You can disable the warning with [`numpy.seterr`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html). Put this before the possible division by zero:

    np.seterr(divide=&#39;ignore&#39;)

That&#39;ll disable zero division warnings globally. If you just want to disable them for a little bit, you can use [`numpy.errstate`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html) in a `with` clause:

    with np.errstate(divide=&#39;ignore&#39;):
        # some code here


----

For a zero by zero division (undetermined, results in a NaN), the error behaviour has changed with numpy version 1.12.0: this is now considered &quot;invalid&quot;, while previously it was &quot;divide&quot;.

Thus, if there is a chance you your numerator could be zero as well, use

    np.seterr(divide=&#39;ignore&#39;, invalid=&#39;ignore&#39;)

or

    with np.errstate(divide=&#39;ignore&#39;, invalid=&#39;ignore&#39;):
        # some code here

See the &quot;Compatibility&quot; section in the  [release notes](https://docs.scipy.org/doc/numpy/release.html#id42), last paragraph before the &quot;New Features&quot; section:

&gt; Comparing NaN floating point numbers now raises the invalid runtime warning. If a NaN is expected the warning can be ignored using np.errstate. 

You could also use `numpy.divide` for division. That way you don&#39;t have to explicitly disable warnings.

    In [725]: np.divide(2, 0)
    Out[725]: 0



`HKObserverQuery` has the following method that supports receiving updates in the background:

    - initWithSampleType:predicate:updateHandler:

The `updateHandler` has a `completionHandler` which has the following [documentation](https://developer.apple.com/library/prerelease/ios/documentation/HealthKit/Reference/HKObserverQuery_Class/index.html):

&gt; This block is passed to the update handler. You must call this block
&gt; as soon as you are done processing the incoming data. Calling this
&gt; block tells HealthKit that you have successfully received the
&gt; background data. If you do not call this block, HealthKit continues to
&gt; attempt to launch your app using a backoff algorithm. If your app
&gt; fails to respond three times, HealthKit assumes that your app cannot
&gt; receive data, and stops sending you background updates.

From looking at other posts it seems like there&#39;s a lot of confusion revolving around this handler. Below are some questions that I have about it:

- When should the handler be called? If called too late, then HK might think that the app never received the query update causing you to hit the background update 3-strikes back-off algorithm. The documentation states that it should be called after handling other queries. Depending on how long it would take to run those queries, it sounds like you could get dangerously close to hitting the background update strikes.
- Why is this needed? Shouldn&#39;t the system know that the app has been launched and has received the background update? When using `CoreBluetooth` in the background it just wakes your app up in the background for 10 seconds. No need to call any handler or deal with the background update 3-strikes.
- If you hit the background update 3-strikes and HK stops sending updates is that permanent? Does HK ever start sending the background updates again? What if there&#39;s a bug that prevented the handler to be called and now you&#39;ve fixed it. Is the app stuck never receiving the updates? Or will it reset when the app is re-launched or updated?
- Does HK keep your app running in the background until the handler is called? Is that part of its purpose or just a side effect? If it&#39;s part of its purpose how long can we run before needing to stop (and hit the first background update strike)?

iOS : Use of HKObserverQuery&#39;s background update completionHandler

Here i cannot understand what is the basic difference between these two methods.


    var events = require(&#39;events&#39;);
    var eventEmitter = new events.EventEmitter();



    var listner1 = function listner1() {
        console.log(&#39;listner1 executed.&#39;);
    }
        
    var listner2 = function listner2() {
        console.log(&#39;listner2 executed.&#39;);    
    }
    
    eventEmitter.addListener(&#39;connection&#39;, listner1);
        
    eventEmitter.on(&#39;connection&#39;, listner2);
        
    eventEmitter.emit(&#39;connection&#39;);

What is the difference between addListener(event, listener) and on(event, listener) method in node.js?

