A Taste of Python - Devdays Toronto 2009

a taste of Presented by Jordan Baker October 23, 2009 DevDays Toronto

About Me • Open Source Developer • Founder of Open Source Web Application and CMS service provider: Scryent - www.scryent.com • Founder of Toronto Plone Users Group - www.torontoplone.ca

Agenda • About Python • Show me your CODE • A Spell Checker in 21 lines of code • Why Python ROCKS • Resources for further exploration

About Python http://www.ﬂickr.com/photos/schoffer/196079076/

About Python • Gotta love a language named after Monty Python’s Flying Circus • Used in more places than you might know

Signiﬁcant Whitespace C-like if(x == 2) { do_something(); } do_something_else(); Python if x == 2: do_something() do_something_else()

Signiﬁcant Whitespace • less code clutter • eliminates many common syntax errors • proper code layout • use an indentation aware editor or IDE • Get over it!

Python is Interactive Python 2.6.1 (r261:67515, Jul 7 2009, 23:51:51) [GCC 4.2.1 (Apple Inc. build 5646)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>>

FIZZ BUZZ 1 2 FIZZ 4 BUZZ ... 14 FIZZ BUZZ

FIZZ BUZZ def fizzbuzz(n): for i in range(n + 1): if not i % 3: print "Fizz", if not i % 5: print "Buzz", if i % 3 and i % 5: print i, print fizzbuzz(50)

FIZZ BUZZ (OO) class FizzBuzzWriter(object): def __init__(self, limit): self.limit = limit def run(self): for n in range(1, self.limit + 1): self.write_number(n) def write_number(self, n): if not n % 3: print "Fizz", if not n % 5: print "Buzz", if n % 3 and n % 5: print n, print fizzbuzz = FizzBuzzWriter(50) fizzbuzz.run()

A Spell Checker in 21 Lines of Code • Written by Peter Norvig • Duplicated in many languages • Simple Spellchecking algorithm based on probability • http://norvig.com/spell-correct.html

The Approach • Census by frequency • Morph the word (werd) • Insertions: waerd, wberd, werzd • Deletions: wrd, wed, wer • Transpositions: ewrd, wred, wedr • Replacements: aerd, ward, wbrd, word, wzrd, werz • Find the one with the highest frequency: were

Norvig Spellchecker import re, collections def words(text): return re.findall('[a-z]+', text.lower()) def train(words): model = collections.defaultdict(int) for w in words: model[w] += 1 return model NWORDS = train(words(file('big.txt').read())) alphabet = 'abcdefghijklmnopqrstuvwxyz' def edits1(word): s = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in s if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in s if len(b)>1] replaces = [a + c + b[1:] for a, b in s for c in alphabet if b] inserts = [a + c + b for a, b in s for c in alphabet] return set(deletes + transposes + replaces + inserts) def known_edits2(word): return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS) def known(words): return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get)

Regular Expressions def words(text): return re.findall('[a-z]+', text.lower()) >>> words("The cat in the hat!") ['the', 'cat', 'in', 'the', 'hat']

Dictionaries >>> d = {'cat':1} >>> d {'cat': 1} >>> d['cat'] 1 >>> d['cat'] += 1 >>> d {'cat': 2} >>> d['dog'] += 1 Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'dog'

defaultdict # Has a factory for missing keys >>> d = collections.defaultdict(int) >>> d['dog'] += 1 >>> d {'dog': 1} >>> int <type 'int'> >>> int() 0 def train(words): model = collections.defaultdict(int) for w in words: model[w] += 1 return model >>> train(words("The cat in the hat!")) {'cat': 1, 'the': 2, 'hat': 1, 'in': 1}

