5 Best Python Projects With Codes That You Can Complete Within An Hour! | by Bharath K | Towards Data Science

Python is a phenomenal programming language for any developer to learn and understand due to its simplicity, ease of use, and versatility. Apart from the continuous developments, improvements, and progression made with each upcoming version, Python has the most supportive and mature community with a ton of productive and helpful resources to assist you in every way possible.
With the help of Python and its equivalent libraries, we can achieve a humungous amount of accomplishments by constructing different types of unique projects. The flexibility of Python allows you to explore any options that you want to, and the tremendous number of wonderful resources will assist you in achieving the task you desire with greater ease. Hence, it is a fantastic idea to start working on numerous Python projects to add to your resume.
I try to cover most of the helpful topics for beginner Data Science enthusiasts and programmers. If you are interested in learning how to master the subject of Data Science within the span of 12 months, you should check out the following guide that suggests the 12 steps that you must follow to achieve this goal.
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In this article, we will look at five different amazing projects that you can build using Python and its libraries. You can effectively compute all the projects mentioned in their respective sections within the time frame duration of an hour. We will look at four simpler Python projects to get started and one slightly more complex Python task with the involvement of artificial intelligence. Let us get started with the construction of our projects!
With the help of Python, it is quite easy to automate most of the tasks that would otherwise be considered tricky or complex for humans. With the help of the appropriate libraries and coding patterns, it is possible to automate your PC to achieve a suitable task with the help of Python. In this section, we will explore a similar project with which we can perform such a type of automation to which will prompt us with alerts reminding us about the tasks to complete.
In this first project, we will look at how we can set up reminder alerts on a timely basis so that you will be notified accordingly. For this task, we will make use of two essential libraries to accomplish the project. The time module imported in Python and the plyer library, which can be installed with a simple pip command, can be used to specify the notification request accordingly. The code block provided below is a great starting point for achieving the desired results for this project.
import timefrom plyer import notificationif __name__ == "__main__":    while True:        notification.notify(            title = "ALERT!!!",            message = "Take a break! It has been an hour!",            timeout = 10        )        time.sleep(3600)
The above code example demonstrates the procedural working for this Python project. However, there are many further improvements and advancements that can be achieved. For the complete explanation of the entire process that you can accomplish with the following project and library, visit the link provided below, as every single concept and attribute related to the following topic is covered in great detail.
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Creating a calculator with Python is an interesting task. While we have explored several concepts of calculators in my previous articles, from simple calculators to perform simple computations to constructing more complex architectures of calculators with differentiation and integration. While the following code blocks made use of pure code and immediate response, in this project, we will focus on creating a more interactive graphic user environment with Python.
For this project, in the first code block, we will declare all the basic requirements and mandatory functions for declaring the expressions, creating the press buttons, and the working of the equals button. Below is the first sample code block for this project. The complete code reference for this sample code block is referenced from the following website. Refer to it for further information and the entire coding procedure.
# Import Tkinterfrom tkinter import *# globally declare the expression variableexpression = ""# Function to update expression in the text entry boxdef press(num): global expression expression = expression + str(num) equation.set(expression)# Function to evaluate the final expressiondef equalpress(): try:  global expression  total = str(eval(expression))equation.set(total)  expression = ""except:equation.set(" error ")  expression = ""
In the next sample code block, we will look at the construction of the basic GUI interface in which you can display the numerous buttons and construct the overall project. For the purpose of this sample code block, I will only display some of the basic elements for creating some basic functionalities. The numbers ranging from one to three can be created as follows, and we can test out the addition operation after clicking the equals button. Click the button elements to display the numbers and perform your desired action accordingly. Once the computation is performed, you can click the equals button to display the final result.
