This is a step by step tutorial of the Numpy module in python for Beginners. Learn about Numpy arrays and setting up Numpy on our system to get started.

What is an Array?

An array is a variable that can store multiple values in itself. An array can hold a large number of values so they can be accessed using index numbers. The index always begins with 0 being the first element of the array and moving to the nth element, which will be the last. These values are of the same data type, which can be one of these strings, integer or float and also many others. Arrays can be of three types such as indexed arrays, associative arrays and multidimensional arrays.

Definition of NumPy:

NumPy is a python based library that helps in the manipulation of the multidimensional arrays with regards to various factors. As a result, this helps in maintaining large and multidimensional arrays and matrics. NumPy stands for Numerical Python. So this open-source help in performing various mathematical functions because it is capable of solving complex problems in arrays. A NumPy array is a group of values with the same data type, and these are given their index number with only non-negative integer numbers. The dimension form up the rank of the array and also integer which gives the size of the array along with dimension form the shape of the array.

History of NumPy:

In the early years, the Python was not supposed to be used for the numerical reason; as a result, it was not so common among the developers. In the year of 2005, Travis Oliphant was trying to develop a unified array package, and the next year it came in to use known with the name of NumPy 1.0 in the year 2006. Everyone can use it as it is open source and free to use.

About NumPy:

I am going to discuss a few reasons why we should use NumPy:

• NumPy helps in the faster processing of the array.
• NumPy is preferred because it can perform operations like Searching and Sorting.
• It can perform all sorts of mathematical and logical operations.
• Arrays can operate fast. As a result, they are better than lists.
• It is good for data analysis.
• It is a good replacement for MATLAB and OCTAVE.
• Supports the use of vector operations.
• It is convenient to perform operations on arrays.
• NumPy is very much helpful in machine learning and data science.
• It helps in high-performance computing and simulations.

Key Features of NumPy:

• It can handle on multidimensional arrays.
• NumPy refers to array objects as ndarray as a result, it provides various supporting functions.
• It can work with all the latest CPU architectures.
• It has tools for using C/C++ and Fortran Code.

Limitations of NumPy:

• NumPy deals with the problem of missing values, but it supports ‘NAN’ which creates confusion among the users.
• Also, it creates issues while comparing values with the help of a python interpreter.
• It requires the allocation of memory for performing functions like addition and insertion, which makes it difficult to manage the space.
• This memory allocation also makes it costly to use.
• Also, it requires shifting in order to use various memory allocations.
• It focuses on working with only numerical data.

NumPy Codebase :

The location for the source code of the NumPy is as follows: https://github.com/numpy/numpy

And also GitHub allows any number of users to use their codebase and they can work on the same codebase as it is enabled by the GitHub.

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