1.
Overview of Python
Programming
Samir Mohanty
samir2artificialintelligence@gmail.com
Mar, 2019

2.
Disclaimer
 The slides presented here are obtained from
several authors of books and from various other
contributors. I hereby acknowledge all the
contributors for their material and inputs.
 I have added and modified a few slides to suit
the requirements of the training.
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3.
Agenda
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3
 Overview of Python basic syntax
 Modules in Python
 Overview of Data structures
 Overview of File I/O
 A few Modules

4.
PYTHON BASICS
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5.
Introduction to Python
 A high-level programming language
 Open source and community driven
 Standard Distribution includes many modules
 Dynamic typed; Automatic Memory management
 Basically interpreted, but source can be compiled
 Easy to learn, yet powerful
 Robust support for OO programming
 Can integrate well with other languages
 Most popular for Data Analytics
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6.
6
Compiling and Interpreting
 Many languages require you to compile (translate) your program into
a form that the machine understands.
 Python is instead directly interpreted into machine instructions.
compile execute
outputsource code
Hello.java
byte code
Hello.class
interpret
outputsource code
Hello.py It generates a machine
independent bytecode
which is handled by the run
time
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7.
Installing
 Python is pre-installed on most Unix systems,
including Linux and MAC OS X
 Set the path (/usr/bin/python) and ensure you have
execute permissions
 The pre-installed version may not be the most
recent one (2.7.15 and 3.7.2 as of Jan 19)
 Download the windows version from
http://python.org/download/
 Install as a standard windows application
 Python comes with a large library of standard
modules
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8.
Setup Python PATH
 Installing Python
 Setting up PATH
For Python:
 C:UsersMR GOKHLEAppDataLocalProgramsPythonPython37
For IDLE
 C:UsersMR GOKHLEAppDataLocalProgramsPythonPython37Libidlelib
 Python as a command processor
 Launch python at DOS shell prompt
 Execute python commands
 exit()
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9.
Launch from command prompt
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10.
IDEs
 There are several options for an IDE
 IDLE – works well with Windows
 Emacs with python-mode or your favorite text
editor
 Eclipse with Pydev
(http://pydev.sourceforge.net/) for Windows &
Linux
 PyCharm Community Edition (open source).
Professional Edition is Closed source. Available
for Windows & Linux
 Spyder ( MIT license). Windows & Linux
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11.
IDLE Development Environment
 IDLE is an Integrated DeveLopment Environment
for Python, typically used on Windows
 Multi-window text editor with syntax highlighting,
auto-completion, smart indent and other.
 Python shell with syntax highlighting.
 Integrated debugger
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12.
IDLE – Development
Environment
 IDLE helps you
program in Python
by:
 color-coding your
program code
 debugging
 auto-indent
 interactive shell
Comments are red
Strings are green
Definitions are blue
Keywords are orange
Output is blue
Shell prompt
 IDLE Shell:
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13.
Python Scripts
 When you call a python program from the
command line, the interpreter evaluates each
expression in the file
 Familiar mechanisms are used to provide
command line arguments and/or redirect input
and output
 Python also has mechanisms to allow a python
program to act both as a script and as a module
to be imported and used by another python
program
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14.
Python Scripts
 Code is interpreted
 You see the result immediately on the shell
window.
 If a statement is in error, execution stops and
error message is displayed.
 The interpretation does not guarantee display
of all errors. Errors are reported only when
encountered, it stops on the first error
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15.
A Code Sample (in IDLE)
x = 34 - 23 # A comment.
y = "Hello" # Another one.
z = 3.45
if z == 3.45 or y == "Hello":
x = x + 1
y = y + " World “ # String concat
print (x)
print (y)
Variables are used as needed.
No specific type of variable. It can hold
any type of data. The most recent
assignment determines the type
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16.
Data in Python
 Everything in Python is an object.
 All objects in Python can be either
mutable or immutable.
 A mutable object can be changed after it
is created, and an immutable object
can’t.
 Objects of built-in types like (int, float,
bool, str, tuple, unicode) are immutable.
Objects of built-in types like (list, set, dict)
are mutable
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17.
Basic Datatypes
 Numbers
 Strings
 List
 Tuple
 Set
 Dictionary
Data structures
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18.
Mutable / Immutable
 Mutable: meaning you can change their
content without changing their identity
 Immutable: are objects that can not be
changed
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19.
Modules & Packages
 Similar to libraries, called Modules
 It can be used (referenced) in standard python
code. Include the modules in the code.
 Usually a File containing python code
 Can define functions, classes, variables
 Multiple modules make up a Package, usually a
directory of python files.
 Official packages are listed in a directory (PyPI)
from where one can download / install
 pip is the tool to download and install packages
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20.
Basic Datatypes
 Integers (default for numbers)
z = 5 / 2 # Answer 2.5, real division
 Floats
x = 3.456
 Strings
 Can use " " or ' ' to specify with "abc" ==
'abc'
 Unmatched can occur within the string:
"matt's"
 Use triple quotes for multi-line strings or strings
than contain both ‘ and “ inside of them:
"""a'b"c""" 26Overview of Python Programming
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21.
Data Structures
 List : An unordered collection of data items
 [35,23.15,"Hello", [2,3,'a','b']]
 Can be used as a stack, queue
 mutable
 Tuple : An unordered collection of data
items used to capture an entity’s data
 (25, 5.5, "My Data", ['a','b'], (1,2,3))
 immutable
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22.
