We all know technology is not only changing trends and habits, but it is also reshaping the way we live our daily lives. We are not talking about centuries ago, but even if we examine the previous two decades, everything, from our communication style to our travel means, from our shopping habits to our entertainment industry, is changing rapidly, and this has both pros and cons. In a broader view, we can say that positive impacts are more dominant than negative effects on our lives. Maybe you have the opposite opinion, but I can prove this with the help of a simple and general example that I am going to discuss in detail with you. Just like almost every other sport and game, poker has some visible technical changes, and people can play poker online. Clients and casinos are getting more and more benefits from technical assistance. We are talking about the facts and ways in which modern technologies are reshaping and redesigning online poker. Different technologies, such as artificial intelligence, are changing the way online poker works, and we will try to find the ways and facts by which this change is being seen by experts.
Collectively, we can say that the advancement in technology has positively revolutionized online poker. There are some negative points about modern technology, but the score of the positive points is higher; therefore, we have talked more about these. The techniques such as AI, machine learning, 3D machines, AR, VR, and some others are the backbone of this change, and it seems like these changes are unending because of the advancement in technology every year. If you are interested in more details then you must visit Ignition starting hands guide for the best tips and tricks on online poker.
The history of online poker is just as important as the history of the internet because, with the advancement of the internet in the 1990s, many games, including online poker, were introduced for game lovers. This growth was so interesting that everyone was attracted to the internet and online gaming, whether they were players or game developers. Many big names in the world of online poker made a great audience out of this niche, and people are moving more and more towards online poker every year.
In the records of online poker, the first visible name is Planet Poker, which was developed in 1998. This was just the start, and the real fame of online poker started in the 2000s. Another name in the poker game is more interesting. After qualifying through an online satellite tournament, Chris Moneymaker won the 2003 World Series of Poker (WSOP) Main Event, helping to popularize the game and attract more players to online poker sites.
In the market for online poker, the next few years were significant, and these played a vital role in attracting a great community to online poker. The main reason for this was, the great bonuses and discounts offered by these online websites and the great community that was involved with amazing skills there. People were getting more and more discounts by simply signing up the new accounts, and this was an attractive point in the growth of online poker.
But in this journey, there were many ups and downs, and here it is important to mention that the industry was rocked in 2006 by the passage of the Unlawful Internet Gambling Enforcement Act (UIGEA) in the United States, which made it illegal for banks to process online gambling transactions. This resulted in a decline in the US market, which had previously been one of the largest for online poker.
Despite this time, the history of online poker has undergone a positive reshaping with the advancement in technology. People seem more convenient to have this game online because of the vast community and more facilities. This will be clear with the help of the points that we are going to share with you.
Now we are talking about some points that will justify that there are many effects of technology on online poker and in every era, we are facing the ups and downs of poker and the conclusion will be made after this discussion.
When players are connected virtually, the first thing that comes to their mind is the transparent results and the security of the rights of the players. The developers have made the system so secure to win the confidence of the players. Modern technology has helped the owners of the system to minimize the chance of cheating and to reduce the chance of using chat bots and cheating codes to win the game. With the advancement of technology, the algorithms are becoming more and more secure to win the trust of thousands of people at the same time.
An interesting point here is to mention of the involvement of the companies like poker operators that are playing a vital role to improve security and to enhance the level of trust towards online poker platforms.
With the advancement in the technologies like artificial intelligence, poker is becoming more and more interesting because of the fantastic features and the availability of the options of personalization that not only gives a quality experience to the players but the rules and options are so clear that even a new player can understand all of them easily without any major issue. This is making online poker more common among the new generation and is still the favorite of the previous one.
For the best gaming experiences, the personalization of the profile and the game is playing an important role. It seems like many controls of the profile are in the hand of the player and he can easily use his profile image in different ways for the best experience.
Data fusion is one of the most significant points in modern technology, and if we purely talk about poker, this game is full of thrill because of its complex decisions and tactics of the game that every player must be pro in to get the best position. The advanced technology helps the player to get a better experience with the help of data fusion. This makes the game more realistic as all the players are in the same database and there is no need of putting the information manually, this makes the user experience better.
With the advancement in artificial technology, machine learning has helped the system to perform better every day by learning from the behaviour of the users every time they play.
The start of the technology was amazing and easy, but with the passage of time and the introduction of new games and platforms, all of them are using modern technology. With the advancement in it, people have the easiest way to reach these platforms, and by the same token, the availability of multiple choices for online poker has made it more interesting to have the game as every online platform has its features and expertise. So, every user has a variety of online poker platforms with the advancement of technology, and this is the winning point of online poker.
