Hello learners! Welcome to the next episode of the arrays, in which we are moving towards the details of the arrays at an advanced level. In the previous lecture, we covered the introductions and fundamentals of arrays, dimensional arrays, and array operations. One must know that the working of the arrays does not end with simple operations, and there is a lot to learn about them. Arrays and their types are important topics in programming, and if we talk about Python, the working and concepts of the array in Python are relatively simple and more effective. The details of the advanced types of arrays will prove this statement. We have a lot of data to share with you, and for this reason, we have arranged this lecture. It is important to understand the reasons behind the reading of this lecture.
What are two-dimensional arrays, and how can we perform them in a Jupyter notebook?
How can we access all the elements of the two-dimensional arrays?
Can we insert the elements in the two-dimensional arrays?
How do we update our 2D array?
Is the appending process easy in the 2D arrays in Python?
Explain three-dimensional arrays and provide an example of how the array is updated.
All of these are important interview questions, and you will learn the concept through its practical implementation, so stay with us till the end of this lecture to get all the answers.
We all know a lot about one-dimensional arrays and have seen these concepts at different levels. In this type, we are dealing with the arrays at an advanced level, and to understand them, we will work on the Jupyter notebook. The second type of array is a little bit different, and we can say it is twice as difficult as the one discussed before. We all know the concept of dimensions from physics classes, and these concepts are also true for arrays. These are also referred to as "multi-dimensional arrays," and we define them as:
“The 2-D arrays are the type of arrays that are shown by two indices, and these contain rows and columns to save the data in the form of matrices.”
As you can guess, these are comparatively more complex than the one-dimensional arrays, and storing the data in them is more useful and has many applications in real life. While performing the example of a 2D array, we are going to use the simpler way where the user just has to mention the numbers of rows and columns and an array is ready.
array-name = [ [d1, d2, .... ,dn], [e1, e2, .... ,en] ]
Here,
d1,d2,...,dn=number of elements in the first dimension
e1,e2,...,en=number of elements in the second dimension
The numbers of the elements vary from zero to infinity. For more detail, have a look at the example given next:
#Initializing the two-dimensional array
array_input = [ [10.9,78.9,111.90] ,[8.0,771.2,2.0] ]
#showing the elements of each dimension separately
print("The elements in the first dimension = " ,array_input[0])
print("The elements in the first dimension = " ,array_input[1])
So you can see that the initializing of the array with the floating elements is done in the first step. Here, the point to notice is the usage of square brackets, and you have to remember that no other type of array is used in this case.
In the previous case, you have seen that if we wanted two or more elements as output, there was a need for more than one print function. Yet, there are also cases where the whole elements of an array are required on the screen. For this, the programmers use different types of iterations according to the requirements and get the results. We have seen the working of the iterations many times in this course and therefore, there is no need to explain why we use it. Arrays are one of the best examples to check the working of the iterations. In all the examples of the arrays, you will see the use of for loop in the nested form because we are dealing with two-dimensional arrays.
One must be wondering how we can add more elements to the two-dimensional arrays because of the matrix-type arrangement of the lament. For this, the method is simple, and by discussing it, we will learn the “input” method of Python. Have a look at the code given next, and we will discuss the details in just a bit.
#importing the array from the Python
import array as twoDArray
#declaring our array with two dimensions of the order 2 by 2
twoDArray = [[2,2,2,2,2,2,2], [6,6]]
print("Array before insertion of elements: ", twoDArray)
#Inserting the elements in the matrix at position three
twoDArray.insert(3, [4,7,4,2,9])
print("Array after insertion of elements: ")
#Using nested for loop to inset the elements one after the other at the required position
for x in twoDArray:
for y in x:
print(y,end = " ")
print()
The following points are to be discussed according to this concept:
Importing the array makes the code easy and effective.
The two-dimensional array is nothing but the arrangement of elements in the form of rows and columns.
The number of rows and columns is not restricted to each other, that is, there is no restriction on using the square array all the time, and the number of elements in rows and columns may be different from each other.
The square brackets are used in the two-dimensional arrays in every case, whether it is the outermost bracket or dimensional bracket.
Special functions such as insert, delete, etc. can be used by using the dot operator with the name of the array.
To insert the elements in the two-dimensional array, a nested for loop is efficient where two loops are being used. The syntax is important in this case.
Other rules of the code are discussed in different lectures in this course.
The next case that we are going to discuss is the updating of the elements in the arrays. Think about the situation when you first declare the array and work with it, but after some time, you have to change some of the elements of the arrays. In such cases, the insertion and deletion operations that must be done are time-consuming. The best way to do this is to update the elements directly. The reason why I mention this detail is that arrays are unchangeable data types in other programming languages such as C++, and once declared, the arrays can not be altered in such languages. Yet, Python gives us the ease to update, delete, insert, and change the arrays in the programs. We know programming is a vast procedure and there are several ways to do the same task, but for you, we have picked the simplest and most interesting way to do so that is given in the code next:
#Importing the array
import array as Updation
#Declaring a two-dimensional array
Updation = [[22,56,4,1], [2,4,6,8,10]]
print("Array before Updation of elements: ", Updation)
#updating the elements in the second array and the third element of the first array
Updation[1] = [44,89,2,0]
Updation[0][3] = 5
print("Array after update of elements: ")
#using nested for loop to print the array
for x in Updation:
for y in x:
print(y,end = " ")
We are now able to understand such codes; therefore, there is no need for long descriptions. But, the point here to notice is, in the same code, merely by declaring the elements at certain positions, the array can be updated and the previous values are then ignored.
In the previous lecture, the element at the end of the one-dimensional array was appended easily. Yet, you must think that more detail is required for the appending of the data because it has to be mentioned where the exact place is where you want to append the data. So, if you remember the previous lecture then you will find it different to append this type of array.
#importing the array
import array as myArray
#initializing our array
myArray = [[1.4,66.90,234.0], [009.6,127.8,34.0,11.34], [0,2]]
print("Elements of my array = ", myArray)
#Using the length operator to check the length before appending the elements
length=len(myArray)
print("Length of the array before appending: ", length)
#The process of appending
myArray.append([7.9,334.90,2303])
#calculating and printing the length after appending elements
length=len(myArray)
print("Length of the array after appending: ", length)
#printing the results after appending
for x in myArray:
for y in x:
print(y,end = " ")
print()
The difference is clear; the number of elements can be easily compared with the help of the length function. We have used the length function many times in this course, and here you can see a better example of how to use it.
The three-dimensional array is the most complex form of the array discussed so far, and usually, at this level, the learner does not go into the details of the three-dimensional arrays to avoid complexity. The 2D array has many interesting applications and has the ability to store a lot of data in a cleaner way; therefore, 3D arrays are not used much for learning purposes, but if we talk about complex applications such as gaming and other fields, the 3D arrays have a great scope.
The form of the 3D array can be understood by keeping the matrix of the order 3x3 in mind. Here is a little information about the initialization of the three-dimensional arrays in the Jupyter notebook.
#initializing my three-dimensional array
ThreeD=[[[22.5,[67.9],[44.4]],[5],[23.8]],[[12,[123],[893]],[0],[78],[126]],[[70],[11]]]
print("The three dimensional array = ", ThreeD)
#Simply updates the elements by mentioning the locations and values.
ThreeD[0][0][0] =11
ThreeD[1][0][0] =21
ThreeD[0][0][1] =111
#Printing the results
print("After updating the elements the array = " ,ThreeD)
Hence, this picture gives us information about the shape and form of the three-dimensional arrays. Do not think that 3D arrays have the form of a 3-by-3 matrix. I suggest you take a deep look at the brackets of the array that we have mentioned here. The complexity of the three-dimensional array is much greater than in previous cases. To make it simple, we have used the single element in the single brackets, but there are more options for the same work that will be unnecessary to learn right now.
Another thing to be mentioned here is that, for simple operations such as displaying and updating the elements, there is no need to import the array every time. Yet, we have used it to display the proper professional coding of the array. We are not talking about the three-dimensional arrays in more detail because they are very difficult to understand and because there are some other options that work better than the three-dimensional arrays. You will learn it in detail in the next lectures.
Therefore, we can say that we have learned a lot about the advanced level of arrays in Python which are two-dimensional arrays and three-dimensional arrays. It is interesting to note that dealing with arrays is much easier in Python than in any other programming language. Therefore, we always say that Python is interesting and easy to learn. The topic ends here, but the detail of data type is not limited to just the types of array. You have to learn a lot about iit,and we are here to share every single and necessary detail in a simple way. So, stay with us in this learning phase.
Hello Python programmers! Welcome to the engineering projects where you will find the best learning data in a precise way. We are dealing with Python nowadays, and today, the topic of discussion is the arrays in the language. We have seen different data types in Python and discussed a lot about them in detail till now. In the previous lecture, we saw the details of the procedures in dictionaries. There are certain ways to store the data in the different types of sequences, and we have highlighted a lot about almost all of them. It is time to discuss the arrays, but before this, it is better to understand the objectives of this lecture:
Introduction to arrays
Difference between contiguous and non-contiguous memory locations
One-dimensional arrays
Functions in arrays
If you are from a programming background, then you have surely heard the name "arrays" and used them in your practice. Yet, if you are a beginner, you must know that arrays are present in almost all types of high-level programming languages. The array is also the way to organize and store the data in a sequential way, but it has some very basic characteristics, most of which are similar to those of other data types, but some are a little bit different, and we will work on both of these types. But before that, let's take a look at the basic definitions of an array:
“An array is a collection of items that are stored in contiguous memory locations. The idea is to group together items of the same type.”
This makes calculating the position of each element easier by simply adding an offset to a base value, i.e., the memory location of the array's first element. These are used to store a gigantic amount of data in different departments and offices, and because it is an old technique, people prefer it to other types such as sets, lists, and dictionaries.