Data Structures
 Set : An unordered set of values ( no
duplicates allowed)
 {2,3,"hello"}
 Dictionary : unordered collection of data
items, each data item is a <key, value>
pair
 {'name': 'John', 1: [2, 4, 3]}
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23.
Standard Operators
 Arithmetic
 Relational
 Logical
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Operator Description
** Exponentiation (raise to the power)
~ + -
Complement, unary plus and minus (method names for the last
two are +@ and -@)
* / % // Multiply, divide, modulo and floor division
+ - Addition and subtraction
>> << Right and left bitwise shift
& Bitwise 'AND'td>
^ | Bitwise exclusive `OR' and regular `OR'
<= < > >= Comparison operators
<> == != Equality operators
= %= /= //= -= +=
*= **=
Assignment operators
is is not Identity operators
in not in Membership operators
not or and Logical operators

24.
Control Structures
 if ..
 if ..else
 if .. elif
 while
 while .. else
 for
 break
 continue
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25.
Functions & Classes
 def fname (abc) :
…
…
return(rval)
 class cname(pclass):
…
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26.
Example function: fact.py
def fact(x):
"""Returns the factorial of its argument, assumed to be a posint"""
if x == 0:
return 1
return x * fact(x - 1)
print
print ("N fact(N)")
print ("---------")
for n in range(10):
print (n, " factorial is : ", fact(n))
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27.
Class definitions
Class ClassName:
<statement-1>
...
<statement-N>
 must be executed
 can be executed conditionally
 creates new namespace
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28.
Class objects
 obj.name references (plus module!):
class MyClass:
"A simple example class"
i = 123
def f(self):
return 'hello world'
>>> MyClass.i
123
 MyClass.f is method object
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29.
Files
 Files are manipulated by creating a file
object
 f = open("points.txt", "r")
 The file object then has new methods
 print f.readline() # prints line from file
 Files can be accessed to read or write
 f = open("output.txt", "w")
 f.write("Important Output!")
 Files are iterable objects, like lists
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30.
Error Capture
 Check for type assignment errors, items
not in a list, etc.
 Try & Except
try:
a block of code that might have an error
except:
code to execute if an error occurs in "try"
 Allows for graceful failure
– important for applications which use system resources
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31.
Popular Modules
 Cryptograpgy
 M2Crypto, OpenSSL
 GUI
 PyGTK, TKInter, PyQt
 Networking
 RPyC, HTTPLib2
 Plotting
 Matplotlib, plotly, SciPy
 Scientific
 Numpy, SciPy,
 Threading
 Threadpool
 Web development
 Django, web2py
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32.
Hands On Bite

33.
Comments
 Start comments with #, rest of line is ignored
 Can include a “documentation string” as the
first line of a new function or class you define
 Development environments, debugger, and
other tools use it: it’s good style to include one
def fact(n):
“““fact(n) assumes n is a positive
integer and returns factorial of n.”””
assert(n>0)
return 1 if n==1 else n*fact(n-1)
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34.
LETS GO HANDS ON..
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35.
Assignment
 Binding a variable in Python means setting a name to
hold a reference to some object
 Assignment creates references, not copies
 Names in Python do not have an intrinsic type, objects
have types
 Python determines the type of the reference automatically based
on what data is assigned to it
 You create a name the first time it appears on the left
side of an assignment expression:
x = 3
 A reference is deleted via garbage collection after any
names bound to it have passed out of scope
 Python uses reference semantics (more later)
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36.
Naming Rules
 Names are case sensitive and cannot start with a
number. They can contain letters, numbers, and
underscores.
bob Bob _bob _2_bob_ bob_2 BoB
 There are some reserved words:
and, assert, break, class, continue,
def, del, elif, else, except, exec,
finally, for, from, global, if, import,
in, is, lambda, not, or, pass, print,
raise, return, try, while
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37.
Naming Conventions
The Python community has these recommend-ed
naming conventions
joined_lower for functions, methods and,
attributes
joined_lower or ALL_CAPS for constants
StudlyCaps for classes
camelCase only to conform to pre-existing
conventions
Attributes: interface, _internal, __private
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38.
Assignment
You can assign to multiple names at the
same time
>>> x, y = 2, 3
>>> x
2
>>> y
3
This makes it easy to swap values
>>> x, y = y, x
Assignments can be chained
>>> a = b = x = 2
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39.
Accessing Non-Existent Name
Accessing a name before it’s been properly
created (by placing it on the left side of an
assignment), raises an error
>>> y
Traceback (most recent call last):
File "<pyshell#16>", line 1, in -toplevel-
y
NameError: name ‘y' is not defined
>>> y = 3
>>> y
3
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40.
Python Program Structure
 modules: Python source files or C extensions
 import, top-level via from, reload
 statements
 control flow
 create objects
 indentation matters – instead of { }
 case sensitive
 objects
 everything is an object
 automatically reclaimed when no longer needed (gc)
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41.
Basic operations
 Assignment:
 size = 40
 a = b = c = 3
 Numbers
 integer, float
 complex numbers: 1j+3, abs(z)
 Strings
 'hello world', 'it's hot'
 "bye world"
 continuation via or use """ long text """"
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42.
String operations
 concatenate with + or neighbors
 word = 'Help' + x
 word = 'Help' 'a'
 subscripting of strings
 'Hello'[2]  'l'
 slice: 'Hello'[1:2]  'el'
 word[-1]  last character
 len(word)  5
 immutable: cannot assign to subscript
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43.
