Kursusindhold

Introduction to Python (copy)

1. Getting Started

First Steps Comments Simple Math: Add & Subtract Simple Math: Multiply & Divide Recap

2. Variables and Types

Data Types Storing Data in Variables Variable Naming Rules Using Variables String Indexing and Length String Slicing and Concatenation Challenge: Inventory Sorting Capstone Recap

3. Conditional Statements

Boolean Data Type Combining Conditions Challenge: Boolean Logic Membership Operators and Type Comparisons Conditional Expressions Challenge: Product Management Capstone Recap

4. Other Data Types

Lists and List Methods Nested Lists Challenge: List Management Tuples and Tuple Methods Tuple Operations Challenge: Tuple Operations Dictionaries and Dictionary Methods Challenge: Pricing Adjustment Capstone Recap

5. Loops

For Loops While Loops Range Function Iterating Over Indexes Challenge: Sales and Shipments Nested Loops Challenge: Automating Stock Control Challenge: Solo Coding Capstone Recap

6. Functions

Built-in Functions Challenge: Zip and Sort Functions User Defined Functions List Behavior in Functions Functions with no Return Modifying Functions Challenge: Product Revenues Capstone Recap

Membership Operators and Type Comparisons

In this chapter, we will explore some nuanced aspects of Python that can significantly enhance how you manage and interact with data in your programs — specifically, Membership Operators and Type Comparisons.

Let's see how Alex uses these tools:

Membership operators are useful when you need to check if specific items or substrings are present within an iterable object. An iterable object in Python is anything that you can loop over, like strings, lists, or tuples. We'll explore lists and tuples in more detail in the next section; for now, understand that membership operators can be applied to more than just strings.

The primary membership operators are in and not in, both of which return a boolean value indicating the presence (or absence) of an item.

Since you have already learned about string indexing and slicing, you're familiar with the concept that strings are iterable. This means you can use membership operators to check for substrings within larger strings.

Consider the following example:


              123
            
itemName = "Strawberries"
in_name = "Straw" in itemName
print("Is 'Straw' in 'Strawberries'?", in_name)

Example Application

Imagine you're managing the product descriptions or categories in your grocery store system. You might receive a long string of product details, and you need to quickly check for specific keywords to categorize or highlight products based on customer preferences or promotional activities:


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# Product description from supplier
product_description = "Fresh organic milk from local farms, pasteurized and homogenized."

# Check if the "organic" and "local" keywords are in the product description
is_organic = "organic" in product_description
is_local = "local" in product_description

# Print the presence of these keywords to decide on marketing strategies
print("Is the product organic?", is_organic)
print("Is the product locally sourced?", is_local)

Verifying Data Types

Understanding the type of data you're dealing with in Python is crucial, especially when managing the diverse needs of a grocery store system. The type() function is invaluable as it helps ensure you're working with the correct data types — such as strings for product names, floats for prices, and integers for stock quantities.

This not only prevents bugs but also makes data manipulations and comparisons more appropriate and reliable.

In the following example, we illustrate how type() can be used to verify that the data entered into the system meets the expected criteria, which is a common necessity in managing grocery store data to prevent errors during checkout or inventory updates:


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# Sample data received from a cashier or inventory management system
product_name = "Almond Milk"
product_price = "3.49"
product_quantity = 30

# Checking if the data types are as expected
correct_name_type = type(product_name) == str
correct_price_type = type(product_price) == float  # Intentional error for demonstration
correct_quantity_type = type(product_quantity) == int

# Print the results to verify data types
print("Is product_name a string?", correct_name_type)
print("Is product_price a float?", correct_price_type)  # Expected: False, actual data type is a string
print("Is product_quantity an integer?", correct_quantity_type)

print("Data type check complete. Please review and correct any 'False' outcomes for data corrections.")

Opgave

Swipe to start coding

Verify the details of a new product added to a grocery store system using membership operators and type comparisons.

Use membership operators to check if the substrings "raw" and "Imported" are in the description variable.
Assign the results to the boolean variables contains_raw and contains_Imported.
Use the type() function to check if price is a float and count is an int.
Assign the results of these type checks to price_is_float and count_is_int.

Output Requirements

Print: Contains 'raw': <contains_raw>.
Print: Contains 'Imported': <contains_Imported>.
Print: Is price a float?: <price_is_float>.
Print: Is count an integer?: <count_is_int>.

Note:

Python is case sensitive, so "imported" and "Imported" are different strings.

Løsning

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Sektion 3. Kapitel 4

Membership Operators and Type Comparisons

Let's see how Alex uses these tools:

The primary membership operators are in and not in, both of which return a boolean value indicating the presence (or absence) of an item.

Consider the following example:


              123
            
itemName = "Strawberries"
in_name = "Straw" in itemName
print("Is 'Straw' in 'Strawberries'?", in_name)

Example Application


              12345678910
            
# Product description from supplier
product_description = "Fresh organic milk from local farms, pasteurized and homogenized."

# Check if the "organic" and "local" keywords are in the product description
is_organic = "organic" in product_description
is_local = "local" in product_description

# Print the presence of these keywords to decide on marketing strategies
print("Is the product organic?", is_organic)
print("Is the product locally sourced?", is_local)

Verifying Data Types

This not only prevents bugs but also makes data manipulations and comparisons more appropriate and reliable.


              12345678910111213141516
            
# Sample data received from a cashier or inventory management system
product_name = "Almond Milk"
product_price = "3.49"
product_quantity = 30

# Checking if the data types are as expected
correct_name_type = type(product_name) == str
correct_price_type = type(product_price) == float  # Intentional error for demonstration
correct_quantity_type = type(product_quantity) == int

# Print the results to verify data types
print("Is product_name a string?", correct_name_type)
print("Is product_price a float?", correct_price_type)  # Expected: False, actual data type is a string
print("Is product_quantity an integer?", correct_quantity_type)

print("Data type check complete. Please review and correct any 'False' outcomes for data corrections.")

Opgave

Swipe to start coding

Verify the details of a new product added to a grocery store system using membership operators and type comparisons.

Use membership operators to check if the substrings "raw" and "Imported" are in the description variable.
Assign the results to the boolean variables contains_raw and contains_Imported.
Use the type() function to check if price is a float and count is an int.
Assign the results of these type checks to price_is_float and count_is_int.

Output Requirements

Print: Contains 'raw': <contains_raw>.
Print: Contains 'Imported': <contains_Imported>.
Print: Is price a float?: <price_is_float>.
Print: Is count an integer?: <count_is_int>.

Note:

Python is case sensitive, so "imported" and "Imported" are different strings.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 3. Kapitel 4

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder