Python programming tutorial for beginners

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

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
Development Environments
what IDE to use?
what IDE to use? http://stackoverflow.com/questions/81584
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
Python 2.6.1 (r261:67515, Feb 11 2010, 00:51:29)
Python 2.6.1 (r261:67515, Feb 11 2010, 00:51:29)
[GCC 4.2.1 (Apple Inc. build 5646)] on darwin
[GCC 4.2.1 (Apple Inc. build 5646)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>>
You can type things directly into a running Python session
You can type things directly into a running Python session
>>> 2+3*4
>>> 2+3*4
14
14
>>> name = "Andrew"
>>> name = "Andrew"
>>> name
>>> name
'Andrew'
'Andrew'
>>> print "Hello", name
>>> print "Hello", name
Hello Andrew
Hello Andrew
>>>
>>>

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

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

Use [ ] to index items in the list
Use [ ] to index items in the list
>>> names[0]
>>> names[0]
‘
‘Ben'
Ben'
>>> names[1]
>>> names[1]
‘
‘Chen'
Chen'
>>> names[2]
>>> names[2]
‘
‘Yaqin'
Yaqin'
>>> names[3]
>>> names[3]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: list index out of range
IndexError: list index out of range
>>> names[-1]
>>> names[-1]
‘
‘Yaqin'
Yaqin'
>>> names[-2]
>>> names[-2]
‘
‘Chen'
Chen'
>>> names[-3]
>>> names[-3]
‘
‘Ben'
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 = "C(=N)(N)N.C(=O)(O)O"
>>> smiles[0]
>>> smiles[0]
'C'
'C'
>>> smiles[1]
>>> smiles[1]
'('
'('
>>> smiles[-1]
>>> smiles[-1]
'O'
'O'
>>> smiles[1:5]
>>> smiles[1:5]
'(=N)'
'(=N)'
>>> smiles[10:-4]
>>> smiles[10:-4]
'C(=O)'
'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 = "C(=N)(N)N.C(=O)(O)O"
>>> smiles.find("(O)")
>>> smiles.find("(O)")
15
15
>>> smiles.find(".")
>>> smiles.find(".")
9
9
>>> smiles.find(".", 10)
>>> smiles.find(".", 10)
-1
-1
>>> smiles.split(".")
>>> smiles.split(".")
['C(=N)(N)N', 'C(=O)(O)O']
['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”:
if "Br" in “Brother”:
print "contains brother“
print "contains brother“
email_address = “clin”
email_address = “clin”
if "@" not in email_address:
if "@" not in email_address:
email_address += "@brandeis.edu“
email_address += "@brandeis.edu“

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

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

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

“
“” is for special characters
” is for special characters
n -> newline
n -> newline
t -> tab
t -> tab
-> backslash
-> 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
Lists are mutable - some useful
methods
methods
>>> ids = ["9pti", "2plv", "1crn"]
>>> ids = ["9pti", "2plv", "1crn"]
>>> ids.append("1alm")
>>> ids.append("1alm")
>>> ids
>>> ids
['9pti', '2plv', '1crn', '1alm']
['9pti', '2plv', '1crn', '1alm']
>>>ids.extend(L)
>>>ids.extend(L)
Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.
Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.
>>> del ids[0]
>>> del ids[0]
>>> ids
>>> ids
['2plv', '1crn', '1alm']
['2plv', '1crn', '1alm']
>>> ids.sort()
>>> ids.sort()
>>> ids
>>> ids
['1alm', '1crn', '2plv']
['1alm', '1crn', '2plv']
>>> ids.reverse()
>>> ids.reverse()
>>> ids
>>> ids
['2plv', '1crn', '1alm']
['2plv', '1crn', '1alm']
>>> ids.insert(0, "9pti")
>>> ids.insert(0, "9pti")
>>> ids
>>> ids
['9pti', '2plv', '1crn', '1alm']
['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:
Tuples: sort of an immutable list
>>> yellow = (255, 255, 0) # r, g, b
>>> yellow = (255, 255, 0) # r, g, b
>>> one = (1,)
>>> one = (1,)
>>> yellow[0]
>>> yellow[0]
>>> yellow[1:]
>>> yellow[1:]
(255, 0)
(255, 0)
>>> yellow[0] = 0
>>> yellow[0] = 0
TypeError: 'tuple' object does not support item assignment
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
>>> names
['ben', 'chen', 'yaqin']
['ben', 'chen', 'yaqin']
>>> gender =
>>> gender = [0, 0, 1]
[0, 0, 1]
>>> zip(names, gender)
>>> zip(names, gender)
[('ben', 0), ('chen', 0), ('yaqin', 1)]
[('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
Keys can be any immutable value
numbers, strings, tuples, frozenset
numbers, strings, tuples, frozenset,
,
not list, dictionary, set, ...
not list, dictionary, set, ...
atomic_number_to_name = {
atomic_number_to_name = {
1: "hydrogen"
1: "hydrogen"
6: "carbon",
6: "carbon",
7: "nitrogen"
7: "nitrogen"
8: "oxygen",
8: "oxygen",
}
}
nobel_prize_winners = {
nobel_prize_winners = {
(1979, "physics"): ["Glashow", "Salam", "Weinberg"],
(1979, "physics"): ["Glashow", "Salam", "Weinberg"],
(1962, "chemistry"): ["Hodgkin"],
(1962, "chemistry"): ["Hodgkin"],
(1984, "biology"): ["McClintock"],
(1984, "biology"): ["McClintock"],
}
}
A set is an unordered collection
with no duplicate elements.

