1598668320

This is a detailed tutorial of the NumPy Array Copy and View. Find out the difference between both of these with the help of illustrative examples.

When we try to duplicate some data in the NumPy we usually have to manipulate data in every way possible. During this manipulation of data, we usually use this method of copying things. When we try to copy we make a new file with the same data so that we can make changes in the new copy. With the help of this, no change will occur in the original copy of the data.

A copy of the data owns the data present in that copy and has no link to the original copy of the data. As a result, we can make any number of changes in the original copy it will have no effect on the version of copies of similar data.

When we copy arrays the new copy of the array is present at some other location in the memory. We need some extra space in order to create the copies of the arrays.

Let us go through an example of a copy:

Output:

```
[9 3 7 6 5]
[9 8 7 6 5]
```

Now we see that first, we create a copy and then we make changes to the original copy. And also no changes will occur in the copy of the array as we make changes in the original array.

#programming #python #numpy

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

1596475108

NumPy consists of different methods to duplicate an original array. The two main functions for this duplication are copy and view. The duplication of the array means an array assignment. When we duplicate the original array, the changes made in the new array may or may not reflect. The duplicate array may use the same location or may be at a new memory location.

It returns a copy of the original array stored at a new location. The copy doesn’t share data or memory with the original array. The modifications are not reflected. The copy function is also known as deep copy.

```
import numpy as np
arr = np.array([20,30,50,70])
a= arr.copy()
#changing a value in original array
arr[0] = 100
print(arr)
print(a)
```

Output

[100 30 50 70]

[20 30 50 70]

Changes made in the original array are not reflected in the copy.

```
import numpy as np
arr = np.array([20,30,50,70])
a= arr.copy()
#changing a value in copy array
a[0] = 5
print(arr)
print(a)
```

Output

[20 30 50 70]

[ 5 30 50 70]

Changes made in copy are not reflected in the original array

#numpy tutorials #numpy copy #numpy views #numpy

1619510796

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

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

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