# Driver codeif __name__ == "__main__": # create a GUI window gui = Tk()# set the background colour of GUI window gui.configure(background="light green")# set the title of GUI window gui.title("Simple Calculator")# set the configuration of GUI window gui.geometry("270x150")# we create an instance of this class equation = StringVar()# create the text entry box for expression_field = Entry(gui, textvariable=equation)# grid method is used for placing expression_field.grid(columnspan=4, ipadx=70)# create a Buttons and place at a particular. button1 = Button(gui, text=' 1 ', fg='black', bg='red',     command=lambda: press(1), height=1, width=7) button1.grid(row=2, column=0)button2 = Button(gui, text=' 2 ', fg='black', bg='red',     command=lambda: press(2), height=1, width=7) button2.grid(row=2, column=1)button3 = Button(gui, text=' 3 ', fg='black', bg='red',     command=lambda: press(3), height=1, width=7) button3.grid(row=2, column=2)plus = Button(gui, text=' + ', fg='black', bg='red',    command=lambda: press("+"), height=1, width=7) plus.grid(row=2, column=3)equal = Button(gui, text=' = ', fg='black', bg='red',    command=equalpress, height=1, width=7) equal.grid(row=5, column=2)clear = Button(gui, text='Clear', fg='black', bg='red',    command=clear, height=1, width=7) clear.grid(row=5, column='1')Decimal= Button(gui, text='.', fg='black', bg='red',     command=lambda: press('.'), height=1, width=7) Decimal.grid(row=6, column=0) # start the GUI gui.mainloop()
For checking out further information on this topic, I would recommend checking out this reference from Geek for Geeks. If you are interested in understanding the concept through a video guide, I would suggest following this video guide on YouTube. If you are curious to learn more about Graphics User Interfaces and the other options that you available to you, check out one of my previous articles that covers seven such tools with some starter codes for the development of projects.
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The audiobook voice-over project, as the name suggests, will involve some textual and voice requirements. For this Python project, we will convert the information to text and get a voice recording that you can automatically listen to. This project will consist of two main phases.
The first phase is the conversion of the textual data into audio recordings, and the second step is to interpret the eBooks into a readable format. For the first task, we will utilize the Google Text-To-Speech using Python, while in the second phase, we will utilize the optical character recognition (OCR) techniques to achieve the best results possible.
To begin with the first phase of the project, we can start exploring the Google Text-To-Speech (GTTS) module to achieve the task of conversion of textual information into an audio file. Once we obtain a playable version of this audio file, we can choose to either keep or remove this particular file. The code for performing the following action is as follows.
from gtts import gTTSimport ostext = "Hello! My name is Bharath."tts = gTTS(text)tts.save("hi.mp3")os.system("hi.mp3")
For gaining further information and learning the intricate details on the working of this library, it is recommended to check out one of my previous articles that extensively covers this topic from the following link provided below.
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For the second phase of this project, we will focus on reading eBooks, which are usually in the PDF (or text files) format, into a textual description such that it is readable by the GTTS module. The reading of information in PDFs or images will require the use of optical character recognition (OCR) technologies. We will use the Pytesseract module for these OCR conversions.
The Pytesseract OCR module is one of the best options to interpret the visual information and extract the textual descriptions from that particular image or document. Let us compute the final code block to construct the project of an audiobook reader with these two technologies.
#Importing the librariesimport cv2import pytesseractfrom PIL import Imagefrom gtts import gTTSfrom playsound import playsound# Specifying the pathpytesseract.pytesseract.tesseract_cmd = r'C:/Program Files/Tesseract-OCR/tesseract.exe'# Reading the image image = cv2.imread('1.png')# Extraction of text from imagetext = pytesseract.image_to_string(image)# Printing the textprint(text)# Create the voice_text variable to store the data.voice_text = ""# Pre-processing the datafor i in text.split():    voice_text += i + ' '    voice_text = voice_text[:-1]voice_texttts = gTTS(voice_text)tts.save("test.mp3")playsound("test.mp3")
To learn more about optical character recognition and the complete working procedure of this library module, I would recommend checking out one of my previous articles on OCR with Python that covers this topic extensively from the link provided below.
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In this section, we will discuss a couple of gaming projects that you can construct with the help of Python. With the help of Python and the variety of modules it offers the users, you can construct a wide array of games. You can build games like hangman, tic tac toe, rock paper scissors, and so much more, including more graphical oriented games like flappy bird or Mario copies with the help of Pygame.
In this first part of the article, we will talk more about how you can use the different libraries that are available in Python to create your own unique games. With the help of the pre-built library modules such as the turtle package and the random library in Python, you can construct a unique project with a slight graphical touch to it.
