{"id":2699,"date":"2026-10-20T18:49:31","date_gmt":"2026-10-20T18:49:31","guid":{"rendered":"https:\/\/prwatech.in\/blog\/?p=2699"},"modified":"2026-10-22T02:19:48","modified_gmt":"2026-10-22T02:19:48","slug":"numpy-tutorial-in-python","status":"publish","type":"post","link":"https:\/\/prwatech.in\/blog\/python\/python-numpy\/numpy-tutorial-in-python\/","title":{"rendered":"NumPy in Python"},"content":{"rendered":"

NumPy Tutorial in Python: Arrays, Functions and Examples<\/b><\/strong><\/h1>\n

Try adding two lists of numbers in plain Python, and you will see the problem at once. The plus sign joins the lists instead of adding the numbers. To add them one by one, you must write a loop. NumPy removes that loop. You write a + b<\/code>, and every pair of numbers is added, in a single line.<\/p>\n

NumPy is the Python library for fast number work. It gives you a data type called the array, a grid of numbers that holds one type of value and does maths on all values together. Almost every data library in Python, such as pandas, SciPy, scikit learn and Matplotlib, is built on top of it. If you want to do data analysis or machine learning in Python, you will use NumPy sooner or later.<\/p>\n

This tutorial starts with installation and takes you through arrays, shape, slicing, linspace, sums, standard deviation, stacking and broadcasting. Every example was run, and the output you see is the real output. We also show what goes wrong in the most common mistakes, with the exact error.<\/p>\n

What is NumPy in Python?<\/h2>\n

NumPy stands for Numerical Python. It is a free, open source library for working with arrays of numbers. Its main object is the ndarray<\/code>, a table of values that all have the same type, and that is indexed by whole numbers. It adds fast maths functions, tools for linear algebra, random numbers and Fourier transforms, and a way to join Python with code written in C, C plus plus and Fortran.<\/p>\n

The simple way to think about it: a Python list holds anything, and an array holds one kind of thing, and because of that, NumPy can do maths on it very quickly.<\/p>\n

Why Use NumPy Instead of a List?<\/h3>\n

Arrays take less memory, are fast and are convenient. Here is a test with one million whole numbers.<\/p>\n

\n\n\n\n\n\n\n\n\n
Python list compared with NumPy array<\/caption>\n
<\/th>\nPython list<\/th>\nNumPy array<\/th>\n<\/tr>\n<\/thead>\n
Memory for 1,000,000 integers<\/th>\nAbout 36 MB<\/td>\n8 MB<\/td>\n<\/tr>\n
Maths on every item<\/th>\nNeeds a loop<\/td>\nOne line, no loop<\/td>\n<\/tr>\n
Types inside<\/th>\nAny mix<\/td>\nOne type<\/td>\n<\/tr>\n
Speed for doubling every item<\/th>\nSlower<\/td>\nFaster<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

The memory figures came from a test I ran. The list figure counts the list and the number objects inside it, and it changes slightly between Python versions. In my run, doubling every item was more than 5 times faster with the array. The exact ratio depends on your computer, so test it on yours.<\/p>\n

Features of NumPy<\/h2>\n
    \n
  • A powerful N dimensional array object.<\/strong> It can have one axis, two axes, or many more.<\/li>\n
  • Broadcasting.<\/strong> NumPy can combine arrays of different shapes, such as adding one number to every item. We show an example below.<\/li>\n
  • Fast maths functions.<\/strong> Sum, mean, standard deviation, square root and hundreds more work on whole arrays.<\/li>\n
  • Tools for linear algebra, Fourier transforms and random numbers.<\/strong><\/li>\n
  • Tools to join Python with C, C plus plus and Fortran code.<\/strong><\/li>\n
  • The base of the data stack.<\/strong> pandas, SciPy, scikit learn and Matplotlib all use NumPy arrays.<\/li>\n<\/ul>\n

    How to Install NumPy?<\/h2>\n

    The same one command works on Windows, Mac and Linux. Open a terminal, or Command Prompt on Windows, and run:<\/p>\n

    \n
    terminal<\/div>\n
    pip install numpy<\/pre>\n<\/div>\n
    If pip is not found, use python3<\/code> or py<\/code> before it:<\/p>\n
    \n
    terminal<\/div>\n
    python -m pip install numpy<\/pre>\n<\/div>\n
    Now check that it worked, and see which version you have:<\/p>\n
    \n
    terminal<\/div>\n
    python -c \"import numpy; print(numpy.__version__)\"<\/pre>\n
    output<\/div>\n
    2.5.3<\/pre>\n<\/div>\n
    Your number may be different. The NumPy 2.5.0 release notes list Python 3.12 to 3.14 as supported versions, and Python 3.11 is dropped. If you are on an older Python, pip may install an older NumPy, which is fine for everything in this tutorial except the descending sort at the end.<\/p>\n
    Installing NumPy on Mac<\/h3>\n
    Open the Terminal and run pip install numpy<\/code>, or python3 -m pip install numpy<\/code> if the first command is not found.<\/p>\n
    Installing NumPy on Windows<\/h3>\n
    The simplest way is the same command in Command Prompt. If you use PyCharm, you can also do it inside the editor. Open File, then Settings, then your project, then Python Interpreter. Click the plus button, search for numpy and click Install Package. The exact menu names differ a little between PyCharm versions, so check them against your version. Then add the line below at the top of your file and you are ready.<\/p>\n
    \n
    python<\/div>\n
    import numpy as np<\/pre>\n<\/div>\n
    Almost everybody writes np<\/code> as the short name. Use np<\/code>.<\/p>\n