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12 Beginner Python Projects - Coding Course

freeCodeCamp.org・142 minutes read

Kyla Gang presents 12 beginner Python projects in a video tutorial, ranging from madlibs to a command-line version of tic-tac-toe. Mistakes are intentionally left in tutorials to highlight problem-solving skills, with an emphasis on string concatenation methods and implementing an unbeatable computer player using the minimax algorithm.

Insights

Python projects range from beginner to advanced, including madlibs, tic-tac-toe AI, and photo editing.
Mistakes in tutorials showcase problem-solving skills, encouraging learning through trial and error.
A guessing game project involves user input, feedback tracking, and dynamic bounds adjustment.
Command-line tic-tac-toe features player classes, board management, and winner determination.
Unbeatable tic-tac-toe AI employs the minimax algorithm for strategic gameplay.

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What beginner Python projects does Kyla Gang present?
Kyla Gang presents 12 beginner Python projects in a video tutorial, ranging from easy to complex.

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Summary

00:00

"Python Projects: Madlibs, Games, Bug Fixes"

Kyla Gang presents 12 beginner Python projects in a video tutorial.
Projects are listed from easiest to most complex, including madlibs, tic tac toe AI, and photo editing.
Mistakes and bugs are intentionally left in tutorials to showcase problem-solving skills.
Viewers are encouraged to subscribe to Kyla Gang's YouTube channel for more coding projects.
A traditional Madlib project is explained using string concatenation methods.
String concatenation methods include using plus signs, curly braces with .format(), and F strings.
An example Madlib project is created using F strings for string concatenation.
User inputs are utilized to fill in blanks in the Madlib project.
A guessing game project is introduced where the computer generates a secret number for the user to guess.
The user inputs guesses, and the computer provides feedback on whether the guess is too high, too low, or correct.

14:22

Implementing Computer Guessing Game with User Feedback

Initialize a feedback variable to track guesses being too high, too low, or correct.
Set the feedback variable to an empty string initially.
Loop over the feedback expression until it represents a correct guess.
Generate a new random number within specified bounds based on user feedback.
Prompt the user to provide feedback on the computer's guess.
Adjust upper or lower bounds based on user feedback (high, low, or correct).
Exit the loop when the computer correctly guesses the number.
Address the issue of random.choice potentially selecting words with spaces or dashes.
Create a function to ensure a valid word is chosen for the hangman game.
Establish tracking mechanisms for guessed letters and valid alphabet characters.

28:07

Python Game: Guess the Word & Tic-Tac-Toe

Set up an empty set named newsletters to track user guesses.
Obtain user input by asking for a character in Python.
Convert the user input to uppercase to ensure consistency.
Add valid user guesses to the used letters set and remove correct guesses from the word letters set.
Check if the user's input is a valid character in the alphabet and not previously guessed.
Implement a loop using a while loop to allow the user to keep guessing until they solve the word.
Display the letters already used by the user and the current word with dashes for unguessed characters.
Introduce the concept of lives in the game, deducting a life for incorrect guesses.
Modify the while loop condition to consider both the remaining word letters and the remaining lives.
Conclude the game by informing the user if they correctly guessed the word or ran out of lives.
Develop a command-line version of tic-tac-toe with options for human vs. computer, human vs. human, or computer vs. computer gameplay.
Create separate classes for players and the game, including a base player class and subclasses for random computer and human players.
Define the game class with a board, a current winner tracker, and methods for printing the board and available moves.

42:35

"Python Tic-Tac-Toe Game Implementation"

To identify available spaces, the index of each spot is appended to the "moves" list.
An alternative method using list comprehension condenses the for loop into a single line.
The "get_move" function is defined for players, with the computer player choosing a random empty spot.
For the human player, input validation is crucial to ensure a valid square selection.
Checks are incorporated to verify the input as a valid number and within the available moves list.
The game's progression involves players taking turns to make moves on the board.
A function "make_move" is created to assign a letter to a square if it's a valid move.
The game loop iterates through moves, updating the board and switching player turns.
A function to check for a winner is implemented, examining rows, columns, and diagonals for a winning sequence.
The game concludes when a winner is determined, with a tie declared if no winner emerges.

