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Can pytest or any testing tool in python generate the unit test automatically?

I started writing pytest for the source code I have. For this I see the code and in test I check if that piece of code returns expected value.
Is there any way to automatically generate test cases for python code, positive and parameterized so that it doesn't take much time if the source code is huge

You can't generate test cases using Pytest, but you can use tools like hypothesis to randomise the particular values that your test cases are given. You still need to specify what cases you want to test though, as well as giving a definition for how it should generate the parameters.
Generally, computers aren't smart enough to know what we want a function to do, so there's no way they can tell whether we intentionally added certain behaviours into our code or whether they are just bugs. Tools like mypy and flake8 can help catch questionable code, but it's still up to you whether that code is correct or not - they can't know for certain.

python-hypothesis: Retrieving or reformatting a falsifying example

Is it possible to retrieve or reformat the falsifying example after a test failure? The point is to show the example data in a different format - data generated by the strategy is easy to work with in the code but not really user friendly, so I'm looking at how to display it in a different form. Even a post-mortem tool working with the example database would be enough, but there does not seem to be any API allowing that, or am I missing something?

You can call note to record additional information during a test, such as your own custom-formatted copy of the generated inputs.
When Hypothesis finds a falsifying example, it will also print out the notes that were recorded the execution of that particular example.

Even a post-mortem tool working with the example database would be enough, but there does not seem to be any API allowing that, or am I missing something?
The example database uses a private format and only records the choices a strategy made to generate the falsifying example, so there's no way to extract the data of the example short of re-running the test.
Stuart's recommendation of hypothesis.note(...) is a good one.

How do you test more complicated functions?

I'm a total amateur/hobbyist developer trying to learn more about testing the software I write. While I understand the core concept of testing, as the functions get more complicated, I feel as though it's a rabbit hole of varations, outcomes, conditions etc. For example...
The function below reads files from a directory into a Pandas DataFrame. A few columns adjustments are made before the data is passed to a different function that ultimately imports the data to our database.
I've already coded a test for the convert_date_string function. But what about this entire function as as whole - how do I write a test for it? In my mind, much of the Pandas library is already tested - thus making sure core functionality there works with my setup seems like a waste. But, maybe it isn't. Or, maybe this is a refactoring question to break this down into smaller parts?
Anyway, here is the code... any insight would be appreciated!
def process_file(import_id=None):
all_files = glob.glob(config.IMPORT_DIRECTORY + "*.txt")
if len(all_files) == 0:
return []
import_data = (pd.read_csv(f, sep='~', encoding='latin-1',
warn_bad_lines=True, error_bad_lines=False,
low_memory=False) for f in all_files)
data = pd.concat(import_data, ignore_index=True, sort=False)
data.columns = [col.lower() for col in data.columns]
data = data.where((pd.notnull(data)), None)
data['import_id'] = import_id
data['date'] = data['date'].apply(lambda x: convert_date_string(x))
insert_data_into_database(data=data, table='sales')
return all_files

There are mainly two kind of tests - proper unit tests, and integration tests.
Unit tests, as the name implies, test "units" of your program (functions, classes...) in isolation (without considering how they interact with other units). This of course require those units can be tested in isolation. For example, a pure function (a function that compute a results from it's inputs, where the result depends only on the inputs and will always be the same for the same inputs, and which doesn't have any side effect) is very easy to test, while a function that reads data from a hardcoded path on your filesystem, makes http requests to a hardcoded url and updates a database (whose connection data are also hardcoded) is almost impossible to test in isolation (and actually almost impossible to test).
So the first point is to write your code with testability in mind: favour small, focused units with a single clear responsability and as few dependencies as possible (and preferably taking their dependencies as arguments so you can pass a mock instead). This is of course a bit of a platonic ideal, but it's a worthy goal still. As a last resort, when you cannot get rid of dependencies or parameterize them, you can use a package like mock that will replace your dependencies with bogus objects having a similar interface.
Integration testing is about testing whole subsystems from a much higher level - for example for a website project, you may want to test that if you submit the "contact" form an email is sent to a given address and that the data are also stored in the database. You obviously want to do so with a disposable test database and a disposable test mailbox.
The function you posted is possibly doing a bit too much - it reads files, builds a panda dataframe, applies some processing, and stores thing in a database. You may want to try and factor it into more functions - one to get the files list, one to collect data from the files, one to process the data etc, you already have the one storing the data in the database - and rewrite your "process_files" (which is actually doing more than processing) to call those functions. This will make it easier to test each part in isolation. Once done with this, you can use mock to test the "process_file" functions and check that it calls the other functions with the expected arguments, or run it against a test directory and a test database and check the results in the database.

