This should work

    rows = session.query(Congress).count()

**EDIT:** Another way related to my first try
    
    from sqlalchemy import func
    rows = session.query(func.count(Congress.id)).scalar()

What are the differences in performance and behavior between using Python&#39;s native `sum` function and NumPy&#39;s `numpy.sum`? `sum` works on NumPy&#39;s arrays and `numpy.sum` works on Python lists and they both return the same effective result (haven&#39;t tested edge cases such as overflow) but different types.
    
	&gt;&gt;&gt; import numpy as np
	&gt;&gt;&gt; np_a = np.array(range(5))
	&gt;&gt;&gt; np_a
	array([0, 1, 2, 3, 4])
	&gt;&gt;&gt; type(np_a)
	&lt;class &#39;numpy.ndarray&#39;)
	
	&gt;&gt;&gt; py_a = list(range(5))
	&gt;&gt;&gt; py_a
	[0, 1, 2, 3, 4]
	&gt;&gt;&gt; type(py_a)
	&lt;class &#39;list&#39;&gt;

	# The numerical answer (10) is the same for the following sums:
	&gt;&gt;&gt; type(np.sum(np_a))
	&lt;class &#39;numpy.int32&#39;&gt;
	&gt;&gt;&gt; type(sum(np_a))
	&lt;class &#39;numpy.int32&#39;&gt;
	&gt;&gt;&gt; type(np.sum(py_a))
	&lt;class &#39;numpy.int32&#39;&gt;
	&gt;&gt;&gt; type(sum(py_a))
	&lt;class &#39;int&#39;&gt;

**Edit:** I think my practical question here is would using `numpy.sum` on a list of Python integers be any faster than using Python&#39;s own `sum`?

Additionally, what are the implications (including performance) of using a Python integer versus a scalar `numpy.int32`? For example, for `a += 1`, is there a behavior or performance difference if the type of `a` is a Python integer or a `numpy.int32`? I am curious if it is faster to use a NumPy scalar datatype such as `numpy.int32` for a value that is added or subtracted a lot in Python code.

For clarification, I am working on a bioinformatics simulation which partly consists of collapsing multidimensional `numpy.ndarray`s into single scalar sums which are then additionally processed. I am using Python 3.2 and NumPy 1.6.

Thanks in advance!

Python&#39;s sum vs. NumPy&#39;s numpy.sum

I&#39;m changing slide with the following code:

    viewPager.setCurrentItem(index++, true);

But it changes too fast. Is there a way to set manually the animation speed?

Change ViewPager animation duration when sliding programmatically

I am using SQLAlchemy in Python, and I want to know how to get the total number of rows in a column. I have variables defined:

    engine = sqlalchemy.create_engine(url, ehco=False)
    Session = sqlalchemy.orm.sessionmaker(bind=engine)
    Session = session()
    metadata = sqlalchemy.MetaData(engine)
    Base = declarative_base(metadata=metadata)

    # A class representing the shape_congress_districts_2012 table
    class Congress(Base):
        __tablename__ = &#39;shape_congress_districts_2012&#39;
        id = geoalchemy.Column(sqlalchemy.Integer, primary_key=True)
        name = geoalchemy.Column(sqlalchemy.Unicode)
        geom = geoalchemy.GeometryColumn(geoalchemy.Polygon(2))
        geom_simple = geoalchemy.GeometryColumn(geoalchemy.Polygon(2))
        area = geoalchemy.Column(sqlalchemy.Float)
        state_id = geoalchemy.Column(sqlalchemy.Integer)
        census_year = geoalchemy.Column(sqlalchemy.Date)

    geoalchemy.GeometryDDL(Congress.__table__)


I want to determine the total number of rows in the table without having to wait a whole bunch of time querying the database. Currently, I have a bit of code:

    rows = session.query(Congress).all()

Then I can access them from list, but this requires me to load everything into memory at once.

