You need to pass the arrays as an iterable (a tuple or list), thus the correct syntax is

    x=np.concatenate((x, s_x))

Warning: `leading or trailing horizontal alignment before iOS 11`

I am getting above warning during compilation on Xcode 9.1 on one of the scenes in a storyboard file. There are other storyboards *(with deployment target iOS 10.0)* and yet the warning is shown to this specific scene on a specific Storyboard file.

The warning gets suppressed if I change deployment target to iOS 11.0 on the scene where warning is shown but I don&#39;t want to do that.

Has anyone come across this case?

leading or trailing horizontal alignment before iOS 11

**Issue**: Can not stop docker containers, whenever I try to stop containers I get the following Error message,

    ERROR: for yattyadocker_web_1  cannot stop container: 1f04148910c5bac38983e6beb3f6da4c8be3f46ceeccdc8d7de0da9d2d76edd8: Cannot kill container 1f04148910c5bac38983e6beb3f6da4c8be3f46ceeccdc8d7de0da9d2d76edd8: rpc error: code = PermissionDenied desc = permission denied

**OS Version/build:** Ubuntu 16.04 | Docker Version 17.09.0-ce, build afdb6d4 | Docker Compose version 1.17.1, build 6d101fb

**Steps to reproduce:**

- Created a rails project with Dockerfile and docker-compose.yml. docker-compose.yml is of version 3.
- Image is built successfully with either `docker build -t &lt;project name&gt; . ` or `docker-compose up --build`
- Containers boots up and runs successfully.
- Try to stop docker compose with docker-compose down.

**What I tried:**: 

- I have to run `sudo service docker restart` and then the containers can be removed.
- Uninstalled docker, removed docker directory and then re installed everything. Still facing same issue. 

**Note**: This configuration was working correctly earlier, but somehow file permissions might have changed and I am seeing this error. I have to run `sudo service docker restart` and then the containers can be removed. But this is highly inconvenient and I don&#39;t know how to troubleshoot this.


**Reference Files:**

    # docker-compose.yml
    version: &#39;3&#39;
    volumes:
      db-data:
        driver: local
      redis-data:
        driver: local  
    services:
      db:
        image: postgres:9.4.1
        volumes:
          - db-data:/var/lib/postgresql/data
        ports:
          - &quot;5432:5432&quot;
        env_file: local_envs.env
      web:
        image: yattya_docker:latest
        command: bundle exec puma -C config/puma.rb
        tty: true
        stdin_open: true
        ports:
          - &quot;3000:3000&quot;
        links:
          - db
          - redis
          - memcached
        depends_on:
          - db
          - redis
          - memcached
        env_file: local_envs.env
      redis:
        image: redis:3.2.4-alpine
        ports:
          # We&#39;ll bind our host&#39;s port 6379 to redis&#39;s port 6379, so we can use
          # Redis Desktop Manager (or other tools) with it:
          - 6379:6379
        volumes:
          # We&#39;ll mount the &#39;redis-data&#39; volume into the location redis stores it&#39;s data:
          - redis-data:/var/lib/redis
        command: redis-server --appendonly yes
      memcached:
        image: memcached:1.5-alpine
        ports:
          - &quot;11211:11211&quot;
      clock:
        image: yattya_docker:latest
        command: bundle exec clockwork lib/clock.rb
        links:
          - db
        depends_on:
          - db
        env_file: local_envs.env
      worker:
        image: yattya_docker:latest
        command: bundle exec rake jobs:work
        links: 
          - db
        depends_on: 
          - db
        env_file: local_envs.env

And Dockerfile:

    # Dockerfile
    FROM ruby:2.4.1
    
    RUN apt-get update &amp;&amp; apt-get install -y nodejs --no-install-recommends &amp;&amp; rm -rf /var/lib/apt/lists/*
    
    ENV APP_HOME /app
    RUN mkdir -p $APP_HOME
    WORKDIR $APP_HOME
    
    ADD Gemfile* $APP_HOME/
    RUN bundle install
    
    ADD . $APP_HOME
    
    RUN mkdir -p ${APP_HOME}/log
    RUN cat /dev/null &gt; &quot;$APP_HOME/log/development.log&quot;
    
    RUN mkdir -p ${APP_HOME}/tmp/cache \
        &amp;&amp; mkdir -p ${APP_HOME}/tmp/pids \
        &amp;&amp; mkdir -p ${APP_HOME}/tmp/sockets
    
