Try this:

    mask = (foo &lt; 40) | (foo &gt; 60)

Note: the `__or__` method in an object overloads the bitwise or operator (`|`), not the Boolean `or` operator.



If you have comparisons within only Booleans, as in your example, you can use the bitwise OR operator `|` as suggested by Jcollado.  But beware, this can give you strange results if you ever use non-Booleans, such as `mask = (foo &lt; 40) | override`. Only as long as `override` guaranteed to be either False, True, 1, or 0, are you fine.

More general is the use of NumPy&#39;s comparison set operators, `np.any` and `np.all`. This snippet returns all values between 35 and 45 which are less than 40 or not a multiple of 3:

    import numpy as np
    foo = np.arange(35, 46)
    mask = np.any([(foo &lt; 40), (foo % 3)], axis=0)
    print foo[mask]
    OUTPUT: array([35, 36, 37, 38, 39, 40, 41, 43, 44])

It is not as nice as with `|`, but nicer than the code in your question.





You can use the [NumPy logical operations][1]. In your example:

    np.logical_or(foo &lt; 40, foo &gt; 60)

  [1]: https://docs.scipy.org/doc/numpy-1.10.0/reference/routines.logic.html




Note that you can use `~` for elementwise negation. 
  
    arr = np.array([False, True])
    ~arr

    OUTPUT: array([ True, False], dtype=bool)

Also `&amp;` does elementwise *and*

    arr_1 = np.array([False, False, True, True])
    arr_2 = np.array([False, True, False, True])

    arr_1 &amp; arr_2

    OUTPUT:   array([False, False, False,  True], dtype=bool)

These also work with *Pandas* Series

    ser_1 = pd.Series([False, False, True, True])
    ser_2 = pd.Series([False, True, False, True])

    ser_1 &amp; ser_2

    OUTPUT:
    0    False
    1    False
    2    False
    3     True
    dtype: bool

    ERROR GServerHandler  - java.io.IOException: Connection reset by peer
    java.io.IOException: Connection reset by peer
            at sun.nio.ch.FileDispatcher.read0(Native Method)
            at sun.nio.ch.SocketDispatcher.read(Unknown Source)
            at sun.nio.ch.IOUtil.readIntoNativeBuffer(Unknown Source)
            at sun.nio.ch.IOUtil.read(Unknown Source)
            at sun.nio.ch.SocketChannelImpl.read(Unknown Source)
            at org.jboss.netty.channel.socket.nio.NioWorker.read(NioWorker.java:323)
            at org.jboss.netty.channel.socket.nio.NioWorker.processSelectedKeys(NioWorker.java:282)
            at org.jboss.netty.channel.socket.nio.NioWorker.run(NioWorker.java:202)
            at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(Unknown Source)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
            at java.lang.Thread.run(Unknown Source)

This log is from a game server implemented using netty. What can cause this exception ? 

When is &quot;java.io.IOException:Connection reset by peer&quot; thrown?

I have a variable called `$final_time_saving` which is just a number of minutes, 250 for example. 

How can I convert that number of minutes into hours and minutes using PHP in this format: 

`4 hours 10 minutes`


Convert number of minutes into hours &amp; minutes using PHP

For example, I would like to create a mask that masks elements with value between 40 and 60:

    foo = np.asanyarray(range(100))
    mask = (foo &lt; 40).__or__(foo &gt; 60)

Which just looks ugly. I can&#39;t write

    (foo &lt; 40) or (foo &gt; 60)

because I end up with:

      ValueError Traceback (most recent call last)
      ...
      ----&gt; 1 (foo &lt; 40) or (foo &gt; 60)
      ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Is there a canonical way of doing element-wise Boolean operations on NumPy arrays with good looking code?




How to perform element-wise Boolean operations on NumPy arrays

For example, I would like to create a mask that masks elements with value between 40 and 60:
<pre><code class="hljs language-ini">foo = np.asanyarray(range(100))
mask = (foo &#x3C; 40).__or__(foo > 60)
</code></pre>
Which just looks ugly. I can't write
<pre><code class="hljs language-scss">(foo &#x3C; 40) or (foo > 60)
</code></pre>
because I end up with:
<pre><code class="hljs language-sql"> ValueError Traceback (most recent call last)
 ...
 ----> 1 (foo &#x3C; 40) or (foo > 60)
 ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
</code></pre>
Is there a canonical way of doing element-wise Boolean operations on NumPy arrays with good looking code?

