Hello students! Welcome to the new tutorial on Python. We all know that Python is one of the most popular programming languages, and there are hundreds or thousands of developers that are earning a handsome amount with the help of this easy programming language. In the previous lecture, we studied the range in the sequence, and in the present class, our concern is having the command on the sets in Python. We know you are curious about the set's details, but before this, I want to share the list of topics that will be covered in this class.
What is a set in the Python programming language?
What are some properties that distinguish the set from other data types?
What is the mutable data type, and how is it related to the set?
Introduction of the Jupyter notebook.
Can we have duplicate elements in the set?
How to add, remove, and update the elements in the set while using the Jupyter notebook.
How can we access the elements using a loop?
Give an example of how to use the length function with sets and why it is important.
All of these are important interview questions, and we will not only find the answer to them but also elaborate on them with the help of simple but understandable examples taken from daily life routines. Your duty is to perform each and every code, not only by copying it from the lecture but also test your knowledge and practising more and more by making your own examples.
Since the last few tutorials on Python, we have been studying a lot about the sequence, which is basically the representation of a collection of data types with homogeneity or heterogeneity in the elements. If we talk about the sets, these have the same properties and procedures as their other group, such as list and range, but a slight difference in their property makes them a different data type. This can be elaborated with the help of its basic definition:
“The set is the type of sequence that contains the group of different data types, and it is the collection of unordered or unindexed data types together.”
Until now, the sequence discussed had been represented exactly as it was written by the programmers in the code. Yet, in the sets, the order is not exactly the same all the time. If you are thinking it is strange, then you must know, in the higher level of programming, this property of the set works great because we get the elements in random orders.
Another difference between the set and the other sequences is the usage of the bracket, or, in other words, the declaration of the sequences. To tell the compiler that we want a set in the sequence, the programmers use curly brackets. You must have noticed that it is very rare to use curly brackets in Python, and therefore we can say that the representation of the set in Python is unique.
As we have a lot of information about the sequences, we can openly discuss the properties of the set, and the reader will easily understand them by comparing them with others. So, here are some of the properties that can be compared:
Sets are represented with curly brackets.
The elements of the set can not be duplicated; that is, all the elements are uniquely defined, and no element should be repeated; otherwise, the compiler will show the output in which the duplicate values are shown only once.
The set is a heterogeneous collection of elements, and therefore, the programmers can add one or more data types to a single set according to their choice.
The set can be empty, that is, declared with zero elements.
The set can be updated after its formation if the programmer wants to make some changes to it afterwards.
There are certain built-in functions of the set that, when used with the sets, have great applications in Python programming.
Each of these properties can be explained well with the help of TensorFlow. We have been using the Jupyter lab of TensorFlow since the start of this tutorial, and now, I want to tell you a better and more professional way to run the code with the help of TensorFlow. For this, you do not have to install any other software but the Jupter notebook already installed on your PC. Simply go to your search bar and run the Jupyter notebook. It will add a new tab with the label "home." Here, go to the “New” dialogue box and select Python 3. This will add the new project to a new tab. You can name it, but by default, it is named "untitled."
If you are practising all the codes with us by hand, you will observe that the Jupyter notebook has a better user experience, and it adds the ending of common syntaxes such as the double quotation and parentheses by itself when the programmer starts them. We will talk more about it in later lectures, but for now, we are moving towards the codes and properties.
The first thing that we want to revise here is the definition of mutable elements:
“In programming languages, mutable objects are those that are used to group different items and can change their value according to the instruction of the programmer.”
We have learned many mutable sequences, such as lists, and here, the point is to revise it to a set and not use the mutable sequences as the elements. Only data types such as strings, integers, etc. can be used as the elements in the set; otherwise, the programmer will face an error. This can be explained with the help of the code given below:
#Starting new list
myList=["Physics", "chemistry", "biology"]
#declaring a new set
mySet={myList,'a','e','i','o','u'}
print(mySet)
As a result, it is demonstrated that programmers can combine simple data types into sets, but it is not possible to create collections of mutable objects or collections of collections within sets.
In the properties, we have mentioned that the process of feeding the duplicate elements into the set is not useful because it checks for each and every element while providing the output, and if the element is being repeated, the sets ignore them. As a result, if we have the element more than once in our input, the number of elements in the input and output are not the same.