When working with arrays, you will always notice the term "contiguous," and it makes sense because it is the best word to describe the structure of the array. It is the finite collection of data in which the elements are arranged strictly one after the other in the successive locations of memory, and in this way, a contiguous structure is obtained just like a ladder in a horizontal way. If you think it is all about knowing about the contiguous memory locations, then you are wrong. But, it is better to understand the concept by comparing it with the help of the opposite of it, that is the non-contiguous memory places.
Sr # |
Feature |
Contiguous Memory Allocation |
Non-Contiguous Memory Allocation |
1 |
Definition |
Contiguous memory locations are the type of allocation that allocates a continuous block of memory without skipping any places. |
Non-contiguous memory allocation, on the other hand, is the type in which memory is not continuous but separate blocks of memory are allocated regardless of how little space exists between them. |
2 |
Complexity |
The control of the operating system is easy in it because the memory can be smoothly be accessed. |
In this case, controlling the OS is comparatively difficult. |
3 |
Execution time |
The execution time is faster as compared to the other type, and it makes sense because the compiler does not have to sense the gap and then jump according to the length of the gap. |
In this case, the execution time is longer than in the first case because the compiler jumps from one memory location to the other. The time is determined by the gaps between the allocated memory. |
4 |
Overhead |
Because there are fewer address translations when dealing with contiguous memory allocation, there is less overhead. |
Because the address translation is greater in this case, we have more overhead. |
5 |
Special Features |
The contiguous memory allocation contains single-partition allocation as well as multi-partition allocation. |
When dealing with non-contiguous memory allocation, paging and segmentation are observed. |
6 |
Type of Fragmentation |
These include both internal and external fragmentation in the case of contiguous memory allocation. |
The internal fragmentation does not occur in this case, and we get only external fragmentation. |
7 |
Swap-in process |
It is noted that the swap-in process can only be arranged in the space that was originally allocated. |
If we talk about the non-contiguous memory allocation, the swap-in process is arranged in any memory allocation, so we get versatility in this case. |
8 |
Wastage of memory |
In this case, the wasted memory varies according to the array we are using. |
It is an advantage of this type of allocation that no memory is wasted in this case. |
9 |
Types |
There are only two types of contiguous memory allocation:
The hint for this point is also given in another feature. |
We observe five types in this case that are mentioned below:
All of this is unnecessary in our case; thus, do not go into detail about this type of situation. |
This is the distinguishing feature of arrays in Python when compared to other programming languages. Arrays, like many other functions and data types, are pre-defined in Python for the convenience of programmers. Because arrays are very general data types that programmers frequently use, it is simple to declare and import arrays in the same line and then begin working with the arrays. There are several ways to use arrays in Python, one of which is to import the arrays. Have a look at the details of this process:
There are different steps that are to be followed when dealing with the arrays in Python, and these are mentioned here:
Import the arrays.
Declare the name.
Use the array in different operations.
Hence using these steps, the arrays are used in different ways and it is interesting to notice that the declaration and the usage of an array are very different in Python as compared to other higher-level programming languages such as C++, C#, etc.
To understand well, let us see all these steps in action, but I want to discuss the type of the arrays with you while performing all these steps so that we may understand both these concepts one after the other.
Here is the point of the array that makes it different from the dictionaries and sets we have mentioned before. The arrays can be made in three ways:
One-dimensional array
Two-dimensional array
Three-dimensional array
Now it's the choice of the programmer according to the requirement, which type of array is he or she using. The details of each of them will be shared with you in just a bit, but before that, open your Jupyter notebook so that we can apply these arrays practically side by side.
Go to the search area of the window and get the Jupyter notebook there.
A screen will appear in front of you, and you have to go to the drop-down menu that is present on the right side of the screen.
Choose python there and wait for the new tab to open on your browser.
Click on the new cell location and start coding.
The most basic and commonly used array is one, and it does not require much effort for you to understand its concept. The very basic structure of the array that we see in the examples and discussion The elements are arranged one after the other, and the index starts at the first place where the zero number is assigned to that location. All the elements are linearly arranged, and it is the simplest form of array that is easy to create and access. In this way, the programmer gets the collection of the same data type in a linear format. The array is then stored in a variable to be used in different ways.
#importing the arrays from the Python library
import array as myArray
result = myArray.array('i', [22,68,123,90,49,167,66,129])
# accessing elements of array and printing it
print("Accessing the first element ", result[0])
print("Accessing the fourth element ", result[3])
The following output is observed as a result:
Hence, the single elements can be accessed in this way. Have you noticed that in the array, we have mentioned the “i” before, and after that, a list is being used? It has a very special reason, and you can check this by deleting the “i” in the code. You will observe that the compiler is throwing an error about the declaration of the Unicode character. Always remember that if the programmer is using the import array method in Python, there is a rule in Python for using arrays that the first argument of the array is always a Unicode character.
Here comes the reason why we are importing the arrays into Python. The primary reason is, it has multiple functions that make the work super easy, and the programmers do not have to write the codes again and again. I am just sharing the very basic and common functions. So let’s jump to the codes:
Take the example in your mind that when you are dealing with a gigantic amount of data at the same array and it becomes tedious to search for the frequency of the particular item. In such cases, when the detail of the element appearing in the array is required, the count function is used. The working of this function can be understood with this example:
#Declaring our array of strings
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
#Using the count function to get frequency
print(result.count(90))
So, when we check the frequency of the number 90, we get the following output:
Hence, we can easily conclude that in our array, the integer 90 appears three times.
The next function that is to be tested is the append function. For this, we add a new element at the end of the array, and doing this is very simple, as you can see in the code given next:
#Declaring our array of integers
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,
11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
#appending a new 90 at the end of array
result.append(90)
print(result)
The next function is reverse(), and as you can guess, the reverse of the array can be obtained with the help of a short command that we are going to test in the code next:
#Declaring our array of strings
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,
11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
result.reverse()
print(result)
As you can see, the order of all the elements is reversed in such a way that the first element is the last and vice versa.
Consequently, in this lecture, we have seen the details of arrays in a different way than when the introduction was done at the start. After that, we have seen what the workings of the contiguous memory location are and how it is different from the other types of contiguous memory locations. Different functions were also discussed in this regard, and more will be discussed in the next lecture.
Hello peeps! Welcome to the new episode of the Python tutorial. We have been working with different types of data collection in Python, and it is amazing to see that there are several ways to store and retrieve data. In the previous lecture, our focus was on the basics of dictionaries. We observed that there are many important topics in the dictionaries, and we must know all of them to make our base of concepts solid. For this, we are now dealing with the dictionaries in different ways, and this tutorial is going to be very interesting because we will pay more heed to the practical work and, by choosing a few cases in our codes, we will apply multiple operations to them. So have the highlights of today’s learning, and then we will move forward with the concepts.
What are nested dictionaries, and how do we use them?
How do you access the elements in the nested dictionary?
Tell us the procedure to add and delete elements from the dictionary.
Can we modify the elements of the dictionary? If yes, then how can we do so?
What is the membership test, and what is the procedure to use it in the dictionary?
Write the simple example in which the iteration is used with the dictionary.
In the previous lecture, we saw simple dictionaries and applied their functions to them. In the present lecture, the details of the dictionaries will be discussed in detail. These functions are not new to us but have been learned while dealing with sets and lists. Yet, the working and the results of every function are different with different types of data, and therefore, we are using the predefined functions again but with a twist. In the previous lecture, we saw the nested dictionary in an example but have not mentioned the details because we were discussing the list in dictionaries and it was not suitable to discuss it at that moment. Yet, have a look at the definition of a nested dictionary:
“A nested dictionary is a type of dictionary in Python that contains more than one dictionary separated by commas in it with different indexes, and each dictionary has a specific number according to its order.”
Accessing the data is a little bit more difficult in the nested dictionaries than in the simpler ones because the index is so important. The dictionaries are then divided with the help of commas between them. Open your Jupyter notebook by following these steps:
Go to the search bar on your PC.
Search for the Jupyter notebook.
Now go to the drop-down menu and choose Python there.
Wait for the new local host to be opened in your browser.
Go to the new cell and start coding.
Copy this code and paste it into the cell. By pushing the run button, you will get the perfect nested dictionary.
Do you know the meaning of our statement that accessing the data from a nested dictionary is a little bit different? It is because we need to type more of the data, and therefore, it is important to mention all the information accurately, as with all types of data, and this becomes tricky for non-programmers.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': '125'},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': '234'},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': '564'}}
#Printing the required value
print("Your required data = ", bookData[2]['coverColor'] )
So, as you can see, the real use of a nested dictionary is to get more readable and clean data every time. These are the advantages of the nested dictionary it is more practical, and a large amount of data can be stored and accessed easily.
It is the plus point of the dictionary that programmers can easily add the element in the nested dictionary by using some simple steps. The point here is to e discussed is to show you that the concept of an empty dictionary is present in Python. Many other data types do not have this characteristic. Hence, the addition of a new element becomes easy while using the dictionary. Have a look at the output of the code and after that, we will discuss the points about it.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Making the room for the new element by creating the empty dictionary
bookData[4]={}
#inserting elements in the next space
bookData[4]['subjectName']= 'English'
bookData[4]['coverColor']= 'yellow'
bookData[4]['Pages']= 611
print("The modified dictionary= ", bookData)
We can conclude the following points from the data given above:
The addition of the new dictionary to the existing nested dictionary is easy.
There is a need for a blank element so that we may fill in the required items.
The programmer in the code has declared the number of the dictionary and not the index, therefore, the data is not started with a zero and instead, the numbers of our choice are used. The programmer may name its dictionary anything no matter if it is the number of the alphabet.
