I&#39;m not sure this will solve your problem, but you can pass `--durations=N` to print the slowest `N` tests after the test suite finishes.

Use `--durations=0` to print all.

You can pass the number with `--durations`

    pytest --durations=0 — Show all times for tests and setup and teardown
    
    pytest --durations=1 — Just show me the slowest
    
    pytest --durations=50 — Slowest 50, with times, … etc

Take refer in: https://medium.com/@brianokken/pytest-durations-0-show-all-times-for-tests-and-setup-and-teardown-848dccac85db

Or: https://docs.pytest.org/en/latest/usage.html#profiling-test-execution-duration

If I have a mysql database running on some host machine, and that host is also running a docker container: How would I access the mysql database from within the docker container that is running on the host?.

For instance, is there a way to publish a hosts port to the container (the inverse of what docker run -p does)?

Access host database from a docker container

It is very unclear for me in which case I would want to use a value receiver instead of always using a pointer receiver.   

To recap from the docs:

    type T struct {
    	a int
    }
    func (tv  T) Mv(a int) int         { return 0 }  // value receiver
    func (tp *T) Mp(f float32) float32 { return 1 }  // pointer receiver

The **docs** also say &quot;For types such as basic types, slices, and small structs, a value receiver is very cheap so unless the semantics of the method requires a pointer, a value receiver is efficient and clear.&quot;

**First point** they docs say a value receiver is &quot;very cheap&quot;, but the question is whether it is cheaper than a pointer receiver. So I made a small benchmark [(code on gist)][1] which showed me, that pointer receiver is faster even for a struct that has only one string field. These are the results:

    // Struct one empty string property
    BenchmarkChangePointerReceiver  2000000000               0.36 ns/op
    BenchmarkChangeItValueReceiver  500000000                3.62 ns/op


    // Struct one zero int property
    BenchmarkChangePointerReceiver  2000000000               0.36 ns/op
    BenchmarkChangeItValueReceiver  2000000000               0.36 ns/op

_(Edit: Please note that second point became invalid in newer go versions, see comments.)_

**Second point** the docs say that a value receiver it is &quot;efficient and clear&quot; which is more a matter of taste, isn&#39;t it? Personally I prefer consistency by using the same thing everywhere. Efficiency in what sense? Performance wise it seems pointer are almost always more efficient. Few test-runs with one int property showed minimal advantage of Value receiver (range of 0.01-0.1 ns/op)

Can someone tell me a case where a value receiver clearly makes more sense than a pointer receiver? Or am I doing something wrong in the benchmark? Did I overlook other factors?


  [1]: https://gist.github.com/chrisport/f28674850ffc3d9238f8

Value receiver vs. pointer receiver

I am running unit tests on a CI server using py.test. Tests use external resources fetched over network. Sometimes test runner takes too long, causing test runner to be aborted. I cannot repeat the issues locally.

Is there a way to make py.test print out execution times of (slow) test, so pinning down problematic tests become easier?

Printing test execution times and pinning down slow tests with py.test

<p>I am running unit tests on a CI server using py.test. Tests use external resources fetched over network. Sometimes test runner takes too long, causing test runner to be aborted. I cannot repeat the issues locally.</p>
<p>Is there a way to make py.test print out execution times of (slow) test, so pinning down problematic tests become easier?</p>


I&#39;m having trouble installing a Python package on my Windows machine, and would like to install it with Christoph Gohlke&#39;s Window binaries. (Which, to my experience, alleviated much of the fuss for many other package installations). However, only .whl files are available.

http://www.lfd.uci.edu/~gohlke/pythonlibs/#jpype

But how do I install .whl files?

Notes
----

- I&#39;ve found [documents on wheel](https://wheel.readthedocs.org/en/latest/), but they don&#39;t seem so staightforward in explaining how to install .whl files.
- This question is a duplicate with [this question](https://stackoverflow.com/questions/27041264/how-to-install-whl-file-in-python-windows), which wasn&#39;t directly answered.

How do I install a Python package with a .whl file?