As we have said, with the advancement in technology, the major work is done on the database of the players, which makes it possible to play with your groups at the same time without any issues. The traditional way of playing poker is to play at a table, and it seems perfect to play with friends in such an environment, but technology has made it possible to play with your own friends on the same table with pre-defined and decided rules according to your mode and choice. Any game with a friend is interesting, and poker is the best option to spend quality time with your friends and loved ones.
This is the nature of the human that more variety means more fun, especially if it is about the games. It is observed that real games do not offer more variety than online games because, because of the advancement in technology, the variations in the games are provided to the players so that every time they play, they enjoy a different type of game according to the mode.
Poker is a mind game, and every type of player needs a different game according to their skills. For example, a player at the beginner level will need some relaxation and want to play with the new players for practice. These types of facilities are provided with an online platform with the help of modern technology that gives challenges to players of the same rank and the same skills. The same is true for vice versa.
Till now, our focus was to justify the points to make clear why the reshaping of online poker is related to modern technology. This time, we are moving to the expert level and will talk deeply about the technology to prove our previous discussion. Online poker and casinos are popular niches globally, and people find it more convenient to play on their mobile devices, desktops, and computers rather than going to the real markets where less security and transparency are provided. To make it more popular, many techniques are being used, and some of these are given next:
One of the most popular technologies that we have seen in the market for online poker is virtual reality, which is not only related to this particular topic but to several fields of gaming. When we think about online poker, the only thing that we might miss is the interaction of the people just like in the real world. To overcome this deficiency, now techniques like virtual reality are used with the help of which, the players can interact and deal with the other players conveniently and can read their profiles to learn about their games and their positions. This helps a lot to the players to make the strategy and to have the perfect move at the perfect time. To make it more real, headsets and motion sensors are used for augmented reality, or simply AR, which helps the user gain knowledge about other players and the rules of the game they are playing so that they may have the strategy to win the game. The information about the objects and the working of the object is also related to it and this gives the player a more engaging and realistic experience.
If we say that blockchain technology has revolutionized the world of online poker, then it will not be wrong because it is proven in many ways, and in such games, the most important feature is the security and privacy that are provided by the blockchain.
To make sure that every transaction is safe and clear, the blockchain provides many features, that help the online platform win the trust of its users. This means that the player will have a tension-free game with the confidence of having the perfect result without any cheating.
If we are talking about modern techniques, then how can we forget the 3D machines? To have an immense and real-time experience in online poker, people are using 3D machines, which give a highly interesting experience in such games. The 3D animation is closer to the real world and the fantastic design gives the best experience with creativity.
The graphics and character modelling of these machines make the game more interesting. The animations make the game more engaging, and the players feel it is more realistic and have a better experience.
We should mention here that the technologies we have mentioned here are not only convenient for the players but also for the developers and the owners of online poker. This gives the player the independence of playing and getting the reward most transparently and conveniently. So, this reshaping of online poker with modern technology is helpful.
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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And online casinos are no different.
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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.
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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:
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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.
Writing an essay is a critical skill that every student must learn.
It’s not an easy task, I understand.
But with deliberate practice and careful attention you can learn to write an essay like a pro.
Essays serve as a vital tool for academic and personal development, allowing individuals to express their thoughts, opinions, and arguments in a structured and coherent manner.
Whether you are writing a college application essay, a research paper, or a personal narrative, the ability to communicate your ideas effectively is essential.
Needless to say, the job of writing an essay can seem daunting, especially if you are a beginner. This guide aims to provide you with a comprehensive overview of the essay writing process, from preparing to write to formatting and finalizing your work.
In this guide, we’ll learn 8 key steps to write an essay in a compelling and concise way.
Let’s get started.
Preparing to write an essay is an essential step in the writing process that can make a significant difference in the quality of your work. In fact, it can make or break your entire writing process.
While you prepare to write an essay, make sure you:
Before you begin writing, make sure you understand the essay prompt. Carefully read the instructions and identify the key requirements, such as the topic, length, formatting guidelines, and due date.
Once you have a clear understanding of the prompt, brainstorm ideas for your essay. This can involve creating a mind map, listing ideas, or doing free-writing exercises. Consider the main points you want to make and how you will support them.
Once you have generated ideas, organize them into a coherent structure. This can involve creating an outline or using a graphic organizer to arrange your ideas logically. Having a clear structure will help you stay focused and ensure that your essay flows smoothly.