#Declaring the set
MySet={21,23.6,55,'Peach', 'Almond', 23.6,21,'Almond'}
#using iteration to print the set
for item in MySet:
print(item, end=" ")
print()
#calculating the length
length=len(MySet)
print('Numbers of elements = ',length)
This property will be more clear with the help of the following screenshot:
Hence, out of eight elements, the two duplicate elements are removed by the compiler, and we only get five elements that were calculated by the length function.
This is an interesting method that is compatible with the set in Python. Consider the situation where the programmer has declared a set and then needs to add an element to the same pre-defined set. In such cases, the addition method is useful, with the help of which the programmer simply uses the syntax of the add method and there is no need to recreate the whole set again.
NameOfSet.add(element to be added)
If the question arises about the position of the element, this will be clear with the help of an example that we are going to check:
#Initializing the set
mySet={'eggs', 'bread', 'jam',23,67,132,55}
print('Elements of my set is= ', mySet)
#adding a new element
mySet.add("oats")
#printing the set with the added element
print('Elements of my set with new element= ', mySet)
Keep the scenario in your mind that we have discussed above, but this time, there is a need to remove the lament from the set, and for this, Python has another method that simply searches for the required element from the set and removes it. Afterwards, the results can be printed on the screen to check whether the task is complete or not. The keyword to remove the element is "discard,” and it is used in the same way as the add keyword.
#Initializing the set
mySet={'eggs', 'bread', 'oat','jam',23,67,132,55}
print('Elements of my set is= ', mySet)
#removing the element "oat"
removeValue=mySet.discard('oat')
#printing the set with the removed element
print('Elements of my set with discarded element= ', mySet)
So, the removal process is also very simple and understandable but the syntax must be kept in mind and before using the final set in this case, always check for the results by printing the elements on the screen as we are doing here because a little mistake on the syntax results in no removal and it may cause the problem in the code. So it is a good practice to have an eye on the elements.
The updating process of the set may include different types of updates, such as increasing the size or changing the elements' sizes. For a better understanding, the best way is to learn how two or more sets can be merged into one large set. In the previous lectures, we have seen this type of process where merging is done with the help of a method. To discuss a new method with you, here we are using the update method. The process and syntax are the same as we have seen in the previous two methods.
setToBeAdded.update(setToBeUpdated)
As a result, the final set has elements from both of these sets. But it is important to notice that both sets have to be declared first, and in the third step, we get the merged or updated search with the help of the command given above.
#Initializing the first set
myFirstSet={'eggs', 'bread', 'oat', 'jam',23,67,132,55}
print('Elements of first set is= ', myFirstSet)
#Initializing the second set
mySecondSet={'Python', 'Java', 'C++'}
print('Elements of second set is= ', mySecondSet)
#Updating the sets
myFirstSet.update(mySecondSet)
#printing the final set
print('Elements of final set= ', myFirstSet)
Hence both of these are merged together and as we are using the sets, the order of the final set is different and unarranged. Well, it is a good practice to check for the numbers of elements using the length function all the time.
We hope by now you have an idea of the for loop and how we use it with different data types in Python. Similar to the list, the programmers can access each and every element with the help of iterations (loops). So, let us review the elements of a set with the help of the for loop.
#declaring our set with the name to-do list.
ToDoList={'assignment', 'coding', 'prayer', 'washing cloths', 'doing dishes'}
#starting for loop
for work in ToDoList:
print(work, end=" ")
If we look at the output, we get the following results:
Hence, it was an interesting tutorial on the sets where we learned a lot about the topic and the details were interesting and related to our daily life. At the start, we saw the basic definition and a brief introduction to the topic. We have seen some properties of the sets that were resembling the types of sequences but these were also different in many ways and we not only studied them in detail but practically proved them in the Jupyter notebook. It was nice to use the Jupyter notebook that we are going to use onward in this series. In the next tutorial, we will put light on some other features so stay tuned with us because we are preparing our next lecture on Python.