55:39

"Diagonal Tic Tac Toe Game with Minimax"

The game involves placing numbers on a board in specific diagonal patterns.
Diagonal one consists of 0, 4, and 8, while diagonal two includes 2, 4, and 6.
To determine a winner, the program checks if all spots in each diagonal match.
If both diagonals match, the game returns true; otherwise, it returns false.
The game is initiated by setting up players, X and O, and creating an instance of Tic Tac Toe.
The game is played by making moves and checking for a winner or a tie.
A pause is added between moves for better readability during gameplay.
An unbeatable computer player is implemented using the minimax algorithm.
The minimax algorithm aims to maximize wins for one player while minimizing losses for the opponent.
The algorithm evaluates possible moves to determine the most optimal path to victory.

01:09:27

"Efficient Algorithm Ensures Strategic Gameplay Success"

To implement a move, the first step involves calling State.make_move(player) and passing the player making the move, followed by passing the new state into mini max by calling self.mini_max(state, alternate player).
The third step in the process is to undo the move by resetting the possible move on the board to an empty space and setting the current winner state back to none.
In the final step, if the player is the max player and the simulated score is greater than the best score, the best dictionary is replaced with the simulated score dictionary; if the player is the mid player and the simulated score is less than the best score, the best dictionary is again replaced with the simulated score dictionary.
The best score dictionary at the end contains the best next possible move and the best score that can arise from it, returning a dictionary of the position and the score.
The algorithm ensures the max player is maximized while minimizing the other player, leading to a strategic gameplay approach.
The genius computer player consistently wins or ties but never loses when playing against a random computer player, showcasing the effectiveness of the algorithm.
Binary search is a divide and conquer algorithm that searches an ordered list efficiently by comparing the target with the middle element and narrowing down the search based on the comparison.
In binary search, the list is divided in half at each step, focusing on the relevant section based on the comparison with the middle element.
Binary search leverages the sorted nature of the list to expedite the search process, recursively narrowing down the search space until the target is found.
By comparing the target with the middle element and adjusting the search range accordingly, binary search proves to be faster than naive searching through the entire list for the target.

01:22:45

Efficient Binary Search Algorithm and Game Creation

Setting bounds on indices: Establishing low and high indices for checking.
Determining low and high values: Setting low to 0 and high to length L minus one.
Calculating midpoint: Midpoint is calculated as the average of low and high indices.
Adjusting bounds based on target location: Modifying low and high based on target position.
Handling cases where target is not in the list: Returning -1 if target is not found.
Implementing search functions: Demonstrating naive search and binary search on a list.
Timing analysis: Comparing the efficiency of naive search and binary search on a sorted list.
Building a command line Minesweeper game: Using recursion and classes to create the game.
Defining play function: Outlining the steps involved in playing the Minesweeper game.
Creating a board object: Utilizing object-oriented programming to represent the game board and bombs.

01:36:19

"Game logic for bomb-digging simulation"

The function "get number of neighboring bombs" calculates the number of bombs surrounding a given row and column.
Variable names like "R" and "C" are used to match the parameters passed into the function.
Neighboring positions are defined using a list, ensuring no out-of-bounds checks are made.
The function iterates through neighboring positions, summing up the number of bombs.
Bounds checking is crucial to prevent going out of bounds, ensuring valid row and column values.
The play function involves creating a board, planting bombs, and prompting the user for their dig location.
The dig function within the board class handles digging at specified row and column indices.
The game continues until all non-bomb spaces are dug, using a while loop to repeat steps until completion.
User input for row and column is split using regex to handle various input formats.
Bounds checking ensures the user inputs a valid location for digging, with the game continuing until all non-bomb spaces are uncovered.