Reading the File >>> text = file('big.txt').read() >>> NWORDS = train(words(text)) >>> NWORDS {'nunnery': 3, 'presnya': 1, 'woods': 22, 'clotted': 1, 'spiders': 1, 'hanging': 42, 'disobeying': 2, 'scold': 3, 'originality': 6, 'grenadiers': 8, 'pigment': 16, 'appropriation': 6, 'strictest': 1, 'bringing': 48, 'revelers': 1, 'wooded': 8, 'wooden': 37, 'wednesday': 13, 'shows': 50, 'immunities': 3, 'guardsmen': 4, 'sooty': 1, 'inevitably': 32, 'clavicular': 9, 'sustaining': 5, 'consenting': 1, 'scraped': 21, 'errors': 16, 'semicircular': 1, 'cooking': 6, 'spiroch': 25, 'designing': 1, 'pawed': 1, 'succumb': 12, 'shocks': 1, 'crouch': 2, 'chins': 1, 'awistocwacy': 1, 'sunbeams': 1, 'perforations': 6, 'china': 43, 'affiliated': 4, 'chunk': 22, 'natured': 34, 'uplifting': 1, 'slaveholders': 2, 'climbed': 13, 'controversy': 33, 'natures': 2, 'climber': 1, 'lency': 2, 'joyousness': 1, 'reproaching': 3, 'insecurity': 1, 'abbreviations': 1, 'definiteness': 1, 'music': 56, 'therefore': 186, 'expeditionary': 3, 'primeval': 1, 'unpack': 1, 'circumstances': 107, ... (about 6500 more lines) ... >>> NWORDS['the'] 80030 >>> NWORDS['unusual'] 32 >>> NWORDS['cephalopod'] 0

Training the Probability Model import re, collections def words(text): return re.findall('[a-z]+', text.lower()) def train(words): model = collections.defaultdict(int) for w in words: model[w] += 1 return model NWORDS = train(words(file('big.txt').read()))

List Comprehensions # These two are equivalent: result = [] for v in iter: if cond: result.append(expr) [ expr for v in iter if cond ] # You can nest loops also: result = [] for v1 in iter1: for v2 in iter2: if cond: result.append(expr) [ expr for v1 in iter1 for v2 in iter2 if cond ]

String Slicing >>> word = "spam" >>> word[:1] 's' >>> word[1:] 'pam' >>> (word[:1], word[1:]) ('s', 'pam') >>> range(len(word) + 1) [0, 1, 2, 3, 4] >>> [(word[:i], word[i:]) for i in range(len(word) + 1)] [('', 'spam'), ('s', 'pam'), ('sp', 'am'), ('spa', 'm'), ('spam', '')]

Deletions >>> word = "spam" >>> s = [(word[:i], word[i:]) for i in range(len(word) + 1)] >>> deletes = [a + b[1:] for a, b in s if b] >>> deletes ['pam', 'sam', 'spm', 'spa'] >>> a, b = ('s', 'pam') >>> a 's' >>> b 'pam' >>> bool('pam') True >>> bool('') False

Transpositions For example: teh => the >>> transposes = [a + b[1] + b[0] + b[2:] for a, b in s if len(b)>1] >>> transposes ['psam', 'sapm', 'spma']

Replacements >>> alphabet = "abcdefghijklmnopqrstuvwxyz" >>> replaces = [a + c + b[1:] for a, b in s for c in alphabet if b] >>> replaces ['apam', 'bpam', ..., 'zpam', 'saam', ..., 'szam', ..., 'spaz']

Insertion >>> alphabet = "abcdefghijklmnopqrstuvwxyz" >>> inserts = [a + c + b for a, b in s for c in alphabet] >>> inserts ['aspam', ..., 'zspam', 'sapam', ..., 'szpam', 'spaam', ..., 'spamz']

Find all Edits alphabet = 'abcdefghijklmnopqrstuvwxyz' def edits1(word): s = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in s if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in s if len(b)>1] replaces = [a + c + b[1:] for a, b in s for c in alphabet if b] inserts = [a + c + b for a, b in s for c in alphabet] return set(deletes + transposes + replaces + inserts) >>> edits1("spam") set(['sptm', 'skam', 'spzam', 'vspam', 'spamj', 'zpam', 'sbam', 'spham', 'snam', 'sjpam', 'spma', 'swam', 'spaem', 'tspam', 'spmm', 'slpam', 'upam', 'spaim', 'sppm', 'spnam', 'spem', 'sparm', 'spamr', 'lspam', 'sdpam', 'spams', 'spaml', 'spamm', 'spamn', 'spum', 'spamh', 'spami', 'spatm', 'spamk', 'spamd', ..., 'spcam', 'spamy'])

Known Words def known(words): """ Return the known words from `words`. """ return set(w for w in words if w in NWORDS)

Correct def known(words): """ Return the known words from `words`. """ return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or [word] return max(candidates, key=NWORDS.get) >>> bool(set([])) False >>> correct("computr") 'computer' >>> correct("computor") 'computer' >>> correct("computerr") 'computer'

