Annalise  Hyatt

Annalise Hyatt


How to Index and Slice NumPy Arrays


Matrix manipulation is one of many fundamental skills needed for Data Science and Machine Learning practitioners. Indexing and slicing arrays generally follow similar procedures. In this tutorial, we will be looking at how these actions are done in Python, specifically NumPy. The intuition you develop will allow you to perform these same operations in other languages such as R, MATLAB, and Julia — mind the starting index.


_The act of indexing is utilizing the shape of the array to __find _specific parts of it. What does this mean?

If you are given a matrix, you know immediately that it will have two dimensions; rows and columns. You may need a number that is in the first row and third column. You may need the entire second column. You may need the first half of the last three rows.

Regardless of what it is you want to achieve, all dimensions follow the same rules and procedures. Once you learn how to work in 2 or 3 dimensions, you can work in all of them.

Indexing Procedure:

  • Add square brackets after variable array.
  • If its shape is not a vector (think Python list,) see how many dimensions the array possesses then list them out in words with commas between them. Think to yourself, if I have 2 dimensions, there are (rows, columns.) There is only one comma needed to list through all dimensions, so I will put one comma inside the square brackets. With 3 dimensions, there are (rows, columns, depth.) There are two commas needed to list through all the dimensions so I will put two commas inside the square brackets.
  • This thinking can then be generalized into a simple rule: putting one less comma in the square brackets than number of dimensions your array is.
  • Use indexing to specify where the information in question is using all dimensions of array.

We will first begin with a vector array. Indexing is the exact same as Python lists:

## Load in numpy
import numpy as np

## Initialize list and convert to np.array
array = np.array([1, 4, 3])
print(array[2], array[0], array[1])
3 1 4

Now, for matrices. numpy accepts a list of lists. Therefore, in the example below, [3,2] will be the first row, [5,4] will be the second row of the matrix, and [7,6] is the third row. In practice, we usually never create our own matrix like this; we often import it in from wherever our source of data comes from. However, for this example, it is best to see inside a simple matrix before learning how to index it.

array = np.array([[3, 2], [5, 4], [7, 6]]

## Number in first row and first column
## Number in second row and second column
## What does this return?
## print(array[2,1])

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How to Index and Slice NumPy Arrays
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.

#miscellaneous #arrays #indexing #multi-dimensional arrays #numpy #python #python tutorial

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 Features - Why we should use Numpy?

Welcome to DataFlair!!! In this tutorial, we will learn Numpy Features and its importance.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

NumPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package. Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use NumPy that converts monotonous code into the compiled form.

numpy features

NumPy Features

These are the important features of NumPy:

1. High-performance N-dimensional array object

This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.

a. One dimensional array

The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.

b. Multidimensional array

In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.

2. It contains tools for integrating code from C/C++ and Fortran

We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.

#numpy tutorials #features of numpy #numpy features #why use numpy #numpy