Give  [`np.ix_`][1] a try:

    Y[np.ix_([0,3],[0,3])]

This returns your desired result:

    In [25]: Y = np.arange(16).reshape(4,4)
    In [26]: Y[np.ix_([0,3],[0,3])]
    Out[26]:
    array([[ 0,  3],
           [12, 15]])

  [1]: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ix_.html

One solution is to index the rows/columns by slicing/striding. Here&#39;s an example where you are extracting every third column/row from the first to last columns (i.e. the first and fourth columns)

    In [1]: import numpy as np
    In [2]: Y = np.arange(16).reshape(4, 4)
    In [3]: Y[0:4:3, 0:4:3]
    Out[1]: array([[ 0,  3],
                   [12, 15]])

This gives you the output you were looking for.

For more info, check out [this page on indexing in `NumPy`](http://docs.scipy.org/doc/numpy/user/basics.indexing.html). 

    print y[0:4:3,0:4:3]

 is the shortest and most appropriate fix .

First of all, your `Y` only has 4 col and rows, so there is no col4 or row4, at most col3 or row3.

To get 0, 3 cols: `Y[[0,3],:]`
To get 0, 3 rows: `Y[:,[0,3]]`

So to get the array you request: `Y[[0,3],:][:,[0,3]]`

Note that if you just `Y[[0,3],[0,3]]` it is equivalent to `[Y[0,0], Y[3,3]]` and the result will be of two elements: `array([ 0, 15])`

 

You can also do this using:

    Y[[[0],[3]],[0,3]]

which is equivalent to doing this using indexing arrays:

    idx = np.array((0,3)).reshape(2,1)
    Y[idx,idx.T]

To make the broadcasting work as desired, you need the non-singleton dimension of your indexing array to be aligned with the axis you&#39;re indexing into, e.g. for an n x m 2D subarray:

    Y[&lt;n x 1 array&gt;,&lt;1 x m array&gt;]

This doesn&#39;t create an intermediate array, unlike CT Zhu&#39;s answer, which creates the intermediate array `Y[(0,3),:]`, then indexes into it.

This can also be done by slicing: `Y[[0,3],:][:,[0,3]]`. More elegantly, it is possible to slice arrays (or even reorder them) by given sets of indices for rows, columns, pages, et cetera:

```python
r=np.array([0,3])
c=np.array([0,3])
print(Y[r,:][:,c]) #&gt;&gt;[[ 0  3][12 15]]
```
for reordering try this:
```python
r=np.array([0,3])
c=np.array([3,0])
print(Y[r,:][:,c])#&gt;&gt;[[ 3  0][15 12]]
```


I opened the Nav example which comes with the standard Bootstrap download (bootstrap-3.0.0\examples\navbar\index.html) and added vertical dividers between two of the links.

However, it doesn&#39;t seem to make any difference to the navigation bar:

    &lt;div class=&quot;navbar-collapse collapse&quot;&gt;
    &lt;ul class=&quot;nav navbar-nav&quot;&gt;
    &lt;li class=&quot;active&quot;&gt;&lt;a href=&quot;#&quot;&gt;Link1&lt;/a&gt;&lt;/li&gt;
    &lt;li&gt;&lt;a href=&quot;#&quot;&gt;Link2&lt;/a&gt;&lt;/li&gt;
    &lt;li class=&quot;divider-vertical&quot;&gt;&lt;/li&gt;
    &lt;li&gt;&lt;a href=&quot;#&quot;&gt;Link3&lt;/a&gt;&lt;/li&gt;


Vertical divider doesn&#39;t work in Bootstrap 3

The EF `OjbectSet.Include(a =&gt; a.parent)` extension is unavailable. I know I could add code to mimic it, but according to https://stackoverflow.com/questions/14518149/entityframework-4-upgraded-to-5-lambda-is-not-available it should be available. I have the `using System.Data.Entity`, and am upgraded to EF 5 in my main project.