Dictionary
Dictionary
>>> symbol_to_name["C"]
>>> symbol_to_name["C"]
'carbon'
'carbon'
>>> "O" in symbol_to_name, "U" in symbol_to_name
>>> "O" in symbol_to_name, "U" in symbol_to_name
(True, False)
(True, False)
>>> "oxygen" in symbol_to_name
>>> "oxygen" in symbol_to_name
False
False
>>> symbol_to_name["P"]
>>> symbol_to_name["P"]
KeyError: 'P'
KeyError: 'P'
>>> symbol_to_name.get("P", "unknown")
>>> symbol_to_name.get("P", "unknown")
'unknown'
'unknown'
>>> symbol_to_name.get("C", "unknown")
>>> symbol_to_name.get("C", "unknown")
'carbon'
'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()
>>> symbol_to_name.keys()
['C', 'H', 'O', 'N', 'Li', 'He']
['C', 'H', 'O', 'N', 'Li', 'He']
>>> symbol_to_name.values()
>>> symbol_to_name.values()
['carbon', 'hydrogen', 'oxygen', 'nitrogen', 'lithium', 'helium']
['carbon', 'hydrogen', 'oxygen', 'nitrogen', 'lithium', 'helium']
>>> symbol_to_name.update( {"P": "phosphorous", "S": "sulfur"} )
>>> symbol_to_name.update( {"P": "phosphorous", "S": "sulfur"} )
>>> symbol_to_name.items()
>>> symbol_to_name.items()
[('C', 'carbon'), ('H', 'hydrogen'), ('O', 'oxygen'), ('N', 'nitrogen'), ('P',
[('C', 'carbon'), ('H', 'hydrogen'), ('O', 'oxygen'), ('N', 'nitrogen'), ('P',
'phosphorous'), ('S', 'sulfur'), ('Li', 'lithium'), ('He', 'helium')]
'phosphorous'), ('S', 'sulfur'), ('Li', 'lithium'), ('He', 'helium')]
>>> del symbol_to_name['C']
>>> del symbol_to_name['C']
>>> symbol_to_name
>>> symbol_to_name
{'H': 'hydrogen', 'O': 'oxygen', 'N': 'nitrogen', 'Li': 'lithium', 'He': 'helium'}
{'H': 'hydrogen', 'O': 'oxygen', 'N': 'nitrogen', 'Li': 'lithium', 'He': 'helium'}

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

Control Flow
Control Flow
Things that are False
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
Things that are True
 The boolean value True
The boolean value True
 All non-zero numbers.
All non-zero numbers.
 Any string containing at least one character.
Any string containing at least one character.
 A non-empty data structure.
A non-empty data structure.

If
If
>>> smiles = "BrC1=CC=C(C=C1)NN.Cl"
>>> smiles = "BrC1=CC=C(C=C1)NN.Cl"
>>> bool(smiles)
>>> bool(smiles)
True
True
>>> not bool(smiles)
>>> not bool(smiles)
False
False
>>> if not smiles
>>> if not smiles:
:
... print "The SMILES string is empty"
... 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"
>>> mode = "absolute"
>>> if mode == "canonical":
>>> if mode == "canonical":
...
... smiles = "canonical"
smiles = "canonical"
... elif mode == "isomeric":
... elif mode == "isomeric":
...
... smiles = "isomeric”
smiles = "isomeric”
...
... elif mode == "absolute":
elif mode == "absolute":
...
... smiles = "absolute"
smiles = "absolute"
... else:
... else:
...
... raise TypeError("unknown mode")
raise TypeError("unknown mode")
...
...
>>> smiles
>>> smiles
' absolute '
' absolute '
>>>
>>>
“raise” is the Python way to raise exceptions

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

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

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

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

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

Range()
Range()
 “
“range” creates a list of numbers in a specified range
range” creates a list of numbers in a specified range
 range([start,] stop[, step]) -> list of integers
range([start,] stop[, step]) -> list of integers
 When step is given, it specifies the increment (or decrement).
When step is given, it specifies the increment (or decrement).
>>> range(5)
>>> range(5)
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
>>> range(5, 10)
>>> range(5, 10)
[5, 6, 7, 8, 9]
[5, 6, 7, 8, 9]
>>> range(0, 10, 2)
>>> range(0, 10, 2)
[0, 2, 4, 6, 8]
[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
Reading files
>>> f = open(“names.txt")
>>> f = open(“names.txt")
>>> f.readline()
>>> f.readline()
'Yaqinn'
'Yaqinn'

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

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

File Output
File Output
input_file = open(“in.txt")
input_file = open(“in.txt")
output_file = open(“out.txt", "w")
output_file = open(“out.txt", "w")
for line in input_file:
for line in input_file:
output_file.write(line)
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
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
>>> import math
>>> math.pi
>>> math.pi
3.1415926535897931
3.1415926535897931
>>> math.cos(0)
>>> math.cos(0)
1.0
1.0
>>> math.cos(math.pi)
>>> math.cos(math.pi)
-1.0
-1.0
>>> dir(math)
>>> dir(math)
['__doc__', '__file__', '__name__', '__package__', 'acos', 'acosh',
['__doc__', '__file__', '__name__', '__package__', 'acos', 'acosh',
'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos',
'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos',
'cosh', 'degrees', 'e', 'exp', 'fabs', 'factorial', 'floor', 'fmod',
'cosh', 'degrees', 'e', 'exp', 'fabs', 'factorial', 'floor', 'fmod',
'frexp', 'fsum', 'hypot', 'isinf', 'isnan', 'ldexp', 'log', 'log10',
'frexp', 'fsum', 'hypot', 'isinf', 'isnan', 'ldexp', 'log', 'log10',
'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan',
'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan',
'tanh', 'trunc']
'tanh', 'trunc']
>>> help(math)
>>> help(math)
>>> help(math.cos)
>>> help(math.cos)

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

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

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

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

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

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

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

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