Top 10 Ideas for Using Python

Have you learned Python? Congratulations, great decision! Remember that, like any skill, you need to keep practicing Python. Here are some cool ideas. Enjoy!

Python is the predominant programming language in the data science ecosystem. It is so popular among data scientists that we tend to associate Python with only tasks related to data science. However, Python is a general-purpose language with several other use cases.

Using Python for data science has lots of benefits. It offers a rich selection of third-party libraries that expedite common tasks. Are you interested in data science? See how you can use your Python skill in the article “Python Data Science Project Ideas” to get even better. However, using Python only for data science is kind of underestimating it.

In this article, we will go over 10 cool ideas for using Python. We will cover a broad range of applications from machine learning to web development. By the end of this article, you will have a comprehensive overview of what Python is used for.

Let’s start with how and why Python was created. Guido van Rossum wanted to create a programming language that met the following expectations:

• Easy and intuitive, yet just as powerful as major competitors.
• Open-source, so anyone can contribute to its development.
• Understandable as plain English.
• Suitable for everyday tasks, allowing for short development times.

With these goals in mind, he created Python in 1991. I think the biggest reason for Python’s popularity is its intuitive syntax and short development times. It is not too complicated to create a reliable product with Python. It further motivates the developers to actually see their product or idea in action. Even if you are not planning on doing a spectacular Python project, this great language can help you out. How? It can make everyday tasks easier, and you will work more efficiently.

There are many reasons for learning Python. The ideas we will go over in this article will shed light on what you can accomplish using Python. Let’s start!

1. Chatbot

Customer satisfaction is of crucial importance for any business. The key to successful customer service is to handle their requests in a timely manner. Chatbots help us achieve this goal.

The advancements in machine learning and natural language processing (NLP) allow for creating very efficient and highly accurate chatbots. Since Python is the go-to language in these areas, your choice of programming language to create a chatbot should be Python.

There are two main types of chatbots: rule-based and self-learning. The rule-based chatbot is an example of traditional programming. The developer creates a set of rules based on the expected input or questions from the users.

The self-learning chatbots involve applying deep learning algorithms and techniques. It learns how to chat with users after an enormous amount of training. Self-learning chatbots have their own challenges, but they are definitely more robust than rule-based ones.

You can create a production-level chatbot using Python. Third-party libraries such as TensorFlow, PyTorch, NLTK, and SpaCy help you a lot by providing functional tools and techniques.

2. Machine Learning Projects

This is another cool reason for using Python. There are numerous areas where you can apply machine learning. Demand forecasting, price prediction, customer churn prediction, and image classification are some examples for which machine learning is extremely useful.

As an example, you can create a machine learning model to predict used car prices. You start with acquiring raw data. It should contain features that have an influence on the price, like age, brand, color, mileage, and so on.

The next step is analyzing data and generating insight. You also need to clean and preprocess the raw data so that it is ready for machine learning algorithms. The performance of a model depends on the quality of the data, so preprocessing the raw data is a vital step of any project.

Data Processing with Python is a great track for learning data processing tools and techniques. It contains 5 fully interactive courses, so you can practice while learning. By the end of this track, you will have learned how to handle CSV, Excel, and JSON files with Python. It also covers string operations which are especially important when working with textual data.

After the data is ready, you can train a machine learning algorithm and let it make predictions on new observations. You can even try to determine the value of your own car!

I strongly recommend improving your Python skills before jumping into machine learning. LearnPython.com is a great platform for learning Python. The Learn Programming with Python track provides a well-designed and structured path.

3. Web Scraping/Crawling

We mentioned above that the first step of a machine learning project is acquiring the raw data. This step is another area for using Python. Used car prices can be collected from the online platforms used for selling and buying used cars.

The process of collecting raw data from the web is called web scraping or crawling. Considering the enormous amount of online traffic, the web might be the biggest data source. Web scraping is a very common practice for collecting data.

There are companies that only do web scraping. They provide tools and services to other companies that need data from the web. Imagine you have an online business selling some goods. You would probably pay money to get your competitors’ prices. You can obtain such data in an organized structure by implementing web scraping techniques.