Hey peeps! Welcome to the new lecture on the sequence data type, where we are discussing the range data type. We are interested in working on deep learning, and for this, we are learning the Python programming language from scratch. If we talk about the previous episode, we saw the byte and byte array methods that were amazing for converting the different data types into bytes. The current lecture will discuss the range data type, which is slightly different from the other types of sequences, so students will learn new and interesting concepts in the lecture; however, before we get into the details of our topic, take a look at today's highlights:
What is the range function?
How can you elaborate on the syntax of the range function in detail?
What are the three types of range functions?
Give us some examples of range functions in Python.
What are some basic questions the answer of which should be kept in mind while using the range function?
The answer to each question above will be provided before the end of this lecture. All the examples will be tried on TensorFlow for better understanding.
The range is a type of sequence in the data type that also represents the group or collection of the items together in different ways, just like other types of sequences. It is also the built-in function in Python, and while using it, the programmer gets the range object. The range is one of my favorite data types because it is easy to use and, in just a few simple steps, it provides us with the sequence of the integers according to our choice. Usually, the loops play an important role while dealing with the range function. Right now, as we have not learned about loops, you will simply have an idea of the workings and output of this data type.
The good thing about using the range function is that, unlike loops, the programmer does not have to use the logic behind the series but just has to put the values in the range function, and the results are great. Keep in mind that the range function is used with the loops, and there is less versatility in the range function when compared with the simple logical loops, but for basic workings, it is important to learn about the range, and this function is great.
The syntax of the range function is also easy, just like its group mates. You have to know about the three parameters and will determine all of them according to your requirements:
MyRange=range(start,stop,step):
for i in range(MyRange)
print(i)
Here, the semicolon at the end indicates that the syntax of the range function is complete, and the compiler now has to calculate the range arguments. Furthermore, if the programmer wants the result to appear on the same line as the interval, he can add end=" " at the end of the print. In this way, the compiler will not jump to the next line, but the results will be printed on the same line with a space between each element. Of course, the programmer has to save the result of the range function into a variable so that it can be used in the other functions. But here, it is important to mention that all of these parameters are not compulsory, but the range function gives you the independence to use one, two, or three of them.
The for loop is the iteration in the programming languages and you will learn them in detail in the coming lectures but for now, keep in mind that the range function alone can not do anything but it is fed into the for loop so that compiler can work on the iterations. The variable (usually i) is used in this loop and the results of the range function are input in this loop.
Another thing that must be mentioned here is the programmer has to choose the number of arguments according to the complexity of the series of numbers he or she wants. So here are the details of each case:
This is the most basic type of range function, in which the programmer simply specifies the point where the compiler has to stop making the range series. In all types of range functions, there is always a need for a stop parameter. Three things are to be mentioned here:
By default, the range starts at zero, and if the user does not have any particular choice for the start, the range function can be used with the only stop parameter.
Only whole numbers are printed on the screen.
The stop number, which is the limit of the range function, will not be printed on the screen.
When you put this value equal to zero, the result will be an empty range and you will get nothing.
for i in range(3):
print(i,end=" ")
Just think about the case where the default value, which is zero, is not to be used. Instead, the programmer has the option of printing the series of numbers without missing any of them and then specifying the start and stop ranges in the range function. But, as in the previous case, the stop number will not be printed on the screen, so you have to give the range of stops that you do not want on the screen, but the number before it is required there.
for i in range(3,34):
print(i,end=" ")
The third function, as expected, is the complete range function, into which the programmer feeds another step parameter. With the help of this, the programmers are able to get the series of numbers that starts from the point they want and have uniform intervals between the numbers and the ending point that is expected by the number. In short, the whole series is under the control of the programmer, but you have to notice that the steps are always uniform. The step function must not be zero and you will get the reason for this statement soon in this lecture. We can put the step value in the code discussed above and in this way, if 2 is the step value, the programmers will have half of the series as given above.
for i in range(3,34,2):
print(i,end=" ")
Here comes the action because, till now, the examples you have seen are simple examples with a simple series, but now, we are dealing with some exceptional cases that will clear some related concepts in your mind. We have divided the examples into some questions, and we will try to get the answers with the help of codes:
Till now, integers are being used in the range function but we know that integers and floats are the two most related data types and will try to attempt the range function with the help of floating values as the parameters.
for i in range(3.5,77):
print(i,end=" ")
As you can see, the compiler is throwing the error that it is not possible to use the float in the range function because it is designed only for integers. The same program will run when you remove the decimal part from the first value, which is the starting point.
