Numpy Array in Python [Everything to know]

Python has a lot of libraries that are used for performing various tasks. Based on the task to be performed, the libraries are grouped accordingly. Python has been an excellent programming language that offers the best environment for carrying out different scientific and mathematical computations. One such library is the Numpy, which is a popular library of Python. It is an open-source library in Python used for performing computations in the engineering and scientific fields. 

The article will focus on the Numpy library along with the Numpy array in Python.

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Numpy library in Python

Numerical data has been an integral part of different sections of research and development. It is the data that holds a generous amount of information. Working with the data is at the core of all scientific studies. The library is one of the best libraries of Python for working with such numerical data. Users of the Numpy array can be the coders who are not experienced yet, or maybe the experienced researchers engaged in industrial research or state-of-the-art scientific research. So, be it, beginners or experienced users, Numpy libraries can be used by almost everyone working in the field of data. The API of the Numpy can be used in SciPy, Pandas, sci-kit-learn, scikit-image, Matplotlib, and several other packages that are developed for applying to scientific and data science packages. 

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The library of Numpy in Python consists of multidimensional arrays and matrix data structures. The library provides the ndarray, which is a homogeneous array object. The Numpy array in Python is in the form of n-dimensional. The library also includes several methods that can be used for performing operations over the array. The library can also be used for performing several mathematical operations over the array. Data structures can be added to the Python that will lead towards the efficient calculation of the different matrices and the arrays. The library also provides several mathematical functions which could be used for operating over the matrices and the arrays.

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Installation and importing of the library

For installing the Numpy in Python, a Python distribution of scientific origin should be used. If the system already has Python installed, the library can be installed through the following command.

Conda installs Numpy, or another command pip installs Numpy can be used.

If Python hasn’t been installed yet on the system, then Anaconda can be used, which is one of the easiest ways to install. Installing the Anaconda doesn’t require installing other libraries or packages separately, such as SciPy, Numpy, Scikit-learn, pandas, etc.

The Numpy library can be imported in Python through the command import Numpy as np.

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The library provides several ways to create arrays in Python in a fast and efficient manner. It also offers ways to modify the arrays or the data within the arrays can be manipulated. The difference between a list to Numpy array is that the data within a Python list can be of different data types, while in the case of a Numpy array in Python, the elements within the array should be homogenous. The elements are of the same data types within the Numpy array. If the elements in the Numpy array were of different data types, then the mathematical functions that could be used over the Numpy array would become inefficient.  

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Comparison of Numpy arrays to list shows that because of the faster and the compact nature of the Numpy arrays, the Numpy arrays are used frequently. Also, because the arrays consume less memory, the Numpy array becomes more convenient for use. The data types of the elements within the array can be specified, as the array uses less memory, and therefore, it provides a mechanism for the specification. The code of the program can therefore be optimized. 

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Numpy Array in Python

The Numpy array is a centralized data structure within the Numpy library. When an array is defined, it consists of arrays arranged in a grid manner, containing information for the raw data. It also contains information on how an element can be located in the array or how an element can be interpreted in an array. The Numpy array consists of elements in a grid that can be indexed in several ways. The elements within the array are of the same data type and are therefore referred to as array dtype.

  • Indexing of the array is done through a tuple of non-negative integers. It can also be indexed through integers, or Booleans, or other arrays.
  • The rank of an array is defined as the dimension number of the array. 
  • The shape of an array is defined as the set of integers that define the array size along the different dimensions. 
  • Initialization of the arrays can be done through a Python list using nested lists for high-dimensional data.
  • The elements within the array can be accessed through square brackets. The indexing of the Numpy array always starts with 0, so while accessing the elements, the first element of the array will be at the 0 positions. Example: b[0] will return the first element in the array b.