Let us discuss the case in which the programmer wants to delete the whole dictionary from the nested collection for some reason. For this, the procedure is not too long and the simple delete command is used for it. For better elaboration, we are using the same example and deleting the second dictionary from our list.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Deleting the 2nd dictionary
del bookData[2]
print("The modified dictionary= ", bookData
Here, the question arises if the single element is to be deleted only, what will be the code? There are only minor changes in the code given above, and only the particular element of the mentioned dictionary will be deleted.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Deleting the specific elements from the dictionary
del bookData[2]['coverColor']
del bookData[3]['subjectName']
print("The modified dictionary= ", bookData)
The next step is to learn about the modification process of the dictionary, and for that, we are choosing another example because it is boring to check the conditions from the same example all the time. For the sake of simplicity, we are using the simple dictionary instead of the nested type of the dictionary.
#Declaring the initial data of the staff
staffInfo = {"2C32": "HR", "2C34": "Chemist", "2C20": "Doctor"}
print("Initial Staff Information: ", staffInfo)
#Changing the staff information
staffInfo["2C32"] = "Compounder"
#Printing the results
print("Updated Staff Information: ", staffInfo)
Hence, in this way, we do not have to first delete and then add the new element in its place; simply changing the element is enough. The new value is masked over the previous one, and in this way, the task becomes easy.
Until now, we have seen some simple and short examples so that the concepts may be easily understood. Yet you must know that in real-life applications, dictionaries are so large that it becomes difficult to notice the existence of specific elements in them. For such cases, it is important to learn the command to check the availability.
# Membership Test for our new dictionary
AvailableFood = {1: "chicken", 2: "beef", 3: "barbeque", 4: "burger", 5: "soup",
6: "salad", 7: "cake", 8: "lasagna", 9: "pizza", 10: "pie",11: "sandwiches",
12: "pasta", 13: "mushrooms", 14: "sausage", 15: "ice cream", 16: "cola", 17: "cupcakes",
18: "chocolate", 19: "biryani", 20: "golgappy", 21: "bread", 22: "jam", 23: "eggs" }
#Checking the availability by using just the key
print(6 in AvailableFood)
#checking if the certain key is "not" available
print(18 not in AvailableFood)
#Using a false value to check the behaviour of the keyword
print(49 in AvailableFood)
If we go into the details of this code, we may get the idea that:
Dictionaries can be used to store a massive amount of data.
The addition of the data is so simple and uncomplicated, and we can access any element easily.
The keys are useful to get the data easily without any complexity in typing in such cases if we use the integers as keys.
The “in” function searches for the required elements of the user and gives the result about the availability of the element.
The “not in” function is totally opposite from the former case, and the programmer can use it to check whether the specific element is absent or not.
The third case is interesting, we simply put the value to check how the “in” function responds when the element is not present in the dictionary, and the result was as expected.
The iteration process is fascinating, and we always try to provide you with examples that you will use in the practical implementation over and over again. The iterations are an important concept, and you will learn them in detail in the coming sessions, but we believe that, for now, you have an idea of what they are and how the programmers use them in their codes. It is time to check what the dictionary will do if we use it in the for loop.
# Membership Test for our new dictionary
AvailableFood = {1: "chicken", 2: "beef", 3: "barbeque", 4: "burger", 5: "soup",
6: "salad", 7: "cake", 8: "lasagna", 9: "pizza", 10: "pie",11: "sandwiches",
12: "pasta", 13: "mushrooms", 14: "sausage", 15: "ice cream", 16: "cola", 17: "cupcakes",
18: "chocolate", 19: "biryani", 20: "golgappy", 21: "bread", 22: "jam", 23: "eggs" }
#using the for loop with the "in" keyword to get the list of each and every food item
for element in AvailableFood:
print(AvailableFood[element])
Hence, all the elements are printed one after the other in a new line, and the programmers can simply get the available food items by using a simple command.
For more practice, I want to check the behaviour of this loop when I use it with the nested dictionary:
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Elements of the dictionaries = ",bookData )
print()
#printing All the dictionaries using iteration.
for myBooks in bookData:
print("Elements of single dictionary using the for loop = ", bookData[myBooks])
The results of the code given above are clean and more understandable:
Hence, the use of iterations makes our output uncomplicated and ordered. So it was all about the dictionary in this episode of the Python programming language. It was amazing to see the different examples, and all of them were related to the dictionaries. The usage of the dictionary in Python is important to understand; therefore, we used the Jupyter notebook to access, add, delete, and modify the elements of nested dictionaries and simple dictionaries with the help of examples. Moreover, the membership test was also interesting to learn and easy. In the end, the iterations made our work easy, and we got a cleaner output with the help of the iterations. We hope it was an attractive way to learn the dictionaries and stay with us for more concepts of Python. Happy learning to all of you.
Hello students! Welcome to the new Python tutorial, where we are learning a lot about this programming language by applying the codes in a Jupyter notebook. It is interesting to see how simple codes with easy syntax can be useful to store, retrieve, and use data in different ways. Many things in Python are so unadorned and pre-defined that programmers may use them in a productive way without any issue.
In the previous lecture, we have seen the details of the sets and how different types of mathematical operations are used to get the required results. We have seen many codes, the examples of which were taken from day-to-day life. In the present lecture, the mission is to learn a new topic that is related to the concepts of previous lectures. Yet, it is important to have a glance at the points that will be discussed in detail here:
Introduction to the dictionaries in the python programming language.
Why do we need dictionaries if we have different ways to store the data?
How can we use dictionaries in Jupyter notebook?
How do you define the concept of keys in dictionaries?
What are some characteristics that make the dictionaries different from others?
How do you justify that using the list with dictionaries makes the working better?
These points are the important interview questions and all of these will be crystal clear in your mind when we will go deep into these concepts one after the other. So stay in this lecture till the end and we will discuss the basic concept today.
To understand the topic, the first question that arises in the mind is what actually constitutes a dictionary and why it is important to understand this concept. An interesting piece of information to share here is that dictionaries are not new concepts; these were defined in the older versions of Python as well, but the difference is that in the older versions, from Python 3.6 onward, dictionaries were considered unordered, and if we talk about the current introduction, we will get the following lines:
"In Python 3.6 and later, a Python dictionary is an ordered collection of data that stores data in the form of pairs in keys or values."
The word “ordered” is not new to us and from the above information, we can understand that in the older versions of Python dictionaries when displayed in the output, does not have the proper indexing and the order of the element was unpredictable but when talking about the updated version (that we are using during this tutorial) the order of the elements in the dictionaries is well defined and the elements will be arranged in the same manner always that is defined by the programmer. Here it is important to notice that usually, the students are using the updated version of Python and there is no need to mention the older versions but the reason to mention this is, the older versions are preferred by the users that have older personal computers or because of any other reason, if they are using the previous versions, then they will find the dictionaries a little bit different from this tutorial.
We have been working with the data types in Python that all are used to have the collection of data and the question here is why we need another entity that can store the data if we have lists, sets, and others. The answer here is simply because the way to store the data is different for all of these and as the topic of discussion today is dictionaries, you must know that it uses the key values to store the data and therefore, it becomes uncomplicated to store the data using the key value mapping format. The most common advantage is, the retrieval of data from the dictionary becomes super easy with the help of these key values. If we talk about the complexity of the dictionaries, we will get the following data:
The best case of a dictionary has O(1) complexity.
The worst case of a dictionary has the O(n) complexity.
Now, that you understand the basics of dictionaries, to make the discussion clear, examples are being used in the Jupyter notebook. We have been using it in our tutorial, and therefore, we know that we simply have to start the software and get the new tab in the browser by clicking on the “new” dialogue box and clicking on “Python 3."
Once you get the worksheet now it's time to get used to all the codes that will clarify the concepts in a better way. So let’s start learning the concept and then simply use all the learning in our codes. This will be easy to understand because we have been working with such types of codes in this whole course but the difference here is the key of the dictionary.
You must know that dictionaries are declared by using the curly brackets in Python and if you have seen our previous tutorial, you must have the idea that the same representation was used in the sets then how can the compiler differentiate between these two and what is the reason to introduce the concept of dictionary in Python? Well, as we move towards the complex problems, we get the idea that simply storing the data is not enough, there is a need of providing the reference of the data and this happens when we use the keys in dictionaries.
“A key in Python dictionary is the specific and unique value assigned to each element and that has the immutable data type.”
The role of keys in the dictionaries will be clear by using the output of the code given next:
#declaring the dictionaries containing the grocery items
GroceryItems={"lettuce":4, "onion":34,"pepper":8,"plum":12, "frozen fruits":22}
print(GroceryItems)
Hence, the keys add more details to the dictionaries, and the data is stored in a better manner while being used in dictionaries. The usage of the keys is still ambiguous in the mind, hence, we are using different examples to express it well. There are different ways to use the dictionaries, and these procedures are given next:
#declaring the dictionaries containing the grocery items
GroceryItems={"lettuce":4, "onion":34,"pepper":8,"plum":12, "frozen fruits":22}
print(GroceryItems)
#retriving the data using the concept of keys in a dictionary
print("The required data = ", GroceryItems["pepper"])
Hence, the data stored in the key can be used easily to retrieve the data. In the example given above, when the person wants to get the number of groceries they want with the help of items.
We have focused on the examples, but before going into the details of them, you must know some important points about the dictionaries.
The dictionaries are declared with the help of curly brackets.
The keys are used with the elements and to declare them, the colon is used along with each element.
The value of the keys can be obtained by mentioning the element.
The dictionaries are ordered and every time when the same dictionary is declared, we get the element as same as that was mentioned in the code.
The usage of any data type is allowed in the dictionaries, and therefore, the working of a large amount of data is possible in the dictionaries.
Same as the sets, duplicate elements in the dictionaries are not possible. It means when the element is repeating in the code, the compiler automatically ignores the second third, or any type of repeating of the same element and therefore, the repetition is not allowed in dictionaries.
Let us now look at some examples of dictionaries where we will use our Python concepts and some functions to interact with the dictionaries.
