What is Numpy Library? What are its uses? What are its features? And why should you learn it?
Numpy library is one of the most important and practical libraries of Python programming language. One of the features that make Python a useful, powerful and popular language is having different libraries. Python is a very practical language. You can even get help from Python in website design. But the most important application of Python is in artificial intelligence and machine learning.
Basically, Python is a language for collecting and analyzing data. For this reason, although there are other powerful programming languages (such as R) for data collection and analysis, Python is the first choice in machine learning projects. One of the reasons is the priority of having a library called Numpy in Python.
In this content, you will get to know the Numpy library and read the answers to the questions posed at the beginning.
Table of Contents
Introducing the Numpy library in Python
Before introducing Numpy, it should be explained a little about machine learning and its processes. Humans can train a system (machine) to do a specific task by using data and building algorithms. In machine learning, a model or algorithm is created by data analysis. That is, it is specified for the machine to take the data, categorize and analyze it based on known steps or processes and deliver the result as an output.
For example, natural language processing experts have taught voice assistants (like Siri) to understand and respond to human language. That is, they built algorithms (models) that take a huge amount of words and sentences, categorize them (structured and systematic) in order to be able to understand them and finally give an appropriate answer (output).
It goes without saying that the model must analyze and structure a huge amount of data. In the Python programming language, arrays are used to categorize and structure data. Arrays are actually containers for grouping and storing data. There are two types of arrays in Python: single-dimensional and multi-dimensional.
When the data is organized, it is easier to perform calculations and analysis. In fact, it is the arrays that are analyzed or, if necessary, mathematical calculations are performed on arrays or array items. But the point is that Python itself cannot do anything but simple mathematical calculations on multidimensional arrays and matrices. A special library, Numpy, is made for that kind of calculation.
The word “Numpy” was created by combining two words: Numeric and Python or Numeric Python. Numpy is a very powerful library with high performance that allows the programmer to manage and manipulate all types of arrays and their items or perform complex mathematical and logical calculations on them.
Numpy library features for machine learning
As mentioned, machine learning is not possible without analyzing huge amounts of data. Of course, in each machine learning project the data and therefore the type of analysis is different. In all projects, complex scientific and mathematical calculations are not supposed to take place. For this reason, Python has various libraries for data analysis. So, the important question must be answered, what makes Numpy different from other libraries?
1. Numpy optimizes and speeds up linear algebra calculations
Numpy is for scientific and mathematical calculations. That is, if it is necessary to perform mathematical calculations to analyze numerical data, Numpy is the best option. With Numpy, linear algebra calculations can be performed in the shortest time. Linear algebra is a branch of mathematics that studies matrices, vectors, and vector spaces.
Linear algebra is used in various sciences (including physics, various branches of engineering, natural sciences and artificial intelligence). Linear algebra calculations (matrices and vectors) make it possible to model almost anything, including natural phenomena. That capability is exactly what made linear algebra useful for artificial intelligence and machine learning. In Numpy and with Numpy arrays, it is possible to create matrices and vectors and perform calculations and various mathematical operations on the items of an array or some of them.
2. Numpy is compatible with other Python programming languages and libraries
The former is what makes Numpy a must-have library for machine learning. That is, the machine learning engineer can apply all kinds of linear algebra calculations for data analysis. Nameless arrays are faster and more efficient than built-in arrays in Python. Because mathematical operations are easier with infinite arrays.
In addition to features related to scientific and mathematical calculations, the Numpy library and its arrays are compatible with other programming languages, including C++ & C. That is, it is possible to integrate other languages in the arrays of Nampa. In fact, Numpy allows developers and programmers to use the features and computing power of languages like C in Python.
Who created Numpy and in what year?
Numpy replaced the Numeric library. In 1995, a programmer named Jim Hugunin, along with several other developers, created Numeric. In 2005, Travis Oliphant, a well-known American data scientist and businessman, launched Numpy by merging Numeric with Numarray (which was a set of plugins for Python) and enhancing the capabilities of both. Numpy is free and open source and is supported and updated by a group of programmers and developers working together.