Lists
 lists can be heterogeneous
 a = ['spam', 'eggs', 100, 1234, 2*2]
 Lists can be indexed and sliced:
 a[0]  spam
 a[:2]  ['spam', 'eggs']
 Lists can be manipulated
 a[2] = a[2] + 23
 a[0:2] = [1,12]
 a[0:0] = []
 len(a)  5
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44.
Basic programming
a,b = 0, 1
# non-zero = true
while b < 10:
# formatted output, without n
print b,
# multiple assignment
a,b = b, a+b
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45.
Control flow: if
x = int(raw_input("Please enter #:"))
if x < 0:
x = 0
print 'Negative changed to zero'
elif x == 0:
print 'Zero'
elif x == 1:
print 'Single'
else:
print 'More'
 no case statement
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46.
Control flow: for
a = ['cat', 'window', 'defenestrate']
for x in a:
print x, len(x)
 no arithmetic progression, but
 range(10)  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
 for i in range(len(a)):
print i, a[i]
 do not modify the sequence being iterated over
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47.
Loops: break, continue, else
 break and continue like C
 else after loop exhaustion
for n in range(2,10):
for x in range(2,n):
if n % x == 0:
print n, 'equals', x, '*', n/x
break
else:
# loop fell through without finding a factor
print n, 'is prime'
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48.
Do nothing
 pass does nothing
 syntactic filler
while 1:
pass
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49.
List methods
 append(x)
 extend(L)
 append all items in list (like Tcl lappend)
 insert(i,x)
 remove(x)
 pop([i]), pop()
 create stack (FIFO), or queue (LIFO)  pop(0)
 index(x)
 return the index for value x
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50.
List methods
 count(x)
 how many times x appears in list
 sort()
 sort items in place
 reverse()
 reverse list
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51.
Functional programming tools
 filter(function, sequence)
def f(x): return x%2 != 0 and x%3 0
filter(f, range(2,25))
 map(function, sequence)
 call function for each item
 return list of return values
 reduce(function, sequence)
 return a single value
 call binary function on the first two items
 then on the result and next item
 iterate
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52.
List comprehensions (2.0)
 Create lists without map(), filter(),
lambda
 = expression followed by for clause + zero
or more for or of clauses
>>> vec = [2,4,6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [{x: x**2} for x in vec}
[{2: 4}, {4: 16}, {6: 36}]
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53.
List comprehensions
 cross products:
>>> vec1 = [2,4,6]
>>> vec2 = [4,3,-9]
>>> [x*y for x in vec1 for y in vec2]
[8,6,-18, 16,12,-36, 24,18,-54]
>>> [x+y for x in vec1 and y in vec2]
[6,5,-7,8,7,-5,10,9,-3]
>>> [vec1[i]*vec2[i] for i in
range(len(vec1))]
[8,12,-54]
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54.
List comprehensions
 can also use if:
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
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55.
del – removing list items
 remove by index, not value
 remove slices from list (rather than by assigning
an empty list)
>>> a = [-1,1,66.6,333,333,1234.5]
>>> del a[0]
>>> a
[1,66.6,333,333,1234.5]
>>> del a[2:4]
>>> a
[1,66.6,1234.5]
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56.
Tuples and sequences
 lists, strings, tuples: examples of
sequence type
 tuple = values separated by commas
>>> t = 123, 543, 'bar'
>>> t[0]
123
>>> t
(123, 543, 'bar')
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57.
Tuples
 Tuples may be nested
>>> u = t, (1,2)
>>> u
((123, 542, 'bar'), (1,2))
 kind of like structs, but no element names:
 (x,y) coordinates
 database records
 like strings, immutable  can't assign to
individual items
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58.
Tuples
 Empty tuples: ()
>>> empty = ()
>>> len(empty)
0
 one item  trailing comma
>>> singleton = 'foo',
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59.
Tuples
 sequence unpacking  distribute elements
across variables
>>> t = 123, 543, 'bar'
>>> x, y, z = t
>>> x
123
 packing always creates tuple
 unpacking works for any sequence
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60.
Dictionaries
 like Tcl or awk associative arrays
 indexed by keys
 keys are any immutable type: e.g., tuples
 but not lists (mutable!)
 uses 'key: value' notation
>>> tel = {'hgs' : 7042, 'lennox': 7018}
>>> tel['cs'] = 7000
>>> tel
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61.
Dictionaries
 no particular order
 delete elements with del
>>> del tel['foo']
 keys() method  unsorted list of keys
>>> tel.keys()
['cs', 'lennox', 'hgs']
 use has_key() to check for existence
>>> tel.has_key('foo')
0
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62.
Conditions
 can check for sequence membership with is and
is not:
>>> if (4 in vec):
... print '4 is'
 chained comparisons: a less than b AND b equals
c:
a < b == c
 and and or are short-circuit operators:
 evaluated from left to right
 stop evaluation as soon as outcome clear
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63.
Conditions
 Can assign comparison to variable:
>>> s1,s2,s3='', 'foo', 'bar'
>>> non_null = s1 or s2 or s3
>>> non_null
foo
 Unlike C, no assignment within expression
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64.
Comparing sequences
 unlike C, can compare sequences (lists,
tuples, ...)
 lexicographical comparison:
 compare first; if different  outcome
 continue recursively
 subsequences are smaller
 strings use ASCII comparison
 can compare objects of different type, but by
type name (list < string < tuple)
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65.
Comparing sequences
(1,2,3) < (1,2,4)
[1,2,3] < [1,2,4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1,2,3) == (1.0,2.0,3.0)
(1,2) < (1,2,-1)
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66.
