Why Numpy is used?

NumPy is one of the most fundamental libraries in Python and perhaps the most useful of them all. NumPy handles large datasets effectively and efficiently. It is a python library for the computation and processing of the multidimensional and single dimensional array elements. It provides various functions which are capable of performing the numeric computations with a high speed. NumPy provides various powerful data structures, implementing multi-dimensional arrays and matrices. These data structures are used for the optimal computations regarding arrays and matrices.

NumPy stands for Numerical Python and is one of the most useful scientific libraries in Python programming. It provides support for large multidimensional array objects and various tools to work with them.

NumPy arrays are called ndarray or N-dimensional arrays and they store elements of the same type and size. It is known for its high-performance and provides efficient storage and data operations as arrays grow in size.

The very first reason to choose python NumPy array is that it occupies less memory as compared to list. Then, it is pretty fast in terms of execution and at the same time, it is very convenient to work with NumPy. So these are the major advantages that Python NumPy array has over list.

To install Python NumPy, go to your command prompt and type “pip install numpy”. Once the installation is completed, go to your IDE (For example: PyCharm) and simply import it by typing: “import numpy as np”

the pre-built function np.array is the simlest way to create an array. We first import numpy as np, meaning that later we will access the numpy functions using the ‘np.functionName’ format. Than we call the function ‘array’ from it. The only required parameter for the array function is data, in a form of list. There are more parameters which also can be found in the numpy.array documentation. We either convert ‘data’ into an array, or we specify the data inside the function, as shown below:

Numpy allows us to use many built-in methods for generating arrays. Let’s examine some of the most used from those built-in methods.

Numpy’s arange function will return evenly spaced values within a given interval. Works similarly to Python’s range() function. The only required parameter is ‘stop’, while all the other parameters are optional:

Numpy provides functions that are able to create arrays of 1’s and 0’s. The required parameter for these functions is ‘shape’.

Arithmetic operations are possible only if the array has the same structure and dimensions. We carry out the operations following the rules of array manipulation. We have both functions and operators to perform these functions.

NumPy indexing is used for accessing an element from an array by giving it an index value that starts from 0. Slicing NumPy arrays means extracting elements from an array in a specific range. It obtains a substring, sub tuple, or sub list from a string, tuple, or list.

In this case, the two indexes are given, the first one for the row and the second one for the column.

Using basic indexing and slicing we can access a particular element or group of elements from an array

Another very interesting reshaping method of NumPy is the transpose() method. It takes the input array and swaps the rows with the column values, and the column values with the values of the rows:

The Linear Algebra module of NumPy offers various methods to apply linear algebra on any numpy array. We can find rank, determinant, trace, etc. of an array. Eigen values of matrices, matrix and vector products (dot, inner, outer, etc. product), matrix exponentiation and solve linear or tensor equations and much more!

Having covered a lot of ground in this article, I am hopeful that you are well acquainted with the some basics of NumPy arrays and are all guns blazing to incorporate it into your data science tasks.

Related posts:

The third season of “The Mandalorian” — the newest providing in the sprawling “Star Wars” saga — premieres March 1. In the “Star Wars” timeline, “The Mandalorian” begins proper after the unique 1977…

Declaring variables is an essential skill in every programming language. Many courses explain creating a variable and assigning a value to it with empty boxes…

Process mapping is a well-known technique for creating a common vision and shared language for improving business results. It helped one management training and development firm realize that people…