What is Python?

Python is an object-oriented, interpreted programming language that was developed in 1991 by Guido van Rossum. Its simple yet powerful syntax is easy to learn and read. Python supports a variety of programming paradigms in addition to object-oriented programming, including functional and procedural programming. It can be used as an extension language for apps that require a programmable interface. Because Python can run on Unix variants like Windows, macOS, and Linux, it’s an extremely portable and versatile language.

Python has an array of uses. It is helpful for developing software and websites, in addition to automating tasks, analyzing, and visualizing data. In terms of web development, Python is often relied on to create the back end of an app or website. This process typically includes transmitting data to and from servers, processing said data, establishing communication with databases, routing URLs, and establishing security measures. 

Python is especially helpful for tasks that must be executed repeatedly. Scripting, or creating code that can be used to automate a process, has many benefits for Python users. It allows them to check multiple files for errors, convert files, perform basic mathematical functions, and locate and delete duplicates in a dataset. Software Engineers can rely on Python to automate new product or feature testing. Even beginners can use Python to automate rudimentary tasks like finding online content or sending texts and emails at predefined intervals. 

Python has several powerful libraries that are applicable to those working with data science or analytics. These free libraries are considered to be some of the most robust available for coding. 

Many professions use Python for their daily tasks, such as Web Developers, Software Engineers, Full Stack Engineers, DevOps Engineers, Python Developers, and Data Analysts. This article will explore how Python is used in the field of data analytics, as well as some of the benefits and drawbacks of working with this programming language.

Uses of Python for Data Analytics

It’s common for Data Analysts to know at least one or more programming languages. Python remains one of the most popular coding languages among Data Scientists and Data Analysts because of how easy it is to learn, as well as its extensive libraries that are well-suited for data analytics. Because Python is highly readable, it saves Data Analysts and Data Engineers time since they do not need to enter as much code to complete tasks. 

Python has applications in every stage of the analytics process, including data mining, data processing, and data visualization. 

  • Python for data mining: The Python library Beautiful Soup is useful when data can’t be retrieved from APIs. This library is a powerful tool for scraping data and then arranging it in a useful format. In addition, the Python library Scrapy allows users to create programs designed to gather structured data from the internet, as well as extract data from APIs. 
  • Python for data processing and modeling: Python has two libraries that are particularly helpful for processing and modeling data: NumPy and Pandas. NumPy, or Numerical Python, can be applied to large datasets to arrange them. NumPy also helps with math operations and their corresponding vectorizations on arrays. Pandas provides users with two data structures: data frames and series. The Pandas library transforms data to the data frame, which enables users to add or remove columns.
  • Python for data visualization: Matplotlib is Python’s first data visualization library. It is 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, hist graphs, and interactive 2D graphs. In addition, the Python library Seaborn, which is based on Matplotlib, is able to transform long numerical lists into engaging pie charts, histograms, heatmaps, or other graphical representations. 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. Plotly is a graphic library that can create a variety of interactive, high-quality data visualizations, such as scatter plots, heatmaps, histograms, box plots, bubble charts, and polar charts.
  • Other uses of Python for data analytics:
    • Python is a helpful tool for quickly and efficiently creating and managing data structures. The Pandas library is useful when working with complex datasets. It allows users to analyze, manipulate, and represent complicated datasets and structures.
    • By using the Python library Scikit-learn, users can create complex models and execute complicated multivariate regressions. This comprehensive machine learning library has applications for statistical modeling, such as regression, clustering, classification, and model selection, among others.

Drawbacks to Using Python for Data Analytics

Although there isn’t one perfect language for data analytics, there are languages that are better suited for particular aspects of the analytics process. Some languages are more helpful for visualization purposes, while others are better at handling large datasets. Since Python is a general-purpose language and was not created solely for Data Analysts, challenges can arise when using Python for data analytics. 

Even though Python is relatively easy to learn, has an extensive support community, and offers a range of helpful libraries, there are a few drawbacks to be aware of when using this programming language in data analysis:

  • Python has limited speed capabilities. It does not operate as quickly as languages like Scala when using native functions.
  • Its data access layers are largely underdeveloped.
  • Python’s dynamic typing feature leads to a slower error-search capability.
  • Python is typically used in server-side programming. It’s not as commonly applied to the client side or in mobile applications because it is not as efficient with memory and has a comparatively slower processing power than other programming languages.
  • When compared to programming languages like Java, Python lacks good documentation.
  • Python requires a large amount of memory to run.

Despite these drawbacks, Python remains one of the most popular programming languages among Data Scientists and Data Analysts. Its flexibility, scalability, and extensive support community make this programming language a go-to for most Data Analysts.

Hands-On Coding Classes

Learning to code is an in-demand skill for those working with data. It can open professional doors and also lead to upward career mobility within a Data Analyst role. Noble Desktop has a variety of coding classes available for interested learners. They are taught in-person in NYC and are also available in the live online format. These classes and bootcamps cover topics like SQL, machine learning, HTML, CSS, and Python.

Noble Desktop’s Python bootcamps provide a great learning option for those who are interested in an intensive learning experience. Courses are available with a focus on topics like Python machine learning, Python for data science, and data science, among others.

In addition, over 100 in-person and live online coding classes are available from a variety of top providers. These small classes are designed for novice coders, as well as intermediate and advanced learners. 

For those searching for a Python coding class nearby, Noble’s Python Classes Near Me tool provides an easy way to locate and browse nearly 100 Python classes that are currently offered in the in-person and live online formats. Course lengths range from six hours to 28 weeks and cost $399-$19,974. The Coding Classes Near Me tool can also be used to browse more than 500 coding classes. These courses are between two hours and 72 weeks in length, and cost between $149 and $27,500.