{"id":23244,"date":"2023-11-08T12:09:30","date_gmt":"2023-11-08T06:39:30","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/python-numpy-tutorial\/"},"modified":"2025-01-06T19:05:54","modified_gmt":"2025-01-06T13:35:54","slug":"python-numpy-tutorial","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/python-numpy-tutorial\/","title":{"rendered":"Python NumPy Tutorial"},"content":{"rendered":"\n

So you've learned the basics of Python<\/a> and you're looking for a more powerful way to analyse data? NumPy is what you need.NumPy is a module for Python that allows you to work with multidimensional arrays and matrices. It's perfect for scientific or mathematical calculations because it's fast and efficient. In addition, NumPy includes support for signal processing and linear algebra operations. So if you need to do any mathematical operations on your data, NumPy is probably the library for you. <\/p>\n\n\n\n

In this tutorial, we'll show you how to use NumPy to its full potential. You'll learn more about arrays as well as operate on them using mathematical functions. <\/p>\n\n\n\n

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NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays <\/a>can be performed. In this Python Numpy Tutorial, we will be learning about NumPy in Python, What is NumPy in Python, Data Types in NumPy, and more. <\/p>\n\n\n\n

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

NumPy in Python is a library that is used to work with arrays and was created in 2005 by Travis Oliphant. NumPy library in Python has functions for working in domain of Fourier transform, linear algebra, and matrices. Python NumPy is an open-source project that can be used freely. NumPy stands for Numerical Python.<\/p>\n\n\n\n

How to install NumPy Python<\/strong>?<\/h2>\n\n\n\n

Installing the NumPy library is a straightforward process. You can use pip to install the library.Go to the command line and type the following: <\/p>\n\n\n\n

pip install numpy \nIf you are using Anaconda distribution, then you can use conda to install NumPy. conda install numpy \nOnce the installation is complete, you can verify it by importing the NumPy library in the python interpreter. One can use the numpy library by importing it as shown below. \nimport numpy \nIf the import is successful, then you will see the following output. \n>>> import numpy \n>>> numpy.__version__ \n'1.17.2' \n<\/pre>\n\n\n\n
NumPy is a library for the Python programming language, and it's specifically designed to help you work with data. <\/p>\n\n\n\n
With NumPy, you can easily create arrays, which is a data structure that allows you to store multiple values in a single variable.<\/p>\n\n\n\n
In particular, NumPy arrays provide an efficient way of storing and manipulating data.NumPy also includes a number of functions that make it easy to perform mathematical operations on arrays. This can be really useful for scientific or engineering applications. And if you're working with data from a Python script, using NumPy can make your life a lot easier. <\/p>\n\n\n\n
Let us take a look at how to create NumPy arrays, copy and view arrays, reshape arrays, and iterate over arrays. <\/p>\n\n\n\n
NumPy Creating Arrays<\/strong><\/h2>\n\n\n\n
Arrays are different from Python lists in several ways. First, NumPy arrays are multi-dimensional, while Python lists are one-dimensional. Second, NumPy arrays are homogeneous, while Python lists are heterogeneous. This means that all the elements of a NumPy array must be of the same type. Third, NumPy arrays are more efficient than Python lists.NumPy arrays can be created in several ways. One way is to create an array from a Python list. Once you have created a NumPy array, you can manipulate it in various ways. For example, you can change the shape of an array, or you can index into an array to access its elements. You can also perform mathematical operations on NumPy arrays, such as addition, multiplication, and division. <\/p>\n\n\n\n
One has to import the library in the program to use it. The module NumPy has an array function in it which creates an array. <\/p>\n\n\n\n
Creating an Array: <\/strong><\/p>\n\n\n\n
import numpy as np \narr = np.array([1, 2, 3, 4, 5]) \nprint(arr) \n<\/code><\/pre>\n\n\n\n
Output: \n<\/strong>[1 2 3 4 5] \n<\/code><\/pre>\n\n\n\n
We can also pass a tuple in the array function to create an array. 2<\/p>\n\n\n\n
import numpy as np \narr = np.array((1, 2, 3, 4, 5)) \nprint(arr) \n<\/code><\/pre>\n\n\n\n
The output would be similar to the above case.<\/p>\n\n\n\n
Dimensions- Arrays: <\/strong><\/p>\n\n\n\n
0-D Arrays: <\/strong><\/p>\n\n\n\n
The following code will create a zero-dimensional array with a value 36. <\/p>\n\n\n\n
import numpy as np \narr = np.array(36) \nprint(arr) \n<\/code><\/pre>\n\n\n\n
Output: \n<\/strong>36 \n<\/code><\/pre>\n\n\n\n
1-Dimensional Array: <\/strong><\/p>\n\n\n\n
The array that has Zero Dimensional arrays as its elements is a uni-dimensional or 1-D array. <\/p>\n\n\n\n
The code below creates a 1-D array, <\/p>\n\n\n\n
import numpy as np \narr = np.array([1, 2, 3, 4, 5]) \nprint(arr) \n<\/code><\/pre>\n\n\n\n
Output: \n<\/strong>[1 2 3 4 5] \n<\/code><\/pre>\n\n\n\n
Two Dimensional Arrays:<\/strong> <\/strong><\/p>\n\n\n\n
2-D Arrays are the ones that have 1-D arrays as its element. The following code will create a 2-D array with 1,2,3 and 4,5,6 as its values. <\/p>\n\n\n\n
import numpy as np \n3\narr1 = np.array([[1, 2, 3], [4, 5, 6]]) \nprint(arr1) \n<\/code><\/pre>\n\n\n\n
Output: \n[[1 2 3] \n[4 5 6]] <\/code><\/pre>\n\n\n\n
Three Dimensional Arrays: <\/strong><\/p>\n\n\n\n
Let us see an example of creating a 3-D array with two 2-D arrays:<\/p>\n\n\n\n
import numpy as np \narr1 = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) print(arr1) \n<\/code><\/pre>\n\n\n\n
Output: \n[[[1 2 3] \n[4 5 6]] \n[[1 2 3] \n[4 5 6]]] <\/code><\/pre>\n\n\n\n
To identify the dimensions of the array, we can use ndim as shown below: <\/p>\n\n\n\n
import numpy as np \na = np.array(36) \nd = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) \nprint(a.ndim) \nprint(d.ndim) \n<\/code><\/pre>\n\n\n\n
Output: \n<\/strong>0 \n3 \n<\/code><\/pre>\n\n\n\n
Operations using NumPy<\/strong><\/h2>\n\n\n\n
Using NumPy, a developer can perform the following operations \u2212<\/p>\n\n\n\n