    EXPOSE 3000



Docker Containers can not be stopped or removed - permission denied Error

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 want to store numpy array into to another numpy array
I am using <code>np.concatenate</code>
This is my code
<pre><code class="hljs language-ini">x=np.concatenate(x,s_x)
</code></pre>
These are the type and the shape of <code>x and s_x</code>
<pre><code class="hljs language-python">Type of s_x: &#x3C;class 'numpy.ndarray'>, Shape of s_x: (173,)
Type of x: &#x3C;class 'numpy.ndarray'> (0,), Shape of x: (0,)
</code></pre>
This is the error being displayed
<pre><code class="hljs language-perl">TypeError: only integer scalar arrays can be converted to a scalar index
</code></pre>

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

When I tried to install python onto homebrew it downloaded it and then an error message popped up at the end that stopped it from completing. When I try to do it again it asks me to do:

    $ brew link python

After entering that the same error message appears:
&lt;pre&gt;permission denied @ dir_s_mkdir - /usr/local/lib&lt;/pre&gt;

I have tried to do:

    $ sudo chown -R $(whoami) /usr/local

And I get an error message that reads: 

&gt; chown: /usr/local: Operation not permitted


How to Fix Permissions on Home-brew on MacOS High Sierra

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?

When I want to typehint a tuple in Python like:

    def func(var: tuple[int, int]):
        # do something

    func((1, 2))    # would be fine
    func((1, 2, 3)) # would throw an error

It is required to give the exact number of items in the tuple. That&#39;s different from list typehinting:

    def func(var: list[int]):
        # do something

    func([1])       # would be fine
    func([1, 2])    # would also be fine
    func([1, 2, 3]) # would also be fine

That&#39;s consequentially, in a way, because of the type of tuples. Because they are designed not to be changed, you have to hardcode the amount of items in it.

So my question is, is there a way to make the number of items in a tuple type hint flexible? I tried something like that but it didn&#39;t work:

    def func(var: tuple[*int]):

TypeHinting tuples in Python

I have a simple question about the .shape function, which confused me a lot. 

    a = np.array([1, 2, 3])   # Create a rank 1 array
    print(type(a))            # Prints &quot;&lt;class &#39;numpy.ndarray&#39;&gt;&quot;
    print(a.shape)            # Prints &quot;(3,)&quot;
    
    b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array
    print(b.shape)                     # Prints &quot;(2, 3)&quot;

What did the .shape exactly do? count how many rows, how many columns,
then the a.shape suppose to be, (1,3), one row three columns, right?





what does numpy ndarray shape do?

What is the difference between `spread operator` and `array.concat()`

&lt;!-- begin snippet: js hide: false console: true babel: false --&gt;

&lt;!-- language: lang-js --&gt;

    let parts = [&#39;four&#39;, &#39;five&#39;];
    let numbers = [&#39;one&#39;, &#39;two&#39;, &#39;three&#39;];
    console.log([...numbers, ...parts]);

&lt;!-- end snippet --&gt;


`Array.concat()` function 


&lt;!-- begin snippet: js hide: false console: true babel: false --&gt;

&lt;!-- language: lang-js --&gt;

    let parts = [&#39;four&#39;, &#39;five&#39;];
    let numbers = [&#39;one&#39;, &#39;two&#39;, &#39;three&#39;];
    console.log(numbers.concat(parts));

&lt;!-- end snippet --&gt;

&gt;Both results are same. So, what kind of scenarios we want to use them? And which one is best for performance?

spread operator vs array.concat()

I have JS array with strings, for example:

    var strArray = [ &quot;q&quot;, &quot;w&quot;, &quot;w&quot;, &quot;e&quot;, &quot;i&quot;, &quot;u&quot;, &quot;r&quot;];

I need to compare for duplicate strings inside array, and if duplicate string exists, there should be alert box pointing to that string.

I was trying to compare it with `for` loop, but I don&#39;t know how to write code so that array checks its own strings for duplicates, without already pre-determined string to compare.


Checking for duplicate strings in JavaScript array

How to add indexes to array in query string?

I tried send data like this:

    axios.get(&#39;/myController/myAction&#39;, { params: { storeIds: [1,2,3] })

And I got this url:

    http://localhost/api/myController/myAction?storeIds[]=1&amp;storeIds[]=2&amp;storeIds[]=3

So, I should to get this url:

    http://localhost/api/myController/myAction?storeIds[0]=1&amp;storeIds[1]=2&amp;storeIds[2]=3

What I should add in my params options to get this url?

