6 Data Types and Operators
- Learn about python data types and operators
- Learn when to use different data types
6.1 Python Data types
In programming, data types define the kind of data a variable can hold, such as numbers, text, or more complex structures, and they determine what operations can be performed on that data.
In Python, common data types include int for integers, float for decimal numbers, str for strings of text, bool for true/false values, list for ordered collections, tuple for immutable collections, set for unique items, and dict for key-value pairs.
Using data types in Python is straightforward: you assign a value to a variable, and Python automatically recognizes the type.
For example, x = 10 creates an integer, whereas x = 10.3 creates a float. A float in a decimal placed number and is stored in python in a 64 bit format.
For an overview of data types use the cheat sheet.
6.1.1 Primitive Data Types
Primitive data types contain single values e.g. numbers and words
The main thing to remember is that sequences of letters like words are called strings and require quotation marks around them:
name = "Alice" creates a string.
The boolean data types use the key words True and False
None can also be used as a placeholder for a variable has no meaningful value yet.
Understanding data types is essential for writing correct programs, performing calculations, and managing data efficiently. Data types behave slightly differently in different languages for example the way numbers a rounded.
6.2 Python operators
operators are symbols or keywords used to perform operations on data and variables, like calculations or comparisons. This of mathematical singles like + 0r <.
Most of the operators in python make sense. Use the cheat sheet table to look them up if you need.
6.3 Composite Data Types - The exciting stuff!
Complex data types is where things really become interesting and the basis for more complex programming
In Python, composite data types allow you to store multiple values in a single variable. They can hold a collection of items, which can be of different data types. The main composite data types in Python are:
Lists
Definition: Ordered, mutable collections that allow duplicate items.
Syntax: Defined using square brackets
[].
Tuples
Definition: Ordered, immutable collections that can also contain duplicates.
Syntax: Defined using parentheses
().
Dictionaries
Definition: Unordered collections of key-value pairs, where each key is unique and must be immutable.
Syntax: Defined using curly braces
{}and using a colon:to separate keys from values.
Sets
Definition: Unordered collections of unique items.
Syntax: Defined using curly braces
{}or theset()function.
Example Initialization and Differences:
Let’s initialize one of each composite data type with a list of 10 animals, and then demonstrate some of the main differences.
# Initialize a list of animals
animal_list = ["Dog", "Cat", "Elephant", "Lion", "Tiger", "Giraffe", "Zebra", "Monkey", "Snake", "Rabbit"]
# Initialize a tuple of animals
animal_tuple = ("Dog", "Cat", "Elephant", "Lion", "Tiger", "Giraffe", "Zebra", "Monkey", "Snake", "Rabbit")
# Initialize a dictionary of animals with their classifications
animal_dict = {
"Dog": "Mammal",
"Cat": "Mammal",
"Elephant": "Mammal",
"Lion": "Mammal",
"Tiger": "Mammal",
"Giraffe": "Mammal",
"Zebra": "Mammal",
"Monkey": "Mammal",
"Snake": "Reptile",
"Rabbit": "Mammal"
}
# Initialize a set of animals
animal_set = {"Dog", "Cat", "Elephant", "Lion", "Tiger", "Giraffe", "Zebra", "Monkey", "Bear", "Rabbit"}
# Show differences between data types
print("Original List:", animal_list)
print("Original Tuple:", animal_tuple)
print("Original Dictionary:", animal_dict)
print("Original Set:", animal_set)
# Mutability Demonstration
# Modifying the list (mutable)
animal_list[0] = "Wolf" # Change "Dog" to "Wolf"
print("Modified List:", animal_list)
# Attempting to modify the tuple (immutable)
animal_tuple[0] = "Wolf"
# This will raise an error
# Adding a new key-value pair to the dictionary (mutable)
animal_dict["Fox"] = "Mammal"
print("Modified Dictionary:", animal_dict)
# Attempting to add a duplicate to the set (will be ignored)
animal_set.add("Dog") # This will not change the set
print("Modified Set (after trying to add 'Dog'):", animal_set)Remember the key differences - use the summary cheat sheet if you need help
Mutability - which data types are mutable?
Order - which are ordered?
Duplicates - which allow duplicates?
6.4 Working with Lists in Python
Lists in Python are versatile and support various operations that allow you to manipulate and interact with the data they contain. Below are some common operations you can perform on lists.
Accessing Elements
You can access list elements using indexing (zero-based).
first_element = my_list[0] # 1
last_element = my_list[-1] # 5
Slicing a List
You can retrieve a portion of a list using slicing.
sub_list = my_list[1:4] # [2, 3, 4]
Adding Elements
Append: Add an element to the end of the list.
my_list.append(6) # [1, 2, 3, 4, 5, 6]
Insert: Insert an element at a specific index.
my_list.insert(2, 2.5) # [1, 2, 2.5, 3, 4, 5, 6]
Removing Elements
Remove: Remove the first occurrence of a specified value.
my_list.remove(2.5) # [1, 2, 3, 4, 5, 6]
Pop: Remove and return an element at a specified index (default is the last element).
last_item = my_list.pop() # list is now [1, 2, 3, 4, 5]
Modifying Elements
You can change the value of an element using its index.
my_list[1] = 20 # [1, 20, 3, 4, 5]
Extending a List
You can add multiple elements to the end of the list using extend().
my_list.extend([6, 7, 8]) # [1, 20, 3, 4, 5, 6, 7, 8]
Sorting a List
Sort the list in ascending order.
my_list.sort() # [1, 3, 4, 5, 6, 7, 8, 20]
Reversing a List
You can reverse the order of elements in a list.
my_list.reverse() # [20, 8, 7, 6, 5, 4, 3, 1]
Finding the Length
Get the number of elements in the list.
length = len(my_list) # 8
Numeric operators e.g. Append multiple repeats
my_list * 2 # [20, 8, 7, 6, 5, 4, 3, 1, 20, 8, 7, 6, 5, 4, 3, 1]
6.5 Exercises
6.6 Summary
- Variables are words used to reference and access a value
- Be clear an concise following guidelines when naming variables
- Comment your code for clarity