Python Tutorial Part 1

Python Tutorial

Haitham El-Ghareeb, Ph.D.
May 2012
Twitter: @helghareeb

Session One - Agenda
• Whetting Your Appetite
• Using Python Interpreter
• Informal Introduction to Python
• More Control Flows

Session Two - Agenda
• Data Structures
• Modules
• Input and Output
• Errors and Exceptions
• Classes

Bonus Session - Agenda
• Unit Testing
– Hopefully!

Python
• easy to learn, powerful programming
language.
• has efficient high-level data structures and a
simple but effective approach to object-
oriented programming.
– elegant syntax
– dynamic typing
– interpreted nature,

Whetting Your Appetite
• You could write a Unix shell script or Windows
batch files for some tasks, but:
– Shell scripts are best at moving around files and
changing text data, not well-suited for GUI
applications or games.
• You could write a C/C++/Java program, but:
– It can take a lot of development time to get even a
first-draft program.

Python
• Simpler to use
• Available on Windows, Mac OS X, and Unix
• Help you get the job done more quickly
• Split your program into modules that can be reused
• Python is an interpreted language:
– Save you considerable time during program
development because no compilation and linking
is necessary.
– Interpreter can be used interactively

Python
• Python is extensible: if you know how to
program in C it is easy to add a new built-in
function or module to the interpreter,
• Named after the BBC show “Monty Python’s
Flying Circus” and has nothing to do with
reptiles.

Using the Python Interpreter
• Python interpreter is usually installed as
/usr/local/bin/python
• Invoking the Interpreter
– A second way of starting the interpreter is python
-c command [arg] ..., which executes the
statement(s) in command
– Some Python modules are also useful as scripts.
These can be invoked using python -m module
[arg] ..., which executes the source file for module

Using Python Interpreter
• Argument Passing
• Interactive Mode

• Error Handling

Source Code Encoding
• It is possible to use encodings different than
ASCII in Python source files.
• The best way to do it is to put one more
special comment line right after the #! line to
define the source file encoding:

Lists
• compound data type
• used to group together other values
• The most versatile
• can be written as a list of comma-separated
values (items) between square brackets.
• List items need not all have the same type.

First Steps toward Programming

For Statement with Shallow Copy

Keyword Arguments
• When a final formal parameter of the form
**name is present, it receives a dictionary
containing all keyword arguments except for
those corresponding to a formal parameter.
• This may be combined with a formal
parameter of the form *name which receives
a tuple containing the positional arguments
beyond the formal parameter list.
• (*name must occur before **name.)

Coding Style - PEP8
• Use 4-space indentation, and no tabs.
• 4 spaces are a good compromise between small
indentation (allows greater nesting depth) and large
indentation (easier to read). Tabs introduce
confusion, and are best left out.
• Wrap lines so that they don’t exceed 79 characters.
• This helps users with small displays and makes it
possible to have several code files side-by-side on
larger displays.
• Use blank lines to separate functions and classes, and
larger blocks of code inside functions.

Coding Style - PEP8
• When possible, put comments on a line of their own.
• Use docstrings.
• Use spaces around operators and after commas, but not
directly inside bracketing constructs: a = f(1, 2) + g(3, 4).
• Name your classes and functions consistently; the
convention is to use CamelCase for classes and
lower_case_with_underscores for functions and methods.
Always use self as the name for the first method argument.
• Don’t use fancy encodings if your code is meant to be used
in international environments. Plain ASCII works best in any
case.

Data Structures - Lists
• list.append(x) Add an item to the end of the list.
• list.extend(L) Extend the list by appending all the
items in the given list.
• list.insert(i, x) Insert an item at a given position.
The first argument is the index of the element
before which to insert, so a.insert(0, x) inserts at
the front of the list, and a.insert(len(a), x) is
equivalent to a.append(x).
• list.remove(x) Remove the first item from the list
whose value is x. It is an error if there is no such
item.

Data Structures - Lists
• list.pop([i]) Remove the item at the given position in
the list, and return it. If no index is specified, a.pop()
removes and returns the last item in the list.
• list.index(x) Return the index in the list of the first item
whose value is x. It is an error if there is no such item.
• list.count(x) Return the number of times x appears in
the list.
• list.sort() Sort the items of the list, in place.
• list.reverse() Reverse the elements of the list, in place.

Functional Programming Tools
• There are three built-in functions that are very
useful when used with lists: filter(), map(), and
reduce().

Filter()
• filter(function, sequence) returns a sequence
consisting of those items from the sequence
for which function(item) is true.
• If sequence is a string or tuple, the result will
be of the same type; otherwise, it is always a
list.
• For example, to compute a sequence of
numbers not divisible by 2 and 3:

Map()
• map(function, sequence) calls function(item)
for each of the sequence’s items and returns a
list of the return values.
• For example, to compute some cubes:

Map()
• More than one sequence may be passed; the
function must then have as many arguments
as there are sequences and is called with the
corresponding item from each sequence (or
None if some sequence is shorter than
another). For example:

Reduce()
• reduce(function, sequence) returns a single
value constructed by calling the binary
function function on the first two items of the
sequence, then on the result and the next
item, and so on.
• For example, to compute the sum of the
numbers 1 through 10:

List Comprehension
• List comprehensions provide a concise way to
create lists.
• Common applications are to make new lists
where each element is the result of some
operations applied to each member of
another sequence or iterable, or to create a
subsequence of those elements that satisfy a
certain condition.

List Comprehension
• A list comprehension consists of brackets
containing an expression followed by a for
clause, then zero or more for or if clauses. The
result will be a new list resulting from
evaluating the expression in the context of the
for and if clauses which follow it.
• For example, this listcomp combines the
elements of two lists if they are not equal:

Sets
• A set is an unordered collection with no
duplicate elements.
• Basic uses include membership testing and
eliminating duplicate entries.
• Set objects also support mathematical
operations like
union, intersection, difference, and symmetric
difference.

Dictionaries
• Dictionaries are sometimes found in other
languages as “associative memories” or
“associative arrays”.
• Unlike sequences, which are indexed by a range
of numbers, dictionaries are indexed by
keys, which can be any immutable type; strings
and numbers can always be keys.
• Tuples can be used as keys if they contain only
strings, numbers, or tuples; if a tuple contains any
mutable object either directly or indirectly, it
cannot be used as a key.

Dictionaries
• You can’t use lists as keys, since lists can be modified in
place using index assignments, slice assignments, or
methods like append() and extend().
• It is best to think of a dictionary as an unordered set of
key: value pairs, with the requirement that the keys are
unique (within one dictionary).
• A pair of braces creates an empty dictionary: {}. Placing
a comma-separated list of key:value pairs within the
braces adds initial key:value pairs to the dictionary; this
is also the way dictionaries are written on output.

Dictionaries
• The main operations on a dictionary are storing a value
with some key and extracting the value given the key.
• It is also possible to delete a key:value pair with del. If
you store using a key that is already in use, the old
value associated with that key is forgotten. It is an error
to extract a value using a non-existent key.
• The keys() method of a dictionary object returns a list
of all the keys used in the dictionary, in arbitrary order
(if you want it sorted, just apply the sorted() function
to it).
• To check whether a single key is in the dictionary, use
the in keyword.

Python Tutorial Part 1

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