Engr. Ranel O. Padon

V. SEQUENCES & DICTIONARIES

PYTHON PROGRAMMING

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PYTHON PROGRAMMING TOPICS

I •  Introduction to Python Programming

II •  Python Basics

III •  Controlling the Program Flow

IV •  Program Components: Functions, Classes, Packages, and Modules

V •  Sequences (List and Tuples), and Dictionaries

VI •  Object-Based Programming: Classes and Objects

VII •  Customizing Classes and Operator Overloading

VIII •  Object-Oriented Programming: Inheritance and Polymorphism

IX •  Randomization Algorithms

X •  Exception Handling and Assertions

XI •  String Manipulation and Regular Expressions

XII •  File Handling and Processing

XIII •  GUI Programming Using Tkinter 2

SEQUENCES AND DICTIONARIES

I •  Introduction to Python Programming

II •  Python Basics

III •  Controlling the Program Flow

IV •  Program Components: Functions, Classes, Packages, and Modules

V •  Sequences (List and Tuples), and Dictionaries

VI •  Object-Based Programming: Classes and Objects

VII •  Customizing Classes and Operator Overloading

VIII •  Object-Oriented Programming: Inheritance and Polymorphism

IX •  Randomization Algorithms

X •  Exception Handling and Assertions

XI •  String Manipulation and Regular Expressions

XII •  File Handling and Processing

XIII •  GUI Programming Using Tkinter 3

Data structures are structures that hold and organize data or information.

DATA STRUCTURES

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Sequences, often called arrays in other languages, are data structures that store related data items.

SEQUENCES

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Python supports three basic sequence data types: a string, a tuple, and a list.

SEQUENCES

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A sequence element may be referenced by writing the sequence name followed by the element’s position number in square brackets ([ ]). The first element in a sequence is the zeroth element.

SEQUENCES

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SEQUENCES

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SEQUENCES

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Sequences can be accessed from the end of the sequence by using negative subscripts.

SEQUENCES

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Some types of sequences are immutable, that is, the sequence cannot be altered (e.g., changing the value of one of its elements). Python strings and tuples are immutable sequences. In other words, they are Read-Only.

SEQUENCES

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SEQUENCES | IMMUTABLE

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Python’s Tuple data structure is similar to Java’s native array and C’s native array, both of which have fixed length.

SEQUENCES

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Some sequences are mutable or can be altered. Python lists are mutable sequences. In other words, they are Read/Write data structure.

SEQUENCES

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SEQUENCES | MUTABLE

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Python’s List data structure is analogous to Java’s ArrayList and Vector classes, both of which are extendable.

SEQUENCES

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The length of the sequence is determined by the function call len(sequence ). This is useful when traversing the sequence.

SEQUENCES

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The length of the sequence is determined by the function call len(sequence ). This is useful when traversing the sequence.

SEQUENCES | len FUNCTION

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SEQUENCES | len FUNCTION

Same result with that of previous implementation, but more maintainable and scalable.

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SEQUENCES | EMPTY STRING

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SEQUENCES | EMPTY LIST

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SEQUENCES | EMPTY TUPLE

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SEQUENCES | TUPLE | SINGLETON

Tuple with single element must still include a comma. Parenthesis is optional in all type of Tuples, singleton or not.

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Sequences can be unpacked, the values stored in the sequence are assigned to various identifiers. Unpacking is a useful programming shortcut for assigning values to multiple variables in a single statement.

SEQUENCES | TUPLE | (UN)PACKING

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SEQUENCES | TUPLE | (UN)PACKING

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SEQUENCES | TUPLE | (UN)PACKING

Tuple packing/unpacking is useful when swapping variables.

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The += augmented assignment statement can insert a value in a list. When the value to the left of the += symbol is a sequence, the value to the right of the symbol must be a sequence also.