We know about the details of lists in the Python programming language, and now we want to connect things together so that we may understand why the students had to learn a lot about lists. Python provides uncomplicated ways to use different functions and concepts and get useful results. In the Jupyter Notebook, we are now inserting the list into our dictionaries in two ways, and in this way, the concepts of lists will also refresh our minds.
During discussion about some important characteristics of dictionaries, it was mentioned that any data type can be inserted into the dictionaries, and this point will be proved with the help of the following code:
#declaring the dictionary of tooth brush
toothBrush = {
"Name of Item": "toothbrush",
"electric": False,
"price": 60,
"available colours": ["red", "white", "blue"]
}
print("Features of tooth brush in detail: ",toothBrush)
print("Price of the tooth brush= ", toothBrush["price"])
When we input this code in the new cell of our local host, after pushing the run button, the following results can be obtained:
Hence, it is proved that any data type in Python can be inserted into the dictionary, and this characteristic gives the programmers the independence to use the collection of data and relate it to a single item in the dictionary. In the example we have just seen, there were multiple options for the same brush about its colours, and hence, in such cases, the programmers use dictionaries to get the required results.
Here comes a different kind of concept that we have not learned in this tutorial until now. The list can also have the dictionaries as the element and if you are wondering why the programmers use them then you must know, the characteristics of the data types matter, and each data type has its own speciality therefore, the combination of both these data types in different ways gives us the variety of uses.
#Create a list of dictionaries that represent the prices of different items
#along with their serial number in the grocery store.
myStoreItems = [{345: 'chicken', 567: 'eggs',
561: 'beef', 879: 'mutton'},
{348: 'milk', 670: 'butter',
127: 'bread', 445: 'tea bags'},
{237: 'chocolate spread', 381: 'jam',
890: 'sauce', 340: 'sandwich'}]
print(myStoreItems)
#accessing the data from the first dictionary
print("The required item is ",myStoreItems[0][561])
#accessing the data from the second dictionary
print("The required item is ",myStoreItems[1][127])
#accessing the data from the second dictionary
print("The required item is ",myStoreItems[2][340])
In this way, we can extract the following concept from this code:
The dictionaries can be used in the lists.
There is the possibility of using different dictionaries in the same code.
The data can be accessed from any dictionary by using the index of the data.
To get the exact element, the programmers have to write the index of the element.
For using the right indexing method, first, we have to mention the dictionary number and then the key of the element.
It's a reminder that indexing starts at 0.
It becomes easy to manage the data with the keys and more data can be stored in a single dictionary in a more manageable way.
The declaration of the dictionaries resembles the sets and we see many features of these two concepts that resemble each other.
In this lecture, we started our discussion with the introduction of dictionaries. The list has many informative features, and the combination of dictionaries with the list makes many useful tasks easy to complete without facing any issues because of the smooth process. The user simply has to feed the required data, and at the appropriate time, the data can be retrieved easily. So, that was all for today. Dictionaries have applications in different ways, and therefore, it is not possible to provide students with all the necessary information in a single lecture. Therefore, in the second part, we will discuss more details about dictionaries and learn a lot through examples.
Online technology has had a profound impact on the real world, revolutionizing various industries and areas of our lives. From e-commerce and communication to education, healthcare, and entertainment, online technology has made many aspects of daily life more convenient, accessible, and efficient.
This technology has fundamentally changed the way people interact with the world around them — and there is no denying — another industry that has been significantly transformed by online technology is — the gaming industry.
With the rise of Live casino online technology and advancements in internet connectivity, players can now connect and compete with others from anywhere in the world. Online technology has also enabled the development of massively multiplayer online games (MMOs) that allow thousands of players to interact in shared virtual worlds.
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However, it is important to note that gambling can be addictive, and players should always gamble responsibly and within their means to ensure that their online casino experience remains enjoyable and is not detrimental to their overall well-being.
An online casino is a virtual platform that allows players to gamble and wager on various casino games via the internet. These games include slots, table games, card games, and live dealer games.
Players can access online casinos using their computer or mobile device, and the games are played using software that simulates the experience of a traditional casino. The outcome of the games is determined by a random number generator , which ensures that the results are fair and unbiased.
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Online casinos use a variety of technologies to provide players with a seamless gaming experience. Some of the most common technologies include:
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Artificial Intelligence:
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Online casinos strive to replicate the real-world casino experience as closely as possible. The following are some ways that online casinos replicate the real-world casino experience:
Online casinos offer a wide range of games that are similar to those found in real-world casinos. Some of the most popular games found in online casinos include:
Online casinos offer online slots as one of their most popular games. They come in different themes, graphics, and features and offer various ways to win.
Online casinos offer a variety of table games, including blackjack, roulette, baccarat, craps, and sic bo. These games are usually played against the computer, but some online casinos offer live dealer versions.
Video poker is a game that combines the elements of slots and poker. Players are dealt a hand of cards and must decide which cards to keep and which to discard in the hopes of making a winning hand.
Online casinos offer live dealer games streamed in real-time from a casino studio. Players can interact with a real dealer and other players, creating a more authentic gaming experience.
These virtual casino games are designed with high-quality graphics and sound effects that help to replicate the look and feel of a real-world casino.
The following are some of the ways that graphics and sound effects enhance the online casino experience:
High-quality graphics make the games more visually appealing, creating a more engaging and entertaining gaming experience.
Graphics and sound effects are used to create a theme and story for each game. This can include anything from a classic slot machine theme that tells a story or transports players to a different time or place.
These graphics and sound effects can offer more realistic exposure. For example, in live dealer games, the sound of the dealer shuffling cards and the cards' visuals can create a more authentic event like you’re part of the entire process.
These visuals and effects are often used to provide feedback and rewards to players. For example, when a player wins a game, the graphics and sound effects may create a celebratory experience to enhance the player's excitement.
Live casino online technology offers live dealer games streamed in real time from a casino studio. This allows players to interact with a real dealer and other players, creating a more authentic gaming atmosphere that both parties can enjoy.
Some of the key aspects of live dealer games include:
Live dealer games are hosted by real dealers who operate the game in real time from a casino studio. Players can interact with the dealer and other players like in a real-world casino.
Many of the most popular table games are available as live dealer games, including blackjack, roulette, baccarat, and casino hold'em.
Live dealer games offer a range of betting options, from low-stakes games to high-stakes games for high rollers.
These games provide the convenience of online gaming, allowing players to enjoy the casino experience from the comfort of their own homes.
Bonuses and promotions are an essential part of the online casino experience, offering players the opportunity to boost their bankroll and increase their chances of winning. Some of the most common types of bonuses and promotions offered by online casinos include:
Welcome bonuses are offered to new players when they sign up for an online casino. These bonuses usually come in the form of a match bonus, where the casino matches a percentage of the player's first deposit.
Free spins are a type of bonus that allows players to spin the reels of a slot machine without having to place a bet. Free spins may be awarded as part of a welcome bonus or as a promotion to existing players.
Reload bonuses are bonuses offered to existing players when they make a deposit. These bonuses are usually smaller than welcome bonuses, but they still offer players the opportunity to boost their bankroll.
Many online casinos offer loyalty rewards programs that reward players for their continued play. These programs may offer players points that can be redeemed for cash or prizes, or access to exclusive bonuses and promotions.
Cashback bonuses offer players a percentage of their losses back as a bonus. For example, a casino may offer a 10% cashback bonus on losses incurred over a certain period.
Online casinos offer a range of payment methods that are similar to those offered in real-world casinos. The following are some of the most common payment methods used by online casinos:
Visa and Mastercard are the most commonly accepted credit and debit cards at online casinos. These payment methods offer fast and convenient transactions, but players should be aware that some banks may decline transactions with online casinos due to local gambling regulations.
E-wallets like PayPal, Neteller, and Skrill are popular payment methods for online casino players. These payment methods offer fast and secure transactions, with many online casinos offering bonuses and promotions for players who use e-wallets.
Bank transfers are a standard payment method for players who want to transfer large amounts of money. However, bank transfers can take several days to process, which may not be ideal for players who want to start playing immediately.
Some online casinos accept cryptocurrencies like Bitcoin and Ethereum as payment methods. Cryptocurrencies offer fast and secure transactions, but players should be aware of the volatility of these currencies and the potential for significant value fluctuations.
In conclusion, online casinos have revolutionized the gambling industry by offering players a convenient and accessible way to play their favorite casino games from the comfort of their own homes.
With advancements in technology, online casinos have been able to replicate the real-world casino experience through features such as live dealer games, high-quality graphics and sound effects, and a wide selection of games.
Additionally, the availability of various payment methods and bonuses, and promotions have made online gambling more attractive to players. However, it is important to note that online gambling can be addictive, and players should always gamble responsibly and within their means.
Needless to say, online casinos provide a convenient and entertaining way to enjoy the thrill of gambling, but it is important to approach online gambling with caution and moderation.
That’s all for today. Hope you’ve enjoyed reading the article. If you have experienced playing in online casinos, we’d appreciate your input in the section below. Thanks for your precious time. Until next time!
Hey people! Welcome to another tutorial on Python programming. We hope you are doing great in Python. Python is one of the most popular languages around the globe, and it is not surprising that you are interested in learning about the comparatively complex concepts of Python. In the previous class, our focus was to learn the basics of sets, and we saw the properties of sets in detail with the help of coding examples. We found it useful in many general cases, and in the present lecture, our interest is in the built-in functions that can be used with the sets. Still, before going deep into the topic, it is better to have a glance at the following headings:
What are the built-in functions?
How do we perform the all() function with examples?
What is the difference between any() and all()?
What are the parameters of sorted()?
From the beginning of this course, we have been using the built-in functions for different purposes, but here, the main idea must be cleared, so for the purpose of revision, have a look at the basic definition of these functions:
"Built-in functions are the pieces of code that have a particular result all the time and are pre-defined in the programming languages so that the programmer does not have to type the same code but simply put the values in the functions and get the required output as expected with minimum effort."
