{"id":79327,"date":"2020-07-15T20:26:29","date_gmt":"2020-07-15T14:56:29","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=79327"},"modified":"2021-05-09T13:13:49","modified_gmt":"2021-05-09T07:43:49","slug":"numpy-features","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/numpy-features\/","title":{"rendered":"NumPy Features – Why we should use Numpy?"},"content":{"rendered":"

Welcome to DataFlair<\/strong>!!! In this tutorial, we will learn Numpy Features and its importance. <\/strong><\/p>\n

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays<\/p>\n

NumPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package.<\/p>\n

Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use NumPy that converts monotonous code into the compiled form.<\/p>\n

\"numpy<\/a><\/p>\n

NumPy Features<\/h2>\n

These are the important features of NumPy:<\/p>\n

1. High-performance N-dimensional array object<\/h3>\n

This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.<\/p>\n

a. One dimensional array<\/h4>\n

The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.<\/p>\n

b. Multidimensional array<\/h4>\n

In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.<\/p>\n

2. It contains tools for integrating code from C\/C++ and Fortran<\/h3>\n

We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.<\/p>\n

3. It contains a multidimensional container for generic data<\/h3>\n

Here generic data refers to the parameterized data type of arrays. It can perform functions on the generic data types. The arrays in NumPy are of homogenous nature. These array elements are assigned parameters. The parameters help increase the diversity of the arrays.<\/p>\n

4. Additional linear algebra, Fourier transform, and random number capabilities<\/h3>\n

It has the capability to perform complex operations of the elements like linear algebra, Fourier transform, etc. We have separate modules for each of the complex functions. We have the linalg module for linear algebra functions.<\/p>\n

Similarly, we have fft functions for Fourier Transform in NumPy. We have a matrix module for applying functions on matrices. We also have special functions for plotting graphs in the matplotlib module of NumPy. Hence, it is a very diverse library to work with arrays.<\/p>\n

5. It consists of broadcasting functions<\/h3>\n

The broadcasting of array is a very useful concept when we work with arrays of uneven shapes. It broadcasts the shape of smaller arrays according to the larger ones. The broadcasting of arrays has some rules and limitations in its implementation.<\/p>\n

For broadcasting one of the arrays needs to be onedimensional or both the arrays are supposed to be of the same shape. There are also a few other limitations on the shape of the arrays.<\/p>\n

6. It had data type definition capability to work with varied databases<\/h3>\n

We can work with arrays of different data types. We can use the dtype function to determine the data type and hence get a clear idea about the available data set.<\/p>\n

With the array definition, we have an additional dtype argument to perform array functions. The knowledge of the data type of array is very important due to the restrictions on NumPy operations.<\/p>\n

Installation of Numpy<\/h2>\n

If you already have python, NumPy can be installed with:
\npip install numpy<\/strong><\/p>\n

If you don\u2019t have python yet, you might want to consider using Anaconda. Anaconda is the easiest way for getting started and has all the major packages pre-installed.<\/p>\n

Importing NumPy<\/h2>\n

To use any package or library in your code, it needs to be made accessible.
\nWe can start using NumPy and its available functionalities using the import statement:
\nimport numpy as np<\/strong><\/p>\n

We import it as np to save time and for standardization of the code.<\/p>\n

Difference between NumPy Array and Python Lists<\/h2>\n

NumPy is an alternative for lists in Python as it holds less memory, has faster processing, and is more convenient to use. The difference between the elements is that the NumPy array has to be homogenous.<\/p>\n

We can maintain homogeneity for the efficient application of the mathematical functions. Arrays in NumPy are more compact when we compare it to lists and the data type specification which leads to code optimization.<\/p>\n

Numpy with other Packages<\/h2>\n

We can combine NumPy with other basic packages like SciPy and Mat-plotlib. The combination implements scientific computations and plotting graphs respectively.<\/p>\n

NumPy Array Basics<\/h2>\n

The most important feature is the N-dimensional array object is called ndarray. It describes the collection of homogeneous elements that uses zero-based indexing. All the elements of the ndarray have equal size of the memory block. Each element in ndarray is the object of a data type object (called dtype).<\/p>\n

Summary<\/h2>\n

Here we come at the end of Numpy Features. NumPy provides a highly efficient multi-dimensional array. It also contains the necessary tools to manipulate and perform operations on these arrays. It is one of the best packages to use for data science implementation.<\/p>\n

Also it is the best alternative for lists. Additionally, it is an important companion to other packages that can be put into use for data manipulation and machine learning.<\/p>\n","protected":false},"excerpt":{"rendered":"

Welcome to DataFlair!!! In this tutorial, we will learn Numpy Features and its importance. NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a...<\/p>\n","protected":false},"author":6,"featured_media":79337,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22401],"tags":[4615,9173,22627],"class_list":["post-79327","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy","tag-features-of-numpy","tag-numpy-features","tag-why-use-numpy"],"yoast_head":"\nNumPy Features - Why we should use Numpy? - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn numpy features to see why you should use numpy - high performance, multidimensional container, broadcasting functions, working with varied databases\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/numpy-features\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NumPy Features - Why we should use Numpy? 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