SEQUENCES

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SEQUENCES

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SEQUENCES

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Lists are not restricted to homogeneous data types. Python programmers typically use lists to store sequences of homogeneous values (values of the same data type)

SEQUENCES

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In general, a program uses a list to store homogeneous values for the purpose of looping over these values and performing the same operation on each value. Usually, the length of the list is not predetermined and may vary over the course of the program.

SEQUENCES

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The for/in structure iterates over a sequence. The for structure starts with the first element in the sequence, assigns the value of the first element to the control variable and executes the body of the for structure. Then, the for structure proceeds to the next element in the sequence and performs the same operations.

SEQUENCES

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SEQUENCES

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SEQUENCES

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Tuples store sequences of heterogeneous data. Each data piece in a tuple represents a part of the total information represented by the tuple. Usually, the length of the tuple is predetermined and does not change over the course of a program’s execution.

SEQUENCES

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SEQUENCES

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Python provides the slicing capability to obtain contiguous regions of a sequence. Slicing creates a new sequence; therefore, when a program passes a slice to a function, the original sequence is not affected.

SEQUENCES | SLICING

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SEQUENCES | SLICING

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is equivalent to the following expressions: sequence [ 0 : len( sequence ) ] sequence [ : len( sequence ) ] sequence [ 0 : ]

SEQUENCES | SLICING

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The dictionary is a mapping construct that consists of key-value pairs. Like a physical dictionary, it has listing of words and definitions. Dictionaries (called associative arrays in PHP, hash maps/tables in Java, and analogous to JavaScript Object Notation), can be thought of as unordered collections of values where each value is accessed through its corresponding key.

DICTIONARIES

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To create an empty dictionary, use empty curly braces.

DICTIONARIES | EMPTY DICTIONARY

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To create a dictionary with values, use a comma-separated sequence of key-value pairs, inside curly braces. Each key-value pair is of the form key : value.

DICTIONARIES

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DICTIONARIES

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DICTIONARIES

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Python dictionary keys must be immutable values, like strings, numbers or tuples, whose elements are immutable. Dictionary values can be of any Python data type.

DICTIONARIES

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Dictionary values are accessed with the expression dictionaryName[ key ].

DICTIONARIES | READ

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Accessing a non-existent dictionary key causes the program to exit and to display a “key error” message. Use a fail-safe approach.

DICTIONARIES | READ

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To insert a new key-value pair in a dictionary, use the statement dictionaryName[ key ] = value.

DICTIONARIES | WRITE

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The statement dictionaryName [ key ] = value modifies the value associated with key, if the dictionary already contains that key. Otherwise, the statement inserts the key-value pair into the dictionary.

DICTIONARIES | WRITE

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A method performs the behaviors (tasks) of an object. To invoke an object’s method, specify the name of the object, followed by the dot (.) access operator, followed by the method invocation.

METHODS

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List method append adds an items to the end of a list.

METHODS | APPEND

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METHODS | APPEND

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List method count takes a value as an argument and returns the number of elements in the list that have that value. If the list contains no elements with the specified value, method count returns 0.

METHODS | COUNT

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METHODS | APPEND

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METHODS | LIST

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METHODS | SORT

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Dictionary method items returns a list of tuples, where each tuple contains a key-value pair. Dictionary method keys returns an unordered list of the dictionary’s keys. Dictionary method values returns an unordered list of the dictionary’s values.

METHODS | ITEMS, KEYS, VALUES

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METHODS | ITEMS, KEYS, VALUES

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METHODS | ITEMS, KEYS, VALUES

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METHODS | DICTIONARIES

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METHODS | DICTIONARIES

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METHODS | LIST | DEL

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METHODS | DICTIONARY | DEL

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Dictionary method copy returns a new dictionary that is a shallow copy of the original dictionary. In a shallow copy, the elements in the new dictionary are references only to the elements in the original dictionary.

METHODS | COPY

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If the programmer wants to create a copy (called a deep copy), that is independent of the original dictionary, Python provides module copy. Function copy.deepcopy returns a deep copy of its argument.

METHODS | DEEP COPY

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METHODS | DEEP COPY

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METHODS | DEEP COPY

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Python arguments are always passed by object reference, that is, the function receives references to the values passed as arguments.