Edit Distance 2 def known_edits2(word): return set( e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS ) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get) >>> correct("conpuler") 'computer' >>> correct("cmpuler") 'computer'

import re, collections def words(text): return re.findall('[a-z]+', text.lower()) def train(words): model = collections.defaultdict(int) for w in words: model[w] += 1 return model NWORDS = train(words(file('big.txt').read())) alphabet = 'abcdefghijklmnopqrstuvwxyz' def edits1(word): s = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in s if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in s if len(b)>1] replaces = [a + c + b[1:] for a, b in s for c in alphabet if b] inserts = [a + c + b for a, b in s for c in alphabet] return set(deletes + transposes + replaces + inserts) def known_edits2(word): return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS) def known(words): return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get)

Comparing Python & Java Versions • http://raelcunha.com/spell-correct.php • 35 lines of Java

import java.io.*; import java.util.*; import java.util.regex.*; class Spelling { " private final HashMap<String, Integer> nWords = new HashMap<String, Integer>(); " public Spelling(String file) throws IOException { " " BufferedReader in = new BufferedReader(new FileReader(file)); " " Pattern p = Pattern.compile("w+"); " " for(String temp = ""; temp != null; temp = in.readLine()){ " " " Matcher m = p.matcher(temp.toLowerCase()); " " " while(m.find()) nWords.put((temp = m.group()), nWords.containsKey(temp) ? nWords.get(temp) + 1 : 1); " " } " " in.close(); " } " private final ArrayList<String> edits(String word) { " " ArrayList<String> result = new ArrayList<String>(); " " for(int i=0; i < word.length(); ++i) result.add(word.substring(0, i) + word.substring(i+1)); " " for(int i=0; i < word.length()-1; ++i) result.add(word.substring(0, i) + word.substring(i+1, i+2) + word.substring(i, i+1) + word.substring(i+2)); " " for(int i=0; i < word.length(); ++i) for(char c='a'; c <= 'z'; ++c) result.add(word.substring(0, i) + String.valueOf(c) + word.substring(i+1)); " " for(int i=0; i <= word.length(); ++i) for(char c='a'; c <= 'z'; ++c) result.add(word.substring(0, i) + String.valueOf(c) + word.substring(i)); " " return result; " } " public final String correct(String word) { " " if(nWords.containsKey(word)) return word; " " ArrayList<String> list = edits(word); " " HashMap<Integer, String> candidates = new HashMap<Integer, String>(); " " for(String s : list) if(nWords.containsKey(s)) candidates.put(nWords.get(s),s); " " if(candidates.size() > 0) return candidates.get(Collections.max(candidates.keySet())); " " for(String s : list) for(String w : edits(s)) if(nWords.containsKey(w)) candidates.put(nWords.get(w),w); " " return candidates.size() > 0 ? candidates.get(Collections.max(candidates.keySet())) : word; " } " public static void main(String args[]) throws IOException { " " if(args.length > 0) System.out.println((new Spelling("big.txt")).correct(args[0])); " } }

import re, collections def words(text): return re.findall('[a-z]+', text.lower()) def train(words): model = collections.defaultdict(int) for w in words: model[w] += 1 return model NWORDS = train(words(file('big.txt').read())) alphabet = 'abcdefghijklmnopqrstuvwxyz' def edits1(word): s = [(word[:i], word[i:]) for i in range(len(word) + 1)] deletes = [a + b[1:] for a, b in s if b] transposes = [a + b[1] + b[0] + b[2:] for a, b in s if len(b)>1] replaces = [a + c + b[1:] for a, b in s for c in alphabet if b] inserts = [a + c + b for a, b in s for c in alphabet] return set(deletes + transposes + replaces + inserts) def known_edits2(word): return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS) def known(words): return set(w for w in words if w in NWORDS) def correct(word): candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word] return max(candidates, key=NWORDS.get)

IDE for Python • IDE’s for Python include: • PyDev for Eclipse • WingIDE • IDLE for Windows/ Linux/ Mac • there’s more

Why Python ROCKS • Elegant and readable language - “Executable Pseudocode” • Standard Libraries - “Batteries Included” • Very High level Datatypes • Dynamically Typed • It’s FUN!

An Open Source Community • Projects: Plone, Zope, Grok, BFG, Django, SciPy & NumPy, Google App Engine, PyGame • PyCon

Resources • PyGTA • Toronto Plone Users • Toronto Django Users • Stackoverﬂow • Dive into Python • Python Tutorial

Thanks • I’d love to hear your questions or comments on this presentation. Reach me at: • jbb@scryent.com • http://twitter.com/hexsprite

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A Taste of Python - Devdays Toronto 2009

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