Modules
 collection of functions and variables,
typically in scripts
 definitions can be imported
 file name is module name + .py
 e.g., create module fibo.py
def fib(n): # write Fib. series up to n
...
def fib2(n): # return Fib. series up to n
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67.
Modules
 import module:
import fibo
 Use modules via "name space":
>>> fibo.fib(1000)
>>> fibo.__name__
'fibo'
 can give it a local name:
>>> fib = fibo.fib
>>> fib(500)
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68.
Modules
 function definition + executable statements
 executed only when module is imported
 modules have private symbol tables
 avoids name clash for global variables
 accessible as module.globalname
 can import into name space:
>>> from fibo import fib, fib2
>>> fib(500)
 can import all names defined by module:
>>> from fibo import *
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69.
Module search path
 current directory
 list of directories specified in PYTHONPATH
environment variable
 uses installation-default if not defined, e.g.,
.:/usr/local/lib/python
 uses sys.path
>>> import sys
>>> sys.path
['', 'C:PROGRA~1Python2.2', 'C:Program
FilesPython2.2DLLs', 'C:Program
FilesPython2.2lib', 'C:Program
FilesPython2.2liblib-tk', 'C:Program
FilesPython2.2', 'C:Program FilesPython2.2libsite-
packages']
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70.
Compiled Python files
 include byte-compiled version of module if there
exists fibo.pyc in same directory as fibo.py
 only if creation time of fibo.pyc matches fibo.py
 automatically write compiled file, if possible
 platform independent
 doesn't run any faster, but loads faster
 can have only .pyc file  hide source
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71.
Standard modules
 system-dependent list
 always sys module
>>> import sys
>>> sys.p1
'>>> '
>>> sys.p2
'... '
>>> sys.path.append('/some/directory')
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72.
Module listing
 use dir() for each module
>>> dir(fibo)
['___name___', 'fib', 'fib2']
>>> dir(sys)
['__displayhook__', '__doc__', '__excepthook__', '__name__', '__stderr__', '__st
din__', '__stdout__', '_getframe', 'argv', 'builtin_module_names', 'byteorder',
'copyright', 'displayhook', 'dllhandle', 'exc_info', 'exc_type', 'excepthook', '
exec_prefix', 'executable', 'exit', 'getdefaultencoding', 'getrecursionlimit', '
getrefcount', 'hexversion', 'last_type', 'last_value', 'maxint', 'maxunicode', '
modules', 'path', 'platform', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setpr
ofile', 'setrecursionlimit', 'settrace', 'stderr', 'stdin', 'stdout', 'version',
'version_info', 'warnoptions', 'winver']
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73.
Classes
 mixture of C++ and Modula-3
 multiple base classes
 derived class can override any methods of its
base class(es)
 method can call the method of a base class with
the same name
 objects have private data
 C++ terms:
 all class members are public
 all member functions are virtual
 no constructors or destructors (not needed)
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74.
Classes
 classes (and data types) are objects
 built-in types cannot be used as base
classes by user
 arithmetic operators, subscripting can be
redefined for class instances (like C++,
unlike Java)
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75.
Class definitions
Class ClassName:
<statement-1>
...
<statement-N>
 must be executed
 can be executed conditionally (see Tcl)
 creates new namespace
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76.
Namespaces
 mapping from name to object:
 built-in names (abs())
 global names in module
 local names in function invocation
 attributes = any following a dot
 z.real, z.imag
 attributes read-only or writable
 module attributes are writeable
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77.
Namespaces
 scope = textual region of Python program where
a namespace is directly accessible (without dot)
 innermost scope (first) = local names
 middle scope = current module's global names
 outermost scope (last) = built-in names
 assignments always affect innermost scope
 don't copy, just create name bindings to objects
 global indicates name is in global scope
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78.
Class objects
 obj.name references (plus module!):
class MyClass:
"A simple example class"
i = 123
def f(self):
return 'hello world'
>>> MyClass.i
123
 MyClass.f is method object
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79.
Class objects
 class instantiation:
>>> x = MyClass()
>>> x.f()
'hello world'
 creates new instance of class
 note x = MyClass vs. x = MyClass()
 ___init__() special method for initialization of
object
def __init__(self,realpart,imagpart):
self.r = realpart
self.i = imagpart
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80.
Instance objects
 attribute references
 data attributes (C++/Java data members)
 created dynamically
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
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81.
Method objects
 Called immediately:
x.f()
 can be referenced:
xf = x.f
while 1:
print xf()
 object is passed as first argument of
function  'self'
 x.f() is equivalent to MyClass.f(x)
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82.
Notes on classes
 Data attributes override method attributes
with the same name
 no real hiding  not usable to implement
pure abstract data types
 clients (users) of an object can add data
attributes
 first argument of method usually called
self
 'self' has no special meaning (cf. Java)
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83.
Another example
 bag.py
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self,x):
self.add(x)
self.add(x)
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84.
Another example, cont'd.
 invoke:
>>> from bag import *
>>> l = Bag()
>>> l.add('first')
>>> l.add('second')
>>> l.data
['first', 'second']
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85.
Inheritance
class DerivedClassName(BaseClassName)
<statement-1>
...
<statement-N>
 search class attribute, descending chain of
base classes
 may override methods in the base class
 call directly via BaseClassName.method
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86.
Multiple inheritance
class DerivedClass(Base1,Base2,Base3):
<statement>
 depth-first, left-to-right
 problem: class derived from two classes
with a common base class
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87.