Python is the preferred language for web scraping as well. The Python community has created many open-source web scraping tools and frameworks. BeautifulSoup is a very popular Python library for collecting and parsing raw data from the web.

4. Data App

We have discussed using Python to do machine learning projects. Let’s assume you have built a model that predicts used car prices. It is so accurate that you want to share it with others. A good way of sharing your model is through a web application.

This is another cool idea for using Python. You can create a web application that takes input from users and feeds them into your trained model. You can then show users the predicted price of their cars.

There are various tools and frameworks that help you create such a web application. A popular one is Streamlit, which is an open-source app framework. It makes it easy to convert your data scripts into a web application. You do not have to have front-end experience. All you need is Python!

5. Sending Emails

Python can also be used for sending emails. If your job requires you to send lots of emails with some standard content, it would be a good idea to write a Python script that sends them for you. It will definitely make your day more productive!

Python comes with built-in packages that can be used for sending emails. The smtplib module allows for sending emails with the Simple Mail Transfer Protocol (SMTP). The email package can be used to generate emails with fancy HTML contents.

Here is a hands-on tutorial that demonstrates how to generate and send emails with Python:

6. Web Development

Python is a good fit for web development as well. Flask, Django, and Pyramid are Python frameworks that can be used for this purpose. Some of the most popular websites like Spotify and Reddit are powered by Python frameworks.

For instance, you can create a website that contains a financial report. It might contain historic prices and trends of several stocks. You can embed charts that provide an informative summary as well.

7. File Renaming Script

As the number of files and directories in your workstation increases, it becomes harder to manage them properly. A structured and standard way for naming files is great for staying organized.

Python makes it easy to work with files and directories. You can rename them, move them around, and even update the content using Python. So, a cool project idea would be to write a Python script that organizes your files.

LearnPython.com offers a great interactive course for Working with Files and Directories in Python. This article provides a more detailed overview of what is in this course and why you should take it.

8. Interactive Data Visualizations

Data visualization is an essential part of data science. Sometimes, we need more than just plain numbers or text to represent data. This is where data visualizations come in handy. It can provide a structured summary of the data.

Interactive data visualizations take it one step further. They are dynamic and can be updated based on user selection. Imagine you have an interactive scatter plot that shows the relationship between the house price and the distance to the city center for a number of cities. You can update the visualization to show houses in only a particular city. We enhance the capabilities of data visualizations by adding interactivity.

Python has a rich selection of data visualization libraries. The Altair library for Python is highly efficient at creating interactive visualizations. You can even create one with multiple plots and embed interactivity between them.

You can enrich your presentations or reports by adding interactive data visualizations.

9. Video Game Development

We’ve all played video games at some point in our lives. Like any other technology, video games have improved a lot in recent years. Both the quality and the number of video games have increased.

Global video game market value is expected to surpass 200 billion dollars by 2023. It is just huge! We see video game companies acquired often for more than a billion dollars. It would be a shame for Python users not to be in this industry!

Python is a quite powerful language in video game development. It provides developers with quick prototyping of video games. As a result, Python is becoming the language of choice in the video game industry.

There are several Python tools and frameworks used for developing video games. A popular one is Pygame, which is a set of Python modules designed for writing video games. If you are new to Pygame, here is an introductory video tutorial:

10. Mask Detection

We have been going through tough times since the beginning of 2020. The global coronavirus pandemic has made life hard for all of us. It brought masks into our lives.

We can turn this into a cool project idea for using Python. You can implement mask detection algorithms with Python frameworks such as TensorFlow, Keras, and OpenCV. It also serves as a great practice project if you plan on working in image processing, image classification, or deep learning in general.

Here is a great tutorial that explains how to do mask detection with Python:

Many Ways to Use Python!

Python has many use cases, which definitely reach beyond data science. We discussed 10 cool ideas for using Python. If you are new to Python, I recommend starting with learning the basics before jumping into working on these cool ideas.

The Learn Programming with Python track is a great starting point. It consists of interactive courses that will get you ready to implement the cool ideas in this article. Here is a more detailed article that explains how to start your adventure with programming and Python.