01:48:44

"Game Over: Dig, Avoid Bombs, Win"

If the game is no longer safe, it indicates a bomb has been hit, leading to a Game Over scenario.
To exit the loop, a break is implemented to prevent further user interaction.
Two outcomes determine the loop's end: either victory with no safe spots left or hitting a bomb.
If all spots have been dug and no bombs hit, the player wins, prompting a congratulatory message.
Conversely, hitting a bomb results in a Game Over message and reveals the entire board.
The board is fully revealed by assigning each spot a value in a list comprehension.
The game is initiated by calling the play function within a "name equals main" statement.
Gameplay involves digging in specific spots, with zeros indicating no nearby bombs.
Recursive digging occurs until a non-zero spot is reached, revealing nearby bombs.
The game concludes with a bomb hit, displaying the entire map and signaling the end of the game.

02:02:02

"Guessing and Mutating: Solving Sudoku Puzzles"

The process involves guessing numbers and mutating a list of lists.
Each guessed number is passed as a puzzle, mutating subsequent values until the end.
The goal is to solve the Sudoku puzzle using new guesses in an array.
If the puzzle is solved, true is returned; otherwise, false is returned.
In case of an invalid check or unsolved puzzle, backtracking is necessary.
Resetting values at incorrect guesses is crucial for moving forward.
A for loop iterates over all possible values for each empty spot in the puzzle.
Trying every combination of numbers is essential to find the solution.
If all combinations fail, the puzzle is deemed unsolvable.
The process is tested and proven to work effectively for solving Sudoku puzzles.

02:15:57

Image Contrast and Blur Adjustment Techniques

Adjusting contrast involves creating a new image copy to modify without changing the original image.
The adjustment is done for x, y, and z positions in the array of the new image.
Contrast adjustment increases the difference from a user-defined midpoint by a factor.
The process involves subtracting the midpoint, scaling by the factor, and adding the midpoint back in.
A vectorized version involves subtracting a constant from every value in the array and adding the constant back.
Increasing contrast involves adjusting the scaling factor and midpoint, while decreasing contrast involves reducing the scaling factor.
The results show a significant difference in contrast levels between increased and decreased contrast images.
Implementing a blur for an image involves averaging a pixel with its surrounding pixels based on a specified kernel size.
The blur process includes iterating through neighbors, summing surrounding pixel values, and dividing by the kernel size.
Applying a kernel to an image involves multiplying pixel values by corresponding kernel values and summing the results to create a new image.

02:29:50

"Markov Chain Model for Poetry Generation"

The new image is created by squaring the values at corresponding indices in two images, then taking the square root of the sum.
An error occurred due to mismatched shapes of the images used in the function.
The edge detection filter is applied to images, resulting in a clear detection of edges.
The graph composer project is introduced, based on Markov chain principles.
In the graph composer project, each word in a text file is transformed into a node connected to words following it.
A Markov chain graph model is explained, where words are vertices connected by directed edges with weights.
The process of generating poetry involves randomly selecting starting words and traversing the graph based on edge weights.
The implementation of the Markov chain model in Python involves defining classes for vertices and graphs.
Functions for adding vertices, edges, and getting next words based on weights are crucial in the Markov chain implementation.
Random selection of next words based on weights is facilitated by the random.choices function in Python.

02:43:08

Creating Probability Maps for Word Selection

Introducing the concept of a probability map to map each word to its probability in separate lists.
Creating two new lists to track neighbors and neighbor weights for each vertex.
Appending vertices to self.neighbors and weights to self.neighbor_weights for every vertex.
Utilizing random.choices with self.neighbors and self.neighbor_weights to make selections.
Generating probability mappings for every vertex under the graph representation.
Initiating the process of composing a pie by obtaining words from a text file.
Lowercasing and removing punctuation from the text to facilitate word extraction.
Splitting the text into words and returning a list of words for further processing.
Building a graph using the extracted words and establishing connections between them.
Implementing a composition function to generate a string of words based on the graph and user-defined length.

02:56:52

Automating File Retrieval and Text Extraction

By importing LS, Python can be utilized to read a folder and retrieve all file names within it, eliminating the need to manually input each file name.
To list all files under a specific artist's name within the folder, the code utilizes f string to pass in the artist's name and then proceeds to extract the words from each song file.
In case of errors like encountering a non-song file such as "dot d s store," the code includes a conditional statement to skip over such files and continue the loop, ensuring the program runs smoothly and generates paragraphs based on the inputted vocabulary from songs.

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