Get the number of rows in table using SQLAlchemy

I am using SQLAlchemy in Python, and I want to know how to get the total number of rows in a column. I have variables defined:
<pre><code class="hljs language-ini">engine = sqlalchemy.create_engine(url, ehco=False)
Session = sqlalchemy.orm.sessionmaker(bind=engine)
Session = session()
metadata = sqlalchemy.MetaData(engine)
Base = declarative_base(metadata=metadata)

# A class representing the shape_congress_districts_2012 table
class Congress(Base):
 __tablename__ = 'shape_congress_districts_2012'
 id = geoalchemy.Column(sqlalchemy.Integer, primary_key=True)
 name = geoalchemy.Column(sqlalchemy.Unicode)
 geom = geoalchemy.GeometryColumn(geoalchemy.Polygon(2))
 geom_simple = geoalchemy.GeometryColumn(geoalchemy.Polygon(2))
 area = geoalchemy.Column(sqlalchemy.Float)
 state_id = geoalchemy.Column(sqlalchemy.Integer)
 census_year = geoalchemy.Column(sqlalchemy.Date)

geoalchemy.GeometryDDL(Congress.__table__)
</code></pre>
I want to determine the total number of rows in the table without having to wait a whole bunch of time querying the database. Currently, I have a bit of code:
<pre><code class="hljs language-ini">rows = session.query(Congress).all()
</code></pre>
Then I can access them from list, but this requires me to load everything into memory at once.

&gt; **Possible Duplicate:**  
&gt; [Flattening a shallow list in Python](https://stackoverflow.com/questions/406121/flattening-a-shallow-list-in-python)  
&gt; [Flatten (an irregular) list of lists in Python](https://stackoverflow.com/questions/2158395/flatten-an-irregular-list-of-lists-in-python)  

&lt;!-- End of automatically inserted text --&gt;

**EDIT:** The question is not *how* to do it - this has been [discussed][1] in other [questions][2] - the question is, which is the *fastest* method?

I&#39;ve found solutions before, but I&#39;m wondering what the fastest solution is to flatten lists which contain other lists of arbitrary length.

For example:

    [1, 2, [3, 4, [5],[]], [6]]

Would become:

    [1,2,3,4,5,6]

There can be infinitely many levels. Some of the list objects can be strings, which mustn&#39;t be flattened into their sequential characters in the output list.


  [1]: https://stackoverflow.com/questions/406121/flattening-a-shallow-list-in-python
  [2]: https://stackoverflow.com/questions/2158395/flatten-an-irregular-list-of-lists

What is the fastest way to flatten arbitrarily nested lists in Python?

I was just re-reading [What’s New In Python 3.0][1] and it states:

&gt; The round() function rounding strategy and return type have changed.
&gt; Exact halfway cases are now rounded to the nearest even result instead
&gt; of away from zero. (For example, round(2.5) now returns 2 rather than
&gt; 3.)

and
the documentation for [round][2]:

&gt; For the built-in types supporting round(), values are rounded to the
&gt; closest multiple of 10 to the power minus n; if two multiples are
&gt; equally close, rounding is done toward the even choice


So, under **v2.7.3**:

    In [85]: round(2.5)
    Out[85]: 3.0
    
    In [86]: round(3.5)
    Out[86]: 4.0

as I&#39;d have expected. However, now under **v3.2.3**:

    In [32]: round(2.5)
    Out[32]: 2
    
    In [33]: round(3.5)
    Out[33]: 4

This seems counter-intuitive and contrary to what I understand about
rounding (and bound to trip up people). English isn&#39;t my native language but
until I read this I thought I knew what rounding meant :-/  I am sure
at the time v3 was introduced there must have been some discussion of
this, but I was unable to find a good reason in my search.

 1. Does anyone have insight into why this was changed to this?
 2. Are there any other mainstream programming languages (e.g., *C, C++, Java, Perl,* ..) that do this sort of (to  me inconsistent) rounding?

What am I missing here?

UPDATE: @Li-aungYip&#39;s comment re &quot;Banker&#39;s rounding&quot; gave me the right search term/keywords to search for and I found this SO question: https://stackoverflow.com/questions/311696/why-does-net-use-bankers-rounding-as-default, so I will be reading that carefully.



  [1]: http://docs.python.org/py3k/whatsnew/3.0.html
  [2]: http://docs.python.org/release/3.1.5/library/functions.html#round

Python 3.x rounding behavior

Let&#39;s say we have a function `add` as follows

    def add(x, y):
        return x + y

we want to apply map function for an array 

    map(add, [1, 2, 3], 2)

The semantics are I want to add 2 to every element of the array. But the `map` function requires a list in the third argument as well.