Based on the type of essay you are writing, you may need to conduct research to support your arguments. This can involve reading academic articles, books, or online sources. Take notes and keep track of your sources to make citing them easier later on.
The introduction of an essay is a crucial step that sets the tone for the rest of the thoughts you’ll sprinkle on the page. It should grab the reader's attention, provide background information on the topic, and present the thesis statement. The key elements to writing a strong introduction include:
Start your introduction with a hook that grabs the reader's attention. This can be a quote, a surprising fact, a question, or a personal anecdote related to the topic.
After the hook, provide some background information on the topic. This can involve defining key terms, giving a brief history of the topic, or explaining why it is important.
The thesis statement is the main argument or point of your story. Make it clear, concise, and specific. It is important to include it at the end of your introduction so that the reader knows what to expect in the rest of the essay.
Your introduction should be brief and to the point. Stop beating around the bush and keep it simple and straightforward. It’s wise to aim for around 10% of the total word count of your essay.
The body paragraphs of an essay are where you present your arguments and support your thesis statement. To make a positive impression and make your prose coherent and concise, you must start your body paragraph with a clear topic sentence that introduces the main point of the paragraph.
Plus, provide evidence to support your argument. You can, for instance, use evidence such as quotes, statistics, or examples to support your argument. Be sure to properly cite your sources. And after presenting evidence, explain how it supports your argument.
This can involve analyzing the evidence, drawing connections to other ideas, or explaining the significance of the evidence. To make your body paragraphs flow well, use transitional words and phrases to connect your ideas and make your writing glide from one sentence to another effortlessly.
Additionally, each paragraph should focus on a single point related to your thesis statement. Avoid introducing new ideas or going off on tangents. Always, use strong, clear language to communicate your ideas effectively. Avoid jargon or overly complex language that can confuse your reader.
Crafting a strong conclusion is a vital part of writing an essay. Of course, you wouldn’t want to make the reader hang in the middle anticipating and guessing your entire point. A good conclusion should summarize your main points, restate your thesis statement, and leave a lasting impression on the reader.
To craft a strong conclusion you must:
Start your conclusion by summarizing the main points you made in your essay. This helps to remind the reader of your key arguments and provides closure to the essay.
Restate your thesis statement in a different way than in the introduction. This reinforces the main point of your essay and reminds the reader of what you set out to accomplish.
End your conclusion with a memorable statement that leaves a lasting impression on the reader. This can be a call to action, a question, a powerful quote, or a thought-provoking idea.
Your conclusion should not introduce new information or arguments. Instead, focus on summarizing your main points and reinforcing your thesis statement.
Similar to the introduction, your conclusion should be brief and concise. It should support the arguments you made in the entire essay. In fact, it exhibits a quick summary of the points you penned down in the essay.
Editing and proofreading are important final steps in the essay writing process. The mistakes and slip-ups come to the surface when you start editing your first draft. One single mistake in the write-up can project an idea of an amateur writer. And you wouldn’t want your entire tedious writing process to go in vain for one single mistake.
The following are some tips for editing and proofreading your essay:
After finishing your essay, take a break and come back to it later. This can help you approach your essay with fresh eyes and catch mistakes you may have missed.
Make sure your essay has a clear structure and flows logically. Check that each paragraph relates to your thesis statement and that your arguments are presented in a clear and coherent manner.
Use a grammar checker or proofread your essay carefully for grammar and spelling errors. Common mistakes include subject-verb agreement, punctuation errors, and misspelled words.
Check that you have properly cited all sources in your essay. Use the appropriate citation style, such as MLA or APA, and make sure all sources are included in your bibliography or works cited page.
Yes, this is important. I have experienced personally that reading out loud provides an entirely different experience. The written words appear different when reading them out loud compared to when you just scan them through your eyes. It can help you catch awkward phrasing, repetition, or other issues with your writing.
Proper formatting and citation help to ensure that your essay is clear, professional, and meets academic standards.
The following are some tips for formatting and citing your essay:
Depending on the subject area and instructor's preference, you may be required to use a specific formatting style, such as MLA, APA, or Chicago. Make sure to follow the guidelines for that style consistently throughout your essay.
Use standard margins and font sizes, such as 1-inch margins and 12-point font. Use a legible font, such as Times New Roman or Arial.
Depending on the formatting style, you may need to include a title page that includes the title of your essay, your name, course name, and date.
In-text citations are used to give credit to sources you have referenced within your essay. Depending on the formatting style, citations can be in parentheses, footnotes, or endnotes.
