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Introduction to Python:

Introduction to Python Chen Lin clin@brandeis.edu COSI 134a Volen 110 Office Hour: Thurs. 3-5

For More Information?:

For More Information? http://python.org/ - documentation, tutorials, beginners guide, core distribution, ... Books include: Learning Python by Mark Lutz Python Essential Reference by David Beazley Python Cookbook , ed. by Martelli, Ravenscroft and Ascher (online at http://code.activestate.com/recipes/langs/python/) http://wiki.python.org/moin/PythonBooks

Python Videos:

Python Videos http://showmedo.com/videotutorials/python “5 Minute Overview (What Does Python Look Like?)” “Introducing the PyDev IDE for Eclipse” “Linear Algebra with Numpy” And many more

4 Major Versions of Python:

4 Major Versions of Python “Python” or “CPython” is written in C/C++ - Version 2.7 came out in mid-2010 - Version 3.1.2 came out in early 2010 “Jython” is written in Java for the JVM “IronPython” is written in C# for the .Net environment Go To Website

Development Environments what IDE to use? http://stackoverflow.com/questions/81584:

Development Environments what IDE to use? http://stackoverflow.com/questions/81584 1. PyDev with Eclipse 2. Komodo 3. Emacs 4. Vim 5. TextMate 6. Gedit 7. Idle 8. PIDA (Linux)(VIM Based) 9. NotePad++ (Windows) 10.BlueFish (Linux)

Pydev with Eclipse:

Pydev with Eclipse

Python Interactive Shell:

Python Interactive Shell % python Python 2.6.1 (r261:67515, Feb 11 2010, 00:51:29) [GCC 4.2.1 (Apple Inc. build 5646)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> You can type things directly into a running Python session >>> 2+3*4 14 >>> name = "Andrew" >>> name 'Andrew' >>> print "Hello", name Hello Andrew >>>

Slide8:

Background Data Types/Structure Control flow File I/O Modules Class NLTK

List:

List A compound data type: [0] [2.3, 4.5] [5, "Hello", "there", 9.8] [] Use len() to get the length of a list >>> names = [“Ben", “Chen", “Yaqin"] >>> len(names) 3

Use [ ] to index items in the list:

Use [ ] to index items in the list >>> names[0] ‘Ben' >>> names[1] ‘Chen' >>> names[2] ‘Yaqin' >>> names[3] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: list index out of range >>> names[-1] ‘Yaqin' >>> names[-2] ‘Chen' >>> names[-3] ‘Ben' [0] is the first item. [1] is the second item ... Out of range values raise an exception Negative values go backwards from the last element.

Strings share many features with lists:

Strings share many features with lists >>> smiles = "C(=N)(N)N.C(=O)(O)O" >>> smiles[0] 'C' >>> smiles[1] '(' >>> smiles[-1] 'O' >>> smiles[1:5] '(=N)' >>> smiles[10:-4] 'C(=O)' Use “slice” notation to get a substring

String Methods: find, split:

String Methods: find, split smiles = "C(=N)(N)N.C(=O)(O)O" >>> smiles.find("(O)") 15 >>> smiles.find(".") 9 >>> smiles.find(".", 10) -1 >>> smiles.split(".") ['C(=N)(N)N', 'C(=O)(O)O'] >>> Use “find” to find the start of a substring. Start looking at position 10. Find returns -1 if it couldn’t find a match. Split the string into parts with “.” as the delimiter

String operators: in, not in:

String operators: in, not in if "Br" in “Brother”: print "contains brother“ email_address = “clin” if "@" not in email_address: email_address += "@brandeis.edu“

String Method: “strip”, “rstrip”, “lstrip” are ways to remove whitespace or selected characters:

String Method: “strip”, “rstrip”, “lstrip” are ways to remove whitespace or selected characters >>> line = " # This is a comment line \n" >>> line.strip() '# This is a comment line' >>> line.rstrip() ' # This is a comment line' >>> line.rstrip("\n") ' # This is a comment line ' >>>

More String methods:

More String methods email.startswith(“c") endswith(“u”) True/False >>> "%s@brandeis.edu" % "clin" 'clin@brandeis.edu' >>> names = [“Ben", “Chen", “Yaqin"] >>> ", ".join(names) ‘Ben, Chen, Yaqin‘ >>> “chen".upper() ‘CHEN'

Unexpected things about strings:

Unexpected things about strings >>> s = "andrew" >>> s[0] = "A" Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'str' object does not support item assignment >>> s = "A" + s[1:] >>> s 'Andrew‘ Strings are read only

“\” is for special characters:

“\” is for special characters \n -> newline \t -> tab \\ -> backslash ... But Windows uses backslash for directories! filename = "M:\nickel_project\reactive.smi" # DANGER! filename = "M:\\nickel_project\\reactive.smi" # Better! filename = "M:/nickel_project/reactive.smi" # Usually works

Lists are mutable - some useful methods:

Lists are mutable - some useful methods >>> ids = ["9pti", "2plv", "1crn"] >>> ids.append("1alm") >>> ids ['9pti', '2plv', '1crn', '1alm'] >>>ids.extend(L) Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L. >>> del ids[0] >>> ids ['2plv', '1crn', '1alm'] >>> ids.sort() >>> ids ['1alm', '1crn', '2plv'] >>> ids.reverse() >>> ids ['2plv', '1crn', '1alm'] >>> ids.insert(0, "9pti") >>> ids ['9pti', '2plv', '1crn', '1alm'] append an element remove an element sort by default order reverse the elements in a list insert an element at some specified position. (Slower than .append())

Tuples: sort of an immutable list:

Tuples: sort of an immutable list >>> yellow = (255, 255, 0) # r, g, b >>> one = (1,) >>> yellow[0] >>> yellow[1:] (255, 0) >>> yellow[0] = 0 Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'tuple' object does not support item assignment Very common in string interpolation: >>> " %s lives in %s at latitude %.1f " % ("Andrew", "Sweden", 57.7056) 'Andrew lives in Sweden at latitude 57.7'

zipping lists together:

zipping lists together >>> names ['ben', 'chen', 'yaqin'] >>> gender = [0, 0, 1] >>> zip(names, gender) [('ben', 0), ('chen', 0), ('yaqin', 1)]

Dictionaries:

Dictionaries Dictionaries are lookup tables. They map from a “key” to a “value”. symbol_to_name = { "H": "hydrogen", "He": "helium", "Li": "lithium", "C": "carbon", "O": "oxygen", "N": "nitrogen" } Duplicate keys are not allowed Duplicate values are just fine

Keys can be any immutable value numbers, strings, tuples, frozenset, not list, dictionary, set, ...:

Keys can be any immutable value numbers, strings, tuples, frozenset , not list, dictionary, set, ... atomic_number_to_name = { 1: "hydrogen" 6: "carbon", 7: "nitrogen" 8: "oxygen", } nobel_prize_winners = { (1979, "physics"): ["Glashow", "Salam", "Weinberg"], (1962, "chemistry"): ["Hodgkin"], (1984, "biology"): ["McClintock"], } A set is an unordered collection with no duplicate elements.

Dictionary:

Dictionary >>> symbol_to_name["C"] 'carbon' >>> "O" in symbol_to_name, "U" in symbol_to_name (True, False) >>> "oxygen" in symbol_to_name False >>> symbol_to_name["P"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'P' >>> symbol_to_name.get("P", "unknown") 'unknown' >>> symbol_to_name.get("C", "unknown") 'carbon' Get the value for a given key Test if the key exists (“in” only checks the keys, not the values.) [] lookup failures raise an exception. Use “.get()” if you want to return a default value.

