What is Python?

Python is a high-level, object-oriented programming language whose straightforward syntax lends itself to readability. Because its basis is English syntax, Python is one of the easiest coding languages to learn. This multipurpose programming language is applicable to almost any situation that uses data, lines of code, or mathematical computations. It allows users to perform advanced data manipulations as well as numerical analysis by using data frames.

Python is the fastest-developing programming language in use today. It can be used for small tasks, such as powering a Reddit moderator bot, as well as more complex endeavors, like working with huge amounts of hedge fund financial data. Because this software is free and open-source, it has a huge community of users around the world.

Python has many professional applications in the world of big data and a variety of libraries that are useful for those tasked with managing and visualizing data.

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What is a Python Library?

In computer programming, a library refers to a bundle of code consisting of dozens or even hundreds of modules that offer a range of functionality. Each library contains a set of pre-combined codes whose use reduces the time necessary to code. Libraries are especially useful for accessing pre-written codes that are repeatedly used, which saves users the time of having to write them from scratch every time.

Python has over 137,000 libraries. The Python Standard Library is comprised of hundreds of modules geared toward executing basic tasks like reading JSON data or sending emails. The Standard Library comes bundled with a Python installation, which means its modules can be used without having to download them. Within Python, each library, or module, has a different purpose. Some of these modules play an important role in fields like data science, data manipulation, data visualization, and machine learning.

This article will explore the ten most popular Python libraries for data analytics.

Ten Most Popular Python Libraries for Data Analytics

Python is a high-level, object-oriented programming language that’s popular among those working with data. It has a variety of libraries that can be used for data visualization:

    • Plotly: This graphic library can create a variety of interactive, high-quality data visualizations, such as scatter plots, heatmaps, histograms, box plots, bubble charts, and polar charts.
    • NumPy: Numerical Python, or NumPy, is Python’s basic numerical computation package. It has many powerful features, such as the n-dimensional array, Fourier transforms, and advanced random number capabilities. NumPy’s extensive collection of high-level mathematical functions support its multi-dimensional arrays and is commonly used for data analytic purposes.
    • SciPy: Short for Scientific Python, SciPy is an open-source, free Python library that is commonly used for high-level computations. SciPy is built on NumPy and contains many high-level commands that aid with manipulating and visualizing data.
    • VisPy: This 2D/3D data visualization library offers users easy options for quickly creating professional-level, interactive visualizations. A variety of interfaces are available for those who have different levels of experience with creating data visualizations. While it’s possible to use one interface to design a basic visualization, VisPy is most useful for those who can work with multiple interfaces, which allows for optimal customization.
    • Pandas: Python Data Analysis, or Pandas, is commonly used in data science, but also has applications for data analytics, wrangling, and cleaning. Pandas offers eloquent syntax, as well as high-level data structures and tools for manipulation.
    • Matplotlib: This is Python’s first data visualization library. It is still considered to be the most popular and widely used data visualization library. Matplotlib can create a variety of graphs, such as line graphs, scatter graphs, histograms, heat plots, and interactive 2D graphs.
    • Seaborn: This data visualization library is based on Matplotlib. It offers a high-level interface that can be used for depicting informative and stunning statistical graphics. At its heart, Seaborn strives to make visualization a core component of the data exploration and analysis process.
    • Scikit-learn: This comprehensive machine learning library is built on SciPy and NumPy. It has applications for statistical modeling, such as regression, clustering, classification, and model selection, among others.
    • Statsmodels: This Python module is designed for statistical modeling. It helps users run statistical tests, explore data, and perform plotting functions.
    • Apache Superset: Even users who do not come from a technical background can use Apache Superset to explore and visualize data. This intuitive model works quickly and has a plethora of tools that can be used to create simple visualizations like line charts, as well as detailed geospatial charts.

These are just a few of the dozens of Python libraries that can be downloaded and used by any Data Analyst to assist with their data analysis and visualization needs.

Hands-on Programming & Data Visualization Classes

An important first step toward learning more about data analytics is enrolling in one of Noble Desktop’s data analytics classes. These beginner-friendly courses are currently available in topics such as Excel, Python, and data science, among other skills necessary for analyzing and visualizing data.

Noble Desktop also offers a variety of programming bootcamps for those who work with data. Courses are offered in topics like Python, JavaScript, and data science, among others. Noble’s bootcamps offer small class sizes, as well as 1-on-1 mentoring, for all participants looking to rigorously explore the most popular programming languages for data analytics.

In addition to Noble’s class listings in computer programming, there are more than 200 live online programming courses currently available from top training providers. These interactive classes are taught in real-time and provide all learners with access to an instructor who is live and ready to provide feedback and answer questions. Courses range from three hours to 72 weeks in duration and cost $149-$27,500.

Do you want to find a nearby coding class in which to enroll? If so, Noble’s Coding Classes Near Me tool provides an easy way to locate and browse over 500 coding classes currently offered in in-person and live online formats. This handy tool ensures that all interested learners can find the course that works best for them. For those searching for a data analytics class nearby, Noble’s Classes Near Me tool also offers an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.