**Note:** I am putting the `add` example for simplicity. My original function is much more complicated. And of course option of setting the default value of `y` in `add` function is out of question as it will be changed for every call.

How to do multiple arguments to map function where one remains the same in python?

I want to create a formatted string with fixed size with fixed position between fields. An example explains better, here there are clearly 3 distinct fields and the string is a fixed size:

    XXX        123   98.00
    YYYYY        3    1.00
    ZZ          42  123.34

How can I apply such formatting to a string in python (2.7)?

How do I make a fixed size formatted string in python?

I am following the Python tutorial and at some point they talk about how the 1st statement of a function can be a String Literal. As far as the example goes, this String Literal seems to be done with three `&quot;`s, giving in the [example][1]

    &quot;&quot;&quot;Print a Fibonacci series up to n.&quot;&quot;&quot;

According to this documentation, this would be used mainly to create some kind of automatically produced documentation.

So I am wondering if someone here could explain to me what are these string literals exactly?

  [1]: http://docs.python.org/release/2.7.3/tutorial/controlflow.html#defining-functions

String literal with triple quotes in function definitions

Is SQL able to do something along the lines of this: `SELECT * FROM table WHERE column = everything`


SQL WHERE column = everything

I want to insert to a table from a select statement, however, there are 3 columns returned from the select statement and the table has 4 columns, I would like to add 0 for all rows in the extra column. Can anyone give me a sample SQL query for that?

Thank you!

Oracle insert from select into table with more columns

I am using a query to alter the charset of a column


    ALTER TABLE `media_value_report` 
        CHANGE `index_page_body` `index_page_body` TEXT CHARACTER  
        SET utf8 NULL DEFAULT NULL

i want to do this for other columns  main_title, landing_page_body as well. But am getting a #1064 error while executing. Can i alter-change multiple columns in  a single query?

I tried but i found in goog search that is not possible to alter in a single query. 



Alter multiple columns in a single statement

I&#39;m getting error:


&gt;Illegal mix of collations (utf8_general_ci,IMPLICIT) and (utf8_unicode_ci,IMPLICIT) for operation &#39;=&#39;&quot;

I tried changing both tables manually to `utf8_general_ci,IMPLICIT` but I&#39;m still getting the error.

Is there a way to convert all tables to `utf8_general_ci,IMPLICIT` and be done with it?

How to convert all tables in database to one collation?

I have a .`sql` file. I am trying to pass connection string details through a Powershell script and invoke an `.sql` file.

I was searching and came up with a cmdlet related to `Invoke-sqlcmd`. While I was trying to find a module corresponding to SQL, I did not find any one in my machine.

Should I install anything in my machine (the machine already has SQL Server Management Studio 2008 R2) to get the modules or is there any easy way to execute `.sql` files using Powershell?

How to execute .sql file using powershell?

How do I specify the column that I want in my query using a model (it selects all columns by default)? I know how to do this with the sqlalchmey session: `session.query(self.col1)`, but how do I do it with with models?  I can&#39;t do `SomeModel.query()`. Is there a way?

Flask SQLAlchemy query, specify column names

Why is this simple test case inserting 100,000 rows 25 times slower with SQLAlchemy than it is using the sqlite3 driver directly?  I have seen similar slowdowns in real-world applications.  Am I doing something wrong?

    #!/usr/bin/env python
    # Why is SQLAlchemy with SQLite so slow?
    # Output from this program:
    # SqlAlchemy: Total time for 100000 records 10.74 secs
    # sqlite3:    Total time for 100000 records  0.40 secs
    
    
    import time
    import sqlite3
    
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy import Column, Integer, String,  create_engine 
    from sqlalchemy.orm import scoped_session, sessionmaker
    
