chance of observing SEO experiments that reach statistical significance. Yet, on the other hand with 10,000 data points, we’ll reach statistical significance – so, what should we do?

Experience has taught me that you can reach significance prematurely, so you’ll want to aim for a sample size that’s likely to hold at least 90% of the time – 220,000 data points are what we’ll need.

This is a really important point because having trained a few enterprise SEO teams, all of them complained of conducting conclusive tests that didn’t produce the desired results when rolling out the winning test changes.

Hence, the above process will avoid all that heartache, wasted time, resources and injured credibility from not knowing the minimum sample size and stopping tests too early.

Assign And Implement

With that in mind, we can now start assigning URLs between test and control to test our SEO theory.

In Python, we’d use the np.where() function (think advanced IF function in Excel), where we have several options to partition our subjects, either on string URL pattern, content type, keywords in title, or other depending on the SEO theory you’re looking to validate.

Use the Python code given here .

Strictly speaking, you would run this to collect data going forward as part of a new experiment. But you could test your theory retrospectively, assuming that there were no other changes that could interact with the hypothesis and change the validity of the test.

Something to keep in mind, as that’s a bit of an assumption!

Once the data has been collected, or you’re confident you have the historical data, then you’re ready to run the test.

In our rank position case, we will likely use a model like the Mann-Whitney test due to its distributive properties.

However, if you’re using another metric, such as clicks, which is poisson-distributed, for example, then you’ll need another statistical model entirely.

The code to run the test is given here .

Once run, you can print the output of the test results:

The above is the output of an experiment I ran, which showed the impact of commercial landing pages with supporting blog guides internally linking to the former versus unsupported landing pages.

In this case, we showed that offer pages supported by content marketing enjoy a higher Google rank by 17 positions (22.58 – 5.87) on average. The difference is significant, too, at 98%!

However, we need more time to get more data – in this case, another 210,000 data points. As with the current sample size, we can only be sure that <10% of the time, the SEO theory is reproducible.

Split Testing Can Demonstrate Skills, Knowledge And Experience

In this article, we walked through the process of testing your SEO hypotheses, covering the thinking and data requirements to conduct a valid SEO test.

By now, you may come to appreciate there is much to unpack and consider when designing, running and evaluating SEO tests. My Data Science for SEO video course goes much deeper (with more code) on the science of SEO tests, including split A/A and split A/B.

As SEO professionals, we may take certain knowledge for granted, such as the impact content marketing has on SEO performance.

Clients, on the other hand, will often challenge our knowledge, so split test methods can be most handy in demonstrating your SEO skills , knowledge, and experience!

More resources: 

• Using Python To Explain Homepage Redirection To C-Suite (Or Any SEO Best Practise)
• What Data Science Can Do for Site Architectures
• An Introduction To Python & Machine Learning For Technical SEO

Featured Image: UnderhilStudio/Shutterstock

Andreas Voniatis is the Founder of Artios, the SEO consulting firm that helps startups grow organically. His experience spans over ...

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• The Hypothesis example database
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The Hypothesis example database ¶

When Hypothesis finds a bug it stores enough information in its database to reproduce it. This enables you to have a classic testing workflow of find a bug, fix a bug, and be confident that this is actually doing the right thing because Hypothesis will start by retrying the examples that broke things last time.

Limitations ¶

The database is best thought of as a cache that you never need to invalidate: Information may be lost when you upgrade a Hypothesis version or change your test, so you shouldn’t rely on it for correctness - if there’s an example you want to ensure occurs each time then there’s a feature for including them in your source code - but it helps the development workflow considerably by making sure that the examples you’ve just found are reproduced.

The database also records examples that exercise less-used parts of your code, so the database may update even when no failing examples were found.

Upgrading Hypothesis and changing your tests ¶

The design of the Hypothesis database is such that you can put arbitrary data in the database and not get wrong behaviour. When you upgrade Hypothesis, old data might be invalidated, but this should happen transparently. It can never be the case that e.g. changing the strategy that generates an argument gives you data from the old strategy.

ExampleDatabase implementations ¶

Hypothesis’ default database setting creates a DirectoryBasedExampleDatabase in your current working directory, under .hypothesis/examples . If this location is unusable, e.g. because you do not have read or write permissions, Hypothesis will emit a warning and fall back to an InMemoryExampleDatabase .

Hypothesis provides the following ExampleDatabase implementations:

A non-persistent example database, implemented in terms of a dict of sets.

This can be useful if you call a test function several times in a single session, or for testing other database implementations, but because it does not persist between runs we do not recommend it for general use.

Use a directory to store Hypothesis examples as files.

Each test corresponds to a directory, and each example to a file within that directory. While the contents are fairly opaque, a DirectoryBasedExampleDatabase can be shared by checking the directory into version control, for example with the following .gitignore :

Note however that this only makes sense if you also pin to an exact version of Hypothesis, and we would usually recommend implementing a shared database with a network datastore - see ExampleDatabase , and the MultiplexedDatabase helper.