Let me tell you the interesting way to get the range series with the help of inter tool chain method. But before this, you have to look at the basic definition of this tool.
“The iter-tool iterator is the pre-define module in python that provides the complex applications of the iteration in simple ways. The methods are defined in this module, and the programmers have to import them before using them.”
So, the chain method is also saved in this method, and when the programmers need to use them in a different way, they simply use the import keyword and use it in programs. As we are dealing with the range function, the iter-tool chain function is used to connect the results of two or more results in the form of a single series. Have a look at the code given next, and then read this paragraph again to get the point.
#import the chain method from the iter-tool library
from itertools import chain
# Printing two methods in a row
print("Concatenating the result")
MyChain = chain(range(4,7), range(34,55,2))
#using the method in the range
for i in MyChain:
print(i, end=" ")
The extraction of the concepts used in this program:
We can import the chain method from the library of itertools that have the iteration tools in it.
To import the method, we use from and import keywords that are represented with the green bold colour in the program.
Concatenation is the process of connecting two or more data types into a single line.
When using concatenation, the for loop is used by making a variable and saving the results of two connected ranges together in the variable.
The independence to use the number of arguments between one to three is the same in the concatenation as in all cases.
In the for loop, when using concatenation, only a variable is used.
The other way to get the same results is by using both ranges with the for loop, but the code will not be very clear in that case.
If the programmer wants to get the results in column form, he or she can simply delete the “end” part in the code.
The simple answer to the question is yes, and when we go into the details, the range function simply gets the indexes the programmer wants and can provide them with the single values they require. In simple words, the programmer tells the range function its stop value, and it assumes the whole series and picks the one number demanded by the programmer. The stop range is described in parentheses when the index to be picked is mentioned in the square bracelets.
#Give the range and pick the element through the index
MyRange = range(5)[2]
print("3rd element out of 5 =", MyRange)
print()
MyRange = range(3,34)[23]
print("23rd element of this range with start and stop value =", MyRange)
print()
MyRange = range(28)[5]
print("5th element of this range with start, stop, and step value =", MyRange)
Hence, the programmer can make a range of choices and then pick one element.
During the discussion of step, we saw the basic discussion of the step argument but keep in mind, if the programmer does not want the step function, he can simply ignore it. There is not need to input the step function as zero because, in such cases, the error will be shown on the screen.
for i in range(3,23,0):
print(i,end=" ")
Hence, from the above code, it is clear that the range of the stop argument is always greater than zero. Moreover, in the same code, if the value of the step argument is greater than the stop argument, it just shows the starting point of the range and does not give the other values or any errors because logically, it is true.
Truss, in this lecture, we saw many interesting concepts about the type of sequence called range function. This is a pre-defined function that is used to represent the group of numbers, and we can control the starting, ending, and interval values between the series of this number according to our wishes. This is always used with the for loop, and different cases of range functions were discussed in this lecture. Stay with us for more Python tutorials.
Modern digital business needs reliable feature flag management. You probably already know that since you're here reading this article. Read it in full, then, and discover what to expect from a real-deal feature management software your company can most certainly benefit from.
Feature flags, also known as feature toggles, are a software development best practice that enables developers to safely and rapidly roll out new features in production. Feature flags provide teams with the ability to easily turn features on or off without having to deploy new code. This allows teams to quickly develop, deploy, and run experiments on their application while minimizing the risk of an unstable deployment. Feature flags can also be used to enable A/B testing and canary releases.
Releasing new features confidently is a goal. Many development companies aim at it, but at the same time they want to avoid a crisis that usually comes from bad execution of new ideas. Any software can be ruined by a single update, you know. What's more, modern consumers will not hesitate to ditch a product with faulty functionality. Restoring confidence is sometimes more difficult than building an app from the scratch. That's why the digital industry must be cautious. Luckily, a pro feature management tool can reduce the risk by providing means for delivering new features available to users selectively and with a certain amount of subtlety.