I have the next code:

    users_table = Table(users_table_name, connection=Core.aws_dynamodb_connection)
    users_table.put_item(data={
      &quot;login&quot;: login,
      &quot;password&quot;: hashlib.sha256(password.encode(&quot;utf-8&quot;)).hexdigest(),
      &quot;profile&quot;: profile,
      &quot;registration_date&quot;: datetime.now() # PROBLEM IS HERE
    })

But when I run it, it fails with error:

&gt; TypeError: Unsupported type &quot;&lt; type &#39;datetime.datetime&#39; &gt;&quot; for value &quot;2015-01-12 05:02:57.053131&quot;

I&#39;ve tried a lot of ways, but it seems that it isn&#39;t possible to save `datetime` to DynamoDB. Btw it works fine in MongoDB.

Is there any solution?

Is it possible to save datetime to DynamoDB?

I&#39;ve been reading a tab-delimited data file in Windows with Pandas/Python without any problems. The data file contains notes in first three lines and then follows with a header. 

    df = pd.read_csv(myfile,sep=&#39;\t&#39;,skiprows=(0,1,2),header=(0))

I&#39;m now trying to read this file with my Mac. (My first time using Python on Mac.) I get the following error.

    pandas.parser.CParserError: Error tokenizing data. C error: Expected 1
    fields in line 8, saw 39

If set the *error_bad_lines* argument for *read_csv* to *False*, I get the following information, which continues until the end of the last row.

    Skipping line 8: expected 1 fields, saw 39
    Skipping line 9: expected 1 fields, saw 125
    Skipping line 10: expected 1 fields, saw 125
    Skipping line 11: expected 1 fields, saw 125
    Skipping line 12: expected 1 fields, saw 125
    Skipping line 13: expected 1 fields, saw 125
    Skipping line 14: expected 1 fields, saw 125
    Skipping line 15: expected 1 fields, saw 125
    Skipping line 16: expected 1 fields, saw 125
    Skipping line 17: expected 1 fields, saw 125
    ...

Do I need to specify a value for the *encoding* argument? It seems as though I shouldn&#39;t have to because reading the file works fine on Windows.

Reading tab-delimited file with Pandas - works on Windows, but not on Mac

Suppose I have a DataFrame with some `NaN`s:

    &gt;&gt;&gt; import pandas as pd
    &gt;&gt;&gt; df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
    &gt;&gt;&gt; df
        0   1   2
    0   1   2   3
    1   4 NaN NaN
    2 NaN NaN   9

What I need to do is replace every `NaN` with the first non-`NaN` value in the same column above it. It is assumed that the first row will never contain a `NaN`. So for the previous example the result would be

       0  1  2
    0  1  2  3
    1  4  2  3
    2  4  2  9

I can just loop through the whole DataFrame column-by-column, element-by-element and set the values directly, but is there an easy (optimally a loop-free) way of achieving this?

How to replace NaNs by preceding or next values in pandas DataFrame?

This is my python code:

    import subprocess
    subprocess.check_output(&quot;ls&quot;,shell=True,stderr=subprocess.STDOUT)

    import subprocess
    subprocess.check_output(&quot;yum&quot;,shell=True,stderr=subprocess.STDOUT)

The first `.check_output()` works well, but the second returns this:

    Traceback (most recent call last):
    File &quot;/usr/lib/x86_64-linux-gnu/gedit/plugins/pythonconsole/console.py&quot;, line 378, in __run
    r = eval(command, self.namespace, self.namespace)
    File &quot;&lt;string&gt;&quot;, line 1, in &lt;module&gt;
    File &quot;/usr/lib/python3.4/subprocess.py&quot;, line 616, in check_output
    raise CalledProcessError(retcode, process.args, output=output)
    subprocess.CalledProcessError: Command &#39;yum&#39; returned non-zero exit status 1

Why does this happen? Is it because `ls` is the original shell command but `yum` is the new package? How can I solve this problem? 

Subprocess check_output returned non-zero exit status 1

In unittest, I can setUp variables in a class and then the methods of this class can chose whichever variable it wants to use...

    class test_class(unittest.TestCase):
        def setUp(self):        
            self.varA = 1
            self.varB = 2
            self.varC = 3
            self.modified_varA = 2

        def test_1(self):
            do_something_with_self.varA, self.varB

        def test_2(self):
            do_something_with_self_modified_varA, self.varC

So in unittest, it was easy to put bunch of tests together that could go under one class and then use many different variables (varA and varB) for different methods. In pytest, I created a fixture in conftest.py instead of a class in unittest, like this...