In general, I wouldn't go down the road of testing pandas or any other dependencies. The way I see it, it is important to make sure that a package that i use is well developed and well supported, then making tests for it will be redundant. Pandas is a very well supported package.
As to your question about the specific function and interest in testing in general, I will highly recommend checking out the Hypothesis python package (you'r in luck - its currently only for python). It provides mock data and generates edge cases for testing purposes.
an example from their docs:
from hypothesis import given
from hypothesis.strategies import text
#given(text())
def test_decode_inverts_encode(s):
assert decode(encode(s)) == s
here you tell it that the function needs to receive text as input, and the package will run it multiple times with different variables that answer the criteria. It will also try all kind of of edge cases.
It can do much more once implemented.

Is there an alternative result for Python unit tests, other than a Pass or Fail?

I'm writing unit tests that have a database dependency (so technically they're functional tests). Often these tests not only rely on the database to be live and functional, but they can also rely on certain data to be available.
For example, in one test I might query the database to retrieve sample data that I am going to use to test the update or delete functionality. If data doesn't already exist, then this isn't exactly a failure in this context. I'm only concerned about the pass/fail status of the update or delete, and in this situation we didn't even get far enough to test it. So I don't want to give a false positive or false negative.
Is there an elegant way to have the unit test return a 3rd possible result? Such as a warning?

In general I think the advice by Paul Becotte is best for most cases:
This is a failure though- your tests failed to set up the system in
the way that your test required. Saying "the data wasn't there, so it
is okay for this test to fail" is saying that you don't really care
about whether the functionality you are testing works. Make your test
reliably insert the data immediately before retrieving it, which will
give you better insight into the state of your system.
However, in my particular case, I am writing a functional test that relies on data generated and manipulated from several processes. Generating it quickly at the beginning of the test just isn't practical (at least yet).
What I ultimately found to work as I need it to use skipTest as mentioned here:
Skip unittest if some-condition in SetUpClass fails

How do I handle dependencies between scenarios in Lettuce?

I like using Lettuce to define test cases. In many cases, it's easy to write Lettuce scenarios in such a way that they can be run either atomically or as part of other scenarios in a feature. However, I find that Lettuce is also a useful tool to try and reason about and implement more complex integration tests. In these cases, it makes sense to break up the tests into scenarios, but define a dependency on a previous scenario. That way I can run a scenario without having to explicitly define which other scenarios need to be run. It also makes the dependency clear in the scenario definition. This might look something like this:
Scenario: Really long scenario
Given some condition
Given another condition
Then something
...
Scenario: A dependent scenario
Given the scenario "Really long scenario" has been run
Given new condition
Then some stuff
...
Then I could do something like:
#step('Given the scenario "([^"]*)" has been run')
def check_scenario(step, sentence):
scenario = get_scenario(sentence) # This what I don't know how to do
if not scenario.ran:
scenario.run()
How do you handle this situation? Are there any gotchas I'm missing with this approach? Taking a quick look through the API docs and the source code, it didn't seem like there was an easy way to retrieve a scenario by it's string.

The only thing I know about is defining new steps which call previously defined steps: Check out the tutorial about that topic. Maybe this can be a good workaround for your problem.

You can use world in order to store data between your scenarios.
#before.each_feature
def feature_setup(feature):
...
world.feature_data = dict()
...
You can access this data from everywhere you have access to world
You can mix it with your terrain.py file in order to save data between steps, scenarios, features.