PASS BY REFERENCE

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If a function receives a reference to a mutable object (e.g., a dictionary or a list), the function can modify the original value of the object. It is as if the object had been passed by reference.

PASS BY REFERENCE

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If a function receives a reference to an immutable object (e.g., a number, a string or a tuple whose elements are immutable values), the function cannot modify the original object directly. It is as if the object had been passed by value.

PASS BY VALUE

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To pass a list argument to a function, specify the name of the list without square brackets.

SEQUENCES AS ARGUMENTS

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SEQUENCES AS ARGUMENTS

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Sequences can contain elements that are also sequences. Such sequences have multiple subscripts. A common use of multiple-subscripted sequences is to represent tables of values consisting of in formation arranged in rows and columns.

SEQUENCES | MULTI-DIMENSIONAL

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To identify a particular table element, we must specify two subscripts:

the first identifies the element’s row,

the second identifies the element’s column.

SEQUENCES | MULTI-DIMENSIONAL

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Python does not support multiple-subscripted sequences directly, but allows programmers to specify single-subscripted tuples and lists whose elements are also single-subscripted tuples and lists, thus achieving the same effect.

SEQUENCES | MULTI-DIMENSIONAL

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The name of every element in a multiple-subscripted sequence is of the form a[ i ][ j ], where a is the name of the sequence, and

i and j are the subscripts that uniquely identify the row and column of each element in the sequence.

SEQUENCES | MULTI-DIMENSIONAL

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SEQUENCES | MULTI-DIMENSIONAL

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SEQUENCES | MULTI-DIMENSIONAL

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SEQUENCES | MULTI-DIMENSIONAL

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SEQUENCES | MULTI-DIMENSIONAL

Getting the sum of all elements in a 2-dimensional list. Useful for Reading values only.

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SEQUENCES | MULTI-DIMENSIONAL

Getting the sum of all elements in a 2-dimensional list using the indices/subscripts. Subscripts are useful also for overriding the array data.

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To compute pure numerical problems (i.e., multi-dimensional arrays), You might want to use the NumPy (Numerical Python) package This package contains modules that handle arrays and provides multi-dimensional array objects for efficient computation.

SEQUENCES | MULTI-DIMENSIONAL

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Operator Description f(parameters)   Function Call x.f(parameters)   Method Call

x[index]   Read from an Element x[index]  =  value   Write to an Element x[start:end]   Slicing/Copying (value,  …)   Tuple Expression/Packing [value,  …]   List Expression

{key:value,  …}   Dictionary Expression

END NOTES

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Make a diagonal_elements(m) function so that a given 2D list, say matrix m, will have all of its diagonal elements be returned in a list be and printed.

def diagonal_elements(m)

print diagonal_elements(my_2d_list)

PRACTICE EXERCISE 1

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Make a scalar_product(m, s) function so that a given 2D list, say matrix m, will have all of its elements be multiplied by the given scalar/number s. You could either modify the input matrix or return an independent copy of the modified matrix

def scalar_product(m, s)

print scalar_product(my_2d_list, 2)

PRACTICE EXERCISE 2

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Make a column_sum(m, c) function so that a given 2D list, say matrix m, will have all of its elements along column c be added. Return a single number

def column_sum(m, c)

print column_sum(my_2d_list, 2)

PRACTICE EXERCISE 3

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Make a transpose function so that a given 2D list, say, will be converted/transposed to this:

PRACTICE EXERCISE 4

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REFERENCES

q  Deitel, Deitel, Liperi, and Wiedermann - Python: How to Program (2001).

q  Disclaimer: Most of the images/information used here have no proper source citation, and I do not claim ownership of these either. I don’t want to reinvent the wheel, and I just want to reuse and reintegrate materials that I think are useful or cool, then present them in another light, form, or perspective. Moreover, the images/information here are mainly used for illustration/educational purposes only, in the spirit of openness of data, spreading light, and empowering people with knowledge. J

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