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.
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Many of the most popular table games are available as live dealer games, including blackjack, roulette, baccarat, and casino hold'em.
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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.
Artificial intelligence is becoming more and more important for data security. In this post, we'll look at how AI may assist businesses in anticipating and thwarting threats. But before going ahead we will explain the terms artificial intelligence and machine Learning.
Artificial intelligence (AI) is a discipline of computer science that focuses on making electrical equipment and software intelligent enough to do human activities. AI is a broad concept and a basic subject of computer science that may be used to a variety of domains including learning, planning, problem solving, speech recognition, object identification and tracking, and other security applications.
Artificial intelligence is divided into numerous subsets. We shall look at two of them in this article:
Deep Learning
Machine Learning
Machine learning (ML)-based computer systems have the capacity to learn and carry out tasks without explicit instructions. These systems find, examine, and comprehend data patterns using ML algorithms and statistical models. Many jobs that are typically completed by people are now routinely carried out automatically using machine learning capabilities.
A machine learning technique called unsupervised learning enables ML algorithms to carry out tasks without clear instructions and produce desired results. Based on analysis and experience, this method determines the best solutions to a problem. When given an input (a task to perform), the model can decide on its own what the optimum course of action is. The model gets better trained and becomes more effective the longer it solves the assignment.
The benefit of ML for many tasks is obvious—machines don't grow bored or upset by repeatedly performing the same monotonous tasks. By automating numerous processes in work chains, they also drastically reduce workloads. Security teams can, for instance, use AI-based solutions (which will be covered later) to automatically detect threats and handle part of them, minimising the amount of human contact necessary for specific security activities.
Data anomalies can be found with the aid of machine learning. You may train algorithms that recognise particular patterns and user behaviour using machine learning. Detecting suspicious behaviour in a workplace, such as an increase in password resets or unexpected requests for sensitive data, will be made possible thanks to this.
Computer vision can also be used to find data trends that might point to a possible system or network vulnerability management violation. Machine learning techniques are employed to forecast future examples of this behaviour based on the environment's present conditions after being trained with historical data on previous successful attacks (e.g., usage patterns).
Besides ML techniques you can rely on the use of VPN. Because you can keep your data from suspicious activities from hackers by installing a VPN. It is easy to set up a VPN on router and once you set it will start monitoring your PCs activities against malicious attacks.
Before an attack ever occurs, AI may identify it and stop it. Understanding how data is gathered, processed, and presented is just as important as looking at the data itself. AI is able to spot warning signals of impending attacks and stop them from executing in the cloud, on a network, or even in real time.
By seeing dangerous activity on your virtual machine (including malware) while you're away from home or work or even on mobile devices, AI can also assist you in protecting yourself against AI-enabled dangers of gadgets and PCs both! Additionally, there are social media platforms like Facebook and Twitter and AI also helps to keep them secure from attackers.
Artificial intelligence is becoming more and more important for data security. AI can assist businesses in identifying dangers, spotting abnormalities, and reaching decisions more quickly than ever before.
It plays a significant role in contemporary data management techniques, which in turn have significant ramifications for enterprises across all industries.
"Domain knowledge" is the capacity for people or computers to comprehend information and take appropriate action without being instructed on its workings or meaning (AKA: natural language processing).
"Machine learning" is the process through which computers or humans can perform jobs utilising data sets without any prior knowledge.
In order to learn from mistakes they made earlier in life and produce better results later on when things get difficult again, both of these strategies depend on increasing volumes of data being gathered over time.
Obtaining a thorough and accurate inventory of all devices, users, and software with access to computer systems. Inventory also heavily relies on categorization and the measurement of business criticality.
Hackers, like everyone else, follow trends, therefore what's popular with hackers changes on a regular basis. AI-based counterintelligence systems can provide current knowledge about global and industry-specific threats to assist in making crucial prioritising decisions on the basis not only on what may be employed to defend your organisation, but also on what is likely to be utilised to attack your organisation.
It is critical to comprehend the significance of the numerous security technologies and verification activities that you have implemented in order to keep a stable security posture. AI can assist you in determining your information security program's strengths and weaknesses.
Accounting for IT assets, threat sensitivity, and control efficacy, AI-based solutions may forecast how and where you will be compromised, allowing you to allocate resources and tools to areas of weakness. AI-derived prescriptive insights can assist you in configuring and improving policies and processes to most greatly increase your organisation's cyber resilience.