Looking the metadata in Assembly System.Data.Entity.dll, v4.0.0.0 (C:\Program Files (x86)\Reference Assemblies\Microsoft\Framework\.NETFramework\v4.5\System.Data.Entity.dll)

	...

	public ObjectQuery&lt;System.Data.Common.DbDataRecord&gt; GroupBy(string keys, string projection, params ObjectParameter[] parameters);
	public ObjectQuery&lt;T&gt; Include(string path);
	public ObjectQuery&lt;T&gt; Intersect(ObjectQuery&lt;T&gt; query);

There is no declaration for the lambda variant of Include here. I&#39;ve checked and the file version is 4.0.30319.17929, as per https://stackoverflow.com/questions/18208102/database-first-generation-entity-framework-5-system-data-entity-vs-entityframewo . The project is generating a 4.5 assembly.

In case it&#39;s relevant, EntityFramework itself is not referenced in this assembly. It just has some data services, so it does include references to System.Data.Entity and the main data layer project.

Any ideas?

Missing Entity Framework Include(lambda) extension

I&#39;m pretty new in `numpy` and I am having a hard time understanding how to extract from a `np.array` a sub matrix with defined columns and rows:

    Y = np.arange(16).reshape(4,4)
If I want to extract columns/rows 0 and 3, I should have:

    [[0 3]
     [12 15]]
I tried all the reshape functions...but cannot figure out how to do this.  Any ideas?

Numpy extract submatrix

I'm pretty new in <code>numpy</code> and I am having a hard time understanding how to extract from a <code>np.array</code> a sub matrix with defined columns and rows:
<pre><code class="hljs language-ini">Y = np.arange(16).reshape(4,4)
</code></pre>
If I want to extract columns/rows 0 and 3, I should have:
<pre><code class="hljs language-lua">[[0 3]
 [12 15]]
</code></pre>
I tried all the reshape functions...but cannot figure out how to do this. Any ideas?

I have some points and I am trying to fit curve for this points. I know that there exist `scipy.optimize.curve_fit` function, but I do not understand documentation, i.e how to use this function.

My points: `np.array([(1, 1), (2, 4), (3, 1), (9, 3)])`

Can anybody explain how to do that?

python numpy/scipy curve fitting

I am unable to create a single table using SQLAlchemy.

I can create it by calling `Base.metadata.create_all(engine)` but as the number of table grows, this call takes a long time.

I create table classes on the fly and then populate them.

    from sqlalchemy import create_engine, Column, Integer, Sequence, String, Date, Float, BIGINT
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import sessionmaker

    Base = declarative_base()

    class HistoricDay():
    	
    	id = Column(Integer, Sequence(&#39;id_seq&#39;), primary_key=True)
    	#  Date, Open, High, Low, Close, Volume, Adj Close
    	date = Column(Date)
    	open = Column(Float)
    	high = Column(Float)
    	low = Column(Float)
    	close = Column(Float)
    	volume = Column(BIGINT)
    	adjClose = Column(Float)
    	
    	def __init__(self, date, open, high, low, close, volume, adjClose):
    		self.date = date
    		self.open = open
    		self.high = high
    		self.low = low
    		self.close = close
    		self.volume = volume
    		self.adjClose = adjClose

    def build_daily_history_table_repr(self):
    		return &quot;&lt;&quot;+self.__tablename__+&quot;(&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;,&#39;{}&#39;)&gt;&quot;.format(self.id, self.date, self.open, self.high, self.low, self.close, self.volume, self.adjClose)
    		
    def build_daily_history_table(ticket):
    	classname = ticket+&quot;_HistoricDay&quot;
    	globals()[classname] = type(classname, (HistoricDay,Base), {&#39;__tablename__&#39; : ticket+&quot;_daily_history&quot;})
    	setattr(globals()[classname], &#39;__repr__&#39;, build_daily_history_table_repr)

