Just tried the following with success.

In your project folder:

    pipenv install ipykernel
    pipenv shell
This will bring up a terminal in your virtualenv like this:

    (my-virtualenv-name) bash-4.4$
In that shell do:

    python -m ipykernel install --user --name=my-virtualenv-name
Launch jupyter notebook:

    jupyter notebook
In your notebook, Kernel -&gt; Change Kernel. Your kernel should now be an option.

[![Change Kernel Screenshot][1]][1]

Source: [IPythonNotebookVirtualenvs][2]


  [1]: https://i.stack.imgur.com/htimC.png
  [2]: https://help.pythonanywhere.com/pages/IPythonNotebookVirtualenvs/

Install and start jupyter inside pipenv:
    
    pipenv install jupyter
    pipenv run jupyter notebook

Any other packages that are installed via pipenv (e.g. `pipenv install numpy`) will also be available to your jupyter notebook session.

Luis&#39; answer works perfectly for jupyter notebooks.

But for hydrogen/atom specifically the recipe is:

    pipenv install ipykernel
    pipenv shell

launch atom from within the pipenv shell

    &gt; atom

Should be good to go!
    

I have a .NET Core 2.0 app and have a problem with authorization. I want to use custom authorization with special requests. Header and standard default authentication.
First, I add configuration in `Startup.cs`:

    public IServiceProvider ConfigureServices(IServiceCollection services)
    {
        // ...
        services.AddAuthorization(options =&gt;
    	{
    		options.AddPolicy(DefaultAuthorizedPolicy, policy =&gt;
    		{
    			policy.Requirements.Add(new TokenAuthRequirement());
    		});
    	});
        services.AddSingleton&lt;IAuthorizationHandler, AuthTokenPolicy&gt;();
        // ...
    }

`AuthTokenPolicy.cs`:

    public class AuthTokenPolicy : AuthorizationHandler&lt;TokenAuthRequirement&gt;
	{	
		protected override Task HandleRequirementAsync(AuthorizationHandlerContext context, TokenAuthRequirement requirement)
		{
			var filterContext = context.Resource as AuthorizationFilterContext;
			var response = filterContext.HttpContext.Response;
			try
			{
				// some validation code

				var isValidToken = isValidTokenTask.Result;
				if (!isValidToken)
				{
					response.StatusCode = 401;
					return Task.CompletedTask;
				}

				response.StatusCode = 200;
				context.Succeed(requirement);
			}
			catch (Exception)
			{
				return Task.CompletedTask;
			}
			return Task.CompletedTask;
		}
	}
and in `HomeController.cs`:

    [Authorize(Policy = Startup.DefaultAuthorizedPolicy)]
    public async Task&lt;IActionResult&gt; IsVisible()

If I use the wrong request.header in `AuthTokenPolicy` I see it, but in the logs I see this error:

&gt; System.InvalidOperationException: No authenticationScheme was specified, and there was no DefaultChallengeScheme found.\r\n   at Microsoft.AspNetCore.Authentication.AuthenticationService.&lt;ChallengeAsync&gt;d__11.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Mvc.ChallengeResult.&lt;ExecuteResultAsync&gt;d__14.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Mvc.Internal.ResourceInvoker.&lt;InvokeResultAsync&gt;d__19.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Mvc.Internal.ResourceInvoker.&lt;InvokeFilterPipelineAsync&gt;d__17.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Mvc.Internal.ResourceInvoker.&lt;InvokeAsync&gt;d__15.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Builder.RouterMiddleware.&lt;Invoke&gt;d__4.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Diagnostics.StatusCodePagesMiddleware.&lt;Invoke&gt;d__3.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at React.AspNet.BabelFileMiddleware.&lt;Invoke&gt;d__5.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at Microsoft.AspNetCore.Authentication.AuthenticationMiddleware.&lt;Invoke&gt;d__6.MoveNext()\r\n--- End of stack trace from previous location where exception was thrown ---\r\n   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()\r\n   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)\r\n   at core.common.Middleware.LoggingMiddleware.&lt;Invoke&gt;d__3.MoveNext() in D:\\Dev\\microservicePDP\\Template\\core.common\\Middleware\\LoggingMiddleware.cs:line 72

After reading [Migrating Authentication and Identity to ASP.NET Core 2.0][1] I&#39;ve added this code in startup.cs


  [1]: https://docs.microsoft.com/en-us/aspnet/core/migration/1x-to-2x/identity-2x
Quotation from the article : 

    services.AddAuthentication(options =&gt; 
    {
        options.DefaultScheme = CookieAuthenticationDefaults.AuthenticationScheme;
        options.DefaultChallengeScheme = OpenIdConnectDefaults.AuthenticationScheme;
    });

&gt; Define a default scheme in 2.0 if one of the following conditions is true:
You want the user to be automatically signed in
You use the [Authorize] attribute or authorization policies without specifying schemes

I added `AuthenticationScheme` and `DefaultChallengeScheme` in `ConfigureServices()`. It didn&#39;t help, the same error here. I&#39;ve tried to use `app.UseAuthentication();` in the `Startup.Configure()` method, with no results.