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Basic operation over the Numpy array

  • The function np.array() is used for creating a Numpy array in Python. The user has to create an array and then have to pass it to a list. A user can also specify the data type in the list.
  • The function np.sort() can be used for sorting a Numpy array in Python. The user can specify the kind, axis, and also order when the function is called. 
  • For getting information on the array’s dimensions or the axis number, the users can use ndarray.ndim. Also, using ndarray.size, will let the user know about the total elements present in the array. 
  • The following commands can be used to know the shape and size of a Numpy array: ndarray.ndim, ndarray.shape, and ndarray.size. For getting an idea of the array’s dimensions or the number of axes of an array, the command ndarray.ndim is used. For getting the details of the total elements number present in the array, the command ndarray.size is used. The command ndarray.shape will return a set of integers indicating the element number stored along the different dimensions in an array. 
  • Numpy arrays can be indexed and sliced in a way similar to that of the lists in Python.
  • Two arrays can be added together by using the symbol “+”. Also, the function sum() can be used to return the sum of all the elements stored in an array. The function can be used in arrays of 1, or 2 dimensions and even high dimensions. 
  • Through the concept of broadcasting in a Numpy array, operations can be carried out over arrays having different shapes. However, the array dimensions should be compatible; otherwise, the program will result in a ValueError.
  • Apart from the function of sum(), the Numpy array provides the functions of the mean for getting the average of the elements, the function prod to get the product of the elements of the array, and also the function std to achieve a standard deviation of the elements of the error. 
  • Users can pass a list of lists to the Numpy array. A list of lists can be passed for creating a 2-D array.

Can an array be reshaped?

Yes, an array can be reshaped by using the function arr.reshape(). This reshapes the array without making any modifications to the array data.

Is it possible to convert an array into different dimensions?

Yes, an array can be converted from a single dimension to a two-dimensional form. The commands np.expand_dims and np.newaxis can be used to increase the array’s dimensions. An array will be increased by one dimension by the use of np.newaxis. If a new axis is to be inserted at a specific position in the array, it can be done using np.expand_dims.

How can an array be created from data that already exists?

An array can be created by specifying the position where slicing should be carried out. Also, two arrays can be stacked vertically using the keyword vstack, and they can be stacked together horizontally through the keyword hstack. For splitting an array, hsplit can be used, which will result in several smaller arrays.

How can the elements within an array be sorted?

The function sort() is used for sorting the elements in an array.

Which function should be used for searching unique elements in an array?

The command np.unique can be used for searching unique elements in a Numpy array. Also, to return the indices of eth unique elements, the user can pass the argument of return_indexto the function np.unique().

How can an array be reversed?

The function np.flip() can be used in a Numpy array to reverse it. Several operations can be carried out over an array once it is created and defined. The library of Python i.e. Numpy provides all the functions and methods required for creating an array and carrying forward with all the mathematical calculations over the elements of the array. There are several libraries offered by Python. If you have an interest in exploring all the libraries and getting an understanding of the different functions, you can check out the course “Executive Programme in Data Science” offered by upGrad. The course is designed for any working professionals and will train you through industry experts. For any queries, you may contact our team of assistance.

What do you mean by an array in Python?

An array is basically a data structure in any programming language, which can contain a specific count of items that are all of the same kind. Arrays are implemented in the various algorithms that are part of a programming language. In Python, you have to create an array by importing the “array” module to your Python program. It consists of two vital components – elements (which are the items stored in an array) and index (which is the numerical position used to identify each element in the array). Using an array, you can perform various functions in Python like insert, delete, update, search, and traverse.

What are data structures in a programming language?

A data structure is a fundamental and vital part of any programming language. It can be described as a specialized structure for storing, organizing, processing, and retrieving data. Data structures can be of basic and advanced kinds, but they are all meant to organize data and suit specific purposes. These help frame data in a way that can make it easier for processing by humans and machines. They offer a streamlined way of accessing and working with data appropriately with the help of various algorithms. In many cases, the basic operations of an algorithm are intricately designed as per the design of the data structure.

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