How to correctly use axios params with arrays

Is there any possible optimization for random access on a very big array (I currently use `uint8_t`, and I&#39;m asking about what&#39;s better)

    uint8_t MyArray[10000000];

when the value at any position in the array is 

 - **0** or **1** for *95%* of all cases, 
 - **2** in *4%* of cases,  
 - between **3** and **255** in
   the other *1%* of cases?

So, is there anything better than a `uint8_t` array to use for this? It should be as quick as possible to loop over the whole array in a random order, and this is very heavy on RAM bandwidth, so when having more than a few threads doing that at the same time for different arrays, currently the whole RAM bandwidth is quickly saturated.

I&#39;m asking since it feels very inefficient to have such a big array (10 MB) when it&#39;s actually known that almost all values, apart from 5%, will be either 0 or 1. So when 95% of all values in the array would only actually need 1 bit instead of 8 bit, this would reduce memory usage by almost an order of magnitude. 
It feels like there has to be a more memory efficient solution that would greatly reduce RAM bandwidth required for this, and as a result also be significantly quicker for random access.




Any optimization for random access on a very big array when the value in 95% of cases is either 0 or 1?

I would like to take an image and change the scale of the image, while it is a numpy array.

For example I have this image of a coca-cola bottle:
[bottle-1][1]

Which translates to a numpy array of shape `(528, 203, 3)` and I want to resize that to say the size of this second image:
[bottle-2][2]

Which has a shape of `(140, 54, 3)`.

How do I change the size of the image to a certain shape while still maintaining the original image? Other answers suggest stripping every other or third row out, but what I want to do is basically shrink the image how you would via an image editor but in python code. Are there any libraries to do this in numpy/SciPy?


  [1]: https://i.stack.imgur.com/n63s2.jpg
  [2]: https://i.stack.imgur.com/vQF7L.jpg

Numpy Resize/Rescale Image

Pretty much what the title says. I&#39;ve read the documentation and I&#39;ve played with the function for a while now but I can&#39;t discern what the physical manifestation of this transformation is. 

What is an intuitive explanation of np.unravel_index?

I&#39;ve noticed that installing Pandas and Numpy (it&#39;s dependency) in a Docker container using the base OS Alpine vs. CentOS or Debian takes much longer. I created a little test below to demonstrate the time difference. Aside from the few seconds Alpine takes to update and download the build dependencies to install Pandas and Numpy, why does the setup.py take around 70x more time than on Debian install?

Is there any way to speed up the install using Alpine as the base image or is there another base image of comparable size to Alpine that is better to use for packages like Pandas and Numpy?



**Dockerfile.debian**

    FROM python:3.6.4-slim-jessie
    
    RUN pip install pandas

**Build Debian image with Pandas &amp; Numpy:**

    [PandasDockerTest] time docker build -t debian-pandas -f Dockerfile.debian . --no-cache
        Sending build context to Docker daemon  3.072kB
        Step 1/2 : FROM python:3.6.4-slim-jessie
         ---&gt; 43431c5410f3
        Step 2/2 : RUN pip install pandas
         ---&gt; Running in 2e4c030f8051
        Collecting pandas
          Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)
        Collecting numpy&gt;=1.9.0 (from pandas)
          Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)
        Collecting pytz&gt;=2011k (from pandas)
          Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
        Collecting python-dateutil&gt;=2 (from pandas)
          Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
        Collecting six&gt;=1.5 (from python-dateutil&gt;=2-&gt;pandas)
          Downloading six-1.11.0-py2.py3-none-any.whl
        Installing collected packages: numpy, pytz, six, python-dateutil, pandas
        Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
        Removing intermediate container 2e4c030f8051
         ---&gt; a71e1c314897
        Successfully built a71e1c314897
        Successfully tagged debian-pandas:latest
        docker build -t debian-pandas -f Dockerfile.debian . --no-cache  0.07s user 0.06s system 0% cpu 13.605 total

