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.
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, array, array) 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 print(array[0,0]) 3 ## Number in second row and second column print(array[1,1]) 4 ## What does this return? ## print(array[2,1])
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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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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 has a variety of built-in functions to create an array.
For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.
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.
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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: https://github.com/rscorrea1/youtube.git
Beginner Python Tutorials: https://www.youtube.com/watch?v=HG_E6EaKY90&list=PLJgwF35R54crXsGuSKR_MtUG2ABU_BFAq
Intermediate Python Tutorials: https://www.youtube.com/watch?v=oNwaOFZDAWo&list=PLJgwF35R54coNbQXGNJyawp-_3CC6I1B4
Learn how to use Jupyter Notebooks: https://www.youtube.com/watch?v=gGYaFfAvYtg
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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
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:
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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 Pandas, Scikit-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.
How 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:
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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.
These are the important features of NumPy:
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.
The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.
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.
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.
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