    In [50]: import platform    
    In [52]: platform.python_implementation()
    Out[52]: &#39;CPython&#39;



How about [platform][1]

it gives you

    platform.python_implementation()


  [1]: http://docs.python.org/2/library/platform.html#platform.python_implementation

I understand that hashes will be different based on different datatypes in SQL Server. One support Unicode another not .... so on (also collation)

I am using char(32) as a datatype but the output is weird. 
Using this

    select HASHBYTES(&#39;MD5&#39;,&#39;MD5Text&#39;)

gives this ouput:

    0xA891DB2DA259280A66FD5F35201CAB6A

and when

    declare @h char(32)
    select @h=HASHBYTES(&#39;MD5&#39;,&#39;MD5Text&#39;)
    select @h,LEN(@h)
output:

Ё‘Ы-ўY( fэ_5 &#171;j 

So I am new to SQL Server.  
Could anyone, please, tell me what datatype should I use to store hashes ??

               

 

What kind of datatype should one use to store hashes?

I have been learning Java for a while now and still learning new syntax tricks and stuff. I came across this in Android source code:

    boolean retry = id == 1;

What does it mean?

What does this line mean in Java: boolean retry = id == 1;

Python has a few different implementations: CPython, Jython, PyPy, etc.  I want to programmatically determine which implementation my code is running on.  How can I do that?

To be specific, write a function called ``get_implementation_name()`` for me:

    impl_name = get_implementation_name()
    if impl_name == &quot;CPython&quot;:
      print &quot;I can abuse CPython implementation details. (I&#39;m a bad, bad man.)&quot;
    elif impl_name == &quot;PyPy&quot;:
      print &quot;Can&#39;t count on reference-counting garbage collection here...&quot;
    else:
      print &quot;I better be careful...&quot;


How can I tell which python implementation I&#39;m using?

Python has a few different implementations: CPython, Jython, PyPy, etc. I want to programmatically determine which implementation my code is running on. How can I do that?
To be specific, write a function called <code>get_implementation_name()</code> for me:
<pre><code class="hljs language-bash">impl_name = get_implementation_name()
if impl_name == "CPython":
 print "I can abuse CPython implementation details. (I'm a bad, bad man.)"
elif impl_name == "PyPy":
 print "Can't count on reference-counting garbage collection here..."
else:
 print "I better be careful..."
</code></pre>

What&#39;s the trivial example of how to generate random colors for passing to plotting functions?

I&#39;m calling scatter inside a loop and want each plot a different color.

    for X,Y in data:
       scatter(X, Y, c=??)

&gt; c:
a color. c can be a single color format string, or a sequence of color specifications of length N, or a sequence of N numbers to be mapped to colors using the cmap and norm specified via kwargs (see below). Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. c can be a 2-D array in which the rows are RGB or RGBA, however.



How to generate random colors in matplotlib?

I connected to a mysql database using python `con = _mysql.connect(&#39;localhost&#39;, &#39;dell-pc&#39;, &#39;&#39;, &#39;test&#39;)`
The program that I wrote takes a lot of time in full execution i.e. around 10 hours. Actually, I am trying to read distinct words from a corpus. 
After reading was finished there was a timeout error.

I checked Mysql default timeouts which were:

    +----------------------------+----------+
    | Variable_name              | Value    |
    +----------------------------+----------+
    | connect_timeout            | 10       |
    | delayed_insert_timeout     | 300      |
    | innodb_lock_wait_timeout   | 50       |
    | innodb_rollback_on_timeout | OFF      |
    | interactive_timeout        | 28800    |
    | lock_wait_timeout          | 31536000 |
    | net_read_timeout           | 30       |
    | net_write_timeout          | 60       |
    | slave_net_timeout          | 3600     |
    | wait_timeout               | 28800    |
    +----------------------------+----------+

How can I change the default timeout ?

How can I change the default Mysql connection timeout when connecting through python?

Prompted by the discussion [here](https://stackoverflow.com/a/14721700/174728)

The [docs](http://docs.python.org/2/library/functions.html#all) suggest some equivalent code for the behaviour of [`all`](http://docs.python.org/2/library/functions.html#all) and [`any`](http://docs.python.org/2/library/functions.html#any)

Should the behaviour of the equivalent code be considered part of the definition, or can an implementation implement them in a non-shortcircuit manner?