I have a function for statistic issues:

    import numpy as np
    from scipy.special import gamma as Gamma

    def Foo(xdata):
        ...
        return x1 * (
                     ( #R is a numpy vector
                      ( ((R - x2)/beta) ** (x3 -1) ) * 
                      ( np.exp( - ((R - x2) / x4) ) ) /
                      ( x4 * Gamma(x3))
                     ).real
                    )

Sometimes I get from the shell the following warning:

    RuntimeWarning: divide by zero encountered in...

I use the numpy `isinf` function to correct the results of the function in other files, so I do not need this warning. 

Is there a way to ignore the message?
In other words, I do not want the shell to print this message.

I do not want to disable all python warnings, just this one.

Ignore divide by 0 warning in NumPy

I have a function for statistic issues:
<pre><code class="hljs language-python">import numpy as np
from scipy.special import gamma as Gamma

def Foo(xdata):
 ...
 return x1 * (
 ( #R is a numpy vector
 ( ((R - x2)/beta) ** (x3 -1) ) * 
 ( np.exp( - ((R - x2) / x4) ) ) /
 ( x4 * Gamma(x3))
 ).real
 )
</code></pre>
Sometimes I get from the shell the following warning:
<pre><code class="hljs language-vbnet">RuntimeWarning: divide by zero encountered in...
</code></pre>
I use the numpy <code>isinf</code> function to correct the results of the function in other files, so I do not need this warning.
Is there a way to ignore the message?
In other words, I do not want the shell to print this message.
I do not want to disable all python warnings, just this one.

With Django REST Framework, a standard ModelSerializer will allow ForeignKey model relationships to be assigned or changed by POSTing an ID as an Integer.

What&#39;s the *simplest* way to get this behavior out of a nested serializer?

Note, I am only talking about assigning existing database objects, *not* nested creation.

I have hacked away around this in the past with additional &#39;id&#39; fields in the serializer and with custom `create` and `update` methods, but this is such a seemingly simple and frequent issue for me that I&#39;m curious to know the best way.


&lt;!-- language: python --&gt;

    class Child(models.Model):
        name = CharField(max_length=20)
    
    class Parent(models.Model):
        name = CharField(max_length=20)
        phone_number = models.ForeignKey(PhoneNumber)
        child = models.ForeignKey(Child)
    
    class ChildSerializer(ModelSerializer):
        class Meta:
            model = Child
    
    class ParentSerializer(ModelSerializer):
        # phone_number relation is automatic and will accept ID integers
        children = ChildSerializer() # this one will not
        
        class Meta:
            model = Parent

DRF: Simple foreign key assignment with nested serializers?

My python code creates a plotly Bar plot but the background is white in color i want to change it into transparent color is that doable 

***My Code:***

    import plotly.plotly as py
    from plotly.graph_objs import *
    py.sign_in(&#39;username&#39;, &#39;api_key&#39;)
    data = Data([
    Bar(
        x=[&#39;Sivaranjani S&#39;, &#39;Vijayalakshmi C&#39;, &#39;Rajeshwari S&#39;, &#39;Shanthi Priscilla&#39;, &#39;Pandiyaraj G&#39;, &#39;Kamatchi S&#39;, &#39;MohanaPriya&#39;, &#39;Madhumitha G&#39;, &#39;Franklin Alphones Raj J&#39;, &#39;Akfaris Almaas&#39;, &#39;Biswajit Champati&#39;, &#39;Priya R&#39;, &#39;Rekha Rajasekaran&#39;, &#39;Sarath Kumar B&#39;, &#39;Jegan L&#39;, &#39;Karthick A&#39;, &#39;Mahalakshmi S&#39;, &#39;Ragunathan V&#39;, &#39;Anu S&#39;, &#39;Ramkumar KS&#39;, &#39;Uthra R&#39;],
        y=[1640, 1394, 1390, 1313, 2166, 1521, 1078, 1543, 780, 1202, 1505, 2028, 2032, 1769, 1238, 1491, 1477, 1329, 2038, 1339, 1458],
        text=[&#39;Scuti&#39;, &#39;Scuti&#39;, &#39;Cygni&#39;, &#39;Scorpii&#39;, &#39;Scuti&#39;, &#39;Pollux&#39;, &#39;Scorpii&#39;, &#39;Pollux&#39;, &#39;Scuti&#39;, &#39;Pollux&#39;, &#39;Scorpii&#39;, &#39;Scorpii&#39;, &#39;Scuti&#39;, &#39;Cygni&#39;, &#39;Scorpii&#39;, &#39;Scuti&#39;, &#39;Scuti&#39;, &#39;Pollux&#39;, &#39;Scuti&#39;, &#39;Pollux&#39;, &#39;Pollux&#39;])])
    plot_url = py.plot(data)