How can I use a custom authorization without authentication?

No authenticationScheme was specified, and there was no DefaultChallengeScheme found with default authentification and custom authorization

I have searched a lot but not found the crystal clear answer for the questions:

1. What is the difference between MediatorLiveData and MutableLiveData?

2. What are the suitable condition to use either of them.

What is difference between MediatorLiveData and MutableLiveData in MVVM

Is there a way to use pipenv with Jupyter notebook?

Or more specifically, with an atom nteract/hydrogen python 3 kernel?



Is there a way to use pipenv with Jupyter notebook?

<p>Is there a way to use pipenv with Jupyter notebook?</p>
<p>Or more specifically, with an atom nteract/hydrogen python 3 kernel?</p>


I am trying to grasp what TimeDistributed wrapper does in Keras.

I get that TimeDistributed &quot;applies a layer to every temporal slice of an input.&quot;

But I did some experiment and got the results that I cannot understand.

In short, in connection to LSTM layer, TimeDistributed and just Dense layer bear same results.

    model = Sequential()
    model.add(LSTM(5, input_shape = (10, 20), return_sequences = True))
    model.add(TimeDistributed(Dense(1)))
    print(model.output_shape)

    model = Sequential()
    model.add(LSTM(5, input_shape = (10, 20), return_sequences = True))
    model.add((Dense(1)))
    print(model.output_shape)

For both models, I got output shape of **(None, 10, 1)**.

Can anyone explain the difference between TimeDistributed and Dense layer after an RNN layer?



What is the role of TimeDistributed layer in Keras?

I get **ValueError: cannot convert float NaN to integer** for following:

    df = pandas.read_csv(&#39;zoom11.csv&#39;)
    df[[&#39;x&#39;]] = df[[&#39;x&#39;]].astype(int)

 * The &quot;x&quot; is a column in the csv file, I cannot spot any *float NaN* in the file, and I don&#39;t understand the error or why I am getting it. 
 * When I read the column as String, then it has values like -1,0,1,...2000, all look very nice int numbers to me.
 * When I read the column as float, then this can be loaded. Then it shows values as -1.0,0.0 etc, still there are no any NaN-s
 * I tried with *error_bad_lines = False* and dtype parameter in read_csv to no avail. It just cancels loading with same exception.
 * The file is not small (10+ M rows), so cannot inspect it manually, when I extract a small header part, then there is no error, but it happens with full file. So it is something in the file, but cannot detect what.
 * Logically the csv should not have missing values, but even if there is some garbage then I would be ok to skip the rows. Or at least identify them, but I do not see way to scan through file and report conversion errors.

Update: Using the hints in comments/answers I got my data clean with this:

    # x contained NaN
    df = df[~df[&#39;x&#39;].isnull()]

    # Y contained some other garbage, so null check was not enough
    df = df[df[&#39;y&#39;].str.isnumeric()]
    
    # final conversion now worked
    df[[&#39;x&#39;]] = df[[&#39;x&#39;]].astype(int)
    df[[&#39;y&#39;]] = df[[&#39;y&#39;]].astype(int)


Pandas: ValueError: cannot convert float NaN to integer

I want to store numpy array into to another numpy array

I am using `np.concatenate`

This is my code

    x=np.concatenate(x,s_x)

These are the type and the shape of `x and s_x`

    Type of s_x: &lt;class &#39;numpy.ndarray&#39;&gt;, Shape of s_x: (173,)
    Type of x: &lt;class &#39;numpy.ndarray&#39;&gt; (0,), Shape of x: (0,)

This is the error being displayed

    TypeError: only integer scalar arrays can be converted to a scalar index

1D numpy concatenate: TypeError: only integer scalar arrays can be converted to a scalar index

I use Visual Studio Code to write Python code with Pylint.

When I press &lt;kbd&gt;Ctrl&lt;/kbd&gt; + &lt;kbd&gt;S&lt;/kbd&gt; (save), the editor wraps a long line into multiple short lines. How do I disable the action or configure wrap column count to 120 (default is 80)?

I have tried ` &quot;python.linting.pylintArgs&quot;: [&quot;--max-line-length=120&quot;]` and `&quot;editor.wordWrapColumn&quot;: 120`, but it didn&#39;t work.