AI-powered systems can give greater context for prioritising and response to safety warnings, for quick incident response, and for surfacing root causes to remediate exposures and avoid future issues.
The explainability of guidance and analyses is critical to leveraging AI to enhance human information security teams. This is critical for gaining buy-in from stakeholders across the organisation, understanding the impact of various information security programmes, and reporting relevant information to all stakeholders involved, including end users, security operations, CISO, audit committees, CIO, CEO, and board of directors.
Although I have been doing this for a while, data security is currently enjoying a comeback. People are more worried than ever about their sensitive data being stolen because hackings are on the rise. The good news is that scalable data protection is possible with artificial intelligence (AI). In this article, we talked about how AI and machine learning combine to find abnormalities in massive datasets and spot trends that point to shady conduct.
Hi Everyone! How are you doing, my friends? Today I bring a crucial topic for PLC programmers, technicians and engineers. We have been working together for a long time using ladder logic programming. We have completed together dozens of projects from real life and industry. One day I was thinking about what we have done in this series of ladder logic programming, and I came across that I missed talking about one essential topic ever. You know what? It’s the PLC troubleshooting and online debugging! After writing a ladder logic program for the project, you can imagine it should operate from the download moment 24/7. As usual, any system goes faulty one day. So we need to go through this matter, showing you how to find our PLC faults, troubleshoot, and go online with the PLC to figure out the cause of the problem. Is it in the program logic? Or inputs and outputs? The hardware has an issue, i.e. battery has died, and the program has gone. For those of you who do not know the meaning of the expression going online with the CPU, do not worry, my friends, as we will show you the operation of the PLC.
That is such an excellent question to open our tutorial by saying that dealing with PLC has mainly three modes of operations: program mode, in which we are allowed to download from PC to PLC, remote mode, in which we are permitted to download and upload to and from PLC, and the run mode that allows us to go online for monitoring and troubleshooting. So Folks, assume we have just completed the programming of the project. Then we tested the logic and downloaded the program to PLC. So what if a fault occurred, as shown in figure 1, and the operator called you for support? That’s what we are going to discuss in this tutorial trying to transfer our experience to you, helping you to get familiar with troubleshooting PLC, and doing live debugging to your program while it is running on the PLC and all IOs connected to the actual device, including sensors and actuators. So let’s get to the tutorial.
First, you bring the PLC programming cable and plug it from PC into PLC. The type of PLC cable depends on the plc type and model. That information you can find in the PLC manual or google it easily. After plugging the PLC cable from PLC into PC, you open the RSLINX software and create a connection using the appropriate driver. The next step is selecting the CPU to see many options to go with one of them. For example, you will see the “download” option used to move the program from PC to PLC; there is also an “upload” option used to get a copy from PLC to PC. And “GO-ONLINE,” which you use to debug the program while running on the PLC. You also need to know, my friends there are many operating modes of the PLC. The program mode allows you to change and modify the program, but the program is not running in that mode of operation. The run mode of operation is also one option to let the program run, and there is no way to do modifications in that mode. The last mode of operation is the remote mode which allows you to make changes and clear the faults, and the program runs simultaneously.
Once you have tried to connect to the CPU, you will see what Figure 2 depicts below. You can notice, everyone, the status in is red “faulted,” and that fault will not allow the program to run. Now you need to do two things. The first is to go into the vault to see what it is and the cause of the fault to find a way to fix it. Second, after setting the fault, you will need to clear the fault. And ultimately, you will change to run mode.
So now, my friends, how to see the fault description to know the reason behind it? It is straightforward, as you can see in figure 3. You go to the fault and right-click to see a popup menu appear. The popup menu has a couple of options like go to error or clear the fault and other options like go online, download, and upload. Now, why not choose to clearing the fault? That might work if the reason behind the fault has gone. But it won’t as long as the fault is still there. If you try, it wbe incorrectrect once you try to move to remote or run mode. By the way, you can clear the error in the program mode only.
Figure 4 shows how to go to the fault to get to know some information about the fault, like which routine in which the fault took place, the precise description and reason for the fault, and the status bits to show you the statuses of PLC. Figure 4 shows the window of the status bits. One tab is the “errors” tab which shows the details about the fault. Now you can see that guy clearly says there is an issue in the expansion module, so you can check the modules and fix the problem, and then you can go ahead clearing the error, as we will show you in the next section.