Private variables
 No real support, but textual replacement
(name mangling)
 __var is replaced by _classname_var
 prevents only accidental modification, not
true protection
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88.
~ C structs
 Empty class definition:
class Employee:
pass
john = Employee()
john.name = 'John Doe'
john.dept = 'CS'
john.salary = 1000
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89.
Exceptions
 syntax (parsing) errors
while 1 print 'Hello World'
File "<stdin>", line 1
while 1 print 'Hello World'
^
SyntaxError: invalid syntax
 exceptions
 run-time errors
 e.g., ZeroDivisionError, NameError,
TypeError
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90.
Handling exceptions
while 1:
try:
x = int(raw_input("Please enter a number: "))
break
except ValueError:
print "Not a valid number"
 First, execute try clause
 if no exception, skip except clause
 if exception, skip rest of try clause and use except
clause
 if no matching exception, attempt outer try statement
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91.
Handling exceptions
 try.py
import sys
for arg in sys.argv[1:]:
try:
f = open(arg, 'r')
except IOError:
print 'cannot open', arg
else:
print arg, 'lines:', len(f.readlines())
f.close
 e.g., as python try.py *.py
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92.
Language comparison
Tcl Perl Python JavaScript Visual
Basic
Speed development     
regexp   
breadth extensible   
embeddable  
easy GUI   (Tk) 
net/web     
enterprise cross-platform    
I18N    
thread-safe   
database access     
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93.
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94.
Sequence types:
Tuples, Lists, and
Strings

95.
Sequence Types
1. Tuple: (‘john’, 32, [CMSC])
 A simple immutable ordered sequence of
items
 Items can be of mixed types, including
collection types
2. Strings: “John Smith”
 Immutable
 Conceptually very much like a tuple
3. List: [1, 2, ‘john’, (‘up’, ‘down’)]
 Mutable ordered sequence of items of
mixed types
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96.
Similar Syntax
 All three sequence types (tuples, strings,
and lists) share much of the same syntax
and functionality.
 Key difference:
 Tuples and strings are immutable
 Lists are mutable
 The operations shown in this section can
be applied to all sequence types
 most examples will just show the
operation performed on one
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97.
Sequence Types 1
 Define tuples using parentheses and commas
>>> tu = (23, ‘abc’, 4.56, (2,3),
‘def’)
 Define lists are using square brackets and
commas
>>> li = [“abc”, 34, 4.34, 23]
 Define strings using quotes (“, ‘, or “““).
>>> st = “Hello World”
>>> st = ‘Hello World’
>>> st = “““This is a multi-line
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98.
Sequence Types 2
 Access individual members of a tuple, list, or
string using square bracket “array” notation
 Note that all are 0 based…
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> tu[1] # Second item in the tuple.
‘abc’
>>> li = [“abc”, 34, 4.34, 23]
>>> li[1] # Second item in the list.
34
>>> st = “Hello World”
>>> st[1] # Second character in string.
‘e’
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99.
Positive and negative indices
>>> t = (23, ‘abc’, 4.56, (2,3),
‘def’)
Positive index: count from the left, starting with 0
>>> t[1]
‘abc’
Negative index: count from right, starting with –1
>>> t[-3]
4.56
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100.
Slicing: return copy of a subset
>>> t = (23, ‘abc’, 4.56,
(2,3), ‘def’)
Return a copy of the container with a subset of
the original members. Start copying at the first
index, and stop copying before second.
>>> t[1:4]
(‘abc’, 4.56, (2,3))
Negative indices count from end
>>> t[1:-1]
(‘abc’, 4.56, (2,3))
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101.
Slicing: return copy of a =subset
>>> t = (23, ‘abc’, 4.56,
(2,3), ‘def’)
Omit first index to make copy starting from
beginning of the container
>>> t[:2]
(23, ‘abc’)
Omit second index to make copy starting at first
index and going to end
>>> t[2:]
(4.56, (2,3), ‘def’)
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102.
Copying the Whole Sequence
 [ : ] makes a copy of an entire sequence
>>> t[:]
(23, ‘abc’, 4.56, (2,3), ‘def’)
 Note the difference between these two lines
for mutable sequences
>>> l2 = l1 # Both refer to 1
ref,
# changing one
affects both
>>> l2 = l1[:] # Independent 113Overview of Python Programming
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103.
The ‘in’ Operator
 Boolean test whether a value is inside a
container:
>>> t = [1, 2, 4, 5]
>>> 3 in t
False
>>> 4 in t
True
>>> 4 not in t
False
 For strings, tests for substrings
>>> a = 'abcde'
>>> 'c' in a
True
>>> 'cd' in a
True
>>> 'ac' in a
False
 Be careful: the in keyword is also used in 114Overview of Python Programming
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104.
The + Operator
The + operator produces a new tuple, list, or
string whose value is the concatenation of its
arguments.
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
>>> “Hello” + “ ” + “World” 115Overview of Python Programming
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105.
The * Operator
 The * operator produces a new tuple, list, or
string that “repeats” the original content.
>>> (1, 2, 3) * 3
(1, 2, 3, 1, 2, 3, 1, 2, 3)
>>> [1, 2, 3] * 3
[1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> “Hello” * 3
‘HelloHelloHello’
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106.
Mutability:
Tuples vs. Lists

107.
Lists are mutable
>>> li = [‘abc’, 23, 4.34, 23]
>>> li[1] = 45
>>> li
[‘abc’, 45, 4.34, 23]
 We can change lists in place.
 Name li still points to the same memory
reference when we’re done.
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108.