	# Initialize the database :: Connection &amp; Metadata retrieval
	engine = create_engine(&#39;mysql+cymysql://root@localhost/gwc?charset=utf8&amp;use_unicode=0&#39;, pool_recycle=3600) # ,echo = True
	
	# SqlAlchemy :: Session setup
	Session = sessionmaker(bind=engine)

	# Create all tables that do not already exist
	Base.metadata.create_all(engine)

	# SqlAlchemy :: Starts a session
	session = Session()

    ticketList = getTicketList()

    for ticket in ticketList:
    	build_daily_history_table(ticket)
    	class_name = ticket+&quot;_HistoricDay&quot;
    	
    	meta_create_all_timer = time.time()
    	# Create all tables that do not already exist
    	# globals()[class_name](&#39;2005-07-24&#39;,0,0,0,0,0,0).create(engine)  #doesn&#39;t work
    	#(globals()[class_name]).__table__.create(engine) #doesn&#39;t work
    	# session.commit() #doesn&#39;t work
    	
    	#Base.metadata.create_all(engine) # works but gets very slow
    	print(&quot;  meta_create_all_timer {}s&quot;.format(time.time()-meta_create_all_timer))
    	
        data = getData(ticket)

    	for m_date, m_open, m_close, m_high, m_low, m_volume, m_adjClose in data:
    		entry = globals()[class_name](m_date, m_open, m_high, m_low, m_close, m_volume, m_adjClose)
    		session.add(entry)
    	
    	session.commit()

I saw in the [documentation][1] that you can do 

    engine = create_engine(&#39;sqlite:///:memory:&#39;)
    
    meta = MetaData()
    
    employees = Table(&#39;employees&#39;, meta,
        Column(&#39;employee_id&#39;, Integer, primary_key=True),
        Column(&#39;employee_name&#39;, String(60), nullable=False, key=&#39;name&#39;),
        Column(&#39;employee_dept&#39;, Integer, ForeignKey(&quot;departments.department_id&quot;))
    )
    employees.create(engine)


However, I&#39;m not able to figure out how to do the same thing as `Table` does, with `declarative_base()`.

How can I do that with classes that inherit from `declarative_base()`?


  [1]: http://docs.sqlalchemy.org/en/rel_0_8/core/metadata.html

How to create only one table with SQLAlchemy?

One frequently finds expressions of this type in python questions on SO. Either for just accessing all items of the iterable

    for i in range(len(a)):
        print(a[i])

Which is just a clumbersome way of writing:

    for e in a:
        print(e)

Or for assigning to elements of the iterable:

    for i in range(len(a)):
        a[i] = a[i] * 2

Which should be the same as:

    for i, e in enumerate(a):
         a[i] = e * 2
    # Or if it isn&#39;t too expensive to create a new iterable
    a = [e * 2 for e in a]

Or for filtering over the indices:

    for i in range(len(a)):
        if i % 2 == 1: continue
        print(a[i])

Which could be expressed like this:

    for e in a [::2]:
        print(e)

Or when you just need the length of the list, and not its content:

    for _ in range(len(a)):
        doSomethingUnrelatedToA()

Which could be:

    for _ in a:
        doSomethingUnrelatedToA()

**In python we have `enumerate`, slicing, `filter`, `sorted`, etc... As python `for` constructs are intended to iterate over iterables and not only ranges of integers, are there real-world use-cases where you need `in range(len(a))`?**

Is there a need for range(len(a))?

Is there a way to make a defaultdict also be the default for the defaultdict? (i.e. infinite-level recursive defaultdict?)

I want to be able to do:

    x = defaultdict(...stuff...)
    x[0][1][0]
    {}

So, I can do `x = defaultdict(defaultdict)`, but that&#39;s only a second level:

    x[0]
    {}
    x[0][0]
    KeyError: 0

There are recipes that can do this. But can it be done simply just using the normal defaultdict arguments?

Note this is asking how to do an infinite-level recursive defaultdict, so it&#39;s distinct to *https://stackoverflow.com/questions/5029934*, which was how to do a two-level defaultdict.

