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This is a detailed tutorial of the NumPy Reshaping Arrays. Learn to change the shape of a NumPy Array with the help of illustrative examples.

In the previous topic, we went through the shape of the array and also how we will be able to define them. We also learnt how we could determine the shape of the array by giving various dimensions.

In this section, we will go through the reshaping of the NumPy arrays. What we mean by reshaping an array is that we give shape to the array as per our needs.

We know that shape of the array is the number of elements in any dimension. So when we are able to determine the no of dimension, we want to have in an array that is what we do reshaping. So by reshaping, we mean adding or subtracting dimensions from an array. And also we can change the number of elements present in every dimension.

Let us go through some example to have a better understanding of this concept:

Table of Contents

In our first example, we are going to convert a NumPy array with elements that are in 1-D into a 2-D array.

#importing the numpy package and

also making an alias as np

import numpy as np

array1 = np. array([ 9, 8, 7, 6, 5, 4, 3, 2

, 1])

a = array1. reshape( 3, 3)

#printing the reshaped array

print( a)

#programming #python #numpy #javascript

1595467140

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.

**_Keeping you updated with latest technology trends, _***Join DataFlair on Telegram*

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.

- import numpy as np
- array=np.
**arange**(20) - array

Output

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

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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

**Lambda function in python**: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

**Syntax: x = lambda arguments : expression**

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

1596180000

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
- Examples
- Negative Slicing
- Using STEP
- Slicing through 2-D Arrays
- Slicing through 3-D arrays

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

1625790120

In this Python tutorial, you will start learning about one of the most important open-source packages in Python (NumPy). Numpy offers:

- Powerful N-Dimensional Arrays for numerical computing
- Plenty of functions to deal with linear algebra, and math in general
- It’s fast and easy to use and most importantly, it is free.

In this introductory video you will be presented to NumPy library, learn how to install it and learn how to create arrays and matrices. Finally, you will understand why NumPy arrays are more efficient than Python Lists when dealing with mathematical operations. We will perform a benchmark comparing NumPy arrays and Python Lists. Let’s get started!

Learn how to use Jupyter Notebooks: https://www.youtube.com/watch?v=gGYaFfAvYtg&t=343s

Playlist: NumPy Course | Video #1

Access the code here: https://github.com/rscorrea1/youtube.git

Timestamp:

0:00 - What is Numpy

0:48 - Course will be presented using Jupyter Notebook

1:07 - Content of the video

1:32 - How to install

2:01 - What is a NumPy Array

2:50 - How to create NumPy Array

3:59 - Why use NumPy Array

5:39 - Speed test Benchmark

9:39 - Next NumPy video announcement

#numpy #python #python numpy tutorial #array #list benchmark

1625801160

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

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