Tuples are immutable
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> t[2] = 3.14
Traceback (most recent call last):
File "<pyshell#75>", line 1, in -toplevel-
tu[2] = 3.14
TypeError: object doesn't support item assignment
You can’t change a tuple.
You can make a fresh tuple and assign its
reference to a previously used name.
>>> t = (23, ‘abc’, 3.14, (2,3), ‘def’)
The immutability of tuples means they’re faster
than lists.
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109.
Operations on Lists Only
>>> li = [1, 11, 3, 4, 5]
>>> li.append(‘a’) # Note the
method syntax
>>> li
[1, 11, 3, 4, 5, ‘a’]
>>> li.insert(2, ‘i’)
>>>li 120Overview of Python Programming
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110.
The extend method vs +
 + creates a fresh list with a new memory ref
 extend operates on list li in place.
>>> li.extend([9, 8, 7])
>>> li
[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7]
 Potentially confusing:
 extend takes a list as an argument.
 append takes a singleton as an argument.
>>> li.append([10, 11, 12])
>>> li
[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7, [10,
11, 12]] 121Overview of Python Programming
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111.
Operations on Lists Only
Lists have many methods, including index, count,
remove, reverse, sort
>>> li = [‘a’, ‘b’, ‘c’, ‘b’]
>>> li.index(‘b’) # index of 1st
occurrence
1
>>> li.count(‘b’) # number of
occurrences
2
>>> li.remove(‘b’) # remove 1st
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112.
Operations on Lists Only
>>> li = [5, 2, 6, 8]
>>> li.reverse() # reverse the list *in place*
>>> li
[8, 6, 2, 5]
>>> li.sort() # sort the list *in place*
>>> li
[2, 5, 6, 8]
>>> li.sort(some_function)
# sort in place using user-defined comparison
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113.
Tuple details
The comma is the tuple creation operator,
not parens
>>> 1,
(1,)
Python shows parens for clarity (best
practice)
>>> (1,)
(1,)
Don't forget the comma!
>>> (1)
1
Trailing comma only required for
singletons others 124Overview of Python Programming
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114.
Summary: Tuples vs. Lists
 Lists slower but more powerful than tuples
 Lists can be modified, and they have lots of
handy operations and mehtods
 Tuples are immutable and have fewer features
 To convert between tuples and lists use the list()
and tuple() functions:
li = list(tu)
tu = tuple(li)
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115.
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116.
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117.
More than just printing
 Python is an object oriented language
 Practically everything can be treated as an
object
 “hello world” is a string
 Strings, as objects, have methods that
return the result of a function on the string
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118.
String Methods
 Assign a string to a
variable
 In this case “hw”
 hw.title()
 hw.upper()
 hw.isdigit()
 hw.islower()
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119.
String Methods
 The string held in your variable remains
the same
 The method returns an altered string
 Changing the variable requires
reassignment
 hw = hw.upper()
 hw now equals “HELLO WORLD”
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120.
Other Python Objects
 Lists (mutable sets of strings)
 var = [] # create list
 var = [‘one’, 2, ‘three’, ‘banana’]
 Tuples (immutable sets)
 var = (‘one’, 2, ‘three’, ‘banana’)
 Dictionaries (associative arrays or ‘hashes’)
 var = {} # create dictionary
 var = {‘lat’: 40.20547, ‘lon’: -74.76322}
 var[‘lat’] = 40.2054
 Each has its own set of methods
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121.
Lists
 Think of a list as a stack of cards, on
which your information is written
 The information stays in the order you
place it in until you modify that order
 Methods return a string or subset of the
list or modify the list to add or remove
components
 Written as var[index], index refers to
order within set (think card number,
starting at 0) 132Overview of Python Programming
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122.
List Methods
 Adding to the List
 var[n] = object
 replaces n with object
 var.append(object)
 adds object to the end of the list
 Removing from the List
 var[n] = []
 empties contents of card, but preserves order
 var.remove(n)
 removes card at n
 var.pop(n)
 removes n and returns its value
133Overview of Python Programming
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123.
Lists in ArcToolbox
You will create lists:
 Layers as inputs
 Attributes to match
 Arrays of objects
You will work with
lists:
 List of field names
 List of selected
features
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124.
Tuples
 Like a list, tuples are iterable arrays of
objects
 Tuples are immutable –
once created, unchangeable
 To add or remove items, you must
redeclare
 Example uses of tuples
 County Names
 Land Use Codes
 Ordered set of functions 135Overview of Python Programming
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125.
Dictionaries
 Dictionaries are sets of key & value pairs
 Allows you to identify values by a
descriptive name instead of order in a list
 Keys are unordered unless explicitly sorted
 Keys are unique:
 var[‘item’] = “apple”
 var[‘item’] = “banana”
 print var[‘item’] prints just banana
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126.
Indentation and Blocks
 Python uses whitespace and indents to
denote blocks of code
 Lines of code that begin a block end in a
colon:
 Lines within the code block are indented at
the same level
 To end a code block, remove the
indentation
 You'll want blocks of code that run only
when certain conditions are met 137Overview of Python Programming
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127.
Conditional Branching
 if and else
if variable == condition:
#do something based on v == c
else:
#do something based on v != c
 elif allows for additional branching
if condition:
elif another condition:
…
else: #none of the above
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128.
Looping with For
 For allows you to loop over a block of code a
set number of times
 For is great for manipulating lists:
a = ['cat', 'window', 'defenestrate']
for x in a:
print x, len(x)
Results:
cat 3
window 6
defenestrate 12
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129.
