Array programming offers an expressive, powerful, and compact syntax. It helps in operating, accessing, and manipulating data in matrices, vectors, and higher-dimensional arrays. In Python language, Numpy is a primary array programming library. It plays a very important part in research analysis pipelines in fields such as physics, chemistry, biology, engineering, material science, astrology, economics, finance, and geology. This was an important part of the software stack that was used in the imaging of the black hole and the discovery of gravitational waves. It keeps acting as an interoperability layer between array computation libraries. With its API, it provides a flexible framework for supporting scientific and industrial analysis.
What is Python Numpy?
Numpy is the most powerful Python library that stands for Numerical Python. It is a general-purpose array processing package that people use in industries for array computing. Numpy provides a high-performance and multidimensional array object and several tools for working with arrays. It is an open-source project that anyone can use freely. Python Numpy is the fundamental package for computing scientifically with Python. Moreover, it can also be used as a systematic multi-dimensional container of generic data.
Numpy is gaining huge popularity and we use it in many production systems. Hence, we need to understand that what this library offers. Python has lists that serve the purpose of arrays; however, they are quite slow to process. Numpy provides array objects that are 50 times faster than traditional Python lists. Ndarray is the array object in Numpy and it offers a lot of support functions that make it easy to work with Python. We use arrays in data science where speed and resources matter a lot.
What one should use Python Numpy?
If you are a Python programmer and you haven’t run into Numpy yet then you are probably missing out. It is an open-source library that programmers use for scientific and numeric computing letting you work with many multi-dimensional arrays efficiently. Also, it is one of the top five packages in Python.
Here are 5 reasons why one should use Python:
- It is fast because it is written in C and gives quick results. Python is a dynamic language. It is interpreted by CPython, it is then converted to bytecode and then executed.
- There are many libraries such as SciPy, pandas, sympy, nose, etc that use Numpy. Both Numpy and SciPy are two sides of a coin. Numpy was created from two packages. It contains both ndarray type and array manipulation functions, but numeric functions as well.
- It allows you to do matrix arithmetic. Numpy’s ndarray lets the users to do dot and inner product of two matrices and also matrix product and raising matrix to a power. Numpy can solve tensor equations and also three different types of matrix inversion.
- It provides a lot of built-in functions. The list is long but it is sufficient to say that there are functions for searching, string operations, random sampling, financial calculations, math functions, linear algebra, statistics, indexing, polynomials, binary, logic, and sorting.
- Numpy has universal functions, also known as ufuncs. They are applicable to each element of the input array. It stores the result in a corresponding output array respectively of the same size. Universal functions are a bit advanced. Array broadcasting is a useful feature of universal functions. It is how different arrays of different sizes and shapes can be used in a function.
Python Numpy Arrays
If you are utilizing Python for Data Science then either you have heard about Numpy or have used it. Most of the statistical analysis that requires storing data in the memory uses Numpy. You might ask now why use Numpy? Aren’t Python lists and other data structures helpful in doing the same thing?
Well, the answer to this question is both yes and no. There is nothing special in Numpy that we can’t do with the help of python lists or other data structures. But, Numpy offers efficient storage and finer handling of data for the mathematical operations that use simple API’s.
The Numpy library is used for creating homogenous n-dimensional arrays (n = 1…n). Its implication would not be possible with heterogeneous data sets. Unlike the lists in Python, all the elements of a Numpy array should be of the same type. Let us see what extra benefits Numpy provides us and how it makes our programming lives easier, especially the ones that deal with mathematical calculations.
- It uses much less memory to store the data.
- We can use Numpy for creating n-dimensional arrays.
- It makes it easy to perform mathematical operations on it.
- Numpy provides three functions named where, nonzero, and count_nonzero for finding elements in an array.
Conclusion
Over the coming decades, developers will face several challenges. New devices will be developing in the future and existing hardware will evolve to meet the reducing returns on Moore’s law. There will be a wider variety of data scientists and a large number of people will use NumPy. Further, the scale of scientific data gathering will increase continuously. Through all the things described in this article, Numpy is controlled to embrace such a changing landscape. It continues to play a significant role in interactive scientific computation. Although, it will require sustained funding from the industries, government, and academia to do so. But most importantly, it will need a new generation of community contributors and graduate students to drive it forward.