    @pytest.fixture(scope=&quot;module&quot;)
    def input1():
        varA = 1
        varB = 2
        return varA, varB

    @pytest.fixture(scope=&quot;module&quot;)
    def input2():
        varA = 2
        varC = 3
        return varA, varC

I feed this input1 and input2 to my functions in a different file (let&#39;s say test_this.py) for two different functions. Here are the questions based on information above...

1. Since I can&#39;t just declare local variables in conftest.py as I can&#39;t simply import this file. Is there a better way of declaring different variables here that can be used in different functions in test_this.py ? I have five different configurations in my actual testing for these variables, defining that many different fixtures in conftest.py and use them as function argument in five different functions in test_this.py sounds painful, I would rather go back to unittest class structure, define my variables and pick and choose what I want

2. Should I just declare global variables in test_this.py and use them in the functions the way I want ? Seems a bit not pythonic. This variables are only used by the functions in this file.

3. Let&#39;s say I have test_that.py and test_them.py as well. If I have some shared variables between these different files, how would I declare them ? just create a file calle variables.py in the directory where all these test files are and do an import whenever I need ? This way I can keep all data in a separate.

4. Is it my impression that pytest discourages using a class to organize your functions ? Every example I read online, it all seem to employ bunch of functions with fixtures only. What is a configuration of defining class and methods and organize tests in pytest ?

5. I have a test scenario where I have to use result of one function into another. With pytest, I have an assert that is at the end of a function not a return so I won&#39;t be able to use this function  as a fixture. How do I accomplish this ? I know this is not a good practice that my one test relies on another but is there a work around ?

Thanks in advance for your answers.



unittest Vs pytest

Testing function I need to pass parameters and see the output matches the expected output.   

It is easy when function&#39;s response is just a small array or a one-line string which can be defined inside the test function, but suppose function I test modifies a config file which can be huge. Or the resulting array is something 4 lines long if I define it explicitly. Where do I store that so my tests remain clean and easy to maintain?

Right now if that is string I just put a file near the `.py` test and do `open()` it inside the test:

    def test_if_it_works():
        with open(&#39;expected_asnwer_from_some_function.txt&#39;) as res_file:
            expected_data = res_file.read()
        input_data = ... # Maybe loaded from a file as well
        assert expected_data == if_it_works(input_data)

I see many problems with such approach, like the problem of maintaining this file up to date. It looks bad as well.
I can make things probably better moving this to a fixture:

    @pytest.fixture
    def expected_data()
        with open(&#39;expected_asnwer_from_some_function.txt&#39;) as res_file:
            expected_data = res_file.read()
        return expected_data

    @pytest.fixture
    def input_data()
        return &#39;1,2,3,4&#39;

    def test_if_it_works(input_data, expected_data):
        assert expected_data == if_it_works(input_data)

That just moves the problem to another place and usually I need to test if function works in case of empty input, input with a single item or multiple items, so I should create one big fixture including all three cases or multiple fixtures. In the end code gets quite messy.

If a function expects a complicated dictionary as an input or gives back the dictionary of the same huge size test code becomes ugly:

     @pytest.fixture
     def input_data():
         # It&#39;s just an example
         return {[&#39;one_value&#39;: 3, &#39;one_value&#39;: 3, &#39;one_value&#39;: 3,
         &#39;anotherky&#39;: 3, &#39;somedata&#39;: &#39;somestring&#39;], 
          [&#39;login&#39;: 3, &#39;ip_address&#39;: 32, &#39;value&#39;: 53, 
          &#39;one_value&#39;: 3], [&#39;one_vae&#39;: 3, &#39;password&#39;: 13, &#39;lue&#39;: 3]}

It&#39;s quite hard to read tests with such fixtures and keep them up to date.

### Update

After searching a while I found a library which solved a part of a problem when instead of big config files I had large HTML responses. It&#39;s [betamax](https://github.com/sigmavirus24/betamax).