Looping with For
 We could use a for loop to perform
geoprocessing tasks on each layer in a list
 We could get a list of features in a feature
class and loop over each, checking
attributes
 Anything in a sequence or list can be used
in a For loop
 Just be sure not to modify the list while
looping
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130.
Modules
 Modules are additional pieces of code that
further extend Python’s functionality
 A module typically has a specific function
 additional math functions, databases,
network…
 Python comes with many useful modules
 arcgisscripting is the module we will use to
load ArcGIS toolbox functions into Python
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131.
Modules
 Modules are accessed using import
 import sys, os # imports two modules
 Modules can have subsets of functions
 os.path is a subset within os
 Modules are then addressed by
modulename.function()
 sys.argv # list of arguments
 filename = os.path.splitext("points.txt")
 filename[1] # equals ".txt"
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132.
Files
 Files are manipulated by creating a file
object
 f = open("points.txt", "r")
 The file object then has new methods
 print f.readline() # prints line from file
 Files can be accessed to read or write
 f = open("output.txt", "w")
 f.write("Important Output!")
 Files are iterable objects, like lists
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133.
Error Capture
 Check for type assignment errors, items
not in a list, etc.
 Try & Except
try:
a block of code that might have an error
except:
code to execute if an error occurs in "try"
 Allows for graceful failure
– important in ArcGIS
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134.
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135.
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136.
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
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137.
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.
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138.
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
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139.
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
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140.
String operators: in, not in
if "Br" in “Brother”:
print "contains brother“
email_address = “clin”
if "@" not in email_address:
email_address += "@brandeis.edu“
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141.
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 '
>>>
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142.
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'
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143.
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
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144.
“” 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
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145.
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())
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146.
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'
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147.
zipping lists together
>>> names
['ben', 'chen', 'yaqin']
>>> gender = [0, 0, 1]
>>> zip(names, gender)
[('ben', 0), ('chen', 0), ('yaqin', 1)]
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148.
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
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149.
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.
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150.
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
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151.
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'}
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152.
 Background
 Data Types/Structure
list, string, tuple, dictionary
 Control flow
 File I/O
 Modules
 Class
 NLTK
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153.
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.
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154.
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
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155.
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
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156.
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“
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157.
Range Test
if (3 <= Time <= 5):
print “Office Hour"
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158.
For
>>> names = [“Ben", “Chen", “Yaqin"]
>>> for name in names:
... print smiles
...
Ben
Chen
Yaqin
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159.
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
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160.
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
>>>
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161.
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]
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162.
 Background
 Data Types/Structure
 Control flow
 File I/O
 Modules
 Class
 NLTK
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163.
Reading files
>>> f = open(“names.txt")
>>> f.readline()
'Yaqinn'
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164.
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
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165.
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
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166.
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”
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167.
 Background
 Data Types/Structure
 Control flow
 File I/O
 Modules
 Class
 NLTK
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168.
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
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169.
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)
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170.
“import” and “from ... import ...”
>>> import math
math.cos
>>> from math import cos, pi
cos
>>> from math import *
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171.
 Background
 Data Types/Structure
 Control flow
 File I/O
 Modules
 Class
 NLTK
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172.
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>
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173.
 Background
 Data Types/Structure
 Control flow
 File I/O
 Modules
 Class
 NLTK
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174.
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175.
186
Expressions
 expression: A data value or set of
operations to compute a value.
Examples: 1 + 4 * 3
42
 Arithmetic operators we will use:
 + - * / addition,
subtraction/negation, multiplication, division
 % modulus, a.k.a. remainder
 ** exponentiation
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176.
187
Integer division
 When we divide integers with / , the
quotient is also an integer.
3 52
4 ) 14 27 ) 1425
12 135
2 75
54
21
 More examples:
 35 / 5 is 7
 84 / 10 is 8
 156 / 100 is 1
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177.
188
Real numbers
 Python can also manipulate real numbers.
 Examples: 6.022 -15.9997 42.0
2.143e17
 The operators + - * / % ** ( ) all work
for real numbers.
 The / produces an exact answer: 15.0 /
2.0 is 7.5
 The same rules of precedence also apply to
real numbers:
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178.
189
Math commands
 Python has useful commands for performing
calculations.
Command name Description
abs(value) absolute value
ceil(value) rounds up
cos(value) cosine, in radians
floor(value) rounds down
log(value) logarithm, base e
log10(value) logarithm, base 10
max(value1, value2) larger of two values
min(value1, value2) smaller of two values
round(value) nearest whole number
sin(value) sine, in radians
sqrt(value) square root
Constant Description
e 2.7182818...
pi 3.1415926...
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179.
190
Variables
 variable: A named piece of memory that
can store a value.
 Usage:
 Compute an expression's result,
 store that result into a variable,
 and use that variable later in the program.
 assignment statement: Stores a value
into a variable.
 Syntax:
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180.
191
 print : Produces text output on the console.
 Syntax:
print "Message"
print Expression
 Prints the given text message or expression value
on the console, and moves the cursor down to the
next line.
print Item1, Item2, ..., ItemN
 Prints several messages and/or expressions on the
same line.
print
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181.
192
 input : Reads a number from user input.
 You can assign (store) the result of input into a
variable.
 Example:
age = input("How old are you? ")
print "Your age is", age
print "You have", 65 - age, "years
until retirement"
Output:
How old are you? 53
Your age is 53
You have 12 years until retirement
input
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182.
193
Repetition (loops)
and Selection (if/else)

183.