I&#39;ll probably just end up using the *bunch* pattern, but when I realized I didn&#39;t know how to do this, it got me interested.

Nested defaultdict of defaultdict

I have a Python script that imports a large CSV file and then counts the number of occurrences of each word in the file, then exports the counts to another CSV file.

But what is happening is that once that counting part is finished and the exporting begins it says `Killed` in the terminal.

I don&#39;t think this is a memory problem (if it was I assume I would be getting a memory error and not `Killed`).

Could it be that the process is taking too long? If so, is there a way to extend the time-out period so I can avoid this?

Here is the code:

	csv.field_size_limit(sys.maxsize)
		counter={}
		with open(&quot;/home/alex/Documents/version2/cooccur_list.csv&quot;,&#39;rb&#39;) as file_name:
			reader=csv.reader(file_name)
			for row in reader:
				if len(row)&gt;1:
					pair=row[0]+&#39; &#39;+row[1]
					if pair in counter:
						counter[pair]+=1
					else:
						counter[pair]=1
		print &#39;finished counting&#39;
		writer = csv.writer(open(&#39;/home/alex/Documents/version2/dict.csv&#39;, &#39;wb&#39;))
		for key, value in counter.items():
			writer.writerow([key, value])
			
And the `Killed` happens after `finished counting` has printed, and the full message is: 

    killed (program exited with code: 137)

What does &#39;killed&#39; mean when a processing of a huge CSV with Python, which suddenly stops?

Is there a simple way to index all elements of a list (or array, or whatever) **except** for a particular index?  E.g., 

- `mylist[3]` will return the item in position 3

- `milist[~3]` will return the whole list except for 3

Index all *except* one item in python

I am coding with Pythons `NumPy` module.  If coordinates of a point in 3D space are described as `[1, 2, 1]`, wouldn&#39;t that be three dimensions, three axis, a rank of three?  Or if that is one dimension then shouldn&#39;t it be points (plural), not point?


Here is the documentation:
&gt; In Numpy dimensions are called axes. The number of axes is rank.
&gt; For example, the coordinates of a point in 3D space [1, 2, 1] is an array of rank 1, because it has one axis. That axis has a length of
&gt; 3.


Source: http://wiki.scipy.org/Tentative_NumPy_Tutorial

In Python NumPy what is a dimension and axis?

In matlab I use

    a=[1,4,6]
    b=[1,2,3]
    corr(a,b)

which returns .9934. I&#39;ve tried `numpy.correlate` but it returns something completely different. What is the simplest way to get the correlation of two vectors?

How to get correlation of two vectors in python

I am having trouble converting a python `datetime64` object into a string. For example:

    t = numpy.datetime64(&#39;2012-06-30T20:00:00.000000000-0400&#39;)

Into:

    &#39;2012.07.01&#39; as a  string. (note time difference)

I have already tried to convert the `datetime64` object to a `datetime` long then to a string, but I seem to get this error:

    dt = t.astype(datetime.datetime) #1341100800000000000L
    time.ctime(dt)
    ValueError: unconvertible time


Content Type	Original Author	Original Content on Stackoverflow
Question	user1595929	View Question on Stackoverflow
Solution 1 - Python	JoshAdel	View Answer on Stackoverflow
Solution 2 - Python	mdml	View Answer on Stackoverflow
Solution 3 - Python	Hima	View Answer on Stackoverflow
Solution 4 - Python	CT Zhu	View Answer on Stackoverflow
Solution 5 - Python	ali_m	View Answer on Stackoverflow
Solution 6 - Python	Father Geppeto	View Answer on Stackoverflow

Numpy extract submatrix

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Solution 3 - Python

Solution 4 - Python

Solution 5 - Python

Solution 6 - Python

Missing Entity Framework Include(lambda) extension

Vertical divider doesn't work in Bootstrap 3

Attributions