As an example, this URL:

    http://example.com/get_image?type=1

should return a response with a `image/gif` MIME type. I have two static `.gif` images,  
and if type is 1, it should return `ok.gif`, else return `error.gif`. How to do that in flask?

How to return images in flask response?

Assume I have this:

    [
      {&quot;name&quot;: &quot;Tom&quot;, &quot;age&quot;: 10},
      {&quot;name&quot;: &quot;Mark&quot;, &quot;age&quot;: 5},
      {&quot;name&quot;: &quot;Pam&quot;, &quot;age&quot;: 7}
    ]


and by searching &quot;Pam&quot; as name, I want to retrieve the related dictionary: ```{name: &quot;Pam&quot;, age: 7}```

How to achieve this ?

Python list of dictionaries search

I am using hand crafted SQL to fetch data from a PG database, using SqlAlchemy. I am trying a query which contains the SQL like operator &#39;%&#39; and that seems to throw SqlAlcjhemy through a loop:

    sql = &quot;&quot;&quot;
           SELECT DISTINCT u.name from user u
            INNER JOIN city c ON u.city_id = c.id
            WHERE c.designation=upper(&#39;fantasy&#39;) 
            AND c.id IN (select id from ref_geog where short_name LIKE &#39;%opt&#39;)
          &quot;&quot;&quot;
    
    # The last line in the above statement throws the error mentioned in the title. 
    # However if the last line is change to:
    # AND c.id IN (select id from ref_geog where short_name = &#39;helloopt&#39;)
    # the script runs correctly.
    #
    # I also tried double escaping the &#39;%&#39; i.e. using &#39;%%&#39; instead - that generated the same error as previously.
    
    connectDb()
    res = executeSql(sql)
    print res
    closeDbConnection()


Any one knows what is causing this misleading error message and how I may fix it?

**[[Edit]]**

Before any one asks, there is nothing special or fancy about the functions included above. For example the function executeSql() simply invokes conn.execute(sql) and returns the results. The variable conn is simply the previously established connection to the database.

Strange SQLAlchemy error message: TypeError: &#39;dict&#39; object does not support indexing

Module `A` includes `import B` at its top. However under test conditions I&#39;d like to [mock][1] `B` in `A` (mock `A.B`) and completely refrain from importing `B`. 

In fact, `B` isn&#39;t installed in the test environment on purpose.

`A` is the unit under test. I have to import `A` with all its functionality. `B` is the module I need to mock. But how can I mock `B` within `A` and stop `A` from importing the real `B`, if the first thing `A` does is import `B`?

(The reason B isn&#39;t installed is that I use pypy for quick testing and unfortunately B isn&#39;t compatible with pypy yet.)

How could this be done?


  [1]: http://www.voidspace.org.uk/python/mock/

How to mock an import

I wonder if it&#39;s possible to install python packages without leaving the IPython shell.


How to install a Python package from within IPython?

What&#39;s the (best) way to solve a **pair of non linear equations using Python.** (Numpy, Scipy or Sympy)

eg:

&gt;  - x+y^2 = 4
&gt;  - e^x+ xy = 3

A code snippet which solves the above pair will be great
&lt;This is not the equations that I want to solve, but the method will be applicable to my case&gt;

How to solve a pair of nonlinear equations using Python?

I&#39;ve got an `ndarray` in python with a `dtype` of `float64`. I&#39;d like to convert the array to be an array of integers. How should I do this?

`int()` won&#39;t work, as it says it can&#39;t convert it to a scalar. Changing the `dtype` field itself obviously doesn&#39;t work, as the actual bytes haven&#39;t changed. I can&#39;t seem to find anything on Google or in the documentation - what&#39;s the best way to do this?

Convert ndarray from float64 to integer

Given a 3 times 3 numpy array

    a = numpy.arange(0,27,3).reshape(3,3)

    # array([[ 0,  3,  6],
    #        [ 9, 12, 15],
    #        [18, 21, 24]])


To normalize the rows of the 2-dimensional array I thought of

    row_sums = a.sum(axis=1) # array([ 9, 36, 63])
    new_matrix = numpy.zeros((3,3))
    for i, (row, row_sum) in enumerate(zip(a, row_sums)):
        new_matrix[i,:] = row / row_sum

There must be a better way, isn&#39;t there?

Perhaps to clearify: By normalizing I mean, the sum of the entrys per row must be one. But I think that will be clear to most people.

How to normalize a 2-dimensional numpy array in python less verbose?