194
The for loop
 for loop: Repeats a set of statements over a group of values.
 Syntax:
for variableName in groupOfValues:
statements
 We indent the statements to be repeated with tabs or spaces.
 variableName gives a name to each value, so you can refer to it in the statements.
 groupOfValues can be a range of integers, specified with the range function.
 Example:
for x in range(1, 6):
print x, "squared is", x * x
Output:
1 squared is 1
2 squared is 4
3 squared is 9
4 squared is 16
5 squared is 25
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184.
195
range
 The range function specifies a range of
integers:
 range(start, stop) - the integers between
start (inclusive)
and stop (exclusive)
 It can also accept a third value specifying the
change between values.
 range(start, stop, step) - the integers
between start (inclusive)
and stop (exclusive) by step
 Example:
for x in range(5, 0, -1):
print x
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185.
196
Cumulative loops
 Some loops incrementally compute a value
that is initialized outside the loop. This is
sometimes called a cumulative sum.
sum = 0
for i in range(1, 11):
sum = sum + (i * i)
print "sum of first 10 squares is", sum
Output:
sum of first 10 squares is 385
 Exercise: Write a Python program that
computes the factorial of an integer.
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186.
197
if
 if statement: Executes a group of
statements only if a certain condition is
true. Otherwise, the statements are
skipped.
 Syntax:
if condition:
statements
 Example:
gpa = 3.4
if gpa > 2.0:
print "Your application is accepted."
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187.
198
if/else
 if/else statement: Executes one block of statements if a certain
condition is True, and a second block of statements if it is False.
 Syntax:
if condition:
statements
else:
statements
 Example:
gpa = 1.4
if gpa > 2.0:
print "Welcome to Mars University!"
else:
print "Your application is denied."
 Multiple conditions can be chained with elif ("else if"):
if condition:
statements
elif condition:
statements
else:
statements
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188.
199
while
 while loop: Executes a group of statements as long as a condition is True.
 good for indefinite loops (repeat an unknown number of times)
 Syntax:
while condition:
statements
 Example:
number = 1
while number < 200:
print number,
number = number * 2
 Output:
1 2 4 8 16 32 64 128
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189.
200
Logic
 Many logical expressions use relational operators:
 Logical expressions can be combined with logical operators:
 Exercise: Write code to display and count the factors of a number.
Operator Example Result
and 9 != 6 and 2 < 3 True
or 2 == 3 or -1 < 5 True
not not 7 > 0 False
Operator Meaning Example Result
== equals 1 + 1 == 2 True
!= does not equal 3.2 != 2.5 True
< less than 10 < 5 False
> greater than 10 > 5 True
<= less than or equal to 126 <= 100 False
>= greater than or equal to 5.0 >= 5.0 True
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190.
201
Text and File Processing

191.
202
 string: A sequence of text characters in a program.
 Strings start and end with quotation mark " or apostrophe ' characters.
 Examples:
"hello"
"This is a string"
"This, too, is a string. It can be very long!"
 A string may not span across multiple lines or contain a " character.
"This is not
a legal String."
"This is not a "legal" String either."
 A string can represent characters by preceding them with a backslash.
 t tab character
 n new line character
 " quotation mark character
 backslash character
 Example: "HellottherenHow are you?"
Strings
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192.
203
Indexes
 Characters in a string are numbered with indexes starting at 0:
 Example:
name = "P. Diddy"
 Accessing an individual character of a string:
variableName [ index ]
 Example:
print name, "starts with", name[0]
Output:
P. Diddy starts with P
index 0 1 2 3 4 5 6 7
character P . D i d d y
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193.
204
String properties
 len(string) - number of characters in a string
(including spaces)
 str.lower(string) - lowercase version of a string
 str.upper(string) - uppercase version of a string
 Example:
name = "Martin Douglas Stepp"
length = len(name)
big_name = str.upper(name)
print big_name, "has", length, "characters"
Output:
MARTIN DOUGLAS STEPP has 20 characters
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194.
205
 raw_input : Reads a string of text from user
input.
 Example:
name = raw_input("Howdy, pardner.
What's yer name? ")
print name, "... what a silly name!"
Output:
Howdy, pardner. What's yer name? Paris
Hilton
Paris Hilton ... what a silly name!
raw_input
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195.
206
Text processing
 text processing: Examining, editing, formatting text.
 often uses loops that examine the characters of a string one by
one
 A for loop can examine each character in a string in sequence.
 Example:
for c in "booyah":
print c
Output:
b
o
o
y
a
h
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207
Strings and numbers
 ord(text) - converts a string into a number.
 Example: ord("a") is 97, ord("b") is 98, ...
 Characters map to numbers using standardized mappings such as
ASCII and Unicode.
 chr(number) - converts a number into a string.
 Example: chr(99) is "c"
 Exercise: Write a program that performs a rotation
cypher.
 e.g. "Attack" when rotated by 1 becomes
"buubdl"
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197.
208
File processing
 Many programs handle data, which often
comes from files.
 Reading the entire contents of a file:
variableName =
open("filename").read()
Example:
file_text =
open("bankaccount.txt").read()
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198.
209
Line-by-line processing
 Reading a file line-by-line:
for line in open("filename").readlines():
statements
Example:
count = 0
for line in open("bankaccount.txt").readlines():
count = count + 1
print "The file contains", count, "lines."
 Exercise: Write a program to process a file of DNA text, such as:
ATGCAATTGCTCGATTAG
 Count the percent of C+G present in the DNA.
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199.
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