**Dockerfile.alpine**

    FROM python:3.6.4-alpine3.7
    
    RUN apk --update add --no-cache g++
    
    RUN pip install pandas

**Build Alpine image with Pandas &amp; Numpy:**

    [PandasDockerTest] time docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache
    Sending build context to Docker daemon   16.9kB
    Step 1/3 : FROM python:3.6.4-alpine3.7
     ---&gt; 4b00a94b6f26
    Step 2/3 : RUN apk --update add --no-cache g++
     ---&gt; Running in 4b0c32551e3f
    fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
    fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
    fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
    fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
    (1/17) Upgrading musl (1.1.18-r2 -&gt; 1.1.18-r3)
    (2/17) Installing libgcc (6.4.0-r5)
    (3/17) Installing libstdc++ (6.4.0-r5)
    (4/17) Installing binutils-libs (2.28-r3)
    (5/17) Installing binutils (2.28-r3)
    (6/17) Installing gmp (6.1.2-r1)
    (7/17) Installing isl (0.18-r0)
    (8/17) Installing libgomp (6.4.0-r5)
    (9/17) Installing libatomic (6.4.0-r5)
    (10/17) Installing pkgconf (1.3.10-r0)
    (11/17) Installing mpfr3 (3.1.5-r1)
    (12/17) Installing mpc1 (1.0.3-r1)
    (13/17) Installing gcc (6.4.0-r5)
    (14/17) Installing musl-dev (1.1.18-r3)
    (15/17) Installing libc-dev (0.7.1-r0)
    (16/17) Installing g++ (6.4.0-r5)
    (17/17) Upgrading musl-utils (1.1.18-r2 -&gt; 1.1.18-r3)
    Executing busybox-1.27.2-r7.trigger
    OK: 184 MiB in 50 packages
    Removing intermediate container 4b0c32551e3f
     ---&gt; be26c3bf4e42
    Step 3/3 : RUN pip install pandas
     ---&gt; Running in 36f6024e5e2d
    Collecting pandas
      Downloading pandas-0.22.0.tar.gz (11.3MB)
    Collecting python-dateutil&gt;=2 (from pandas)
      Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
    Collecting pytz&gt;=2011k (from pandas)
      Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
    Collecting numpy&gt;=1.9.0 (from pandas)
      Downloading numpy-1.14.1.zip (4.9MB)
    Collecting six&gt;=1.5 (from python-dateutil&gt;=2-&gt;pandas)
      Downloading six-1.11.0-py2.py3-none-any.whl
    Building wheels for collected packages: pandas, numpy
      Running setup.py bdist_wheel for pandas: started
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: still running...
      Running setup.py bdist_wheel for pandas: finished with status &#39;done&#39;
      Stored in directory: /root/.cache/pip/wheels/e8/ed/46/0596b51014f3cc49259e52dff9824e1c6fe352048a2656fc92
      Running setup.py bdist_wheel for numpy: started
      Running setup.py bdist_wheel for numpy: still running...
      Running setup.py bdist_wheel for numpy: still running...
      Running setup.py bdist_wheel for numpy: still running...
      Running setup.py bdist_wheel for numpy: finished with status &#39;done&#39;
      Stored in directory: /root/.cache/pip/wheels/9d/cd/e1/4d418b16ea662e512349ef193ed9d9ff473af715110798c984
    Successfully built pandas numpy
    Installing collected packages: six, python-dateutil, pytz, numpy, pandas
    Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
    Removing intermediate container 36f6024e5e2d
     ---&gt; a93c59e6a106
    Successfully built a93c59e6a106
    Successfully tagged alpine-pandas:latest
    docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache  0.54s user 0.33s system 0% cpu 16:08.47 total

Why does it take ages to install Pandas on Alpine Linux

I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti).  I&#39;m new to GPU, and would like to make sure I&#39;m properly using the GPU to accelerate Numpy/Python.  I searched on the internet for a while, but didn&#39;t find a simple tutorial that addressed my questions.  I&#39;d appreciate it if someone can give me some pointers:

1) Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, ... etc)?  Or do I have code in a specific way to exploit the GPU for fast computation?

2) Can someone recommend a good tutorial/introductory material on using Numpy/Python with GPU (nvidia&#39;s)?

Thanks a lot!

Content Type	Original Author	Original Content on Stackoverflow
Question	Santhosh	View Question on Stackoverflow
Solution 1 - Python	Nils Werner	View Answer on Stackoverflow

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

Python Problem Overview

Python Solutions

Solution 1 - Python

Docker Containers can not be stopped or removed - permission denied Error

leading or trailing horizontal alignment before iOS 11

Attributions