## INTRODUCTION

Welcome to this comprehensive discussion on what is NumPy in Python. NumPy was created in 2005 by an American data scientist and businessman Travis Oliphant. It is a Python library that is used for working with arrays and is an open-source project that can be used free of cost. The full form of NumPy is Numerical Python. It can also be used in matrices, linear algebra, random number generation, and Fourier transform. It is also used in the industry for array computing, and its main object is the homogeneous multidimensional array.

## 1.Lists in Python:

Python is a general-purpose coding language that can be used for software development and other types of programming besides web development. Lists in Python are used to store multiple items of multiple data like integers, strings, floats, etc., in a single variable. They are a basic python data structure that enables you to update, delete or add individual values in it. The values in a list are defined by a square bracket []. Python also provides concise syntax to access sublists; this process is called slicing. A list can also have another list as an item, which is known as a nested list.

## 2.NumPy Overview:

NumPy Program in Python:

NumPy has an extensive set of built-in functions and a basic mathematical computing package for the Python language. It has various properties, which include the execution of computational and mathematical NumPy array calculations in the element-wise method. It supports multi-dimensional, fast arrays or ndarrays and can efficiently store and manipulate data. NumPy also has certain tools for writing and reading text data or binary and integrates language codes like C and C.

## 3.Properties of Ndarray:

What is NumPy array in Python?

The main data structure in NumPy is the Ndarray which is a short name for an N-dimensional array. It contains data of the same type as integers or floating-point values. Other properties of Ndarray are as follows:

• Any number of values can be updated, removed, or added from an array.
• It can hold only a homogeneous type of data.
• It is a multidimensional array.
• It is efficient, fast and also supports mathematical functions.

## 4.Purpose of Ndarray:

Any program can have more than one algorithm having any business logic, such as element-wise mathematical operations or functions or computations. These algorithms may need to share the data so that data scientists can manipulate it. The purpose of Ndarray here is to mainly simplify this task and is accessible throughout any program for efficiency and fast computing.

## 5.Creating and Printing Ndarray:

The first step is importing the NumPy library, where you can name it as ‘np’. For the creation of an array, use the syntax np.array. Then to print the array, type the print command, which a variable name will follow.

You can create various other types of arrays which include an array of zeroes using the zeroes method or an array containing only ones. You can also create empty arrays using NumPy, where NumPy can fill the array with any random number. The syntax will change for every different kind of array being created.

## 6.Basic operations in NumPy:

Using basic NumPy operations, you can apply mathematical, comparison, and logical operations in an array. These are useful in data wrangling. NumPy uses the indices of the elements in each array to carry out the basic operations.

## 7.NumPy Functions in Python:

NumPy provides a large number of mathematical functions. Some of the functions are described as follows:

• Universal Function or ufunc – A universal function or ufunc operates on Ndarrays in an element-by-element fashion, supporting the broadcasting of array, typecasting, and other standard features. Each ufunc takes array inputs and produces array outputs using various functions.
• Shape Manipulation – The shape of the basic NumPy array can be changed using NumPy library functions. Some of the methods used for array shape manipulation during the data wrangling phase are – flatten, resize, stack, reshape, and split. The ravel function flattens the data set into a single row. Similarly, other functions include Reshape, Resize, and so on.
• Broadcasting – This function is used to carry out arithmetic operations between arrays of different shapes. When NumPy operates on two arrays, it mainly compares their shape element-wise.
• Linear Algebra – In linear algebra, transpose is one of the methods used by data scientists, which helps them fix problems in the data. Other linear algebra functions consist of inverse and trace functions. The inverse is a method to inverse the arrays and can be applied to only square matrices. Trace is another method that lets you perform any sum on the diagonal data elements of the array. Trace can only be used for diagonals. It is an incremental order that can be applied from left to right and not vice versa. The sum here provides the sum of entire data elements.

## CONCLUSION

Since the incorporation of NumPy in2005, it has gained huge popularity and is considered to be one of the key Python libraries to be used. It is being used in a large number of production systems nowadays.

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