Here is the relevant excerpt from cpython/Lib/test/test_builtin.py

    def test_all(self):
        self.assertEqual(all([2, 4, 6]), True)
        self.assertEqual(all([2, None, 6]), False)
        self.assertRaises(RuntimeError, all, [2, TestFailingBool(), 6])
        self.assertRaises(RuntimeError, all, TestFailingIter())
        self.assertRaises(TypeError, all, 10)               # Non-iterable
        self.assertRaises(TypeError, all)                   # No args
        self.assertRaises(TypeError, all, [2, 4, 6], [])    # Too many args
        self.assertEqual(all([]), True)                     # Empty iterator
        S = [50, 60]
        self.assertEqual(all(x &gt; 42 for x in S), True)
        S = [50, 40, 60]
        self.assertEqual(all(x &gt; 42 for x in S), False)

    def test_any(self):
        self.assertEqual(any([None, None, None]), False)
        self.assertEqual(any([None, 4, None]), True)
        self.assertRaises(RuntimeError, any, [None, TestFailingBool(), 6])
        self.assertRaises(RuntimeError, all, TestFailingIter())
        self.assertRaises(TypeError, any, 10)               # Non-iterable
        self.assertRaises(TypeError, any)                   # No args
        self.assertRaises(TypeError, any, [2, 4, 6], [])    # Too many args
        self.assertEqual(any([]), False)                    # Empty iterator
        S = [40, 60, 30]
        self.assertEqual(any(x &gt; 42 for x in S), True)
        S = [10, 20, 30]
        self.assertEqual(any(x &gt; 42 for x in S), False)

It doesn&#39;t do anything to enforce the shortcircuit behaviour

Is the shortcircuit behaviour of Python&#39;s any/all explicit?

I have a dataset with multi-index columns in a pandas df that I would like to sort by values in a specific column.  I have tried using sortindex and sortlevel but haven&#39;t been able get the results I am looking for.  My dataset looks like:

        Group1    Group2
        A B C     A B C
    1   1 0 3     2 5 7
    2   5 6 9     1 0 0
    3   7 0 2     0 3 5 

I want to sort all data and the index by column C in Group 1 in descending order so my results look like:

        Group1    Group2
        A B C     A B C
     2  5 6 9     1 0 0
     1  1 0 3     2 5 7
     3  7 0 2     0 3 5 


Is it possible to do this sort with the structure that my data is in, or should I be swapping Group1 to the index side?


Multi Index Sorting in Pandas

How do I access the corresponding groupby dataframe in a groupby object by the key?

With the following groupby:

    rand = np.random.RandomState(1)
    df = pd.DataFrame({&#39;A&#39;: [&#39;foo&#39;, &#39;bar&#39;] * 3,
                       &#39;B&#39;: rand.randn(6),
                       &#39;C&#39;: rand.randint(0, 20, 6)})
    gb = df.groupby([&#39;A&#39;])

I can iterate through it to get the keys and groups:

    In [11]: for k, gp in gb:
                 print &#39;key=&#39; + str(k)
                 print gp
    key=bar
         A         B   C
    1  bar -0.611756  18
    3  bar -1.072969  10
    5  bar -2.301539  18
    key=foo
         A         B   C
    0  foo  1.624345   5
    2  foo -0.528172  11
    4  foo  0.865408  14

I would like to be able to access a group by its key:

    In [12]: gb[&#39;foo&#39;]
    Out[12]:  
         A         B   C
    0  foo  1.624345   5
    2  foo -0.528172  11
    4  foo  0.865408  14

But when I try doing that with `gb[(&#39;foo&#39;,)]` I get this weird `pandas.core.groupby.DataFrameGroupBy` object thing which doesn&#39;t seem to have any methods that correspond to the DataFrame I want.

The best I could think of is:

    In [13]: def gb_df_key(gb, key, orig_df):
                 ix = gb.indices[key]
                 return orig_df.ix[ix]
        
             gb_df_key(gb, &#39;foo&#39;, df)
    Out[13]:
         A         B   C
    0  foo  1.624345   5
    2  foo -0.528172  11
    4  foo  0.865408  14  

but this is kind of nasty, considering how nice pandas usually is at these things.  
What&#39;s the built-in way of doing this?

