Indexing and Slicing of 1D, 2D and 3D Arrays in Numpy

This article shows the indexing and slicing of Numpy arrays from the basic to advanced level.

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Indexing and Slicing of 1D, 2D and 3D Arrays in Numpy
Luna  Hermann

Luna Hermann


NumPy Array Tutorial - Python NumPy Array Operations and Methods

The most important feature of NumPy is the homogeneous high-performance n-dimensional array object. Data manipulation in Python is nearly equivalent to the manipulation of NumPy arrays. NumPy array manipulation is basically related to accessing data and sub-arrays. It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

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Numpy Array Basics

Arrays in NumPy are synonymous with lists in Python with a homogenous nature. The homogeneity helps to perform smoother mathematical operations. These arrays are mutable. NumPy is useful to perform basic operations like finding the dimensions, the bite-size, and also the data types of elements of the array.

NumPy Array Creation

Array Creation in NumPy

1. Using the NumPy functions

NumPy has a variety of built-in functions to create an array.

a. Creating one-dimensional array in NumPy

For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.

  1. import numpy as np
  2. array=np.arange(20)
  3. array


array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19])

We can check the dimensions by using array.shape.

#numpy tutorials #array in numpy #numpy array #python numpy array

Kennith  Blick

Kennith Blick


Arrays Indexing and Slicing - Python NumPy Tutorial #4

NumPy Tutorial with examples!
In this Python tutorial, you will learn all you need to know about NumPy arrays indexing and slicing (including advanced techniques such as Boolean Indexing). To make it easier for you to understand the concepts, the Jupyter Notebook also include self-explained images.

Playlist: Python from Scratch | Video #4
Access the code here:

Beginner Python Tutorials:
Intermediate Python Tutorials:
Learn how to use Jupyter Notebooks:

#python #numpy #python numpy tutorial #slicing #arrays

Josiah  Herzog

Josiah Herzog


NumPy Array Slicing (Python Tutorial)

This article explains the NumPy array Slicing. Learn to slice an array of any dimension into another array having items of a given range with examples.

Table of Contents

NumPy Array Slicing

The content present in the NumPy arrays can be made accessible, and also we can make changes thorough indexing as we got to know in the previous module. Another way of data manipulation in arrays in NumPy is though slicing through the arrays. We can also try changing the position of the elements in the array with the help of their index number. Slicing is the extension of python’s basic concept of changing position in the arrays of N-d dimensions.

Slicing, in very simple words, means changing the position of elements from one given index to another given index.

We give the value of slice in this form [Start: End], and there is also another way in which we also define step attribute which could be written in this form [ Start:End:Step]

Some steps we need to keep in mind while giving these parameters:

  • If we do not give any value for start, then it is considered 0 by default.
  • If we do not give any value for the end, then it would consider the length of the array in the given dimension.
  • Also, if we do not give value for the step, then it is considered 1 by default.

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Bailee  Streich

Bailee Streich


Linear Algebra for Data Scientists with NumPy - Analytics India Magazine

A geek in Machine Learning with a Master’s degree in…

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NumPy is an essential Python library to perform mathematical and scientific computations. NumPy offers Python’s array-like data structures with exclusive operations and methods. Many data science libraries and frameworks, including PandasScikit-Learn, Statsmodels, Matplotlib and SciPy, are built on top of NumPy with Numpy arrays in their building blocks. Some frameworks, including TensorFlow and PyTorch, introduce NumPy arrays or NumPy-alike arrays as their fundamental data structure in the name of tensors.

NumPy in data scienceHow NumPy becomes the base of Data Science computing system (source)

Data Science relies heavily on Linear Algebra. NumPy is famous for its Linear Algebra operations. This article discusses methods available in the NumPy library to perform various Linear Algebra operations with examples. These examples assume that the readers have a basic understanding of NumPy arrays. Check out the following articles to have a better understanding of NumPy fundamentals:

  1. Fundamental Concepts of NumPy
  2. Basic Programming with NumPy
  3. Top Resources to Learn NumPy

#developers corner #linear algebra #matrices #numpy #numpy array #numpy dot product #numpy matrix multiplication #numpy tutorial #svd #vectors

NumPy Applications - Uses of Numpy

In this Numpy tutorial, we will learn Numpy applications.

NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays.NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.

numpy applications

Numpy Applications

1. An alternative for lists and arrays in Python

Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.

2. NumPy maintains minimal memory

Arrays in NumPy are objects. Python deletes and creates these objects continually, as per the requirements. Hence, the memory allocation is less as compared to Python lists. NumPy has features to avoid memory wastage in the data buffer. It consists of functions like copies, view, and indexing that helps in saving a lot of memory. Indexing helps to return the view of the original array, that implements reuse of the data. It also specifies the data type of the elements which leads to code optimization.

3. Using NumPy for multi-dimensional arrays

We can also create multi-dimensional arrays in NumPy.These arrays have multiple rows and columns. These arrays have more than one column that makes these multi-dimensional. Multi-dimensional array implements the creation of matrices. These matrices are easy to work with. With the use of matrices the code also becomes memory efficient. We have a matrix module to perform various operations on these matrices.

4. Mathematical operations with NumPy

Working with NumPy also includes easy to use functions for mathematical computations on the array data set. We have many modules for performing basic and special mathematical functions in NumPy. There are functions for Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, etc.

#numpy tutorials #applications of numpy #numpy applications #uses of numpy #numpy