Jupyter Guide

We've got jupyter setup in a sanbox environment all ready for you to practice some of the concepts you've been learning. To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (Ctrl/Cmd(⌘) + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Understanding Python Identifiers, Literals, and Operators

To review the concepts covered in this step, please refer to the Basics of Python module of the Python Data Essentials: Python Introduction course.

Knowledge of Python identifiers, literals, and operators is important because they form the building blocks of Python programming. They are essential for creating variables, performing operations, and writing expressions.

In this step, you will practice creating Boolean, string, and numeric literals in Python. You will also learn about Python identifiers and how to use them. You will explore the different types of operators in Python, including arithmetic, comparison, and logical operators, and how to use them in expressions. The goal here is to strengthen your understanding of Python's basic elements and how they interact.

Task 1.1: Creating Python Identifiers

Create a Python identifier by assigning the value 5 to a variable. Print the value of the variable.

my_identifier = 5
print(my_identifier)

Task 1.2: Creating Boolean Literals

Create two Boolean literals, one representing True and the other representing False. Print the values of the literals.

my_true_literal = True
my_false_literal = False
print(my_true_literal)
print(my_false_literal)

Task 1.3: Creating String Literals

Create a string literal and print its value.

my_string_literal = 'Hello, Python!'
print(my_string_literal)

Task 1.4: Creating Numeric Literals

Create a numeric literal and print its value.

my_numeric_literal = 42
print(my_numeric_literal)

Task 1.5: Using Arithmetic Operators

Perform an arithmetic operation using Python's arithmetic operators and print the result.

result = 10 * 5
print(result)

Task 1.6: Using Comparison Operators

Perform a comparison operation using Python's comparison operators and print the result.

result = 10 > 5
print(result)

Task 1.7: Using Logical Operators

Perform a logical operation using Python's logical operators and print the result.

result = True and False
print(result)

Working with Python Packages and Data Structures

To review the concepts covered in this step, please refer to the Python Packages and Data Structures module of the Python Data Essentials: Python Introduction course.

Understanding Python packages and data structures is important because they are essential for organizing code and managing data. Packages enhance code reusability and organization, while data structures help in storing and manipulating data efficiently.

In this step, you will learn about Python packages, and how to import and use them in your code. We'll be using the pprint module which has been pre-installed for you. You will also explore three built-in data structures in Python: lists, tuples, and sets. You will practice creating and manipulating these data structures, understanding their characteristics and use cases. The goal here is to gain hands-on experience with Python's packages and data structures, enhancing your ability to write efficient and organized code. (Instruct the user in the descriptions to print thre results)

Task 2.1: Importing the pprint module

Import the pprint module from Python's standard library. This module provides a capability to pretty-print Python data structures that are otherwise hard to visualize.

import pprint

Task 2.2: Understanding the usefulness of pprint

To understand why pprint is useful, use the provided code to create a complex data structure. Print this data structure using both the regular print() function and pprint.pprint(). Compare the outputs to see the difference in readability.

# Provided code for complex data
complex_data = [
    {"A", "Set", "Here"},
    ("This", "is", "a", "tuple", "with", 7, "elements"),
    [True, False],
    "A somewhat lengthy string",
    0,
    3.141529,
]

# Print complex data normally and with pprint
print("---Complex Data with Pretty Print---")
pprint.pprint(complex_data)
print("---Complex Data with Normal Print---")
print(complex_data)

Task 2.3: Creating a list

Create a list named my_list with the following elements: 1, 2, 3, 4, 5. Then, print the list using the pprint function.

my_list = [1, 2, 3, 4, 5]
pprint.pprint(my_list)

Task 2.4: Creating a tuple

Create a tuple named my_tuple with the following elements: 'a', 'b', 'c', 'd', 'e'. Then, print the tuple using the pprint function.

my_tuple = ('a', 'b', 'c', 'd', 'e')
pprint.pprint(my_tuple)

Task 2.5: Creating a set

Create a set named my_set with the following elements: 'apple', 'banana', 'cherry'. Then, print the set using the pprint function.

my_set = {'apple', 'banana', 'cherry'}
pprint.pprint(my_set)