Disable auto wrap long line in Visual Studio Code

Lets say I have following `pandas` `DataFrame`:

    import pandas as pd
    df = pd.DataFrame({&quot;A&quot;:[1,pd.np.nan,2], &quot;B&quot;:[5,6,0]})

Which would look like:

    &gt;&gt;&gt; df
         A  B
    0  1.0  5
    1  NaN  6
    2  2.0  0

First option
------------

I know one way to check if a particular value is `NaN`, which is as follows:

    &gt;&gt;&gt; df.isnull().ix[1,0]
    True

Second option (not working)
-------------

I thought below option, using `ix`, would work as well, but it&#39;s not:

    &gt;&gt;&gt; df.ix[1,0]==pd.np.nan
    False

I also tried `iloc` with same results:

    &gt;&gt;&gt; df.iloc[1,0]==pd.np.nan
    False

However if I check for those values using `ix` or `iloc` I get:

    &gt;&gt;&gt; df.ix[1,0]
    nan
    &gt;&gt;&gt; df.iloc[1,0]
    nan

So, **why is the second option not working?** Is it possible to check for `NaN` values using `ix` or `iloc`?


Checking if particular value (in cell) is NaN in pandas DataFrame not working using ix or iloc

I have some asyncio code which runs fine in the Python interpreter (CPython 3.6.2). I would now like to run this inside a Jupyter notebook with an IPython kernel.

I can run it with

    import asyncio
    asyncio.get_event_loop().run_forever()

and while that seems to work it also seems to block the notebook and doesn&#39;t seem to play nice with the notebook.

My understanding is that Jupyter uses Tornado under the hood so I tried to [install a Tornado event loop as recommended in the Tornado docs](http://www.tornadoweb.org/en/stable/asyncio.html): 

    from tornado.platform.asyncio import AsyncIOMainLoop
    AsyncIOMainLoop().install()

However that gives the following error:

    ---------------------------------------------------------------------------
    AssertionError                            Traceback (most recent call last)
    &lt;ipython-input-1-1139449343fc&gt; in &lt;module&gt;()
          1 from tornado.platform.asyncio import AsyncIOMainLoop
    ----&gt; 2 AsyncIOMainLoop().install()
    
    ~\AppData\Local\Continuum\Anaconda3\envs\numismatic\lib\site- packages\tornado\ioloop.py in install(self)
        179         `IOLoop` (e.g.,     :class:`tornado.httpclient.AsyncHTTPClient`).
        180         &quot;&quot;&quot;
    --&gt; 181         assert not IOLoop.initialized()
        182         IOLoop._instance = self
        183 
    
    AssertionError: 

Finally I found the following page: http://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Asynchronous.html

so I added a cell with the following code:

    import asyncio
    from ipykernel.eventloops import register_integration
    
    @register_integration(&#39;asyncio&#39;)
    def loop_asyncio(kernel):
        &#39;&#39;&#39;Start a kernel with asyncio event loop support.&#39;&#39;&#39;
        loop = asyncio.get_event_loop()
    
        def kernel_handler():
            loop.call_soon(kernel.do_one_iteration)
            loop.call_later(kernel._poll_interval, kernel_handler)
    
        loop.call_soon(kernel_handler)
        try:
            if not loop.is_running():
                loop.run_forever()
        finally:
            loop.run_until_complete(loop.shutdown_asyncgens())
            loop.close()

and in the next cell I ran:

    %gui asyncio

That worked but I don&#39;t really understand why and how it works. Can someone please explain that to me?

How do I run Python asyncio code in a Jupyter notebook?

I&#39;m on a windows machine without admin right and I would like to run jupyter on chrome, while the default browser is another.

I have a local installation of the Anaconda distribution and my first option to start jupyter would be through the Anaconda Navigator, but maybe I have to do something else. Because it is a local installation the command line `jupyter notebook` produces no results.

When I paste the url address in the default browser I have (something like `http://localhost:8892/notebooks/Home/Exercices/Testing1.ipynb` the chrome page asks me for a password or token. I have no password and I do not know what a token is.

Is there a way to change the browser of the Anaconda Navigator? or how can I start jupyter with Chrome? 

How to change the default browser used by jupyter notebook in windows

A super basic question: why is the following not rendering in Markdown - which happens to be in a `jupyter` notebook

**Raw code**

    ### Results
    
    | --- | --- | --- |
    | Stretch/Untouched | ProbDistribution | Accuracy |
    | --- | --- | --- |
    | Stretched | Gaussian | .843 |


**Code as it looks in `jupyter` in edit mode**

[![enter image description here][1]][1]

**Rendering in `jupyter`**

[![enter image description here][2]][2]

So the table did *not* render properly

**Update** I did some twiddling and now it renders.. but **still uncertain why the original code did not work**

[![enter image description here][3]][3]


  [1]: https://i.stack.imgur.com/ZezAR.png
  [2]: https://i.stack.imgur.com/MbXEq.png
  [3]: https://i.stack.imgur.com/tyXde.png

Tables in Markdown (in Jupyter)

I use Jupyter notebook. I accidentally deleted a cell, and I can&#39;t go step back.

How can I recover the notebook cell?


How to recover deleted Jupyter notebook cell?

I want to remove a certain environment created with conda. How can I achieve that? Let&#39;s say I have an active testenv environment. I tried, by following [documentation][1], with:

    $ conda env remove

    CondaEnvironmentError: cannot remove current environment. deactivate and run conda remove again

I then deactivate it:

    $ source deactivate

I try running again the command to remove it and I still get the same error. What is going wrong here?


  [1]: https://conda.io/docs/commands/env/conda-env-remove.html

Removing Conda environment

I am learning Python virtual environment. In one of my small projects I ran

    pipenv run python myproject.py

and it created a virtualenv for me in `C:\Users\USERNAME\.virtualenvs`

I found it also created or modified some files under my project source code directory. I am just wondering how to cleanly delete this virtualenv and reverse my project back to a no-virtualenv state.

I am using python 3.6.4, and PyCharm.

How to remove a virtualenv created by &quot;pipenv run&quot;

My Ubuntu 16.04.03 is installed with Python 3.5.2. How do I setup pipenv to use Python 3.6 when my system does not have python 3.6?   

    $ pipenv --python 3.6
    Warning: Python 3.6 was not found on your system…
    You can specify specific versions of Python with:
      $ pipenv --python path/to/python



How to setup a pipenv Python 3.6 project if OS Python version is 3.5?

How can I deactivate my pipenv environment? 

With other tools I&#39;ve been able to do something like `source deactivate`, but that has no affect here.

Create an environment:

    pipenv --three

Activate the environment:

    source $(pipenv --venv)/bin/activate

But how to deactivate?

Deactivate a pipenv environment

I am using [`pipenv`](https://pipenv.kennethreitz.org/) to handle Python package dependencies.

The Python package is using two packages (named `pckg1` and `pckg2`) that rely on the same package named `pckg3`, **but from two different versions**. Showing the dependency tree :

    $ pipenv graph
      pckg1==3.0.0
        - pckg3 [required: &gt;=4.1.0]
      pckg2==1.0.2
        - pckg3 [required: ==4.0.11]

An attempt to install dependencies :

    $ pipenv install

    Warning: Your dependencies could not be resolved. You likely have a mismatch in your sub-dependencies.
    You can use $ pipenv install --skip-lock to bypass this mechanism, then run $ pipenv graph to inspect the situation.
    Hint: try $ pipenv lock --pre if it is a pre-release dependency.
    Could not find a version that matches pckg3==4.0.11,==4.1.0,&gt;=4.1.0 (from -r C:\Users\user\AppData\Local\Temp\pipenv-o7uxm080-requirements\pipenv-hwekv7dc-constraints.txt (line 2))
    Tried: 3.3.1, 3.3.2, 3.3.3, 3.4.0, 3.4.2, 4.0.0, 4.0.0, 4.0.1, 4.0.1, 4.0.2, 4.0.2, 4.0.3, 4.0.3, 4.0.4, 4.0.4, 4.0.6, 4.0.6, 4.0.8, 4.0.8, 4.0.9, 4.0.9, 4.0.10, 4.0.10, 4.0.11, 4.0.11, 4.1.0, 4.1.0, 4.1.1, 4.1.1, 4.1.2, 4.1.2, 4.2.1, 4.2.1, 4.3.0, 4.3.0
    There are incompatible versions in the resolved dependencies.

As suggested, `pip install --skip-lock` does the trick, but the dependency tree is still unresolved.

I would love to tell `Pipenv` to override `pckg2`&#39;s requirement, and specify `pckg3&gt;=4.1.0`.

How can this be resolved?

How to resolve Python package dependencies with pipenv?

The problem originates when I start by cloning a git project that uses pipenv, so it has a Pipfile + Pipfile.lock. I want to use a virtual environment with the project so I run `pipenv shell`. I now have a virtual environment created and I am inside the virtual environment. The project obviously has a lot of dependencies (listed in the Pipfile). I don&#39;t want to have to go through the list in the Pipfile one by one and install them using `pipenv install &lt;package_name&gt;`. Is there a pipenv/pip command that installs all the packages from a Pipfile I already have? Or maybe I need to set up the environment differently than running `pipenv shell`?

Content Type	Original Author	Original Content on Stackoverflow
Question	emehex	View Question on Stackoverflow
Solution 1 - Python	Luis Meraz	View Answer on Stackoverflow
Solution 2 - Python	dahe	View Answer on Stackoverflow
Solution 3 - Python	emehex	View Answer on Stackoverflow

Is there a way to use pipenv with Jupyter notebook?

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Solution 3 - Python

What is difference between MediatorLiveData and MutableLiveData in MVVM

No authenticationScheme was specified, and there was no DefaultChallengeScheme found with default authentification and custom authorization

Attributions