Build Your Own AdBlocker in (Literally) 10 Minutes

Browser extensions always fascinated me.They are quite underrated these days, especially with the move from desktop to mobile browsing. However, I love the idea of customizing the websites I visit to my own liking, such as to enhance my privacy online.Today I decided to share with you how to build a fully-functional extension to block LinkedIn ads, in just 10 minutes.Believe it or not, extensions are probably the easiest thing you can build, so let’s cut to the chase and in order to really do this in 10 minutes.

_Disclaimer: _This extension will only work for Chromium-based browsers e.g. Chrome, Brave, Opera, Microsoft Edge (yeah, really), etc. In other words, it won’t work on Firefox or Safari.

Setup

_As always, all the code is on GitHub. To make things really simple, our extension will not actually have a pop-up (the little window that appears when you click an extension’s icon) — it will simply be working in the background.
As such, all we really need are 3 files. These are:manifest.json , background.js and linkedin.js.
Thus, Step 1 is: Make an empty repository and create those files inside.Now, let’s go through their content.

manifest.json

manifest.json is the only essential file an extension needs. In our case, it will contain metadata about the extension, the permissions it needs to operate, and the script it should run in the background. Here’s what it looks like:

{
	    "manifest_version": 2,
	    "name": "LinkedIn AdBlocker",
	    "description": "Blocking ads.",
	    "version": "0.0.1",
	    "author": "<AUTHOR_NAME>",
	    "browser_action": {
	        "default_title": "LinkedIn AdBlocker"
	    },
	    "permissions": [
	        "tabs",
	        "webNavigation",
	        "https://www.linkedin.com/"
	    ],
	    "background": {
	        "scripts": [
	            "extension.js"
	        ]
	    }
	}

Besides the metadata, here’s a bit of info on the important things this manifest is setting up for us:

Permissions

permissions establish what your extension is allowed to do, such as getting the URL of the current page, or adding JavaScript to a website.When you’re publishing your extension somewhere like the Chrome Web Store, you’ll be asked to justify each permission you’re requesting access to, in order to ensure the security and privacy of your users.Here, we’re asking for permissions for tabs and webNavigation in order to know when we’re visiting a new website, as well as what that website is. We need this to check if the website is LinkedIn so we can launch our adblocking script.

#javascript #privacy #software-engineering #programming #productivity

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Build Your Own AdBlocker in (Literally) 10 Minutes
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Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.
 

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!


What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  1. By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
array.array()
  1. Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
arr.array()
  1. Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODEC TYPEPYTHON TYPESIZE
'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8
'f'floatfloat4
'd'doublefloat8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.


import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:


import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python 

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:  
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
        - 10:33  Finding the length of an array
        - 11:44  Adding Elements
        - 15:06  Removing elements
        - 18:32  Array concatenation
       - 20:59  Slicing
       - 23:26  Looping  


Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  • By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
array.array()
  • Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
arr.array()
  • Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODEC TYPEPYTHON TYPESIZE
'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8
'f'floatfloat4
'd'doublefloat8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming 

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

Riyad Amin

Riyad Amin

1571046022

Build Your Own Cryptocurrency Blockchain in Python

Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.

Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.

In this article, we are going to explain how you can create a simple blockchain using the Python programming language.

Here is the basic blueprint of the Python class we’ll use for creating the blockchain:

class Block(object):
    def __init__():
        pass
    #initial structure of the block class 
    def compute_hash():
        pass
    #producing the cryptographic hash of each block 
  class BlockChain(object):
    def __init__(self):
    #building the chain
    def build_genesis(self):
        pass
    #creating the initial block
    def build_block(self, proof_number, previous_hash):
        pass
    #builds new block and adds to the chain
   @staticmethod
    def confirm_validity(block, previous_block):
        pass
    #checks whether the blockchain is valid
    def get_data(self, sender, receiver, amount):
        pass
    # declares data of transactions
    @staticmethod
    def proof_of_work(last_proof):
        pass
    #adds to the security of the blockchain
    @property
    def latest_block(self):
        pass
    #returns the last block in the chain

Now, let’s explain how the blockchain class works.

Initial Structure of the Block Class

Here is the code for our initial block class:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()

As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:

self — just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.

index — it’s used to track the position of a block within the blockchain.

previous_hash — it used to reference the hash of the previous block within the blockchain.

data—it gives details of the transactions done, for example, the amount bought.

timestamp—it inserts a timestamp for all the transactions performed.