***The graph looks like this***

![enter image description here][1]


  [1]: http://i.stack.imgur.com/KudRA.jpg

Setting Background color to transparent in Plotly plots

I have a really big DataFrame and I was wondering if there was short (one or two liner) way to get the a count of non-NaN entries in a DataFrame. I don&#39;t want to do this one column at a time as I have close to 1000 columns. 

    df1 = pd.DataFrame([(1,2,None),(None,4,None),(5,None,7),(5,None,None)], 
                        columns=[&#39;a&#39;,&#39;b&#39;,&#39;d&#39;], index = [&#39;A&#39;, &#39;B&#39;,&#39;C&#39;,&#39;D&#39;])

        a   b   d
    A   1   2 NaN
    B NaN   4 NaN
    C   5 NaN   7
    D   5 NaN NaN


Output:

    a: 3
    b: 2
    d: 1

Count number of non-NaN entries in every column of Dataframe

I activated a [virtualenv][1] which has pip installed. I did

```lang-none
pip3 install Django==1.8
```

and Django successfully downloaded. Now, I want to open up the Django folder. Where is the folder located?

Normally it would be in &quot;downloads&quot;, but I&#39;m not sure where it would be if I installed it using pip in a virtualenv.

  [1]: http://pypi.python.org/pypi/virtualenv





Where does pip install its packages?

[Visual Studio Code][1] was recently released and I liked the look of it and the features it offered, so I figured I would give it a go.

I downloaded the application from the [downloads page][2], fired it up, messed around a bit with some of the features ... and then realized I had no idea how to actually execute any of my Python code!

I really like the look and feel/usability/features of Visual Studio Code, but I can&#39;t seem to find out how to run my Python code, a real killer because that&#39;s what I program primarily in.

Is there is a way to execute Python code in Visual Studio Code?

  [1]: https://code.visualstudio.com/
  [2]: https://code.visualstudio.com/Download







How to execute Python code from within Visual Studio Code

I want to create a Pandas DataFrame filled with NaNs. During my research I found [an answer][1]:

    import pandas as pd
    
    df = pd.DataFrame(index=range(0,4),columns=[&#39;A&#39;])
This code results in a DataFrame filled with NaNs of type &quot;object&quot;. So they cannot be used later on for example with the `interpolate()` method. Therefore, I created the DataFrame with this complicated code (inspired by [this answer][2]):

    import pandas as pd
    import numpy as np

    dummyarray = np.empty((4,1))
    dummyarray[:] = np.nan

    df = pd.DataFrame(dummyarray)
This results in a DataFrame filled with NaN of type &quot;float&quot;, so it can be used later on with `interpolate()`. Is there a more elegant way to create the same result?

  [1]: https://stackoverflow.com/questions/13784192/creating-an-empty-pandas-dataframe-then-filling-it
  [2]: https://stackoverflow.com/questions/1704823/initializing-numpy-matrix-to-something-other-than-zero-or-one

Elegant way to create empty pandas DataFrame with NaN of type float

I have a script that generates two-dimensional `numpy` arrays with `dtype=float` and shape on the order of `(1e3, 1e6)`.  Right now I&#39;m using `np.save` and `np.load` to perform IO operations with the arrays.  However, these functions take several seconds for each array.  Are there faster methods for saving and loading the entire arrays (i.e., without making assumptions about their contents and reducing them)?  I&#39;m open to converting the arrays to another type before saving as long as the data are retained exactly.