For easier usage I created a fixture:
    
    from betamax import Betamax

    @pytest.fixture
    def session(request):
        session = requests.Session()
        recorder = Betamax(session)
        recorder.use_cassette(os.path.join(os.path.dirname(__file__), &#39;fixtures&#39;, request.function.__name__)
        recorder.start()
        request.addfinalizer(recorder.stop)
        return session

So now in my tests I just use the `session` fixture and every request I make is being serialized automatically to the `fixtures/test_name.json` file so the next time I execute the test instead of doing a real HTTP request library loads it from the filesystem:

    def test_if_response_is_ok(session):
       r = session.get(&quot;http://google.com&quot;)

It&#39;s quite handy because in order to keep these fixtures up to date I just need to clean the `fixtures` folder and rerun my tests.

Pytest where to store expected data

Suppose there are two packages in a project: `some_package` and `another_package`.

    # some_package/foo.py:
    def bar():
        print(&#39;hello&#39;)

&lt;!-- --&gt;

    # another_package/function.py
    from some_package.foo import bar

    def call_bar():
        # ... code ...
        bar()
        # ... code ...

I want to test `another_package.function.call_bar` mocking out `some_package.foo.bar` because it has some network I/O I want to avoid. 

Here is a test:

    # tests/test_bar.py
    from another_package.function import call_bar

    def test_bar(monkeypatch):
        monkeypatch.setattr(&#39;some_package.foo.bar&#39;, lambda: print(&#39;patched&#39;))
        call_bar()
        assert True

To my surprise it outputs `hello` instead of `patched`. I tried to debug this thing, putting an IPDB breakpoint in the test. When I manually import `some_package.foo.bar` after the breakpoint and call `bar()` I get `patched`.

On my real project the situation is even more interesting. If I invoke pytest in the project root my function isn&#39;t patched, but when I specify `tests/test_bar.py` as an argument - it works.

As far as I understand it has something to do with the `from some_package.foo import bar` statement. If it&#39;s being executed before monkeypatching is happening then patching fails. But on the condensed test setup from the example above patching does not work in both cases.

And why does it work in IPDB REPL after hitting a breakpoint?

Pytest monkeypatch isn&#39;t working on imported function

**Question**

How can I import helper functions in test files without creating packages in the `test` directory?

&lt;br /&gt;
**Context**

I&#39;d like to create a test helper function that I can import in several tests.  Say, something like this:

    # In common_file.py
    
    def assert_a_general_property_between(x, y):
        # test a specific relationship between x and y
        assert ...


    # In test/my_test.py

    def test_something_with(x):
        some_value = some_function_of_(x)
        assert_a_general_property_between(x, some_value)

*Using Python 3.5, with py.test 2.8.2*

&lt;br /&gt;  

**Current &quot;solution&quot;**

I&#39;m currently doing this via importing a module inside my project&#39;s `test` directory (which is now a package), but I&#39;d like to do it with some other mechanism if possible (so that my `test` directory doesn&#39;t have packages but just tests, and the tests can be run on an installed version of the package, as is recommended [here in the py.test documentation on good practices](http://pytest.org/latest/goodpractises.html#choosing-a-test-layout-import-rules)).




Create and import helper functions in tests without creating packages in test directory using py.test

I recently discovered `pytest`. It seems great. However, I feel the documentation could be better.

I&#39;m trying to understand what `conftest.py` files are meant to be used for.

In my (currently small) test suite I have one `conftest.py` file at the project root. I use it to define the fixtures that I inject into my tests.

I have two questions:

1. Is this the correct use of `conftest.py`? Does it have other uses?
2. Can I have more than one `conftest.py` file? When would I want to do that? Examples will be appreciated.

More generally, how would you define the purpose and correct use of `conftest.py` file(s) in a py.test test suite?

Content Type	Original Author	Original Content on Stackoverflow
Question	Mikko Ohtamaa	View Question on Stackoverflow
Solution 1 - Python	Bruno Oliveira	View Answer on Stackoverflow
Solution 2 - Python	Phuong	View Answer on Stackoverflow

Printing test execution times and pinning down slow tests with py.test

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Value receiver vs. pointer receiver

Access host database from a docker container

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