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Introduction to Python (copy)
course content

Kursinnhold

Introduction to Python (copy)

Introduction to Python (copy)

1. Getting Started
First StepsCommentsSimple Math: Add & SubtractSimple Math: Multiply & DivideRecap
2. Variables and Types
Data TypesStoring Data in VariablesVariable Naming RulesUsing VariablesString Indexing and LengthString Slicing and ConcatenationChallenge: Inventory Sorting CapstoneRecap
3. Conditional Statements
Boolean Data TypeCombining ConditionsChallenge: Boolean LogicMembership Operators and Type ComparisonsConditional ExpressionsChallenge: Product Management CapstoneRecap
4. Other Data Types
Lists and List MethodsNested ListsChallenge: List ManagementTuples and Tuple MethodsTuple OperationsChallenge: Tuple OperationsDictionaries and Dictionary MethodsChallenge: Pricing Adjustment CapstoneRecap
5. Loops
For LoopsWhile LoopsRange FunctionIterating Over IndexesChallenge: Sales and ShipmentsNested LoopsChallenge: Automating Stock ControlChallenge: Solo Coding CapstoneRecap
6. Functions
Built-in FunctionsChallenge: Zip and Sort FunctionsUser Defined FunctionsList Behavior in FunctionsFunctions with no ReturnModifying FunctionsChallenge: Product Revenues CapstoneRecap

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Data Types

To begin, we will start by exploring Data Types — the building blocks of programming.

Like a well-organized grocery store, Python categorizes items so they are easy to find and use. Similarly, in Python, we categorize data into types to make it easier to manipulate and operate on. Let's dive into some common data types.

Understanding Data Types

In Python, every piece of data has a type. As your grocery store might have different sections for fruits, vegetables, and beverages, Python organizes data into integers, floats, strings, and more.

Here's a brief look at some fundamental data types you'll frequently use in Python:

Integers

An integer (int) represents whole numbers without decimals, such as the number of items in a grocery cart — for example, 3 apples or 10 oranges.

Floating-point Numbers

A floating-point number (float) is used for numbers with decimal points, such as the price of products — like 1.99 for bananas or 2.50 for a gallon of milk.

Strings

A string (str) is a sequence of characters representing text, such as the names of products in your grocery store: "apple", "banana", or "oat milk".

Booleans

A boolean (bool) data type has two possible values, True or False, and is used for conditions like checking if an item is in stock or not.

Example Practical Application

To get a feel for how these data types work, we will use the type() function inside a print() statement to display how Python interprets different data types. This should show you how Python handles various kinds of information.

Here's how it works:


              
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# Displaying integers
print(type(25))

# Displaying floating-point numbers
print(type(6.25))

# Displaying strings
print(type("Olive Oil"))

# Displaying booleans 
print(type(120 > 95))
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Understanding data types is crucial because it determines what operations can be performed on a given piece of data. Just as you separate grocery store items into appropriate sections based on the type of item, data must be handled according to its type for Python to execute properly.

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Drag and drop the appropriate values into the fields to describe the product.

Name (string):
Quantity (int):
Price (float):
Is Available (bool):

Click or drag`n`drop items and fill in the blanks

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