You Know, For Kids!

Swear words are a powerful motivator for novice programmers, and an unfortunate byproduct of advanced ones.

Teaching the computer to swear at you in random, creative ways is a powerful experience. It’s easy in most modern operating systems that include scripting languages (thanks for nothin’, Windows!*). So, you out there with the kids, the friends who want to learn, the curious, pass the magical power of curse words along.

* Yes, it’s even possible on Windows thanks to vbscript! On Mac / Linux / Unix, use your favorite: Python / Ruby / Bash / ….!

(inspired by Juliet at how-do-the-young-start-programming-nowadays)

Quibble, a Damn Small Query Langauge (DSQL) Using Python

This intermediate-level article will demonstrate how do use the filter idiom, delegation tables, list generators and the operator module to create a compact but expandable query langauge for querying data.

When many people hear the word ‘query’, their minds jump to Structured Query Language (SQL).  Now I love SQL as much as anyone[1].  Using SQL for queries is wonderful when one’s data is already loaded into a SQL database[2].  Sometimes the Real World (TM) conspires against this, since:

  • the data might be heterogeneous
  • the data might be easy to express in Python terms, but tedious to refector into a normalized form.  As a quick example, consider a dict of sets, which would require a join and a foreign key and actual *gasp* schema design.
  • one might not have access to a database (though with SQLite being embedded in Python from 2.5 onward, this is less an issue)
  • one might have irrational biases against schemas and the straightjacketing that they impose on agile development, and programmer whimsy.  I suffer from this bias myself, and attend regular SQL indoctination meetings, but so far it’s not sticking!  NoSQL Forever!
  • SQL is enterprisey, but not Web2.0, man!

That said, SQL has lots of advantages:

  • Exteremely flexible, complex querying
  • Widely deployed
  • (etc, etc.)

Let’s begin by building a list of dictionaries to query against.  These could be any list of object that support a dictionary interface.  Note that these objects are heterogeneous.  Also note they are quite contrived, and rather boring.

# a list of dicts to query against
data = [
    dict(a=None, b=1, c=[1,2,3]),
    dict(a=13, d=dict(a=1,b=2)),
    dict(c=13,e="some string"),
    dict(c=10,e="some other string"),
    dict(a=10,e="some other string"),
    {('author','email'): ('Gregg Lind','gregg.lind at')},

Now that we have some data, we’re going to build a simple query language called Quibble [3] to search against it.  We will be using the filter/pipeline idiom.  The filter idiom is quite simple:  if the an object matches some condition, keep it; else continue on.  On Unix, this is a very simple type of pipeline; when one wants venture capital, call it “map-reduce”.  While Python has a filter function (, the list comprehension builtin will be quite a bit simpler to use for our dumb purposes.

Next we will build a delegation table.  This simple mapping maps names like “<=” to functions.  When people talk about the power of ‘functions are first-class objects’, which is part of what they’re on about.  We can make this mapping of function shorthand names mapped to *unevaluated functions*.

To make our lives easier, Quibble will use a simple convention for defining what is a valid query operator.  An ‘operator function’ must take exactly two argument, following this format:

     my_operator(some_dict[key], value)

Luckily for us [4], the functions in the python operator module mostly take this form.  Having this same calling convention will make it possible to just drop the ‘right’ function in.

import operator
operators = {
    "<" :,
    "<=" : operator.le,
    "==" : operator.eq,
    "!=" :,
    ">=" :,
    ">"  :,
    "in" : operator.contains,
    "nin" : lambda x,y: not operator.contains(x,y),

Note that with ‘nin’, we had to wrap it.   Python’s lambda statement makes this easy, and the resulting code is still easy-to-read.  We could also use a true named function here, like this:

def nin_(x,y):
    return x not in y

Or a simpler lambda:

"nin" :  lambda x,y:  x not in y,
def query(D,key,val,operator="=="):
    D:  a dictionary
    key:  the key to query
    val:  the value
    operator:  "==", ">=", "in", et all.

    Returns elements in D such that operator(D.get(key,None), val) is true
        op = operators[operator]
    except KeyError:
        raise ValueError, "operator must be one of %r" % operators
    return [x for x in D if op(x.get(key,None),val)]

print "1st version"
print query(data,'a',1)
print query(data,'c',None,'!=')

Excellent.  Time to retire to a private island.  Oh wait, you want to define new functions?  Chain these queries together?  It should handle exceptions?  We can fix those.

