What is NumPy?
NumPy (Numerical python) is an open-source library of Python which was created in 2005 by “Travis Oliphant” for scientific and numerical computing.
NumPy works with n-dimensional arrays and provides various functions and operates to perform on them. Elements in NumPy arrays are of the same data type and array in NumPy is called ndarray.
Why Use NumPy?
- NumPy provides very easy and effective techniques to handle large data.
- NumPy uses less memory and stores data in a contiguous block of memory.
- NumPy has a simple and easy syntax which makes it easy to learn and helps to better understand code.
- NumPy with SciPy together can be used in place of MATLAB.
Features of NumPy
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High-performance NumPy provides various operators and functions that can perform on elements of an array (single or multidimensional array.)
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Iintegrating code from C/C++ and Fortran NumPy is partially written in python and most of the part of the NumPy library is written in c language so it provides tools for integrating the functionalities available in other languages.
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Multidimensional container The n-dimensional arrays are usually of the same fixed-size multidimensional container of items that are of the same type. The elements of the n-dimension array are specified by separate data-type objects i.e dtype. NumPy can perform various operations on these array elements.
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Linear algebra, Fourier transform, and random number capabilities NumPy can perform various complex operations on the ndarrays like Fourier transformation, linear algebra, etc. For each complex function, NumPy in python provides a separate module.
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Broadcasting functions When we are working with an array of different shapes and sizes, broadcasting is very useful. It broadcast the smaller array according to the larger array and then we can easily perform various functions on the array of the same shape.
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Work with varied databases Using NumPy, we can work with various data types and determine the kind of data using the dtype function.
The Benefits of Choosing NumPy
Most libraries are built on the NumPy library. By learning NumPy you can set a strong foundation in the field of data science.
- NumPy has lots of inbuilt functions which can reduce the lines of code by reducing loops, which helps to make code quick and easy to understand.
- There are a large group of contributors who are working on it and making it fast, user-friendly, and bug-free.
Audience
This NumPy Tutorial is designed for those who want to learn about the NumPy library and its different features. You will be at a moderate level of expertise after completing this tutorial.
Prerequisite
- Basic knowledge of Python programming language.
- NumPy should be installed on an environment that you are using for writing NumPy code. If you are using an online IDE or compiler, then there is no need to install NumPy.
Why is NumPy Faster Than Lists?
NumPy is faster than lists because it uses contiguous blocks of memory and optimized C code. Some other factors that contribute to NumPy's speed advantage over lists include:
- This allows for efficient data access and numerical operations, while lists are implemented in slower pure Python.
- Vectorization, which allows NumPy to perform operations on entire arrays at once, rather than iterating over each element individually.
Which Language is NumPy written in?
NumPy is written in a combination of Python, C, and C++. C and C++ are used for performance-critical parts, while Python is used for higher-level functionality and user interface.
About this NumPy Tutorial
- This Python NumPy tutorial gives you a basic understanding of NumPy and the prerequisite that one should have before starting with NumPy.
- Some benefits of NumPy, which help to understand why NumPy is important.
Take-Away Skills from This NumPy Tutorial
- NumPy is fast and very easy to learn NumPy because of its quick and easy syntax.
- The in-built functions of NumPy can be used to handle large data easily.