Some useful dictionary methods:

Some useful dictionary methods >>> symbol_to_name.keys() ['C', 'H', 'O', 'N', 'Li', 'He'] >>> symbol_to_name.values() ['carbon', 'hydrogen', 'oxygen', 'nitrogen', 'lithium', 'helium'] >>> symbol_to_name.update( {"P": "phosphorous", "S": "sulfur"} ) >>> symbol_to_name.items() [('C', 'carbon'), ('H', 'hydrogen'), ('O', 'oxygen'), ('N', 'nitrogen'), ('P', 'phosphorous'), ('S', 'sulfur'), ('Li', 'lithium'), ('He', 'helium')] >>> del symbol_to_name['C'] >>> symbol_to_name {'H': 'hydrogen', 'O': 'oxygen', 'N': 'nitrogen', 'Li': 'lithium', 'He': 'helium'}

Slide25:

Background Data Types/Structure list, string, tuple, dictionary Control flow File I/O Modules Class NLTK

Control Flow:

Control Flow Things that are False The boolean value False The numbers 0 (integer), 0.0 (float) and 0j (complex). The empty string "". The empty list [], empty dictionary {} and empty set set(). Things that are True The boolean value True All non-zero numbers. Any string containing at least one character. A non-empty data structure.

If:

If >>> smiles = "BrC1=CC=C(C=C1)NN.Cl" >>> bool(smiles) True >>> not bool(smiles) False >>> if not smiles : ... print "The SMILES string is empty" ... The “else” case is always optional

Use “elif” to chain subsequent tests:

Use “elif” to chain subsequent tests >>> mode = "absolute" >>> if mode == "canonical": ... smiles = "canonical" ... elif mode == "isomeric": ... smiles = "isomeric” ... elif mode == "absolute": ... smiles = "absolute" ... else: ... raise TypeError("unknown mode") ... >>> smiles ' absolute ' >>> “raise” is the Python way to raise exceptions

Boolean logic:

Boolean logic Python expressions can have “and”s and “or”s: if (ben <= 5 and chen >= 10 or chen == 500 and ben != 5): print “Ben and Chen“

Range Test:

Range Test if (3 <= Time <= 5): print “Office Hour"

For:

For >>> names = [“Ben", “Chen", “Yaqin"] >>> for name in names: ... print smiles ... Ben Chen Yaqin

Tuple assignment in for loops:

Tuple assignment in for loops data = [ ("C20H20O3", 308.371), ("C22H20O2", 316.393), ("C24H40N4O2", 416.6), ("C14H25N5O3", 311.38), ("C15H20O2", 232.3181)] for (formula, mw) in data: print "The molecular weight of %s is %s" % (formula, mw) The molecular weight of C20H20O3 is 308.371 The molecular weight of C22H20O2 is 316.393 The molecular weight of C24H40N4O2 is 416.6 The molecular weight of C14H25N5O3 is 311.38 The molecular weight of C15H20O2 is 232.3181

Break, continue:

Break, continue >>> for value in [3, 1, 4, 1, 5, 9, 2]: ... print "Checking", value ... if value > 8: ... print "Exiting for loop" ... break ... elif value < 3: ... print "Ignoring" ... continue ... print "The square is", value**2 ... Use “break” to stop the for loop Use “continue” to stop processing the current item Checking 3 The square is 9 Checking 1 Ignoring Checking 4 The square is 16 Checking 1 Ignoring Checking 5 The square is 25 Checking 9 Exiting for loop >>>

Range():

Range() “range” creates a list of numbers in a specified range range([start,] stop[, step]) -> list of integers When step is given, it specifies the increment (or decrement). >>> range(5) [0, 1, 2, 3, 4] >>> range(5, 10) [5, 6, 7, 8, 9] >>> range(0, 10, 2) [0, 2, 4, 6, 8] How to get every second element in a list? for i in range(0, len(data), 2): print data[i]

Slide35:

Background Data Types/Structure Control flow File I/O Modules Class NLTK

Reading files:

Reading files >>> f = open(“names.txt") >>> f.readline() 'Yaqin\n'

Quick Way:

Quick Way >>> lst= [ x for x in open("text.txt","r").readlines() ] >>> lst ['Chen Lin\n', 'clin@brandeis.edu\n', 'Volen 110\n', 'Office Hour: Thurs. 3-5\n', '\n', 'Yaqin Yang\n', 'yaqin@brandeis.edu\n', 'Volen 110\n', 'Offiche Hour: Tues. 3-5\n'] Ignore the header? for (i,line) in enumerate(open(‘text.txt’,"r").readlines()): if i == 0: continue print line

Using dictionaries to count occurrences:

Using dictionaries to count occurrences >>> for line in open('names.txt'): ... name = line.strip() ... name_count[name] = name_count.get(name,0)+ 1 ... >>> for (name, count) in name_count.items(): ... print name, count ... Chen 3 Ben 3 Yaqin 3

File Output:

File Output input_file = open(“in.txt") output_file = open(“out.txt", "w") for line in input_file: output_file.write(line) “w” = “write mode” “a” = “append mode” “wb” = “write in binary” “r” = “read mode” (default) “rb” = “read in binary” “U” = “read files with Unix or Windows line endings”

Slide40:

Background Data Types/Structure Control flow File I/O Modules Class NLTK

Modules:

Modules When a Python program starts it only has access to a basic functions and classes. (“int”, “dict”, “len”, “sum”, “range”, ...) “Modules” contain additional functionality. Use “import” to tell Python to load a module. >>> import math >>> import nltk

import the math module:

import the math module >>> import math >>> math.pi 3.1415926535897931 >>> math.cos(0) 1.0 >>> math.cos(math.pi) -1.0 >>> dir(math) ['__doc__', '__file__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'exp', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'hypot', 'isinf', 'isnan', 'ldexp', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'] >>> help(math) >>> help(math.cos)

“import” and “from ... import ...”:

“import” and “from ... import ...” >>> import math math.cos >>> from math import cos, pi cos >>> from math import *

Slide44:

Background Data Types/Structure Control flow File I/O Modules Class NLTK

Classes:

Classes class ClassName(object): <statement-1> . . . <statement-N> class MyClass(object): """A simple example class""" i = 12345 def f(self): return self.i class DerivedClassName(BaseClassName): <statement-1> . . . <statement-N>

Slide46:

Background Data Types/Structure Control flow File I/O Modules Class NLTK

http://www.nltk.org/book NLTK is on berry patch machines!:

http://www.nltk.org/book NLTK is on berry patch machines! >>>from nltk.book import * >>> text1 <Text: Moby Dick by Herman Melville 1851> >>> text1.name 'Moby Dick by Herman Melville 1851' >>> text1.concordance("monstrous") >>> dir(text1) >>> text1.tokens >>> text1.index("my") 4647 >>> sent2 ['The', 'family', 'of', 'Dashwood', 'had', 'long', 'been', 'settled', 'in', 'Sussex', '.']

Classify Text:

Classify Text >>> def gender_features(word): ... return {'last_letter': word[-1]} >>> gender_features('Shrek') {'last_letter': 'k'} >>> from nltk.corpus import names >>> import random >>> names = ([(name, 'male') for name in names.words('male.txt')] + ... [(name, 'female') for name in names.words('female.txt')]) >>> random.shuffle(names)

Featurize, train, test, predict:

Featurize, train, test, predict >>> featuresets = [(gender_features(n), g) for (n,g) in names] >>> train_set, test_set = featuresets[500:], featuresets[:500] >>> classifier = nltk.NaiveBayesClassifier.train(train_set) >>> print nltk.classify.accuracy(classifier, test_set) 0.726 >>> classifier.classify(gender_features('Neo')) 'male'

from nltk.corpus import reuters :

from nltk .corpus import reuters Reuters Corpus: 10,788 news 1.3 million words. Been classified into 90 topics Grouped into 2 sets, "training" and "test“ Categories overlap with each other http://nltk.googlecode.com/svn/trunk/doc/book/ch02.html

Reuters:

Reuters >>> from nltk.corpus import reuters >>> reuters.fileids() ['test/14826', 'test/14828', 'test/14829', 'test/14832', ...] >>> reuters.categories() ['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', ...]

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