How to access pandas groupby dataframe by key

What&#39;s all this fuss about Python and CPython *(Jython,IronPython)*, I don&#39;t get it:

[python.org][1] mentions that CPython is:

&gt; *The &quot;traditional&quot; implementation of Python (nicknamed CPython)*

[yet another Stack Overflow question][2] mentions that:

&gt; *CPython is the default byte-code interpreter of Python, which is written in C.*

Honestly I don&#39;t get what both of those explanations practically mean but what I thought was that, *if I use CPython does that mean when I run a sample python code, it compiles it to C language and then executes it as if it were C code*

**So what exactly is CPython and how does it differ when compared with python and should I probably use CPython over Python and if so what are its advantages?**


  [1]: http://www.python.org/
  [2]: https://stackoverflow.com/a/2324217/2425215

Python vs Cpython

I&#39;ve been hearing a lot about the [PyPy][1] project. They claim it is 6.3 times faster than the [CPython][2] interpreter on [their site][3].

Whenever we talk about dynamic languages like Python, speed is one of the top issues. To solve this, they say PyPy is 6.3 times faster.

The second issue is parallelism, the infamous [Global Interpreter Lock][4] (GIL). For this, PyPy says it [can give GIL-less Python][5].

If PyPy can solve these great challenges, what are its weaknesses that are preventing wider adoption? That is to say, what&#39;s preventing someone like me, a typical Python developer, from switching to PyPy *right now*? 

  [1]: http://en.wikipedia.org/wiki/PyPy
  [2]: http://en.wikipedia.org/wiki/CPython
  [3]: http://speed.pypy.org
  [4]: http://en.wikipedia.org/wiki/Global_Interpreter_Lock
  [5]: http://pypy.org/tmdonate.html


Why shouldn&#39;t I use PyPy over CPython if PyPy is 6.3 times faster?

I am trying to understand how Python works (because I use it all the time!). To my understanding, when you run something like python script.py, the script is converted to bytecode and then the interpreter/VM/CPython–really just a C Program–reads in the python bytecode and executes the program accordingly.

How is this bytecode read in? Is it similar to how a text file is read in C? I am unsure how the Python code is converted to machine code. Is it the case that the Python interpreter (the python command in the CLI) is really just a precompiled C program that is already converted to machine code and then the python bytecode files are just put through that program? In other words, is my Python program never actually converted into machine code? Is the python interpreter already in machine code, so my script never has to be?

How exactly is Python Bytecode Run in CPython?

Can I extend syntax in python for dict comprehensions for other dicts, like the OrderedDict in `collections` module or my own types which inherit from `dict`?  

Just rebinding the `dict` name obviously doesn&#39;t work, the `{key: value}` comprehension syntax still gives you a plain old dict for comprehensions and literals.  

    &gt;&gt;&gt; from collections import OrderedDict
    &gt;&gt;&gt; olddict, dict = dict, OrderedDict
    &gt;&gt;&gt; {i: i*i for i in range(3)}.__class__
    &lt;type &#39;dict&#39;&gt;

So, if it&#39;s possible how would I go about doing that?  It&#39;s OK if it only works in CPython.  For syntax I guess I would try it with a `O{k: v}` prefix like we have on the `r&#39;various&#39; u&#39;string&#39; b&#39;objects&#39;`.

*note:*  Of course we can use a generator expression instead, but I&#39;m more interested seeing how hackable python is in terms of the grammar.

OrderedDict comprehensions

I was playing around with timeit and noticed that doing a simple list comprehension over a small string took longer than doing the same operation on a list of small single character strings. Any explanation? It&#39;s almost 1.35 times as much time.

    &gt;&gt;&gt; from timeit import timeit
    &gt;&gt;&gt; timeit(&quot;[x for x in &#39;abc&#39;]&quot;)
    2.0691067844831528
    &gt;&gt;&gt; timeit(&quot;[x for x in [&#39;a&#39;, &#39;b&#39;, &#39;c&#39;]]&quot;)
    1.5286479570345861

What&#39;s happening on a lower level that&#39;s causing this?

Content Type	Original Author	Original Content on Stackoverflow
Question	Stuart Berg	View Question on Stackoverflow
Solution 1 - Python	unutbu	View Answer on Stackoverflow
Solution 2 - Python	Jakob Bowyer	View Answer on Stackoverflow

How can I tell which python implementation I'm using?

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

What does this line mean in Java: boolean retry = id == 1;

What kind of datatype should one use to store hashes?

Attributions