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. It allows users to perform advanced data manipulations as well as numerical analysis by using data frames. This multipurpose programming language is applicable to almost any situation that uses data, lines of code, or mathematical computations.
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 working in Data Analytics.
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 composed 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 two of Python’s most popular data analytics libraries, NumPy and Pandas, to see which one comes out ahead.
Benefits of Using NumPy for Data Analytics
NumPy, short for Numerical Python, is one of Python’s core packages for scientific computing. This library is made up of multidimensional array objects, as well as a set of routines designed to process them. NumPy is a powerful tool for performing a variety of logical and mathematical tasks.
The following are some of the main advantages of working with NumPy for data analytics:
- NumPy is particularly useful for creating data objects with N dimensions.
- Its framework performs quickly and smoothly when working on homogenous datasets.
- When used for numerical calculations, NumPy arrays use less memory than Python lists. It also allows users to specify the types of data in the contents, which can optimize code.
- NumPy can efficiently store data and data operations, especially as arrays increase in size.
- It is not difficult to perform mathematical operations on the data stored in NumPy.
- NumPy allows users to increase their workflow speed.
- It is able to interface with other Python packages. Since NumPy has been around for a relatively long time, nearly all machine learning and data analytics packages for Python use NumPy in some capacity.
Benefits of Using Pandas for Data Analytics
Pandas is an open-source BSD-licenced Python package that is built on top of NumPy. It is generally used for machine learning tasks, as well as data analytics and data science. Pandas offers user-friendly, easy-to-use data structures and analysis tools for working with time series and numeric data.
Pandas is considered to be one of the best data-wrangling packages. It also functions well with various other data science Python modules. By combining the functionality of Matplotlib and NumPy, Pandas offers users a powerful tool for performing data analytics and visualization.
The following list highlights some of the most helpful features Pandas offers for data analytics:
- Pandas is known for its exceptional ability to represent and organize data.
- The Pandas library was created to be able to work with large datasets faster and more efficiently than any other library. It excels at analyzing huge amounts of data.
- Data can be imported to Pandas from a variety of file formats, such as SQL, Excel, and JSON, among others.
- When a Pandas user writes a line or two of code, it’s possible to perform tasks that would require more than ten or fifteen lines of code using Java or C++. This efficiency helps novices work with Pandas.
- Pandas is considered to be a robust library that features an array of features and commands that make data analysis easier.
- Because Python is one of the most popular programming languages in the world, learning how to code in Pandas for Python is a versatile and marketable skill set that can gain the attention of employers.
- Users can edit and customize Pandas by selecting from its extensive feature list.
Which One Comes Out Ahead?
In terms of which Python library comes out ahead for data analytics, the answer depends on what the library is intended to be used for. Pandas is most commonly used for data wrangling and data manipulation purposes, and NumPy objects are primarily used to create arrays or matrices that can be applied to DL or ML models. Whereas Pandas is used for creating heterogenous, two-dimensional data objects, NumPy makes N-dimensional homogeneous objects.
When accessing data, NumPy can access data only by using index positions, while Pandas is a bit more flexible and allows for data access via index positions or index labels. In terms of speed, the DataFrames used in pandas tend to be slower than Numpy arrays, so NumPy’s speed generally outperforms that of Pandas.
Generally speaking, for users who are working with homogenous, mathematical data, NumPy is a better library. And for those users who are working to understand a client’s data, as well as perform any alterations or transformations on the data, Pandas is a better option.
While both Pandas and NumPy are powerful Python libraries with their own unique uses and features, both play an integral role in the field of data analytics. These packages can be used together or separately for your organization’s data analysis, manipulation, and preparation 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. For those interested in learning more specifically about NumPy, Pandas, and Matplotlib, Noble’s Machine Learning Bootcamp provides industry-relevant, hands-on training.
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 Data Analytics Classes Near Me tool 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.