For a better understanding of the sets and the practice of these in codes, the best way is to use the built-in functions so that whenever you are coding these built-in functions arise instantly into your mind and the effort to code the same line again and again in different programs. There are certain types of built-in functions, the details of which were covered in the previous lecture, so for now, we are trying to introduce the new functions and repeat just the most common ones with the sets.
The first function of discussed by us is very interesting and has application in many general cases. This function checks the element of the set one after the other and on the basis of a combination of results, it provides the output. The working of all() is a little bit tricky. Consider the case when we define a set with a particular data type and all function and then keeps that data type in the memory. Afterwards, it checks the result by following the sequence of combinations and then provides a single output in the form of true or false. The combination of all() is given in the table:
True |
False |
Result |
All values |
No value |
True |
No value |
All values |
False |
One value |
Remaining |
False |
Remaining |
One value |
False |
Empty set |
True |
|
Hence, just like the logical gates, the all() function works on the logics, and this will be proved when you see these examples:
Before going into the details of the all() function, have a look at the code where only integers anf floats are used with different values.
# integer set with non-zero values
myIntegerSet = {33,6,5,12,44,9}
print(all(myIntegerSet))
# Float and false value
myFloatSet = {33.6,55.9,12,4, False}
print(all(myFloatSet))
# Float with only one false value
FloatWithOneFalse = {1.8, 3.55, 4.34, False}
print(all(FloatWithOneFalse))
# Integer set with only one true value
integerWihOneTrueValue= {44,False,0}
print(all(integerWihOneTrueValue))
# empty set
emptySet={}
print(all(emptySet))
The following points are extracted from the results given above:
The digit zero is always considered false if you are using the float or integer sets.
The all() function checks each and every element and stores the results in memory, then checks for the combination and provides the results.
It is obvious that if the set contains the value "false,” then it will be considered the false function, and the compiler will not consider it a string.
The empty set is always true.
Now, this can also be performed with the string sets, and all the strings will be considered true. Hence, any element with double quotation marks will be considered a string, and the compiler will provide the output in the form of a single result.
Your task is to design the code using the information given above and check the results by matching the table given above.
If you have understood the previous function, then this will be more fun for you because, in some cases, we can say that the any() function is the opposite of the all() function. Therefore, they are named so. So, look at the code of the any() function, where only the name of the function is being replaced, and you will see the clear difference in the results.
# integer set with non-zero vlaues
myIntegerSet = {33,6,5,12,44,9}
print(any(myIntegerSet))
# Float and false value
myFloatSet = {33.6,55.9,12,4, False}
print(any(myFloatSet))
# Float with only one false value
FloatWithOneFalse = {1.8, 3.55, 4.34, False}
print(any(FloatWithOneFalse))
# Integer set with only one true value
integerWihOneTrueValue= {44,False,0}
print(any(integerWihOneTrueValue))
# empty set
emptySet={}
print(any(emptySet))
The results can easily be understood, and this time, we have extracted the table of combinations from the output of our code:
True |
False |
Result |
All values |
No value |
True |
No value |
All values |
False |
One value |
Remaining |
True |
Remaining |
One value |
True |
Empty set |
False |
|
So, the same task is for you as we have given the exercide with the all() function.
It is a different kind of function and is meant to represent the set in a better manner. While using it with the set, the output is obtained has the number of index with it automatically. It is useful in the long sets where the number of elements matters, and by using this, the programmer does not have to do the counting manually, but the index number is shown with each element. For this, the results are saved into the variable, and then the results are fed into the list. If it is confusing at the moment, have a look at the code given next:
#Making the set of books
Books={'Biology', 'Chemistry', 'Physics', 'English', 'General Knowledge',
'geography', 'poems', ' grammer','programming', 'software', 'technology'}
#declaring the varialbe
i=enumerate(Books)
#using the varialbe in list and printing the results
print(list(i))
So, all the books are arranged and have an index according to their position. Take the case into your mind, where the set contains hundreds or thousands of elements, and the indexing helps us to understand the collection.
The next function that is going to be discussed is the sorted() function, that plays an important role in the numeric calculations. Usually, programmers feed the data in an unordered manners.
If all the discussion above is not enough for you to understand the whole concept, then do not worry because these examples and their explanation will help you understand the concepts deeply.
It is a simple task, and the sorting is easy when you have few elements, but with the complex cases, the sorting needs logic. You will see some examples with the parameters in the same lecture, but for a start, have a look at the code below:
So, the sorting process of integers and
#Declaring the set
print("The unsorted set = ", myIntegers)
print()
#Using sorted() function on the set
print("The sorted set= ", sorted(myIntegers))
This is the big array, and it is difficult to arrange it manually; therefore, we are using sets here to eliminate the duplicate elements and to sort them with the help of a simple command.
Once the set is complete, it becomes difficult to read and understand the data of the set if it is large. In such cases, the sorted function is used, which sorts, or, in simpler words, arranges the data into a specific order. To learn about the syntax of this function, you must know three parameters:
This is the general term used to present a collection of things, groups, loops, or sequences. These are the necessary parts of the sorted function, and as “set” is the collection of data types, we can use it in this function to sort the elements according to our wishes. Without this parameter, the compiler will show the error.
It is a relatively difficult parameter to grasp, but it is responsible for the function's versatility. It may be tedious to realize that the set you are entering is only sorted in ascending and descending order. In programming, the data is stored in the form of different collections of numbers, and the key is the way to tell the compiler which pattern must be followed for sorting the data. This will be clear with the help of an example, and after that, we will examine the results:
The second parameter in the sorted() function is optional, and it is used to ask the programmer about the way to sort the data, that is if the programmer needs the iteration in an ascending or descending manner. It just has two possible inputs: true or false. As the name of the parameter indicates, if the reverse is set to true, the results will be in reverse order, that is, from the highest value to the lowest. But by default, it is always true, and if the programmer inputs true or even does not use this parameter, the output of the set will be in ascending rather than descending order.
# My string list declaration
mySet ={ ('apple'), ('red'), ('black'), ('strawberry')}
# sort set taking length as key and reversing the order
Result = sorted(mySet, key=len, reverse=True)
# print set
print('Sorted results:', Result)
So, it was an interesting tutorial on the sets, and with the help of different examples, we have learned built-in functions such as all(), any(), and the sorted function.
Hey, learners! Welcome to the next tutorial on Python with examples. We have been working with the collection of elements in different ways, and in the previous lecture, the topic was the built-in functions that were used with the sets in different ways. In the present lecture, we will pay heed to the basic operations of the set and their working in particular ways. We are making sure that each and every example is taken simply and easily, but the explanation is so clear that every user understands the concept and, at the same time, learns a new concept without any difficulty. The discussion will start after this short introduction of topics:
How do we declare and then find the difference between the two sets?
What are the built-in functions for operations, and how do we use them in the codes?
How do we apply the operation of symmetric differences on the sets?
What is chaining in iteration, and how do we use it? Give some examples.
How do you define the union of the sets?
What is an intersection and how it is related to the sets?
What is the procedure to find whether the sets are equal or not? Give us some examples in Python.
All of these are important interview questions, and we are using the concept in this lesson to find the answer to each question in detail. The Jupyter notebook is being used for practical work. To open it, go to the search bar in Windows and search for the Jupyter notebook. After that, go to the “new” dialogue box and start Python 3 to create a new notebook.
We all come from a programming background, and it is not surprising to know that different mathematical operations are used in programming to do useful tasks. In this lecture, you will learn the importance of mathematical functions with regard to sets. We have been working on the set since the last two lectures, and we are now aware of the details of working on sets. But during the programming process, the code contains different types of calculations, and large programs are extremely difficult to run without the involvement of mathematical operations. We will explain this with the help of different examples, and this is going to be fun because these concepts have been studied by us since our early education, and now is the time to know the reasons for that study.
This is the simplest set operation and it involves the difference between the elements of the set in their corresponding values. In simple words, if we are dealing with two sets named A and B then the difference between them is a new set containing all the elements that are in set A but not in set B. In Python, there are two ways to get the difference of the set in two ways:
The difference using the sign
The difference using the function
Both of these are discussed in detail here. The difference with the sign is simple as we minus the values in the simple mathematical question. A minus sign is used between the names of the set, and we get the result.
On the other hand, if we talk about the function, Python has a built-in function that calculates the difference, and it is a more professional way to use the functions for such operations. These two methods are simply described in the example given next:
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of difference
differenceWithSign=A-B
differenceWithFunction=(A.difference(B))
#Printing results
print("A-B= ",differenceWithSign)
print("Difference with function= ", differenceWithFunction)
It is obvious that both methods give us the same results. So it totally depends on the programmer to choose the method, but here is a reminder that the order of the names of sets is important here because if we write B-A then the results will be entirely different.
If you found the difference between the sets easy then you will find it more interesting because it is similar to the difference of the set but the only change is, in the resultant set, the uncommon elements of set A and B both are included instead of only the first set. In this way, the output of the entries from both the sets and no set is ignored. To denote the symmetric difference, the “^” sign is used. By the same token, the function for the symmetric difference also exists in Python,n and the keyword “symmetric_difference” is used for this. Have a look at the code and output for this case:
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of symmetric difference
SymmetricDifferenceWithSign=A^B
SymmetricDifferenceWithFunction=(A.symmetric_difference(B))
#Printing results
print("A^B= ",SymmetricDifferenceWithSign)
print("Difference with function= ", SymmetricDifferenceWithFunction)
Compare the results with the previous operation, and the difference will be clear.
In the previous lecture, we have been working with the itertools. The current case is to check whether the itertool works with the sets or not. You will see that the union of the two sets has the same results as the chain of the two sets, but for the sake of learning, we are working on both concepts. Hence, have a look at the code and understand it. After that, type the code on your own and check the results.
from itertools import chain
apple={12,8,34,5,90,3,2}
banana={1,7,90,33,2,1,5}
totalSales=set(chain(apple,banana))
print(totalSales)
By simply declaring and using the sets in a chain format, we are getting the result of the sales of both fruits, but in the cases where both sales were equal, the entry is ignored as we are using the set that ignores the duplicates.