The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.

As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.

Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.

So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.

Building the Chain

The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.

It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.

Let’s talk about the helper methods.

Adding the Constructor Method

Here is the code:

class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()

The init() constructor method is what instantiates the blockchain.

Here are the roles of its attributes:

self.chain — this variable stores all the blocks.

self.current_data — this variable stores information about the transactions in the block.

self.build_genesis() — this method is used to create the initial block in the chain.

Building the Genesis Block

The build_genesis() method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.

To create it, we’ll call the build_block() method and give it some default values. The parameters proof_number and previous_hash are both given a value of zero, though you can give them any value you desire.

Here is the code:

def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
 def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block

Confirming Validity of the Blockchain

The confirm_validity method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.

As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.

Thus, the confirm_validity method utilizes a series of if statements to assess whether the hash of each block has been compromised.

Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.

Here is the code:

def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True

Declaring Data of Transactions

The get_data method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.

Here is the code:

def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True

Effecting the Proof of Work

In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.

For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.

This way, it discourages spamming and compromising the integrity of the network.

In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.

Finalizing With the Last Block

Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.

Here is the code:

def latest_block(self):
        return self.chain[-1]

Implementing Blockchain Mining

Now, this is the most exciting section!

Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.

If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.

Here is the mining method in this simple cryptocurrency blockchain project:

def block_mining(self, details_miner):
            self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)

Summary

Here is the whole code for our crypto blockchain class in Python:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()
    def __repr__(self):
        return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()
    def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
    def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block
    @staticmethod
    def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True
    def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True        
    @staticmethod
    def proof_of_work(last_proof):
        pass
    @property
    def latest_block(self):
        return self.chain[-1]
    def chain_validity(self):
        pass        
    def block_mining(self, details_miner):       
        self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awared with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)  
    def create_node(self, address):
        self.nodes.add(address)
        return True
    @staticmethod
    def get_block_object(block_data):        
        return Block(
            block_data['index'],
            block_data['proof_number'],
            block_data['previous_hash'],
            block_data['data'],
            timestamp=block_data['timestamp']
        )
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
    sender="0", #this means that this node has constructed another block
    receiver="LiveEdu.tv", 
    amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)

Now, let’s try to run our code to see if we can generate some digital coins…

Wow, it worked!

Conclusion

That is it!

We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.

We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.

Learn More

Thanks for reading !

Do you have any comments or questions? Please share them below.

#python #cryptocurrency

Awesome  Rust

Awesome Rust

1658878980

Git Branchless: Branchless Workflow for Git Built on Rust

Branchless workflow for Git

(This suite of tools is 100% compatible with branches. If you think this is confusing, you can suggest a new name here.)

About

git-branchless is a suite of tools which enhances Git in several ways:

It makes Git easier to use, both for novices and for power users. Examples:

It adds more flexibility for power users. Examples:

  • Patch-stack workflows: strong support for "patch-stack" workflows as used by the Linux and Git projects, as well as at many large tech companies. (This is how Git was "meant" to be used.)
  • Prototyping and experimenting workflows: strong support for prototyping and experimental work via "divergent" development.
  • git sync: to rebase all local commit stacks and branches without having to check them out first.
  • git move: The ability to move subtrees rather than "sticks" while cleaning up old branches, not touching the working copy, etc.
  • Anonymous branching: reduces the overhead of branching for experimental work.
  • In-memory operations: to modify the commit graph without having to check out the commits in question.
  • git next/prev: to quickly jump between commits and branches in a commit stack.
  • git co -i/--interactive: to interactively select a commit to check out.

It provides faster operations for large repositories and monorepos, particularly at large tech companies. Examples:

  • See the blog post Lightning-fast rebases with git-move.
  • Performance tested: benchmarked on torvalds/linux (1M+ commits) and mozilla/gecko-dev (700k+ commits).
  • Operates in-memory: avoids touching the working copy by default (which can slow down git status or invalidate build artifacts).
  • Sparse indexes: uses a custom implementation of sparse indexes for fast commit and merge operations.
  • Segmented changelog DAG: for efficient queries on the commit graph, such as merge-base calculation in O(log n) instead of O(n).
  • Ahead-of-time compiled: written in an ahead-of-time compiled language with good runtime performance (Rust).
  • Multithreading: distributes work across multiple CPU cores where appropriate.
  • To my knowledge, git-branchless provides the fastest implementation of rebase among Git tools and UIs, for the above reasons.

