ReadMe.md

Chapter 02 | List Data | Working with ordered data

Data and Processing

Programming is the job of working with data. We are inclined to know these with data.

• acquiring data
• processing data
• understanding data.

The first challenge to work with data is to store it efficiently. Python provides us with these options.

• Lists
• Dictionaries
• Tuples
• Sets

Basic Data Type

We can use the basic data type, to work with single data value. Basic data types are:-

• Numbers
  • A variables takes the type from the values what is assigned.
  • wait_time = random.randint(1,60)
• Strings
  • Python dynamically assigns type to a variable.
  • word = "bottles"
• Objects
  • Everything is an object in Python.
  • Since objects can be assigned to variables, an interesting combination occurs in Python.
  • Objects have state (attributes and values) and behavior (methods).
  • Python's objected oriented programming is optional. We can still write programs in python without using classes.

Built-in Data Structures.

Four Built-in Data Structures in Python, which means this is available as languages basic features without any export.

• Lists : an ordered mutable collection of objects
  • It is similar to array in other programming languages, with some added functionality.
  • It is zero indexed.
  • Lists are dynamic or mutable, that is it has APIs to manipulate the size/content of the lists.
  • Lists are also heterogeneous.
• Tuples : and ordered immutable collection of objects
  • It is an immutable list or it is a constant list.
  • Tuples are important when we send data to some function and do not expect it to be modified.
• Dictionaries : an unordered set of key/value pairs.
  • In other programming language, Dictionaries may be known as associative array, map, symbol or hash.
  • Each unique key has a value associated with it.
  • Dictionaries are unordered and mutable.
  • The ordering inside Dictionaries are random, we cannot expect that the order in which we inserted the data is the order in which it will come out.
• Sets : and unordered set of unique objects.
  • Sets helps in removing duplicates from data.
  • Sets are mutable and also unordered.

List

• List is created using a [] brackets.
  • odds = [1,3,5]
• List is a great choice for storing similar objects
• When the data has identifiable structure we should consider using something else other than list.

List Creation

• Empty list
  • prices = []
• List can contain heterogeneous data.
  • car_details = ['Toyota', 'RAV4', 2.2, 60807]
• List can contain list in itself.
  • odds_and_ends = [[1,2,3], ['a', 'b', 'c'], ['One', 'Two', 'Three']]

List Operation

• in : Find a object inside a List
  • 'a' in 'aeiou'
• not in : Check is a object is not inside a list.
• append() : Add an object to the end of the list.
• remove() : Takes an object's value and removes the first occurrence of the value.
  • It raises an error if the value is not present in the list.
• pop() : takes an optional index value as its arguments.
  • pop removes and returns and object from a list.
  • If no index value is specified it removes the last element of the list.
  • Calling pop on empty list raises error.
• append() : adds a single object to an existing list.
  • passing an empty list in appends, adds that empty list to the existing list.
• extend() : takes a list of object as its argument and adds each of its object to an existing list.
  • No changes when an empty list is passed in extend.
• Both extend() and append() add elements to the end of the list.
• insert() : takes an index value and an object as its arguments.

Bullet Point

• List is a great way of storing a collection of related objects.
• List are very similar to array in other languages, but with added functionality, list in python can be extended.
• List is denoted by square brackets, with items separated by comma. [}
• An empty list is represented by [].
• in operator helps in identifying if an element is present in the list.
• Due to methods like append(), extend(), insert() and remove() we can add and remove elements from list.

List Operation's Continued

• = : assignment operator can be used to copy a list, but both the list will point to same content, one is modified other will also be.
• copy() : copy and existing list to another list.
  • Never use assignment operator to copy a list, always use copy method.
• List supports both indexing using positive numbers and negative, also it support range of index.
  • Positive index is same as all programming languages.
    • It counts from left to right.
  • Negative index gives the element from end of the list
    • It counts from right to left.
    • -1 : always gives the last element of the list.
• List supports Start, Stop and Step in indexing, which is called slice
  • START: The value where the range begins, default is 0
  • STOP: The value where the range end, but the value not included, the default is end of list.
  • STEP: The step value of how the range will be generated, default is 1.
  • letters[start:stop:step]
• Slice works on any sequence in Python.
  • Slice does not change the original list in any form.
• List method change the state of list.

List Iteration

• Python for loop knows these things about the list.
  • Where the start of the list is
  • How many object it contains
  • Where the end of the list is

paranoid_android = "Marvin"
letters = list(paranoid_android)

for char in letters:
    print("\t", char)

• On each iteration char refers to the current object.
• letters is the list to iterate over.
• The statement which is indented is executed on every loop.
• In place of the full sequence, we can give just the slice in the for loop.

Bullet Points

• List understand the square brackets notation, which can be used to select the individual objects from any list.
• Like a lot of other programming language, Python also starts counting from 0.
• Python can use negative index to access the elements of the list.
• Python supports start, stop and step in the brackets notation, which helps in creating a slice of the list.