I have a matrix `A` and I want 2 matrices `U` and `L` such that `U` contains the upper triangular elements of A (all elements above and not including diagonal) and similarly for `L`(all elements below and not including diagonal). Is there a `numpy` method to do this?

e.g 
    
    A = array([[ 4.,  9., -3.],
               [ 2.,  4., -2.],
               [-2., -3.,  7.]])

    U = array([[ 0.,  9., -3.],
               [ 0.,  0., -2.],
               [ 0.,  0.,  0.]])

    L = array([[ 0.,  0.,  0.],
               [ 2.,  0.,  0.],
               [-2., -3.,  0.]])

Extract upper or lower triangular part of a numpy matrix

How do you save/load a scipy sparse `csr_matrix` in a portable format?  The scipy sparse matrix is created on Python 3 (Windows 64-bit) to run on Python 2 (Linux 64-bit).  Initially, I used pickle (with protocol=2 and fix_imports=True) but this didn&#39;t work going from Python 3.2.2 (Windows 64-bit) to Python 2.7.2 (Windows 32-bit) and got the error:

    TypeError: (&#39;data type not understood&#39;, &lt;built-in function _reconstruct&gt;, (&lt;type &#39;numpy.ndarray&#39;&gt;, (0,), &#39;[98]&#39;)).

Next, tried `numpy.save` and `numpy.load` as well as `scipy.io.mmwrite()` and `scipy.io.mmread()` and none of these methods worked either.

Save / load scipy sparse csr_matrix in portable data format

Can I use `~A` to invert a numpy array of booleans, instead of the rather awkward functions `np.logical_and()` and `np.invert()`? 

Indeed, `~` seems to work fine, but I can&#39;t find it in any nympy reference manual, and - more alarmingly - it certainly does _not_ work with scalars (e.g. `bool(~True)` returns `True` !), so I&#39;m a little bit worried ...

Inverting a numpy boolean array using ~

I would like the element-wise logical OR operator. I know &quot;or&quot; itself is not what I am looking for.

I am aware that AND corresponds to `&amp;` and NOT, `~`. But what about OR? 

  [1]: https://stackoverflow.com/questions/21415661/logic-operator-for-boolean-indexing-in-pandas &quot;here&quot;
  [2]: https://stackoverflow.com/questions/15998188/how-can-i-obtain-the-element-wise-logical-not-of-a-pandas-series

Element-wise logical OR in Pandas

In pandas, I&#39;d like to create a computed column that&#39;s a boolean operation on two other columns. 

In pandas, it&#39;s easy to add together two numerical columns. I&#39;d like to do something similar with logical operator `AND`. Here&#39;s my first try:

    In [1]: d = pandas.DataFrame([{&#39;foo&#39;:True, &#39;bar&#39;:True}, {&#39;foo&#39;:True, &#39;bar&#39;:False}, {&#39;foo&#39;:False, &#39;bar&#39;:False}])
    
    In [2]: d
    Out[2]: 
         bar    foo
    0   True   True
    1  False   True
    2  False  False

    In [3]: d.bar and d.foo   ## can&#39;t
    ...
    ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

So I guess logical operators don&#39;t work quite the same way as numeric operators in pandas. I tried doing what the error message suggests and using `bool()`:


    In [258]: d.bar.bool() and d.foo.bool()  ## spoiler: this doesn&#39;t work either
    ...
    ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().


I found a way that works by casting the boolean columns to `int`, adding them together and evaluating as a boolean.
    
    In [4]: (d.bar.apply(int) + d.foo.apply(int)) &gt; 0  ## Logical OR
    Out[4]: 
    0     True
    1     True
    2    False
    dtype: bool
    
    In [5]: (d.bar.apply(int) + d.foo.apply(int)) &gt; 1  ## Logical AND
    Out[5]: 
    0     True
    1    False
    2    False
    dtype: bool
    
This is convoluted. Is there a better way?


Content Type	Original Author	Original Content on Stackoverflow
Question	jb.	View Question on Stackoverflow
Solution 1 - Python	jcollado	View Answer on Stackoverflow
Solution 2 - Python	dirkjot	View Answer on Stackoverflow
Solution 3 - Python	ViennaMike	View Answer on Stackoverflow
Solution 4 - Python	Roko Mijic	View Answer on Stackoverflow

How to perform element-wise Boolean operations on NumPy arrays

Python Problem Overview

Python Solutions

Solution 1 - Python

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Convert number of minutes into hours & minutes using PHP

When is "java.io.IOException:Connection reset by peer" thrown?

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