See also the User guide and Design goals.

Demos

Repair

Undo almost anything:

  • Commits.
  • Amended commits.
  • Merges and rebases (e.g. if you resolved a conflict wrongly).
  • Checkouts.
  • Branch creations, updates, and deletions.

Why not git reflog?

git reflog is a tool to view the previous position of a single reference (like HEAD), which can be used to undo operations. But since it only tracks the position of a single reference, complicated operations like rebases can be tedious to reverse-engineer. git undo operates at a higher level of abstraction: the entire state of your repository.

git reflog also fundamentally can't be used to undo some rare operations, such as certain branch creations, updates, and deletions. See the architecture document for more details.

What doesn't git undo handle?

git undo relies on features in recent versions of Git to work properly. See the compatibility chart.

Currently, git undo can't undo the following. You can find the design document to handle some of these cases in issue #10.

  • "Uncommitting" a commit by undoing the commit and restoring its changes to the working copy.
    • In stock Git, this can be accomplished with git reset HEAD^.
    • This scenario would be better implemented with a custom git uncommit command instead. See issue #3.
  • Undoing the staging or unstaging of files. This is tracked by issue #10 above.
  • Undoing back into the middle of a conflict, such that git status shows a message like path/to/file (both modified), so that you can resolve that specific conflict differently. This is tracked by issue #10 above.

Fundamentally, git undo is not intended to handle changes to untracked files.

Comparison to other Git undo tools

  • gitjk: Requires a shell alias. Only undoes most recent command. Only handles some Git operations (e.g. doesn't handle rebases).
  • git-extras/git-undo: Only undoes commits at current HEAD.
  • git-annex undo: Only undoes the most recent change to a given file or directory.
  • thefuck: Only undoes historical shell commands. Only handles some Git operations (e.g. doesn't handle rebases).

Visualize

Visualize your commit history with the smartlog (git sl):

Why not `git log --graph`?

git log --graph only shows commits which have branches attached with them. If you prefer to work without branches, then git log --graph won't work for you.

To support users who rewrite their commit graph extensively, git sl also points out commits which have been abandoned and need to be repaired (descendants of commits marked with rewritten as abcd1234). They can be automatically fixed up with git restack, or manually handled.

Manipulate

Edit your commit graph without fear:

Why not `git rebase -i`?

Interactive rebasing with git rebase -i is fully supported, but it has a couple of shortcomings:

  • git rebase -i can only repair linear series of commits, not trees. If you modify a commit with multiple children, then you have to be sure to rebase all of the other children commits appropriately.
  • You have to commit to a plan of action before starting the rebase. For some use-cases, it can be easier to operate on individual commits at a time, rather than an entire series of commits all at once.

When you use git rebase -i with git-branchless, you will be prompted to repair your commit graph if you abandon any commits.

Installation

See https://github.com/arxanas/git-branchless/wiki/Installation.

Short version: run cargo install --locked git-branchless, then run git branchless init in your repository.

Status

git-branchless is currently in alpha. Be prepared for breaking changes, as some of the workflows and architecture may change in the future. It's believed that there are no major bugs, but it has not yet been comprehensively battle-tested. You can see the known issues in the issue tracker.

git-branchless follows semantic versioning. New 0.x.y versions, and new major versions after reaching 1.0.0, may change the on-disk format in a backward-incompatible way.

To be notified about new versions, select Watch » Custom » Releases in Github's notifications menu at the top of the page. Or use GitPunch to deliver notifications by email.

Related tools

There's a lot of promising tooling developing in this space. See Related tools for more information.

Contributing

Thanks for your interest in contributing! If you'd like, I'm happy to set up a call to help you onboard.

For code contributions, check out the Runbook to understand how to set up a development workflow, and the Coding guidelines. You may also want to read the Architecture documentation.

For contributing documentation, see the Wiki style guide.

Contributors should abide by the Code of Conduct.

Download details:
Author: arxanas
Source code: https://github.com/arxanas/git-branchless
License: GPL-2.0 license

#rust #rustlang #git