Now everyone, after checking the fault reason and getting rid of that issue, it is time to clear the fault and return the CPU to regular operation by switching it back to run mode. Remember, if the fault causes are not eradicated correctly, the fault will return once you try to switch to remote or ryn mode. Figure 5 shows how to clear the fault by right click on the fault highlighted in red and choosing “clear fault.” Now let’s see what happens.
Figure 6 shows a message confirming to clear the fault because going with that will remove the saved information about the fault.
Figure 7 shows that the fault has been successfully cleared, and the program switched to remote program mode meaning the CPU is removed from the fault, but the program still is not running and need to switch to run mode. Remember, my friends, up to this point, we were curious if the fault reason had cured correctly until the CPU switched successfully to run mode.
Figure 8 shows the CPU has switched to run mode in green! That means the errors have cleared, and the causes have gone. Now we need to move on to showing you how to do debugging by going online with your program that is running on the PLC. So let’s see that together, folks.
Figure 9 shows the direct step to connect to the PLC by clicking the comms menu and selecting your PLC guy.
Figure 10 shows the connection has been established, and the remote running green appears on the top left, as you can see, my friends.
Figure 11 shows how to go online by clicking on the menu at the top left corner, as shown below, and you will see a popup menu that comes out showing their options: Download, Upload, and GO Online options. Choose the last to complete the online going procedure. So now you are connected and online with the PLC, meaning you can monitor all the parameters and values and see the IO’s statuses.
Thanks, my friends, for following up with me in this tutorial I see it’s essential for you all to practice because I can not imagine a PLc programmer who does not know such practice. Next time will bring a new project from the factory to continue learning and practice together the PLC ladder logic programming. So please stay safe and be ready to meet soon with a new tutorial.
Hi, my friends and welcome back. I am happy to meet you again with a new tutorial of our PLC ladder logic programming series tutorials. Today we will complete what we started the last tutorial on the Elevator control project. We have a bunch of duties to complete together today. So let’s save time and jump into work immediately.
Figure one shows the details that might help me describe the project between our hands. We have an elevator car that travels up and down and can stop on one of four floors based on the passengers’ requests. We have 6 push buttons on the wall next to the elevator door that can send requests to call the elevator. In addition, there is a control panel inside the elevator cabinet in which there are push buttons to request stations to reach floors 1, 2, 3, or 4. So now we are requested to manage all the scenarios and all requests received from inside and outside the elevator considering the priority of these requests to save energy and time management for the elevator traveling route. In the next section, the detailed requirements are listed.
The elevator should save all requests from the time they are requested until they are processed and cleared from the to-do list.
The elevator should prioritize the requests in their direction of movement. For example, while he goes down, the requests for places located below his position will be given priority to do first. Similarly, when he goes up, the requests above the position where he is will be given priority. In that we, the waiting time will be reduced, and the energy and overall travelling distance will also be minimized. Because we have a lot to present in this significant project, let’s move on directly to the design and the ladder logic program that handles these requirements.
As usual, we are going to apply the divide-and-conquer rule. So the project will be divided into subtasks, and each task will be managed by one subroutine all subroutines are called in the main routine in sequence. Figure 2 shows the program structure which is composed of 5 subroutines. In the first routine, all initializations are called to be executed. So what’s in these subroutines? That is what we are going to show you below. So please follow up, my friends.
Figure 1 shows the ladder logic code of the first subroutine that initializes the work to start with the first floor as the initial position and going up as the initial moving direction and activate the action of performing the subsequent request or waiting for an incoming one.
The next subroutine is the heart of the logic, as it saves the requests and energizes the light indicators of the requested doors. Figure 3 shows a portion of the routine’s ladder logic code. As you notice, my friends, the first line is to tell the compiler that this is the start of the subroutine. The rung number 1 check if the request push button of the first floor inside the elevator’s car has been pressed and the elevator is not on the first floor; it will light the button lamp to show there is one request in the queue until the time comes to perform that request then the light will be turned off as we will demonstrate to you, guys. The next rungs, rungs number 2, 3, and 4, do the same for the corresponding keys inside the elevator’s car. If a push button is pressed, and its requested floor is not the current floor where the elevator is, then the corresponding lamp indicator is lighted, showing the request is saved in the queue, and when it is processed, the lamp indicator will be turned off. Now, moving on in the code, rung number 5 handles the outdoor request from pressing the first floor up push button. It simply checks the floor location. If it is not on the first floor, it lights; the lamp indicator of that push button shows that the request is in progress and will be turned off once it is processed. The following rungs do similarly the same function. But notice we have one push button for floors 1 and 4 while there are 2 push buttons for floors 2 and 3. So there are two rungs for handling the up and down direction requests.