A work cited page lists all of the sources you have referenced in your essay. Make sure to follow the appropriate formatting guidelines for your chosen citation style.
Many citation tools, such as EasyBib and Zotero, can help you format citations correctly and manage your sources.
Checking for plagiarism can ensure that you have not inadvertently used someone else's work without proper citation. To check for plagiarism in your essay you must:
There are many plagiarism detection software programs available, such as Turnitin, Grammarly, and Copyscape. These programs compare your essay to a vast database of existing works to identify any instances of plagiarism.
Look for similarities between your essay and other works by comparing key phrases, sentences, or concepts. This can be done manually or using online tools such as Google Scholar.
If you use someone else's words or ideas in your essay, make sure to use quotation marks and properly cite the source. This helps to avoid plagiarism and gives credit where it is due.
If you paraphrase someone else's work, make sure to put it in your own words and properly cite the source. Paraphrasing is not an excuse for plagiarism, as it still involves using someone else's work.
Getting feedback on your essay can be a valuable way to improve your writing and make sure your arguments are clear and effective. Here are some tips for getting feedback on your essay:
Ask someone you trust to read your essay and provide feedback. Choose someone who can give you honest and constructive criticism.
Your instructor can provide valuable feedback on your essay and help you improve your writing skills. Ask for specific areas to work on or things to improve.
Many schools and universities have writing centers where you can get help with your writing. Staff at the writing center can provide feedback on your essay and help you improve your writing skills. Moreover, you can also get help from an essay writing service to make your essay compelling and concise from start to finish.
There are many online writing resources available that can provide feedback on your essay, such as Grammarly and Hemingway Editor. These resources can help you identify areas for improvement and give you suggestions for how to improve your writing.
In a peer review, you exchange essays with another student and provide feedback on each other's work. This can be a valuable way to get feedback on your essay and see how others approach the same topic.
Writing an essay is an essential skill that can benefit you in many ways. By following the steps outlined in this beginner's guide, you can learn how to prepare, plan, write, edit, and proofread your essay effectively.
Remember to start by selecting a topic that interests you, and to organize your thoughts and ideas before you begin writing.
Be sure to write a strong introduction, well-developed body paragraphs, and a compelling conclusion that summarizes your main points. Don't forget to edit and proofread your essay carefully to ensure that it is clear, concise, and error-free.
Finally, remember to properly format and cite your essay to meet academic standards. With practice and perseverance, you can master the art of essay writing and communicate your ideas effectively in writing.
Although 3D printing feels like a relatively new development, there are lots of promising projects underway. A scheme to build 46 eco-homes has been approved in the UK’s first 3D printed development , for example, and the same is happening in Australia to provide housing for remote indigenous communities in rural areas .
But how can 3D printing be applied in business? Here’s a breakdown on how it can be used and the opportunities it creates.
3D printing refers to technology that can form materials using computer designs. The earliest signs of 3D printing came about in 1981. Dr. Hideo Kodama created a rapid prototyping machine that built solid parts using a resin and a layer-by-layer system.
Using a bottom-up technique, the material is layered until a tangible item is created. We are still in very early days when it comes to 3D printing, but engineers are optimistic about how it can be applied on a large scale across industries. There’s great potential for using 3D printing in manufacturing and home building.
3D printing begins with a design stage. This is the 3D modelling stage where you can uncover the best path to follow to get the most out of the design, such as the materials used. You will also be able to use this information to determine the cost and speed of your project, adjusting where necessary.
3D printing equipment is powered by a system of control cables such as those from RS to facilitate autonomous 3D printing applications. Data connections are also used to transmit the design to printing equipment.
3D printing is commonly used for prototyping ahead of launching major manufacturing projects. It allows product designers to get a life-size glimpse at the proposed product, enabling them to identify any faults or improvements before going ahead with more expensive resources and materials. While 3D printing can be done to a large scale, it can be done to a much smaller scale too to create smaller, cost-effective prototype models.
The attention that is given to the design process and modelling stage means companies can analyse the production method used to create the desired output. Sometimes there will be limitations such as the fact that 3D printing can only work when adding layers on top of one another, which means features like overhangs can’t be catered towards in a simple manner. Regardless, 3D printing can still cater to things that traditional manufacturing can’t.
3D printing can be used to minimise demand on time and manpower. It can be used to tackle more intricate tasks at a larger scale. Aerospace was one of the first industries to utilise this, as well as biomedical and mechanical engineering. In some cases, conventional manufacturing simply can’t replicate the detail at such a large scale.