Introduction to Python Programming.ppt

Introduction to Python
TINA SUSAN THOMAS

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
>>>

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

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

Unexpected things about strings
>>> s = "andrew"
>>> s[0] = "A"
TypeError: 'str' object does not support item
assignment
>>> s = "A" + s[1:]
>>> s
'Andrew‘
Strings are read only

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

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
>>> yellow = (255, 255, 0) # r, g, b
>>> one = (1,)
>>> yellow[0]
>>> yellow[1:]
(255, 0)
>>> yellow[0] = 0
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
>>> names
['ben', 'chen', 'yaqin']
>>> gender = [0, 0, 1]
>>> zip(names, gender)
[('ben', 0), ('chen', 0), ('yaqin', 1)]

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, ...
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
>>> 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"]
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
>>> 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'}

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

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

For
>>> for name in names:
... print smiles
...
Ben
Chen
Yaqin

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 C24H40N4O2 is 416.6
The molecular weight of C14H25N5O3 is 311.38

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” 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]

Reading files
>>> f = open(“names.txt")
>>> f.readline()
'Yaqinn'

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

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
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”

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 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 math
math.cos
>>> from math import cos, pi
cos
>>> from math import *

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>

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
>>> 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
>>> 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
 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/bo
ok/ch02.html

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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