What is Python Used For? 11 Python Uses
The fact that Python has a wide range of applications has already been addressed several times in this essay. Let's examine the specific situations where Python is useful in the real world.
1. Python for Data Analysis
In every modern company, data has become a genuinely priceless asset, and the majority of businesses are interested in collecting, modifying, and analyzing pertinent data to get actionable business insights from it. And here is where Python excels over all others.
Apart from its extensive standard library, Python is particularly useful since it offers an incredible array of supplementary modules made exclusively for analytical applications.
NumPy and pandas are two of the most well-known Python data analysis libraries. With the help of these tools, you can do practically anything with your data, including organizing and cleaning it, investigating statistics, or showing any hidden trends.
There are many other libraries available that you can use for other data-related activities, such as web scraping, hypothesis testing, and data visualization, in addition to these two.
2. Python for Data Visualization
Data visualization is a standalone component of data analysis that enables us to present the information, whether it is unprocessed or cleaned and transformed, in a way that is more engaging and illuminating.
Python is used in this situation once more, providing a large range of capabilities for data visualization. The most well-known of them are matplotlib and seaborn, which is built on it. They enable us to produce a plethora of visualizations, ranging from the most basic (line plots, bar charts, and histograms) to the most complex (animated plots, streamlines, cluster maps, joy plots, etc.).
Take our courses Introduction to Data Visualization with Matplotlib and Introduction to Data Visualization with Seaborn to learn more about how to utilize these two Python packages.
In addition, Python offers a bewildering array of additional graphical libraries, both general-purpose (like Plotly, Bokeh, or Altair) and more specialized (like missingno for visualizing missing numbers, Toyplot for making interactive plots for electronic publication, GeoPandas for making maps, etc.).
3. Python for Machine Learning
Most tasks in data science revolve around machine learning (ML). It exemplifies a branch of artificial intelligence (AI) that uses algorithms to give computers the ability to recognize patterns and trends in past data and forecast future data.
We may develop models that reliably predict a company's customer churn rate, calculate a person's risk of contracting a specific disease, determine the best locations for taxi vehicles, etc. using ML techniques.
4. Python for Software Development
Python is utilized at every stage of software development, including build control, automated continuous compilation, prototyping, bug tracking, testing, and software maintenance, in addition to its broad range of applications in data science fields.
This programming language's flexibility and power allow it to be used to create programs that are both reasonably simple and extremely advanced multi-protocol apps or those that entail complex numeric computations.
Python enables us to create AI or ML-based audio or video applications as well as any other kind of software, such as GUIs, APIs, or application programming interfaces.
5. Python for Web Development
While we would typically use languages like HTML, CSS, and JavaScript to develop a website's front end, or the portion that people can see, we would frequently choose Python to build a website's back end.
With specialized built-in modules that allow for data exchange with servers, information processing, database access, URL routing, content management, and website security, Python is utilized for several popular frameworks (like Django or Flask) for these uses.
Google, Facebook, Instagram, YouTube, Dropbox, and Reddit are just a few of the well-known websites and programs built with Python.
6. Python for Task Automation/Scripting
Python is a fantastic programming language for creating scripts that automate certain monotonous jobs. Another name for this procedure is scripting.
You can use scripting, in particular, to operate with files and directories. You could, for instance, create, rename, convert, split, merge, or delete files, as well as change the content of the files or check them for errors, duplicates, or certain text patterns.
Additionally, Python automation can be used to find and download data from the internet, finish and submit web forms, and send recurring emails or notifications.
7. Monitoring the stock market
8. Web scraping
9. Keeping updated a to-do list
10. Creating bots
11. Generating lists of random names, colors, phone numbers,
currencies, etc.