At the simplest level, the results of union and chain are the same, but at a higher level, the working of both of these is different; therefore, it is important to understand both concepts and practice them with the sets.
Now, let us show you an interesting example of the same function where a set is used with the string entries. When applying the chain to that particular set, the same alphabets are ignored. The programmers can represent the results as joining or separate results, and this is explained well with the following code:
from itertools import chain
A = "Python"
B = "Programming"
C = "Tutorial"
output1 = set(chain(A, B, C))
print("before joining the set :", output1)
output2 = ''.join(res)
print("After joining the set :", output2)
We know the message is not conveyed in detail and therefore, it is proved that sets are more useful when dealing with numbers.
The name of this operation is self-explanatory if we have two sets named A and B, then the union of these sets means a new set that contains all the elements of set A and set B. We have read this in our mathematics classes, but here, this will be done with the help of coding. There are some special signs that are used to define the operations, just like we indicated the difference with the help of minus signs. When we talk about the union of the sets, we use the | sign for this, and Python also has the function for the union.
There is no need for long explanations for this operation, and we are simply changing the sign of the previous example and providing you with an example of how the difference and union give you entirely different results by changing just the operation.
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of Union
unionWithSign=A|B
unionWithFunction=(A.union(B))
#Printing results
print("A | B= ",unionWithSign)
print("Union with function= ", unionWithFunction)
As a result, the resultant set contains the values of both sets, but have you noticed that the resultant set has fewer values than the collection of both sets A and B? It is because we are using sets, and the values that are identical are just shown once.
If you have understood the concept of the union of sets, then you must keep it in mind, and then by comparing the process of intersection, the concept of both these operations will be clear in your mind. It is because usually people seem confused between these two.
Let us take the case where we are discussing the recipes of two dishes that are chicken and fish and want to know the common ingredients to write in the list. For this, we will use the intersection of the set, and both of these will contain the name of the ingredients, which means these are the string sets. During the process of the intersection of sets "fish” and "chicken," the resultant set contains all the elements that are common in both sets, and all other entries are totally ignored. In this way, the person will be able to understand what things he needs from the same section of the store and where he has to go for more shopping. This will be done with the help of the following code:
#Declaring two sets of string
fish={"Onion", "Tomato", "Garlic", "Fish", "Salt", "Carrot", "Eggs", "Ginger"}
chicken={"Chicken", "Salt", "Potato", "Onion", "Garlic", "pepper"}
#Using both methods of intersection
intersectionWithSign=fish & chicken
intersectionWithFunction=(fish.intersection(chicken))
#Printing results with the same ingredients
print("fish & chicken= ",intersectionWithSign)
print("Intersection with function= ", intersectionWithFunction)
When dealing with different types of sets, let's say set Apple and set Banana, where the shopkeeper stores the data of the weekly sales of cartons, the seller wants to take the record of the day and check if the sales are the same or not. Then, he simply checks the results by applying the equal signs for the two times between the sets and getting the result. Keep in mind that the order of the set does not matter here as the set does not have the index values. So, no matter if the sales were not equal on the same day, if the weekly sales are matching exactly, then the sets will be equal.
#Making the set of apples and banana with the sales
apple={34,78.9,32,89.7,33,12.6,55}
banana={12.6,78.9,33,34,32,89.7,55}
#using if loop to check the results and print the output
if apple==banana:
print("Sales of apple and banana are the same")
else:
print("Sales of apple and banana are not the same")
Now, to elaborate on this more, the sets that are used in the previous examples are fed into the same code, and then by running the code, we can conclude the results.
#Making the set with the sales
apple={12,8,34,5,90,3,2}
banana={1,7,90,33,2,1,5}
#using if loop to check the results and print the output
if apple==banana:
print("Sales of apple and banana are the same")
else:
print("Sales of apple and banana are not the same")
The “if” loop is an important iteration, and this is the best way to explain how we can use it for the bi-conditional cases. These loops will soon be used in many complex codes in this tutorial.
Hence, it was a fantastic tutorial on sets, and we learned a lot with the help of different operations. We had an idea about the sets and their characteristics, but in this lecture, the operations and working of the sets are well explained, and we see the difference, symmetric difference, chain, union, intersection, and equality operations of the sets in detail. I hope you get the point that we are trying to make clear. For more interesting tutorials, stay on the same website.
Artificial intelligence is becoming more and more important for data security. In this post, we'll look at how AI may assist businesses in anticipating and thwarting threats. But before going ahead we will explain the terms artificial intelligence and machine Learning.
Artificial intelligence (AI) is a discipline of computer science that focuses on making electrical equipment and software intelligent enough to do human activities. AI is a broad concept and a basic subject of computer science that may be used to a variety of domains including learning, planning, problem solving, speech recognition, object identification and tracking, and other security applications.
Artificial intelligence is divided into numerous subsets. We shall look at two of them in this article:
Deep Learning
Machine Learning
Machine learning (ML)-based computer systems have the capacity to learn and carry out tasks without explicit instructions. These systems find, examine, and comprehend data patterns using ML algorithms and statistical models. Many jobs that are typically completed by people are now routinely carried out automatically using machine learning capabilities.
A machine learning technique called unsupervised learning enables ML algorithms to carry out tasks without clear instructions and produce desired results. Based on analysis and experience, this method determines the best solutions to a problem. When given an input (a task to perform), the model can decide on its own what the optimum course of action is. The model gets better trained and becomes more effective the longer it solves the assignment.
The benefit of ML for many tasks is obvious—machines don't grow bored or upset by repeatedly performing the same monotonous tasks. By automating numerous processes in work chains, they also drastically reduce workloads. Security teams can, for instance, use AI-based solutions (which will be covered later) to automatically detect threats and handle part of them, minimising the amount of human contact necessary for specific security activities.
Data anomalies can be found with the aid of machine learning. You may train algorithms that recognise particular patterns and user behaviour using machine learning. Detecting suspicious behaviour in a workplace, such as an increase in password resets or unexpected requests for sensitive data, will be made possible thanks to this.
Computer vision can also be used to find data trends that might point to a possible system or network vulnerability management violation. Machine learning techniques are employed to forecast future examples of this behaviour based on the environment's present conditions after being trained with historical data on previous successful attacks (e.g., usage patterns).
Besides ML techniques you can rely on the use of VPN. Because you can keep your data from suspicious activities from hackers by installing a VPN. It is easy to set up a VPN on router and once you set it will start monitoring your PCs activities against malicious attacks.
Before an attack ever occurs, AI may identify it and stop it. Understanding how data is gathered, processed, and presented is just as important as looking at the data itself. AI is able to spot warning signals of impending attacks and stop them from executing in the cloud, on a network, or even in real time.
By seeing dangerous activity on your virtual machine (including malware) while you're away from home or work or even on mobile devices, AI can also assist you in protecting yourself against AI-enabled dangers of gadgets and PCs both! Additionally, there are social media platforms like Facebook and Twitter and AI also helps to keep them secure from attackers.
Artificial intelligence is becoming more and more important for data security. AI can assist businesses in identifying dangers, spotting abnormalities, and reaching decisions more quickly than ever before.
It plays a significant role in contemporary data management techniques, which in turn have significant ramifications for enterprises across all industries.
"Domain knowledge" is the capacity for people or computers to comprehend information and take appropriate action without being instructed on its workings or meaning (AKA: natural language processing).
"Machine learning" is the process through which computers or humans can perform jobs utilising data sets without any prior knowledge.
In order to learn from mistakes they made earlier in life and produce better results later on when things get difficult again, both of these strategies depend on increasing volumes of data being gathered over time.
Obtaining a thorough and accurate inventory of all devices, users, and software with access to computer systems. Inventory also heavily relies on categorization and the measurement of business criticality.
Hackers, like everyone else, follow trends, therefore what's popular with hackers changes on a regular basis. AI-based counterintelligence systems can provide current knowledge about global and industry-specific threats to assist in making crucial prioritising decisions on the basis not only on what may be employed to defend your organisation, but also on what is likely to be utilised to attack your organisation.
It is critical to comprehend the significance of the numerous security technologies and verification activities that you have implemented in order to keep a stable security posture. AI can assist you in determining your information security program's strengths and weaknesses.
Accounting for IT assets, threat sensitivity, and control efficacy, AI-based solutions may forecast how and where you will be compromised, allowing you to allocate resources and tools to areas of weakness. AI-derived prescriptive insights can assist you in configuring and improving policies and processes to most greatly increase your organisation's cyber resilience.
AI-powered systems can give greater context for prioritising and response to safety warnings, for quick incident response, and for surfacing root causes to remediate exposures and avoid future issues.
The explainability of guidance and analyses is critical to leveraging AI to enhance human information security teams. This is critical for gaining buy-in from stakeholders across the organisation, understanding the impact of various information security programmes, and reporting relevant information to all stakeholders involved, including end users, security operations, CISO, audit committees, CIO, CEO, and board of directors.
Although I have been doing this for a while, data security is currently enjoying a comeback. People are more worried than ever about their sensitive data being stolen because hackings are on the rise. The good news is that scalable data protection is possible with artificial intelligence (AI). In this article, we talked about how AI and machine learning combine to find abnormalities in massive datasets and spot trends that point to shady conduct.
Welcome to today's article on our comprehensive Raspberry Pi 4 programming guide. As we saw in the previous article, the Raspberry Pi 4 may power a single seven-segment display. In addition, we also interfaced a Raspberry Pi with 4 Seven-Segment Display Modules to display the time. However, this guide will show you how to construct a Raspberry Pi 4 crypto miner that uses very little electricity.