One remaining thing for this subroutine is that the last rung confirms that when no requests are there so, the light on the first floor is turned on, showing that the elevator is on the first floor. Now my friends, let’s move to the next subroutine.
The next subroutine is called “track the vertical travel.” As you see, guys, the code checks in rung number 1 if the elevator is on the first floor, then it lit the first-floor light indicator and unlatched or de-energized the previous floor light, which is on the second floor and stops and opens the door. Now, when it reaches the second floor, indicated by the I:5 encoder, it lit the second floor and de-energized the lights of the next floors up and down, which are the first and third floors. Also, it checks if the second floor is being requested or in the queue list, then it stops the elevator and opens the door for getting or charging people who requested the second floor.
Figure 7 shows the ladder logic code of the second and last part of this routine; rung number 3, for example, checks if the elevator has reached the third floor; in this case, it lit the third-floor lamp indicator and turned off the next floors’ lamp indicators, i.e. floor 2 when it moves up from floor 2 to floor 3, or floor 4 when it goes down from floor 4 to floor 3. Also, if it goes down and the GO-Down request of the third floor is pressed, meaning there is a request in the queue, then it stops the elevator and opens the door for charging or discharging people who target the third floor. But, if it is going down and the Go-down, it stops the elevator and opens the door for charging or discharging the people. And the last rung, number 4, handles the 4th-floor requests. If it is on the 4th floor, it just lights the light indicator on the 4th floor and turns the light on the 3rd floor.
Now, my friends, the next routine is called “stop and open,” that handles opening the door at each destination. Figure 8 shows the logic; in rung number 1, if the door is not open for 2 seconds, then it is open the door, gives some time and then performs the subsequent request. When the door is opened, the request for the first floor is cleared. When it reaches floor number 2, and the door opens, if it goes up, clear the floor lightly and push the button to request the second floor, similarly, for the third and fourth floors.
Figure 9 shows the ladder logic code of the routine that decides which door will be bypassed and closing the door and go. It is called “ Do next or wait.” You can see, guys, it easily compares the moving direction and the requests in the queue based on the light status of the push buttons of all floors. Then bypass and resume with the door closed or wait and open the door for charging or discharging people.
Figure 10 shows the routine for closing the door at destinations. The routine is called “close door and move.” It simply checks if the door is closed and going up is requested, then lunch the move up the direction. But, if it is requested to move down, then latch the motor moving down. If no requests are moving up or down, enable the between-floors status. Now, my friends, we have explored all subroutines, and the time to test our logic and ladder logic program and subroutines has come. So let’s see if it is correct or needs some amendments.
Figure 11 shows the initial state of the program while the elevator stops at the first floor, and the light indicators show the light of the first floor is lit, as you can see, my friends. Now let us command the push buttons requesting many requests to test.
From the initial position where the elevator is on the first floor, figure 12 shows the elevator has been requested as follows: on the second floor, it is requested to move up. On the third floor, it is requested to move down. On the fourth floor, it is requested to move down.
According to the requests and the sequences, it starts the journey from the first floor and stops at the second floor, as shown in figure 12.
Continuing his journey, he bypasses the third floor because it needs to go down. Show it gives priority to the request on the 4th floor, which is in his moving direction, as shown in figure 13.
Now, after reaching the most up, it changes direction and goes down to reach the third floor, which is the last request, as shown in figure 14. Notice, my friends, every time it reaches one requested place, it clears the light of the belonging request.
Clearly, the test successfully showed one scenario with all the test cases of moving, stopping, and bypassing. Thank you, friends, for following up on these points, and I hope you have enjoyed the project we have done together in this tutorial. Also, I promise the next tutorial will come with a new project from real life and learn more and enjoy practicing ladder logic programing together. So stay safe and be ready for our next tutorial.