What Is NumPy?

Python is a popular and flexible programming language that you can use for a wide range of programs and applications. One reason behind Python's versatility is the large number of libraries it offers to make developing programs more efficient. One of the libraries Python offers is NumPy, a fundamental library for its programmers working on scientific computing concepts. As a free, open-source library, NumPy is easily accessible to users. Read on to learn more.

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What is NumPy?

NumPy, short for numerical Python, is one of Python’s many programming libraries for scientific computing. With NumPy, you have access to multidimensional arrays and routines that enable you to efficiently perform various operations on arrays. These operations include mathematical functions such as random simulations, statistical operations, and linear algebra. NumPy also integrates with other programming languages outside of Python. For example, the NumPy C-API allows you to extend access to NumPy functions with C programming.

What does NumPy do?

NumPy allows you to reduce memory usage, optimize code, and simplify development, making it critical for performance. A library that enables efficient development is valuable since you can prototype algorithms faster and perform exploratory analysis on data. Even if you don’t have much programming experience, are new to Python, or have years of expertise, you can still benefit from using NumPy since it uses a manageable syntax that makes learning accessible.

While Python benefits from being a relatively simple programming language compared to other languages, it doesn’t have the same computational power as alternatives, such as C. However, NumPy helps eliminate this gap, making Python a computationally powerful language while still offering the simplicity Python users expect.

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Jobs that use NumPy

NumPy is widely used throughout engineering and science careers, such as in data scientist roles. Data scientists use NumPy to analyze, gather statistical insights from data sets, and develop machine learning models, multidimensional data structures, and mathematical formulas. It's also popular among software developers, web developers, and video game developers. 

NumPy skills

Before beginning to work with NumPy, developing a robust foundation in Python programming is essential. Learning Python is a worthwhile endeavor, as it’s one of the top in-demand programming languages. It stands out as a beginner-friendly, versatile language, giving you several potential career paths to pursue. You also have several options available regarding how you approach learning Python, whether through earning a college degree, such as a bachelor’s degree in computer science, a boot camp, or self-learning through online courses.

Pros and cons of NumPy

NumPy's primary benefits include being beginner-friendly and allowing users to tackle complicated mathematical operations. However, using NumPy has several benefits beyond its user-friendly operation and a few limitations. The following offers a look at the advantages you can experience with NumPy and some scenarios where alternative libraries may come out ahead compared to NumPy.

Pros

• NumPy enables you to efficiently program mathematical calculations on large data sets for scientific computing.

• NumPy integrates with other programming languages, namely C and C++, and other Python libraries such as Matplotlib, scikit-learn, and pandas.

• NumPy adds high-powered data structures to your program that increase the number of mathematical functions you can use.

• The arrays in NumPy use less memory and operate faster than when using Python lists.

Cons

• Compared to pandas, another popular data science library in Python, pandas offers more flexible access to data than NumPy.

• NumPy may not be ideal for performing tasks involving transforming or altering data since it’s better suited for homogeneous data. 

Other Python libraries: Alternatives to NumPy

NumPy is one of Python's fundamental libraries and is especially useful for data science, but Python offers other packages for data science as well. Let’s look at some NumPy alternatives.

1. pandas

Pandas is an open-source library in Python that is used for data manipulation and analysis. With pandas, you can import data from different sources and formats before merging and preparing the data for analysis. 

What is the difference between NumPy and pandas?

NumPy is typically easier to use, while pandas tends to be faster. When working with massive data sets, NumPy is generally more effective, but pandas provides you with more options for dealing with absent data. Also, while NumPy utilizes C arrays, pandas employs Python objects internally, making work easier for you.

2. TensorFlow

TensorFlow is a library that makes machine learning more accessible, allowing you to develop models for various applications, whether mobile devices, desktops, or the cloud. TensorFlow supports the entire machine learning process, from data processing to model building and training.

3. SciPy

SciPy is a library users often use alongside NumPy. It extends NumPy’s capabilities, giving you access to more data structures and algorithms. Applications for SciPy algorithms include differential equations, statistics, eigenvalue problems, linear algebra, and image processing.

How to install NumPy

You can install NumPy in Python using the command “conda install numpy” or “pip install numpy”. After installing NumPy, you can begin using NumPy commands in your code by importing functions with the command “import numpy as np”. 

How to learn NumPy: Tutorials and other resources

Whether you’re new to NumPy or have some experience, several resources can help you grow your abilities. NumPy Learn offers several tutorials, including books and videos, so that you can learn the basics. The site also has resources for more complex problems, such as linear algebra and advanced indexing. You can also find boot camps focusing on data science and programming with Python, including NumPy.

Getting started with NumPy on Coursera

NumPy is a powerful tool for various scientific and engineering scenarios. It integrates with other languages and Python libraries, adding data structures to expand one's ability to perform mathematical functions, making it valuable for many use cases. 

Expand your knowledge of Python and NumPy with one of the highly-rated courses you can find on Coursera. For example, Python for Data Science, AI, and Development from IBM covers NumPy and pandas, Python data structures, and other key fundamentals. Alternatively, you might check out Expressway to Data Science: Python Programming Specialization from the University of Colorado Boulder. This three-course series can help you discover fundamental Python concepts and gain experience with popular data science libraries, including NumPy and data visualization packages.

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