Cryptocurrencies have been the subject of widespread conversation for some time now. It's possible to use your computer to create them, and they can be used as currency. Because of this, the Raspberry Pi can also be used for Bitcoin mining. It's also possible to mine other cryptocurrencies. One drawback of mining is that the cost of electricity often exceeds the revenue it brings in. So, let's check out how to construct a solar-powered, money-making cryptocurrency miner with a Raspberry Pi.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Raspberry Pi 4 | Amazon | Buy Now | |
A pool account
Bitcoin Wallet
Raspberry Pi
Raspbian image SD card
USB Bitcoin miner
Crypto mining, the digital equivalent of the gold mining industry, involves a combination of complex mathematical calculations and blind luck. Mining is crucial for cryptocurrencies as it is the only way to update the distributed ledger (Blockchain).
Despite Bitcoin's popularity, there are other digital currencies available. All cryptocurrencies use blockchains to ensure that all transactions are legitimate and that users cannot spend the same cryptocurrency more than once.
To simplify things for the unfamiliar in the web3 environment, let's say that a blockchain is a distributed ledger that maintains track of all transactions made over it. Similar to how a bank keeps a record of who gave money to whom, how much was sent, and when it was sent, blockchain stores this unchangeable data within distributed blocks linked together via a network.
Users, known as miners or validator nodes, provide the network's computational power to verify all of the blockchain's transactions. This blog post will not delve further into smart contracts, which are computer programs that can be set up to run automatically on a blockchain if and only if specific criteria are met.
Bitcoin and Ethereum miners are sometimes pictured as a large server farm full of powerful graphics processing unit (GPU) or application-specific integrated circuit (ASIC) devices that work tirelessly to solve complex cryptographic puzzles issued by the blockchain in exchange for financial rewards. The consensus technique for validating submissions and awarding incentives varies from blockchain to blockchain.
Raspberry Pi users can choose from several different coins to mine, but not all are profitable. The most profitable option is the one you should choose. The USB miner is crucial to mining since it dramatically boosts productivity. In mining, you have two primary options:
For anyone interested in beginning mining using a USB miner like NEWPAC, selecting a cryptocurrency that uses the SHA-256 algorithm is a must. Bitcoin (BTC), Bitcoin Cash (BCH), Bitcoin SV (BSV), and many others are just some of the cryptocurrencies that use the SHA-256 algorithm. However, Bitcoin is the most lucrative and should be explored first if you plan to mine using a Raspberry Pi.
The Raspberry Pi's central processing unit (CPU) can be used to begin mining in the absence of a dedicated USB miner. In such a scenario, you should go with Monero (XMR), the coin that can be mined with the least effort using a Raspberry Pi.
After calculating electricity and equipment costs, I found that bitcoin mining with a regular computer could have been more worthwhile. Most bitcoins are now mined using specialized computers called ASIC bitcoin miners; nevertheless, amateurs and enthusiasts still have some success mining by joining a mining pool. What if we set up a mining rig powered by a Raspberry Pi and solar panels and "deducted" the cost of the equipment? As the number of miners for Bitcoins increases, the difficulty of mining rises, and the rewards for miners decrease, the industry has become very competitive. Despite this discouraging information, I've decided to move on with this plan and shift my focus to alternative crypto assets.
Since we are utilizing a Raspberry Pi rather than an ASIC bitcoin miner, individual crypto mining was not an option. Despite my best efforts, I could not locate any mining pools that supported the Raspberry Pi operating system among the many available for Windows and macOS. Since Linux miners are written for the x86 architecture, Raspberry Pi cannot participate in the mining process. Linux mining software that runs on x86 processors like those found on most personal computers is supported.
Please note that the purpose of this paper is to promote further study of blockchain technology and cryptocurrencies, not to create any of those assets. The techniques outlined here are workarounds that need to be endorsed by the developers. Instead, you can download the free software linked with your preferred mining pool and install it on your personal computer.
We'll first sign up for an account on minergate, a crypto mining pool with over 3.5 million users worldwide that supports Bitcoin, Gold, Zcash, Ethereum, Ethereum, and monero. Since Monero is the only crypto I have had success with, this guide will focus solely on that one.
Turn on your Raspberry Pi.
Press Ctrl-T or launch a Terminal window in Raspberry Pi OS using Desktop. Please use the standard login procedures while using Raspberry Pi Lite.
If you're already in the Terminal, you can install the updates and prerequisites immediately.
sudo apt-get update && sudo apt-get upgrade -y
sudo apt install git automake autoconf libcurl4-openssl-dev libjansson-dev libssl-dev libgmp-dev
cd cpuminer-multi
Please use the below three commands to compile the mining code. This process will take a few minutes if you're using a Raspberry Pi 4.
sudo ./autogen.sh
sudo ./configure
sudo ./build.sh
Let's begin monero mining once we've installed and set up the mining program on our Raspberry Pi. To activate the miner, run the following line in the Terminal, substituting YOUR EMAIL with the address you used to create your minergate account.
./cpuminer -a cryptonight -o stratum+tcp://xmr.pool.minergate.com:45700 -u YOUR_EMAIL
The mining software will begin running, and if you're lucky, you'll see some 'accepted' shares marked with a "yes."
Please log in to minegate/internal so we can inspect your Minergate Dashboard. This can be done on a PC or laptop using the Chromium web browser or on a Raspberry Pi using the Raspberry Pi Desktop interface. Find the Monero icon at the bottom of your screen. The ONLINE status will be displayed if Monero is connected and functioning correctly. Congratulations! You have started Monero mining!
Now that we have a basic understanding of blockchain and cryptocurrencies, the issue of which currency is superior naturally emerges. The original cryptocurrency was Bitcoin, but there are now thousands of others, each with unique characteristics.
Though Bitcoin transactions may be traced back to specific senders and recipients through their hash values, this is a significant drawback of the cryptocurrency.
Monero is a cryptocurrency with unique rules in this regard. It's likewise mineable and based on a blockchain, but unlike bitcoin, the transactions here are anonymous and difficult to track. This is why most exchanges will not let you buy or sell Monero and why mining is the best option if you want some.
Many more cryptocurrencies exist besides Bitcoin and Monero, such as the technically superior coins Ethereum and the humorous currency Dogecoin. The Raspberry Pi can be used to mine a large number of them.
We'll utilize the Crontab approach to ensure that our cryptocurrency miner is always running on our Raspberry Pi.
crontab -e
If you haven't already, you'll see the message "no crontab for pi, Choose an editor" when you try to set the crontab.
Select 1 and press Enter.
Clicking here will launch a new crontab file; once it has opened, go to the bottom and add the following command, substituting YOUR EMAIL with the email you used to sign up for your Minergate account.
@reboot sudo /cpuminer-multi/cpuminer -a cryptonight -o stratum+tcp://xmr.pool.minergate.com:45700 -u YOUR_EMAIL
To keep your crontab, hit Ctrl-X and then y.
Type "sudo reboot" into the Terminal to restart the Pi.
After being powered on for almost 8 hours, my Raspberry Pi 4 has successfully calculated 357 good shares. Successful miners receive compensation when their shares are valued. If I do the math and get the appropriate answer, but my Pi is slower than another computer, I get a bad share. Only the first miner will be compensated if a miner submits a valid response before anyone else. Every invalid share is a penalty for the miner because of the possibility of fraud. I began to worry when my first four shares were flagged as invalid.
357 good shares = 0.000001410642 Monero = 0.00015569 USD
For 8 hours, I earned $0.000100, which is less than a penny. I was required to have at least 0.05 Monero (equivalent to about $5.811 USD) to make a withdrawal. (As of the date this article was published, the exchange rate was.) To attain the minimum withdrawal criterion of 0.05 Monero would take me 3,762 years at a rate of accumulating 0.000001410642 Monero per 8 hours.
As was mentioned at the outset of this piece, the aim of this activity was education regarding bitcoin, not financial gain.
Mined cryptocurrency rewards are divided up based on hash rates and shares. My hash rate swung between 1.6 and 33.3 hashes per second. The pool averaged 10.27 MH/s, around 3 million times faster than my Pi. As a point of comparison, 1 MH/s equals 1,000,000 hashes/ sec.
Additionally, a tiny commission is added to your transactions by the Minergate. Choose a Pay Per Share structure or one based on chance (with more significant potential gain).
Many 'time out' and send line failed' errors appeared on my Pi as I wrote this essay. On occasion, a Pi reboot was required, but on other occasions, the miner resumed operations without any more intervention.
Even though my Raspberry Pi wasn't a "money maker" in the cryptocurrency mining game, we still had a great time seeing it effectively compute and accumulate excellent shares.
A person can easily mine bitcoins at home with minimal equipment. A powered external USB hub may be the way to go if you want to avoid shelling out the cash for a desktop PC. Bitcoin mining can be facilitated and made more profitable by using a powered external USB hub. Raspberry Pi version B, compatible with most PCs, is also readily available and inexpensive. You can use Bitcoins to buy and sell on websites or keep them safe in a digital wallet
when you have Bitcoins.
Remember that large commercial Bitcoin miners employing thousands of computers will be your main competition. Still, a Pi 4 mining system is a fantastic (and entertaining) method of earning Bitcoins with little work. Because of the high cost of maintaining the hardware, mining Bitcoin using a Pi 4 is not financially sound. For Bitcoin mining, you'll also need hardware that's up to the task.
To be sure, a Pi 4 mining system can be a fantastic (and entertaining) method of earning Bitcoins without much effort on your part. However, even if you only make a few Satoshi, you'll still gain valuable experience and knowledge, so it's a good use of time. Be mindful of your monthly electricity costs, though.
Although you might make a few dollars mining on a Raspberry Pi, you won't become filthy rich overnight. Your electric bill may skyrocket if you've amassed a sizable Raspberry Pi fleet for mining. You can generate a small profit with a solar panel designed for the Raspberry Pi. The revenues won't make you rich, though; mining Monero with a Pi 4 and 100H/s of hashing power will net you just $1 per year. Making an annual average of $20 from mining using a USB miner is possible with Bitcoin.