A more fundamental problem with this filter approach is that defining “or” conditions is quite awkward, since filters reduce the input set at each stage, but we will clean this up as well (but it will be ugly).

Let’s add some functionality.

  • operator can be any two argument function
  • return an iterator instead of a list
  • tee the original input, just in case it too is an iterator, we don’t want to exhaust it.
  • adds a keynotfound argument, to change what happens if the key isn't found in the dict
import itertools
import inspect
def _can_take_at_least_n_args(f,n=2):
    ''' helper to check that a function can take at least two unnamed args'''
    (pos, args,kwargs, defaults) = inspect.getargspec(f)
    if args is not None or len(pos) >= n:
        return True
        return False

def query(D,key,val,operator="==", keynotfound=None):
    D:  a list of dictionaries
    key:  the key to query
    val:  the value
    operator:  "==", ">=", "in", et all, or any two-arg function
    keynotfound:  value if key is not found

    Returns elements in D such that operator(D.get(key,None), val) is true
    D = itertools.tee(D,2)[1]  # take a teed copy

    # let's let operator be any two argument callable function, *then*
    # fall back on the delegation table.
    if callable(operator):
        if not _can_take_at_least_n_args(operator,2):
            raise ValueError ("operator must take at least 2 arguments")
            # alternately, we could wrap it in a lambda, like:
            # op = lambda(x,y): operator(x),
            # but we have to check to see how many args it really wants (inc. 0!)
        op = operator
        op = operators.get(operator,None)
    if not op:
        raise ValueError, "operator must be one of %r, or a two-argument function" % operators

    def try_op(f,x,y):
            ans = f(x,y)
            return f(x,y)
        except Exception, exc:
            return False

    return (x for x in D if try_op(op, x.get(key,keynotfound),val))

print "2nd version"
print list(query(data,'a',1))
print list(query(data,'c',None,'!='))
at_fakaroo = lambda k,v:  "fakearoo" in k[1] # v will be irrelevant
print list(query(data, ('author','email'), None, at_fakaroo, keynotfound=('','')))

That is looking quite a bit more powerful!  It still has lots of problems:

  • ‘or’ isn’t well supported.
  • we handle all errors in the function equivalently — by eating them!  This will make it really hard to debug, since none of us writes perfect code.
  • chaining queries is doable via nesting, but it’s ugly (see below).
  • relies on the dictionary interface
  • awkward to peer inside nested components
  • doesn’t handle attribute lookup easily (but could be modified to, using getattr

Let’s try to make a “Queryable” object that chains operations via method calls (something like
SQLAlchememy generative selects

class Queryable(object):
    def __init__(self,D):
        self.D = itertools.tee(D,2)[1]

    def tolist(self):
        return list(itertools.tee(self.D,2)[1])

    def query(self,*args,**kwargs):
        return Queryable(query(self.D,*args,**kwargs))

    q = query

print "3rd version, Queryable"
# c > 10 and "other" in e
Q = Queryable(data).q('c',8,'>')
print Q.tolist()
Q = Q.q('e', 'other', 'in')
print Q.tolist()

This is OKAY, and but it still has plenty of codesmell.

  • lots of tee madness
  • ugly “tolist” method
  • we’re the query optimizer… we’re guaranteed that at least one pass will be O(n), since there is no indexing, and no smarts at all in the querying.

Next steps / alternatives:

Knowing when to give up!

Like any domain specific language, Quibble (as written here) walks a very fine line between functionality and complexity (okay it stumbles over the line drunkenly, but not by too much!) If we need much more complexity in our queries (or object model) then we’re back to writing python, and investigating a proper solution (SQL, Mongo, etc.) is probably worthwhile!  For a simple reporting language, or debugging, or a simple command line interface, this might be plenty.

Happy Yule!


1. Not true, I hate it.

2. Unless it’s super complex to query, involves lots of joins, or the query optimizer is off drunk at the pub, or stars are poorly aligned.

3. Quibble — from Query Bibble, Bibble being an ancient Etruscan word for a teething ring.

4. Well, actually, not lucky at all.  Like most scientific papers, this article pretends that inquiry is orderly.  I knew that I wanted to talk about the operator module, and most of the functions in operator take this form, so it seems like a sensible first-approximation convention.

No Geek Bulls**t Programming Class (Results so Far)

The Project

Create an accessible ‘learn to program’ class, using Python. Undo damage and barriers to access around geek culture, endemic sexism and racism, and models that say that “only certain people can program”.

