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.