We have developed a cryptocurrency miner that generates no additional costs whatsoever. The hash rate is a severe drawback of this design. Bitcoin mining on the Pi 4 is only profitable if the values of cryptocurrencies are supposed to remain the same. The upfront investment in equipment is more than the yearly return on investment from mining. One's perspective could alter if one were to speculate on the possibility of dramatically increasing prices. Those who are just sitting on unused hardware are in the same boat. A little setup is not worthwhile. The following guide will teach you how to set up a fingerprint sensor on your Raspberry Pi 4.
Thank you for being here for today's tutorial of our in-depth Raspberry Pi programming tutorial. The previous tutorial demonstrated the proper wiring of the photoresistor sensor to the GPIO pins. Finally, we learned how it might be included in a Python script for data collection and analysis needs. We also looked at the functions of each component in the circuit. However, I'll walk you through installing a Pi 4 Print Server in this guide. While installing the program is straightforward, setting it up so that a Windows network can locate the print server requires a little more effort. Rather than spending hundreds of dollars upgrading to a laser printer, you may easily upgrade your current USB printer to laser quality by installing a print server.
Because of this software, you no longer have to have the printer physically linked to a single computer, and you may place it wherever you choose and share it with as many computers as you like. In addition, it's a fantastic method of printer sharing that eliminates the need for a pricey tower computer to be on and active all the time. CUPS is the program we'll be using to make this happen. Common Unix Printing System, or CUPS, is the foundation of Linux printing applications. But, the program facilitates communication between your computer and printer. It would help if you visited available printing to verify that the CUPS printing software supports your printer model.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Raspberry Pi 4 | Amazon | Buy Now | |
Raspberry Pi 4
Wi-Fi
USB Printer
Since the Raspberry Pi print server is included in the Debian Jessie distribution, setting it up is a breeze. In this lesson, I'll be using Raspbian, so if you're unfamiliar with it and would like to learn how to set it up, check out my guide on how to do so.
We must ensure the Raspberry Pi is up-to-date with the most recent software to get started. Just type in the appropriate instructions into the terminal to accomplish this.
sudo apt update
sudo apt upgrade
We can begin setting up the print software after the Pi 4 has been upgraded. Here, we will be setting up CUPS.
CUPS, short for Common Unix Printing System, is a printing system designed for computers running UNIX-like operating systems. The software transforms the host computer into a print server. A CUPS-enabled server may receive print jobs from various client devices, sort them, and send them to the correct printer for output. Conveniently, this program can handle the administration of your printers, whether they're linked locally through USB or remotely via the network. Using the terminal, enter the following command to install the software. Considering HP has CUPS that support its open source project, HP printers, in particular. Even if your specific printer model isn't listed as being directly supported by CUPS, you may still be able to find a compatible generic driver online that will get the job done. These links will take you to a list of CUPS-compatible printers.
sudo apt install cups
We still have some work to do after CUPS's installation is complete. The first step is to include the pi user in the lpadmin set of users. With this group, the pi user can manage CUPS settings without logging in as the superuser.
sudo usermod -a -G lpadmin pi
To make sure it functions properly on your home network, there is one more thing we must do to CUPS: make it available to every computer on your network. At this time, Cups is configured to refuse connections from addresses outside the local network. By entering the following two commands, we can make it listen to all incoming connections:
sudo cupsctl --remote-any
sudo systemctl restart cups
After this, any machine on the network can send prints to the Pi 4 print server. The following command can be used if you need to know your Raspberry Pi's local IP Address.
hostname -I
If you know your Raspberry Pi's IP address, you can use it to access the website at the address below. Be sure to replace "192.168.1.105" with your IP address.
We'll examine how to configure SAMBA so that Windows can find the Raspberry Pi print server. Furthermore, we will demonstrate how to install a printer using the CUPS interface.
A proper SAMBA configuration is required if you use your print server in conjunction with Windows. To get SAMBA up and running with the CUPS print drivers, we'll have to install it and tweak its settings.
First, check that SAMBA is installed; to do so, we can use the terminal's built-in install command. Just by typing this into the terminal, we can accomplish our goal.
sudo apt install samba
Now that SAMBA is installed on our Pi 4, we can access its config file and make some changes. The following command will cause the file to be opened in the nano text editor:
Sudo nano /etc/samba/smb.conf
Once the file has been opened, it must be scrolled to the end. To do this quickly, press the Control key plus the V key. The following lines should be added or edited once you reach the very end of the file. The file already contained the "[printers]" and "[print$]" sections; all I had to do was update the values to reflect the following.
[printers]
comment = All Printers
browseable = no
path = /var/spool/samba
printable = yes
guest ok = yes
read only = yes
create mask = 0700
[print$]
comment = Printer Drivers
path = /var/lib/samba/printers
browseable = yes
read only = no
guest ok = no
To save the file, hit CTRL+X, Y, and ENTER. SAMBA needs to be restarted to pick up the updated settings. The following command, when entered into the terminal, will restart SAMBA.
sudo systemctl restart smbd
It's easy to set up a printer using CUPS, but first, we need to open the program's graphical user interface. For the IP address of your Raspberry Pi, enter "hostname" into the terminal.
hostname -I
To access the IP configuration page for your Raspberry Pi, type the following into your web browser and enter the IP address you just jotted down. Replace "192.168.1.105" with your IP address when entering this address.
The following homepage is what you should see. Here, we'll go to "Administration" on the main menu.
You'll be directed to Cups's control panel when you click here. On this page, select the "Add Printer" option.
The "Add Printer" screen has been brought up, allowing us to choose the printer we wish to configure Cups with. That printer is a Canon MG2500 series machine. When you've made your print choices, click the "Continue" button.
Ensure the printer is turned on and plugged into the Raspberry Pi through a USB connection if it does not appear here. If your Raspberry Pi still doesn't show up, try restarting it while ensuring your printer is on and connected.
Choose your printer's model from the dropdown menu here. CUPS will automatically identify the printer model and install the appropriate driver when possible. However, this may only sometimes work, so you may need to sift through the list to locate the proper driver manually. Once you've double-checked everything and are pleased, click the "Add Printer" button.
After completing the steps on this screen, the printer will have been added successfully. Here, you can give it a name and a summary that mean whatever you choose. If you have more than one printer in your residence, specifying its location will make your life easier. If you want other computers to be able to use the printer, you must also turn on "Share This Printer." If everything looks good, hit the "Continue" button.
After finishing the printer setup process, you will see the screen shown in the image below. Several of the printer's more nuanced settings are accessible through this panel—the number of pages printed, the quality of the printout, and so forth.
Having finished setting up our Raspberry Pi print server, we will now discuss how to add it to Windows. Having SAMBA set up earlier in the course should make this step less painless.
Installing a CUPS printer on Windows requires selecting the driver that will allow Windows to communicate with and comprehend the printer. Launching "My Computer" or "This PC" and then clicking "network" in the left-hand navigation pane is a quick method to get to Windows' network page, where you can get started. When you get there, you should see a screen like the one below, where your Raspberry Pi's hostname (in my instance, RASPBERRYPI) is displayed. If you double-click your Raspberry Pi's share, it may prompt you to log in. If entering anything other than "enter" fails to log you in, try "pi."
The printers used with your Pi 4 print server should now be displayed on the screen. Select the printer you wish to use by double-clicking on it.
You'll see the cautionary message below if you try to double-click this. Select "OK" to proceed with the tutorial.
Select your printer brand on the left, and then select your printer model from the available drivers for that brand on the right. If your printer isn't listed here, you can identify its model online and install the necessary drivers. For me, that meant tracking down the Canon MG2500 series. When you've decided which printer to use, you may move forward by clicking the "Ok" button.
The procedure will now initiate a link to your printer. Select "Printer" > "Set as Default Printer" to make this the system's default printer.
Now that the printer has been installed on your computer, you can use it with any application that supports printing. By printing a test page, you may verify that the printer is configured correctly.
If you're having trouble printing a file, check to see if you've picked the correct printer driver in CUPS and Windows. Ensure the printer is turned on as well; the Canon MG2500 series, for example, do not immediately restart when a print job is delivered. Adding Apple AirPrint capability to your Pi 4 print server is a great way to expand its capabilities.
Apple's AirPrint printing technology eliminates the requirement for users of Apple products to acquire and install the separate printing software. By adding AirPrint functionality, you may quickly and effortlessly print from your iOS smartphone to any nearby printer. You can run an AirPrint server from your Raspberry Pi, and Cups is the software that will power it. It will take care of talking to your printer on your Raspberry Pi's behalf.
The "Avahi daemon" must be set up before AirPrint may be used on your computer. The following command will install the package onto your Raspberry Pi.
sudo apt install avahi-daemon
Using this package, you can make Apple's Zeroconf design a reality. Bonjour has become widely used to refer to this type of network architecture. Using Bonjour, AirPrint can link disparate gadgets like an iPhone and a Raspberry Pi. Once you've selected the files you'd like to print, the Bonjour daemon will forward them to the designated printer.
Let's restart the machine to see whether the AirPrint server has worked appropriately, and everything is ready. Execute this command to force the Raspberry Pi to restart.
sudo reboot
After rebooting your Raspberry Pi, you can check to see if anything went wrong. This should get you to the point where you can print from any AirPrint-enabled device.
Have you succeeded in following this guide and setting up a Pi 4 network print server? If you've followed these steps carefully, your Raspberry Pi should be ready to function as a network AirPrint server. We were able to accomplish this by putting the Avahi daemon in place. This daemon implements the bonjour protocol used by AirPrint. Feel free to leave a message below if you have any thoughts, suggestions, or problems you'd want to discuss. The following tutorial will review the steps for monitoring a patient's heart rate with a Raspberry Pi 4.