Bits and Bites (at TC ExC0)

Choose Your Own Pyventure (Wikibook)

Results So Far

So far there have been two class sessions. The gender mix (self-identified) is about 50/50/0 male/female/(genderqueer, intersex) and we have 10 students or so. The self-identified goals of students included: building programs for work, changing careers, remedying previous bad programming class experiences, (rarer) learning python specifically (after knowing some other language).

Lessons Learned (and some Theories)

# Make the class accessible

  • No alpha male bulls**t
  • No pissfighting over languages, programming backgrounds, etc. remember, even experts start as newbies.
  • create safer, accessible spaces (physically accessible, make childcare credits available, advertise to underserved communities. avoid gender / sexuality assumptions, respect pronouns. Enforce safer space.)

# emotions matter in the learning experience

  • acknowledge the complexity of programming
  • programmers are made, not born
  • programming is hard to do, hard to learn
  • explain that it was hard for you to learn as well.
  • remind learners that making mistakes is how one learns to program

# Start far back. Go back further. Most students know little about how the computer works.

  • they haven’t seen / heard of / used the command line / terminal
  • they don’t know the difference between the shell and the python environment
    • they try things like ” >>> python “
  • there will be mac and windows users, prepare for both
  • some learners will have programmed before, some will not

# Have a goal / main project for the course

  • connect with students.
  • build toward a full project
  • lessons should iteratively replace / improve / expand on code made during previous lessons
  • no math. Math algorithms are boring and irrelevant for most people. Python makes strings easy. Easy strings makes for easy to discuss, real-world data

# Don’t get bogged down in syntax. People don’t care. Python has awesome syntax, mostly.

  • Gloss over warts and complexities
  • Avoid jargon

# Don’t get bogged down in datatypes. Don’t mention unicode. Ignore tuples.

  • Do mention strings, “numbers” (encompassing ints and floats)
  • dictionaries before lists. Associating keys and values parallels associating variable names with values. After teaching dicts, lists are trivial.

# relate functions and data structures. They are intertwined and need to be taught in parallel.

  • Functions exist to process data structures, and data exist to feed functions

# Ignore Objects and Object-Oriented Programming

  • OO isn’t hard, but it is confusing, especially for newbies
  • More importantly, it’s *irrelevant* for most early programming tasks

# Now matters more than Complete

  • Use Wikibooks or Google Docs for ease in sharing materials. (if repeating, we might choose GDocs — Wikibooks is too much machinery)
  • Don’t worry about getting all the details right


Learning Programming: Dissecting a Turd

As mentioned in my introduction to this series, learning to program is a series of lurching steps forward, sideways, and back, leading (if all goes well) to a better holistic understanding of the process.

In many rationalist disciplines, there are learning exercises that require grokking the whole and the parts in an iterative process, until a fullness is achieved. As learning occurs, one filters out more or the irrelevant detail. Think of learning to drive, or to speak a new language, or music theory. Without knowing “the point”, the big picture, the details are just noise, and tedia. Most programming (cf: math, stats) books and courses stay focused on the details far too long. What’s missing are activities that combine the micro and macro views fluidly. Code analysis fits the bill nicely.

One of the unsung marvels of the open-source movement is that it exposes mountains of good and bad code for all to see, of every imaginable pattern and anti-pattern.  Some of the code even works, despite of (or because of) its ugliness.

Learning to read others’ code is an under-explored pedagogy tool. It’s well and good to read the sort of well-commented, rational, toy examples that one finds in books, but it’s quite another thing entirely to learn to read other peoples’ eccentric, human-created code, warts and all.

Python has some nice advantages for this sort of exercise*. It has an interactive interpreter, and even the worst python code is still pretty easy to pick apart, for most reasonable length examples, using common modules. Give me Twisted, and I can give you write-only code, but that’s my failing, not the tool.

So, as my service / penance for all the bad code I’ve unleashed, I’ve decided to comment and describe the weird mishmash of ideas and patterns existing in a first generation, but working, application I wrote.

– read on for the code, the analysis, and more embarrassing warts… >

Learning Programming: Introduction

More people should know how to program*.  More resources are available than ever to help one learn.  Pc’s are ubiquitous, and the open source movement and the internet make powerful languages of every flavour (C, Python, Haskell, R…) and difficulty easily available, along with extensive documentation.   It should be a cakewalk, right? So why aren’t there more programmers?

read on for the thrilling conclusion!