What is NumPy?

If you are familiar with data science, you have probably already heard of NumPy, the Python library used to work with large volumes of data. In this article we explore in depth the features and operation of this powerful programming tool.

If you are interested in the world of data and want expert training in this field, at Universidad Europea we have a Degree in Mathematical Engineering, where you can get the skills and knowledge to succeed in this area.

Definition of NumPy and its advantages

NumPy, short for ‘Numerical Python’, is a Python library designed to handle vectors and matrices, facilitating numerical calculations and analysis of large volumes of data. It is widely used in scientific programming, especially in fields such as data science, engineering and mathematics.

The NumPy package offers several key advantages for data analysis:

• It reduces memory consumption.
• It improves processing speed.
• It is accessible to users of all Python skill levels.

Features of NumPy

The main advantage of the NumPy library is its ability to manipulate large datasets using features and objects such as the following:

• Multi-dimensional arrays: NumPy uses a type of objects known as ‘ndarray’. The abbreviation ‘nd’ stands for ‘n-dimensional’ and indicates that these arrays can have any number of dimensions. This feature allows for efficient handling of structured data such as vectors, arrays and other multidimensional structures, facilitating operations in multiple dimensions.
• Array attributes: provide crucial information such as the shape, the number of dimensions (ndim), the data type (dtype) and the total size of the arrays. These attributes are fundamental to understanding the structure of the data stored in ndarray and are used extensively in indexing, slicing and data manipulation operations.
• Image analysis: NumPy allows images to be represented as multidimensional arrays, simplifying their manipulation and processing with operations such as resizing, applying filters and pixel adjustment.
• Mathematical and statistical functionality: NumPy offers tools for advanced mathematical calculations, including linear algebra. It also provides functions for calculating fundamental statistics such as mean, median, standard deviation and variance.
• Random data generation: NumPy includes a specific module to generate distributions and random numbers, very useful in simulations and tests related to data science.

NumPy: How to use it

The use of NumPy is essential in many fields related to data processing due to its ability to efficiently handle large numerical data sets. Several areas have shown remarkable improvements thanks to the adoption of NumPy, some of them are:

• Machine learning and data science: NumPy provides the foundation for manipulating and preparing data, as well as for implementing efficient machine learning algorithms. In addition, its integration with other libraries such as Pandas (for data analytics) and Matplotlib (for visualisation) facilitates the creation of complete and powerful workflows for data analytics and predictive modelling projects.
• Image and signal processing: Enables efficient representation and manipulation of data matrices, essential for applications ranging from image enhancement to signal processing in engineering and scientific research.
• Computer science: In computational science, NumPy is widely used to solve differential equations and perform numerical simulations and computations at scale.
• Quantum computing: In the emerging field of quantum computing, NumPy is presented as a tool for handling data and complex mathematical operations that are fundamental to developing and simulating quantum algorithms.

How to specialise in NumPy?

To master NumPy, start by learning Python, which is recognised as one of the most advanced programming languages. Python's uses range from applications in big data, data miningand machine learning to web development and the creation of games and 3D graphics.

In addition to the mastery of Python, there are specialised training courses, such as the master in Big Data Analytics at Universidad Europea, where the use of the NumPy library for data analysis is explored in depth.