In the code block shown below, we are defining a function where we are drawing a racing track, and once the track is complete, we plan to place a couple of turtles so that they can race each other. The motion of the races can be randomized with the random library, and each result of the roll will be different each time, and hence which turtle wins the race will also be different each time.
def treat():    speed(0)    penup()    goto(-140, 140)    for step in range(15):        write(step, align='center')        right(90)        for num in range(8):            penup()            forward(10)            pendown()            forward(10)        penup()        backward(160)        left(90)        forward(20)            turtle1 = Turtle()    turtle1.color('red')    turtle1.shape('turtle')
While the above code block is a sample code for the project that we plan to build, you can either continue from here on your own with some unique ideas or refer to one of the other projects that I constructed as a fun Python project for Halloween. If you are interested in building a similar turtle race project as specified above, then check out the following link provided below. It is a detailed guide on how you can create any type of Python game with a unique and intriguing idea without the requirement of too much programming knowledge.
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For the second part, you can construct a multitude of projects with Pygame. It is one of the best libraries in Python that allows you to work on many different gaming projects. You can build simpler project ideas with this gaming library or construct more complex projects with deep learning and reinforcement learning. If you are interested in learning more about developing games and why you should develop one yourself with Python and Artificial Intelligence, then check out the following article from the link provided below.
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Unlike our previously discussed projects, the sentiment analysis project will involve more of other equivalent topics related to AI, such as machine learning and deep learning. However, I find that there are numerous variations of sentiment analysis that you can perform on many different levels, and the complexity can be constantly improved with each variation of any specific action, similar to the calculator project described in the second point of this article.
Let us firstly understand what sentiment analysis is, and how you can perform the following in Python with a sample code block. Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
For performing the task of sentiment analysis, we can achieve the most successful results with the help of numerous methods. Usually, for beginners, the best approach is to start with numerous machine learning algorithms after cleaning the text data available to them and performing all the basic operations of tokenization. Machine learning algorithms like logistic regression and Naïve Bayes are methods to consider for approaching this problem.
The project we will construct in this article will use some elements of natural language processing toolkit and the inbuilt Naïve Bayes algorithm. We are trying to use the movie corpus dataset, and perform some type of sentiment analysis. The complete code block shown below is taken from the following link. Check it out for further information and details on the following topic.
# Load and prepare the datasetimport nltkfrom nltk.corpus import movie_reviewsimport randomdocuments = [(list(movie_reviews.words(fileid)), category)              for category in movie_reviews.categories()              for fileid in movie_reviews.fileids(category)]random.shuffle(documents)# Define the feature extractorall_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())word_features = list(all_words)[:2000]def document_features(document):    document_words = set(document)    features = {}    for word in word_features:        features['contains({})'.format(word)] = (word in document_words)    return features# Train Naive Bayes classifierfeaturesets = [(document_features(d), c) for (d,c) in documents]train_set, test_set = featuresets[100:], featuresets[:100]classifier = nltk.NaiveBayesClassifier.train(train_set)# Test the classifierprint(nltk.classify.accuracy(classifier, test_set))
If you are interested in approaching the following problem statement with a more deep learning approach, then you should consider starting off finding a solution using LSTMs. Then, you can approach the following problem with a more complex approach of sequence to sequence models with attention or with the use of 1-Dimensional convolutional layers, etc. As a starting point, I would recommend checking out the following link to get started with the deep learning approach for the sentiment analysis project.
If you are interesting in learning more about natural language processing and other similar tasks that you can perform with NLP and deep learning, feel free to check out one of my previous projects that covers the topic of next word prediction. In this project, I go deeper into how you can construct a next word prediction model with the help of deep learning and conduct predictive searches using LSTM.
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“The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code — not in reams of trivial code that bores the reader to death.” — Guido van Rossum
The simplicity of Python allows developers to straight away five into the creation of exceptional projects. With the help of the diverse and phenomenal tools that are available in Python, enthusiasts can build almost any type of project that they want to explore. You can build any kind of AI, Data Science, computer vision, or natural language project that you desire and want to, thanks to the versatility of this programming language.
In this article, we covered the topic of how to construct five different Python projects, namely, automating your PC, GUI calculator, Audiobook voice-over, Python Hangman game, and sentiment analysis. With the code blocks, the various references, and other resources, you should easily be able to build these projects without too much difficulty.
After working on these projects, take up some of your own choices and start exploring and experimenting with these other options as well. Keep building more and more projects till you learn a lot more. If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles that you might enjoy reading!
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!