Fastest save and load options for a numpy array

Is it possible to save a numpy array appending it to an already existing npy-file --- something like `np.save(filename,arr,mode=&#39;a&#39;)`?

I have several functions that have to iterate over the rows of a large array. I cannot create the array at once because of memory constrains. To avoid to create the rows over and over again, I wanted to create each row once and save it to file appending it to the previous row in the file. Later I could load the npy-file in mmap_mode, accessing the slices when needed.

save numpy array in append mode

*This question contains its own answer at the bottom. Use preallocated arrays.*

Following-up from [this question][1] years ago, is there a canonical &quot;shift&quot; function in numpy? I don&#39;t see anything from [the documentation][2].

Here&#39;s a simple version of what I&#39;m looking for:

    def shift(xs, n):
        if n &gt;= 0:
            return np.r_[np.full(n, np.nan), xs[:-n]]
        else:
            return np.r_[xs[-n:], np.full(-n, np.nan)]

Using this is like:

    In [76]: xs
    Out[76]: array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
    
    In [77]: shift(xs, 3)
    Out[77]: array([ nan,  nan,  nan,   0.,   1.,   2.,   3.,   4.,   5.,   6.])
    
    In [78]: shift(xs, -3)
    Out[78]: array([  3.,   4.,   5.,   6.,   7.,   8.,   9.,  nan,  nan,  nan])

&lt;sub&gt;This question came from my attempt to [write a fast rolling_product][3] yesterday. I needed a way to &quot;shift&quot; a cumulative product and all I could think of was to replicate the logic in `np.roll()`.&lt;/sub&gt;

----------

So `np.concatenate()` is much faster than `np.r_[]`. This version of the function performs a lot better:

    def shift(xs, n):
        if n &gt;= 0:
            return np.concatenate((np.full(n, np.nan), xs[:-n]))
        else:
            return np.concatenate((xs[-n:], np.full(-n, np.nan)))

----------

An even faster version simply pre-allocates the array:

    def shift(xs, n):
        e = np.empty_like(xs)
        if n &gt;= 0:
            e[:n] = np.nan
            e[n:] = xs[:-n]
        else:
            e[n:] = np.nan
            e[:n] = xs[-n:]
        return e

*The above proposal is the answer. Use preallocated arrays.*

  [1]: https://stackoverflow.com/q/5859144/478288
  [2]: http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html
  [3]: https://stackoverflow.com/a/30386409/478288



Shift elements in a numpy array

I am trying to find the count of distinct values in each column using Pandas. This is what I did.

    import pandas as pd
    import numpy as np
    
    # Generate data.
    NROW = 10000
    NCOL = 100
    df = pd.DataFrame(np.random.randint(1, 100000, (NROW, NCOL)),
                      columns=[&#39;col&#39; + x for x in np.arange(NCOL).astype(str)])

I need to count the number of distinct elements for each column, like this:

    col0    9538
    col1    9505
    col2    9524

What would be the most efficient way to do this, as this method will be applied to files which have size greater than 1.5GB?


----------

Based upon the answers, `df.apply(lambda x: len(x.unique()))` is the fastest ([notebook](https://colab.research.google.com/drive/1xsrG3iUR5fkouRtBTWA2oO8YJWYqNk4G)).

```%timeit df.apply(lambda x: len(x.unique()))
10 loops, best of 3: 49.5 ms per loop
%timeit df.nunique()
10 loops, best of 3: 59.7 ms per loop
%timeit df.apply(pd.Series.nunique)
10 loops, best of 3: 60.3 ms per loop
%timeit df.T.apply(lambda x: x.nunique(), axis=1)
10 loops, best of 3: 60.5 ms per loop
```

Finding count of distinct elements in DataFrame in each column

An often used statement like `(void)x;` allows to suppress warnings about unused variable `x`. But if I try compiling the following, I get some results I don&#39;t quite understand:

    int main()
    {
        int x;
        (short)x;
        (void)x;
        (int)x;
    }

Compiling this with g++, I get the following warnings:

    $ g++ test.cpp -Wall -Wextra -o test
    test.cpp: In function ‘int main()’:
    test.cpp:4:13: warning: statement has no effect [-Wunused-value]
         (short)x;
                 ^
    test.cpp:6:11: warning: statement has no effect [-Wunused-value]
         (int)x;
               ^

So I conclude that casting to `void` is very different from casting to any other types, be the target type the same as `decltype(x)` or something different. My guess at possible explanations is:

* It is just a convention that `(void)x;` but not the other casts will suppress warnings. All the statements equally don&#39;t have any effect.
* This difference is somehow related to the fact that `void x;` isn&#39;t a valid statement while `short x;` is.

Which of these if any is more correct? If none, then how can the difference in compiler warnings be explained?

What does casting to `void` really do?

I&#39;m getting the user&#39;s location through 

    Location location = LocationServices.FusedLocationApi.getLastLocation(
                mGoogleApiClient);

This line of code is inside a method and before calling this method I do check for Android run time permissions. Only if the permission is available from the user then I call this method. Code is working perfectly.

The problem is that Android Studio still shows an error on this line not recognising that I&#39;ve already checked before calling this function.

    Call requires permission which may be rejected by user: code should explicitly check to see if permission is available (with `checkPermission`) or explicitly handle a potential `SecurityException`
Now how do I remove this warning? I&#39;ve already checked for permissions and don&#39;t want  to check again just to remove this warning. I&#39;ve tried adding @SuppressWarnings() but don&#39;t know the exact String to pass into this.             **@SuppressWarnings({&quot;all&quot;})** works but it is obviously not recommended.

How do I remove this warning? 

**EDIT 1 :** This is my exact code -

    private void checkPermissions() {
        if (ContextCompat.checkSelfPermission(context, Manifest.permission.ACCESS_FINE_LOCATION)
                == PackageManager.PERMISSION_GRANTED)
            getLocation();  //Method called if I have permission
    }

    private void getLocation() {
        //Android studio shows warning at this line.
        Location location = LocationServices.FusedLocationApi.getLastLocation(
                mGoogleApiClient);
    }

But if I put the permission check inside getLocation() method then the warning disappears. **@SuppressWarnings({&quot;MissingPermission&quot;})** did not work.

**EDIT 2:** I&#39;ve discovered that the only way to suppress the warning are - 

Adding this comment on top of that particular piece of code - 

    //noinspection ResourceType

or adding this - 

    @SuppressWarnings({&quot;ResourceType&quot;})

Android Studio - remove Security Exception warning

Since version 15, [IntelliJ warns me about code duplicates][1].

In some cases this might be intentional, so I want to ignore/suppress this warning by using the `@SuppressWarnings` annotation. But what is the correct value for this?


  [1]: https://www.jetbrains.com/help/idea/2016.1/analyzing-duplicates.html?origin=old_help

Edit: I&#39;m not asking for disabling this kind of inspection completely as in question https://stackoverflow.com/questions/36627002/is-it-possible-to-disable-duplicate-code-detection-in-intellij

Suppress duplicate warnings in IntelliJ IDEA by annotation

I keep getting annoyed by the &quot;Can be private&quot; warning, however my `FirebaseRecyclerAdapter` will not work in that case. So, is there a `@SuppressWarnings` for that?

**What I&#39;ve tried:**

`@SuppressWarnings(&quot;all&quot;)` but that&#39;s not what I want.

**Note:** I am using Android Studio

What value for @SuppressWarnings do I use to suppress the &quot;Can be private&quot; warning?

PyCharm provides some helpful warnings on code style, conventions and logical gotchas. It also provides a notification if I try to commit code with warnings (or errors).

Sometimes I consciously ignore these warnings for particular lines of code (for various reasons, typically to account for implementation details of third-party libraries). I want to suppress the warning, but just for that line (if the warning crops up on a different line where I&#39;m not being deliberate, I want to know about it!)

How can I do that in PyCharm? (Following a universal Python convention strongly preferable.)

Can I get PyCharm to suppress a particular warning on a single line?

Consider

    #include &lt;iostream&gt;
    int main()
    {
        double a = 1.0 / 0;
        double b = -1.0 / 0;
        double c = 0.0 / 0;
        std::cout &lt;&lt; a &lt;&lt; b &lt;&lt; c; // to stop compilers from optimising out the code.    
    }

I have always thought that `a` will be +Inf, `b` will be -Inf, and `c` will be NaN. But I also hear rumours that strictly speaking the behaviour of floating point division by zero is *undefined* and therefore the above code cannot considered to be portable C++. (That theoretically obliterates the integrity of my million line plus code stack. Oops.)

Who&#39;s correct?

Note I&#39;m happy with *implementation defined*, but I&#39;m talking about cat-eating, demon-sneezing *undefined behaviour* here.

The behaviour of floating point division by zero

Just threw together a simple test, not for any particular reason other than I like to try to have tests for all my methods even though this one is quite straightforward, or so I thought.

        [TestMethod]
        public void Test_GetToolRating()
        {
            var rating = GetToolRating(45.5, 0);
            Assert.IsNotNull(rating);
        }
       

        private static ToolRating GetToolRating(double total, int numberOf)
        {
            var ratingNumber = 0.0;

            try
            {
                var tot = total / numberOf;
                ratingNumber = Math.Round(tot, 2);
            }
            catch (Exception ex)
            {
                var errorMessage = ex.Message;
                //log error here
                //var logger = new Logger();
                //logger.Log(errorMessage);
            }

           
            return GetToolRatingLevel(ratingNumber);
        }

As you can see in the test method, I AM dividing by zero. The problem is, it doesn&#39;t generate an error. See the error window display below.

[![Error List View from VS2017][1]][1]


  [1]: https://i.stack.imgur.com/Fjx6O.png

Instead of an error it is giving a value of infinity? What am I missing?So I googled and found that doubles divided by zero DON&#39;T generate an error they either give null or infinity. The question becomes then, how does one test for an Infinity return value?

Divide by zero and no error?

**Case 1:**

    #include &lt;iostream&gt;
    
    int main()
    {
        double d = 15.50;
        std::cout&lt;&lt;(d/0.0)&lt;&lt;std::endl;
    }

It compiles without any warnings and prints `inf`. OK, C++ can handle division by zero, ([see it live](https://wandbox.org/permlink/qnpr8cM4W7rKlNid)).

But,

**Case 2:** 

    #include &lt;iostream&gt;
    
    int main()
    {
        double d = 15.50;
        std::cout&lt;&lt;(d/0)&lt;&lt;std::endl;
    }

The compiler gives the following warning ([see it live](https://wandbox.org/permlink/9cRSsWqewKxqN2CT)):

    warning: division by zero [-Wdiv-by-zero]
         std::cout&lt;&lt;(d/0)&lt;&lt;std::endl;
	 
Why does the compiler give a warning in the second case? 

Is `0 != 0.0`?

**Edit:**

    #include &lt;iostream&gt;
    
    int main()
    {
        if(0 == 0.0)
            std::cout&lt;&lt;&quot;Same&quot;&lt;&lt;std::endl;
        else
            std::cout&lt;&lt;&quot;Not same&quot;&lt;&lt;std::endl;
    }

**output:**

    Same



C++ warning: division of double by zero

I don&#39;t quite understand why I don&#39;t get a division by zero exception:

```c++
int d = 0;
d /= d;
```

I expected to get a division by zero exception but instead `d == 1`.

Why doesn&#39;t `d /= d` throw a division by zero exception when `d == 0`?

Content Type	Original Author	Original Content on Stackoverflow
Question	overcomer	View Question on Stackoverflow
Solution 1 - Python	dddsnn	View Answer on Stackoverflow
Solution 2 - Python	fixxxer	View Answer on Stackoverflow

Ignore divide by 0 warning in NumPy

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

What is the difference between addListener(event, listener) and on(event, listener) method in node.js?

iOS : Use of HKObserverQuery's background update completionHandler

Attributions