Introduction to Python

Learn Python in three hours Some material adapted from Upenn cmpe391 slides and other sources Overview History Installing & Running Python Names & Assignment Sequences types: Lists, Tuples, and Strings Mutability

Brief History of Python Invented in the Netherlands, early 90s by Guido van Rossum Named after Monty Python Open sourced from the beginning Considered a scripting language, but is much more

Scalable, object oriented and functional from the beginning Used by Google from the beginning Increasingly popular Pythons Benevolent Dictator For Life Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another's code; too little and expressive-ness is endangered.

- Guido van Rossum The Python tutorial is good! Running Python The Python Interpreter Typical Python implementations offer both an interpreter and compiler

Interactive interface to Python with a read-eval-print loop [[email protected] ~]$ python Python 2.4.3 (#1, Jan 14 2008, 18:32:40) [GCC 4.1.2 20070626 (Red Hat 4.1.2-14)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> def square(x): ... return x * x ... >>> map(square, [1, 2, 3, 4]) [1, 4, 9, 16]

>>> Installing Python is pre-installed on most Unix systems, including Linux and MAC OS X The pre-installed version may not be the most recent one (2.6.2 and 3.1.1 as of Sept 09) Download from Python comes with a large library of standard modules There are several options for an IDE IDLE works well with Windows

Emacs with python-mode or your favorite text editor Eclipse with Pydev ( 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 with stepping, persistent breakpoints, and call stack visibility

Editing Python in Emacs Emacs python-mode has good support for editing Python, enabled enabled by default for .py files Features: completion, symbol help, eldoc, and inferior interpreter shell, etc. Running Interactively on UNIX On Unix % python >>> 3+3 6

Python prompts with >>>. To exit Python (not Idle): In Unix, type CONTROL-D In Windows, type CONTROL-Z + Evaluate exit() Running Programs on UNIX Call python program via the python interpreter % python Make a python file directly executable by Adding the appropriate path to your python interpreter as the first line of your file

#!/usr/bin/python Making the file executable % chmod a+x Invoking file from Unix command line % Example script: #! /usr/bin/python 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, fact(n) 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 Example of a Script #! /usr/bin/python """ reads text from standard input and outputs any email

addresses it finds, one to a line. """ import re from sys import stdin # a regular expression ~ for a valid email address pat = re.compile(r'[-\w][-.\w]*@[-\w][-\w.]+[a-zA-Z]{2,4}') for line in stdin.readlines(): for address in pat.findall(line): print address results python> python Getting a unique, sorted list import re from sys import stdin pat = re.compile(r'[-\w][-.\w]*@[-\w][-\w.]+[a-zA-Z]{2,4}) # found is an initially empty set (a list w/o duplicates)

found = set( ) for line in stdin.readlines(): for address in pat.findall(line): found.add(address) # sorted() takes a sequence, returns a sorted list of its elements for address in sorted(found): print address results python> python [email protected] python> Simple functions: """factorial done recursively and iteratively""" def fact1(n): ans = 1 for i in range(2,n): ans = ans * n return ans def fact2(n):

if n < 1: return 1 else: return n * fact2(n - 1) Simple functions: 671> python Python 2.5.2 >>> import ex >>> ex.fact1(6) 1296 >>> ex.fact2(200)

78865786736479050355236321393218507000000L >>> ex.fact1 >>> fact1 Traceback (most recent call last): File "", line 1, in NameError: name 'fact1' is not defined >>> The Basics 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 Enough to Understand the Code

Indentation matters to code meaning Block structure indicated by indentation First assignment to a variable creates it Variable types dont need to be declared. Python figures out the variable types on its own. Assignment is = and comparison is == For numbers + - * / % are as expected Special use of + for string concatenation and % for string formatting (as in Cs printf)

Logical operators are words (and, or, not) not symbols The basic printing command is print Basic Datatypes Integers (default for numbers) z = 5 / 2 # Answer 2, integer division Floats x = 3.456 Strings

Can use or to specify with abc == abc Unmatched can occur within the string: matts Use triple double-quotes for multi-line strings or strings than contain both and inside of them: abc Whitespace Whitespace is meaningful in Python: especially indentation and placement of newlines Use a newline to end a line of code Use \ when must go to next line prematurely

No braces {} to mark blocks of code, use consistent indentation instead First line with less indentation is outside of the block First line with more indentation starts a nested block Colons start of a new block in many constructs, e.g. function definitions, then clauses 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: its good style to include one def fact(n): fact(n) assumes n is a positive integer and returns facorial of n. assert(n>0) return 1 if n==1 else n*fact(n-1) 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) 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 Naming conventions The Python community has these recommended 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

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 Accessing Non-Existent Name Accessing a name before its been properly created (by placing it on the left side of an assignment), raises an error >>> y Traceback (most recent call last): File "", line 1, in -toplevely NameError: name y' is not defined >>> y = 3

>>> y 3 Sequence types: Tuples, Lists, and Strings 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 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 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 >>> st >>> st string

= Hello World = Hello World = This is a multi-line that uses triple quotes. 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 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 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)) 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) 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 copies, two refs The in Operator Boolean test whether a value is inside a container: >>> t >>> 3 False

>>> 4 True >>> 4 False = [1, 2, 4, 5] in t in t not in t 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 the syntax of for loops and list comprehensions 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 Hello World 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 Mutability: Tuples vs. Lists 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 were done. Tuples are immutable >>> t = (23, abc, 4.56, (2,3), def) >>> t[2] = 3.14 Traceback (most recent call last): File "", line 1, in -topleveltu[2] = 3.14 TypeError: object doesn't support item assignment

You cant 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 theyre faster than lists. 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 [1, 11, i, 3, 4, 5, a] 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]] 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 occurrence >>> li [a, c, b] Operations on Lists Only >>> li = [5, 2, 6, 8]

>>> li.reverse() >>> li [8, 6, 2, 5] # reverse the list *in place* >>> li.sort() >>> li [2, 5, 6, 8] # sort the list *in place*

>>> li.sort(some_function) # sort in place using user-defined comparison 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 Empty tuples have a special syntactic form >>> () () >>> tuple() () 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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