    Base = declarative_base()
    DBSession = scoped_session(sessionmaker())
    
    class Customer(Base):
        __tablename__ = &quot;customer&quot;
        id = Column(Integer, primary_key=True)
        name = Column(String(255))
    
    def init_sqlalchemy(dbname = &#39;sqlite:///sqlalchemy.db&#39;):
        engine  = create_engine(dbname, echo=False)
        DBSession.configure(bind=engine, autoflush=False, expire_on_commit=False)
        Base.metadata.drop_all(engine)
        Base.metadata.create_all(engine)
    
    def test_sqlalchemy(n=100000):
        init_sqlalchemy()
        t0 = time.time()
        for i in range(n):
            customer = Customer()
            customer.name = &#39;NAME &#39; + str(i)
            DBSession.add(customer)
        DBSession.commit()
        print &quot;SqlAlchemy: Total time for &quot; + str(n) + &quot; records &quot; + str(time.time() - t0) + &quot; secs&quot;
    
    def init_sqlite3(dbname):
        conn = sqlite3.connect(dbname)
        c = conn.cursor()
        c.execute(&quot;DROP TABLE IF EXISTS customer&quot;)
        c.execute(&quot;CREATE TABLE customer (id INTEGER NOT NULL, name VARCHAR(255), PRIMARY KEY(id))&quot;)
        conn.commit()
        return conn
    
    def test_sqlite3(n=100000, dbname = &#39;sqlite3.db&#39;):
        conn = init_sqlite3(dbname)
        c = conn.cursor()
        t0 = time.time()
        for i in range(n):
            row = (&#39;NAME &#39; + str(i),)
            c.execute(&quot;INSERT INTO customer (name) VALUES (?)&quot;, row)
        conn.commit()
        print &quot;sqlite3: Total time for &quot; + str(n) + &quot; records &quot; + str(time.time() - t0) + &quot; sec&quot;
    
    if __name__ == &#39;__main__&#39;:
        test_sqlalchemy(100000)
        test_sqlite3(100000)

I have tried numerous variations (see http://pastebin.com/zCmzDraU )


Why is SQLAlchemy insert with sqlite 25 times slower than using sqlite3 directly?

Just a quick question: SQLAlchemy [talks about][1] calling `sessionmaker()` once but calling the resulting `Session()` class each time you need to talk to your DB. For me that means the second I would do my first `session.add(x)` or something similar, I would first do

    from project import Session
    session = Session()

What I did until now was to make the call `session = Session()` in my model _once_ and then always import the same session anywhere in my application. Since this is a web-applications this would _usually_ mean the same (as one view is executed).

But where is the difference? What is the disadvantage of using one session all the time against using it for my database stuff until my function is done and then creating a new one the next time I want to talk to my DB?

I get that if I use multiple threads, each one should get their own session. But using `scoped_session()`, I already make sure that problem doesn&#39;t exist, do I?

Please clarify if any of my assumptions are wrong.


  [1]: http://docs.sqlalchemy.org/en/rel_0_7/orm/session.html#session-frequently-asked-questions &quot;Session FAQ&quot;

SQLAlchemy: Creating vs. Reusing a Session

I&#39;m a beginner in SQLAlchemy and found query can be done in 2 method:

Approach 1:

    DBSession = scoped_session(sessionmaker())
    class _Base(object):
        query = DBSession.query_property()

    Base = declarative_base(cls=_Base)

    class SomeModel(Base):
        key   = Column(Unicode, primary_key=True)
        value = Column(Unicode)

    # When querying
    result = SomeModel.query.filter(...)

Approach 2

    DBSession = scoped_session(sessionmaker())
    Base = declarative_base()

    class SomeModel(Base):
        key   = Column(Unicode, primary_key=True)
        value = Column(Unicode)

    # When querying
    session = DBSession()
    result = session.query(SomeModel).filter(...)


Is there any difference between them?

What&#39;s the difference between Model.query and session.query(Model) in SQLAlchemy?

I am developing an app with flask and SQL Alchemy.
I need to display the queries executed to generate a page alongside the time each query took for debugging

What&#39;s the best way to do it?

Content Type	Original Author	Original Content on Stackoverflow
Question	dbmikus	View Question on Stackoverflow
Solution 1 - Python	Diego Navarro	View Answer on Stackoverflow

Get the number of rows in table using SQLAlchemy

Python Problem Overview

Python Solutions

Solution 1 - Python

Change ViewPager animation duration when sliding programmatically

Python's sum vs. NumPy's numpy.sum

Attributions