Intro to Python libraries
Published: 2024-04-18 16:18:02

## **Introduction:**

Python has become very popular, thanks in part to its extensive library ecosystem that caters to a wide range of needs. Among these libraries, three stand out for their versatility and utility in creating functional applications with a good user experience: inquirer, matplotlib, and tabulate. In this beginner's guide, we'll explore how these libraries can be used to build practical applications that not only perform tasks efficiently but also provide a seamless user interface.

**1. Getting Started with Inquirer:**

Inquirer is a Python library that simplifies the process of creating interactive command-line interfaces (CLIs) by providing a set of common interface elements such as prompts, checkboxes, and text inputs. Its intuitive API makes it easy to build applications that engage users and collect input effectively.

Imagine you're tasked with building a quiz application similar to those used in schools or companies for assessments. With inquirer, you can quickly implement the necessary prompts to gather responses from users in a structured manner. Let's take a look at a simple example:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/j5in1xd9tpgms0xvzhd2.png)  

In this code snippet, we are displaying 10 random questions from our table of quiz questions and then shuffling the answers.  By simply using inquirer.list_input() and passing in the choices we create the ability for the user to simply arrow up and down the choices to select their answer.  

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nsf2v7efpnq0zup1sube.png)

This is only one example from our application.  We incorporated into many areas of our CLI.  Inquirer offers a variety of prompt types and many more customization options to tailor the user experience to your application's needs. You can explore the full range of features in the [official documentation](https://github.com/magmax/python-inquirer).

**2. Visualizing Data with Matplotlib:**

Matplotlib is a powerful library for creating static, animated, and interactive visualizations in Python. It's widely used in scientific computing, data analysis, and machine learning for its flexibility and ease of use. One of its key features is its ability to generate high-quality plots with just a few lines of code.

Suppose you're working on a project to analyze student grades and visualize their performance. Matplotlib can help you create informative plots that highlight trends and patterns in the data. Let's consider a scenario where you want to plot student grades on a scatter plot and visualize the class average:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zp3vi2aw2ymnlfcerq02.png)

In this code snippet, we use Matplotlib to create a scatter plot of student grades, with each student represented by a point on the graph. The user's score is another scatterplot, with only one value, which allows us to change it's color.  We then add a dashed red line to indicate the class average. By visualizing the data in this way, we can quickly identify outliers and assess overall performance.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/x1cjsd8u49aoxl0itdng.png)

Matplotlib offers a wide range of plotting functions and customization options to suit various needs. You can explore the full capabilities of Matplotlib in the [official documentation](https://matplotlib.org/).

**3. Generating Tables with Tabulate:**

Tabulate is a lightweight Python library for formatting tabular data in a visually appealing manner. It provides a simple interface for generating tables in CLI applications, making it ideal for displaying structured information to users.

Continuing with our quiz application example, let's say you want to present users with a summary of their quiz results in a neat tabular format. Tabulate makes it easy to achieve this with minimal effort:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8b8ieul2krwvmltq5upp.png)

In this code snippet, we use Tabulate to create a formatted table of quiz results, including columns for the quiz name, score, and the date the quiz was taken. The tablefmt='fancy_grid' option adds a visually appealing grid layout to the table, enhancing readability.  

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/kqeqyhjd4stk36wf5t9d.png)

Tabulate offers various table formatting options and styles to suit different preferences. You can explore the full range of features in the [official documentation](https://pypi.org/project/tabulate/).

**4. Exploring Further Possibilities:**

The examples provided in this guide only scratch the surface of what can be achieved with inquirer, matplotlib, and tabulate. As you continue your journey in Python development, there's a vast world of possibilities waiting to be explored within these libraries.

With inquirer, you can dive deeper into its capabilities by incorporating more complex prompts and validation mechanisms. For instance, you can implement conditional logic to dynamically adjust the questions based on previous user responses. Additionally, you can explore advanced styling options to create visually appealing interfaces that align with your application's design aesthetic. Inquirer's extensive documentation provides valuable insights and examples to guide you through these advanced features.

Matplotlib offers an extensive array of plotting functions and customization options beyond what we've covered in this guide. Experiment with different plot types such as histograms, bar charts, and heatmaps to visualize your data from various perspectives. Delve into the realm of 3D plotting or interactive visualizations using Matplotlib's interactive mode or integration with other libraries like PyQt or Tkinter. By exploring the full spectrum of Matplotlib's capabilities, you can create visually stunning visualizations that effectively communicate insights from your data.

Tabulate provides a solid foundation for generating tables in CLI applications, but there's much more you can do to enhance the presentation and functionality of your tables. Explore advanced formatting techniques such as multi-level headers, custom alignments, and styling options to tailor the appearance of your tables to your specific requirements. You can also integrate tabulate with other libraries or frameworks to enhance interactivity, such as incorporating clickable table cells or enabling sorting and filtering capabilities. The possibilities for customization are virtually limitless, allowing you to create polished and professional-looking tables that elevate the user experience of your CLI applications.

**Conclusion:**

In this beginner's guide, we've explored how to create functional applications with Python libraries such as inquirer, matplotlib, and tabulate. These libraries offer powerful tools for building interactive command-line interfaces, visualizing data, and formatting tabular information.

As you continue to explore these libraries and integrate them into your projects, remember to refer to the official documentation and seek out additional resources for further learning. With practice and experimentation, you'll be well on your way to mastering the art of Python development.

Happy coding!