A file-based database loaded from a GitHub Actions artifact.

You can use this for sharing example databases between CI runs and developers, allowing the latter to get read-only access to the former. This is particularly useful for continuous fuzzing (i.e. with HypoFuzz ), where the CI system can help find new failing examples through fuzzing, and developers can reproduce them locally without any manual effort.

You must provide GITHUB_TOKEN as an environment variable. In CI, Github Actions provides this automatically, but it needs to be set manually for local usage. In a developer machine, this would usually be a Personal Access Token . If the repository is private, it’s necessary for the token to have repo scope in the case of a classic token, or actions:read in the case of a fine-grained token.

In most cases, this will be used through the MultiplexedDatabase , by combining a local directory-based database with this one. For example:

Because this database is read-only, you always need to wrap it with the ReadOnlyDatabase .

A setup like this can be paired with a GitHub Actions workflow including something like the following:

In this workflow, we use dawidd6/action-download-artifact to download the latest artifact given that the official actions/download-artifact does not support downloading artifacts from previous workflow runs.

The database automatically implements a simple file-based cache with a default expiration period of 1 day. You can adjust this through the cache_timeout property.

For mono-repo support, you can provide a unique artifact_name (e.g. hypofuzz-example-db-frontend ).

A wrapper to make the given database read-only.

The implementation passes through fetch , and turns save , delete , and move into silent no-ops.

Note that this disables Hypothesis’ automatic discarding of stale examples. It is designed to allow local machines to access a shared database (e.g. from CI servers), without propagating changes back from a local or in-development branch.

A wrapper around multiple databases.

Each save , fetch , move , or delete operation will be run against all of the wrapped databases. fetch does not yield duplicate values, even if the same value is present in two or more of the wrapped databases.

This combines well with a ReadOnlyDatabase , as follows:

So your CI system or fuzzing runs can populate a central shared database; while local runs on development machines can reproduce any failures from CI but will only cache their own failures locally and cannot remove examples from the shared database.

Store Hypothesis examples as sets in the given Redis datastore.

This is particularly useful for shared databases, as per the recipe for a MultiplexedDatabase .

If a test has not been run for expire_after , those examples will be allowed to expire. The default time-to-live persists examples between weekly runs.

Defining your own ExampleDatabase ¶

You can define your ExampleDatabase , for example to use a shared datastore, with just a few methods:

An abstract base class for storing examples in Hypothesis’ internal format.

An ExampleDatabase maps each bytes key to many distinct bytes values, like a Mapping[bytes, AbstractSet[bytes]] .

Save value under key .

If this value is already present for this key, silently do nothing.

Return an iterable over all values matching this key.

Remove this value from this key.

If this value is not present, silently do nothing.

Move value from key src to key dest . Equivalent to delete(src, value) followed by save(src, value) , but may have a more efficient implementation.

Note that value will be inserted at dest regardless of whether it is currently present at src .

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Django security releases issued: 5.0.7 and 4.2.14.

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Thanks to Elias Myllymäki for the report.

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Edit entry URL not linking correctly in Django (Python crash course 3rd Ed.)

I'm working through the Python Crash Course book by Eric Matthes. In Chapter 19 i start to run into problems. The 'Edit Entry' section does not load correctly when clicking the link. The link displays where it should, with the text that it should, but when clicking the link i get this message in my browser:

runserver shows this output in the terminal:

Django Version 5.0.6 Python Version 3.12.4

Here is the relevant code i have so far in the project for reference:

I've reviewed my code against the code in the book, as well as the related github page, to see if maybe there were syntax errors, typos, updated code on his github, etc. and everything looks correct. When i enter the URL in my browser it brings me to the correct page, but the link seems to be adding in all the text, including curly braces, that is referenced. The quotations seem to be properly formatted and matched up. A google search did not proved any seemingly relevant answers, more of what i found seemed to address the problem of getting braces into the output, rather than keeping them from 'leaking' into it. I can't find any real difference between how other links/urls were added to the project and this one. everything else works as expected.

• 1 I don't see any code like {url 'learning_logs:edit_entry' entry.id} in the code you've shared. Please ensure that you share a minimal reproducible example with us that we can run to reproduce your problem. Some debugging steps you should follow: Do you have any unsaved file? If so you should save them. Check if you have any on click handlers attached to that anchor you're clicking. (Potentially by JavaScript code you're not showing to us?) –  Abdul Aziz Barkat Commented 2 days ago
• maybe topic.id is not what you think it is. print it before rendering –  folen gateis Commented 2 days ago

Most likely you have this in one of your templates <a href="{url 'learning_logs:edit_entry' entry.id}">your link</a> .

You should replace that with <a href="{% url 'learning_logs:edit_entry' entry.id %}">your link</a> , then it should all work again :)

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