The above allows test execution, including A and B testing. Different user segments provide responds that are often negative, but they are valuable. Smart feature deployment is all about getting the data from the user-side, making adjustments without a massive crisis in case of a failure. This is why a good feature management software is an essential investment for any serious company from the digital industry.
There are many feature management tools out there, but not all of them are actually worth their price tags. The thing is, it must be a comprehensive product. Feature releases can become a complicated process, especially when software development teams work on large projects that consist of layers upon layers of code. It is not unusual these days that a digital endeavor is, in fact, a cluster of products cooperating with each other. Therefore, truly the best feature management software ought to provide an ability to control all these processes. A surprisingly cheap or even free feature flagging platform will be limited to basics only. Basics, however, are not enough nowadays.
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If you plan to run a start-up, it helps to have some basic understanding of IT management. Here are some key pointers on how best to manage your business IT systems:
You should know how your IT systems work and how they can be managed. This will help you keep them working well, which is especially important if your business depends on them for important functions like payroll or customer service. The earlier in the process that someone understands their role and responsibilities in running the company's technology, the better off they'll be when troubleshooting problems arise later on down the road.
Make sure your hardware and software are secure.
Ensure that you have the right licenses for your software.
Use cloud storage for your data security and recovery.
Conduct regular backups using your cloud system or external hard drive
Use a firewall to protect your network from unauthorized access.
Install antivirus, anti-spyware and anti-malware software on each computer (and smartphone) in the organization so that all data can be protected from outside threats.
Cloud storage is one of the best ways to store your data in a secure, accessible and reliable way. Further, Cloud storage makes it easy to access your files from anywhere and at any time. It reduces costs because you don’t need to buy or maintain expensive hard drives, which means that you can save up money for other things like marketing materials or new equipment.
Cloud storage also has some great benefits: like It keeps your information safe from hackers who might want access to it so that they can steal it or sell it on black market sites.
It's important to have a backup of your company's data , which is why you'll need to take steps to ensure that it doesn't get lost or damaged. The most basic way of backing up your files is by using an external hard drive or USB. If you're using the cloud system provided by your hosting provider, then there is also an option for automatic backups (but this might not be suitable for all businesses).
You should also keep in mind that not all kinds of information will fit onto one computer file—for example, some documents contain links between multiple pages; others have embedded images that could be lost if they weren't backed up before being deleted from their original source material.
If you need help with an IT issue or have technical questions, it is essential to get it resolved quickly. This can be a time-consuming process and require specialist skills that many startups don't have in-house.
It's also important to keep your business running smoothly as well as making sure that any problems are dealt with promptly by the right people at the right time (and not just left until later). While hiring an IT expert may seem like a great idea at first glance—especially if they're willing to offer their services for free—it's more likely that you will end up spending more money than you would have had by using other options.
It's important to understand how your IT systems work, so that you know what services you need from providers and how to keep them working well. This will allow you to make informed decisions about what kind of support is required in the future. If a provider offers a specific service or product, then it might be worth considering whether this meets your needs rather than buying something else that does not meet those needs directly. For this purpose you can use different IP for different locations to get best advantages from expertise. You can also use IP from Saudi Arabia to check
any issue regarding IT management if your clients are based in UK or USA. You can also hire expertise from these locations too.
Because If there are any areas where an expert could help with advice or training then this would also be beneficial for both parties involved - especially if there are technical problems with one part of the system which could be fixed by someone who has experience with similar systems elsewhere.
If you are planning to run a start-up, it helps to have a basic understanding of IT management. Make sure your hardware and software are secure, use cloud storage for your data security and recovery, get IT support when you need it - both for advice and for technical issues. This is absolutely essential if you’re not an IT expert yourself.
Hey pupils! Welcome to the new tutorial on deep learning, where we are in the section on Python learning. In the previous lecture, we were discussing the tuple data type, which is a sub-class of sequences. In the present lecture, the discussion will be about the byte sequence and byte array. The whole discussion is cleared with the help of practical implementation in TensorFlow by using simple and easy codes. If you understand the concepts of list and tuple, then this lecture will be easy for you. Yet, before going into the details of this topic, you must know the highlights of the content discussed in this lecture:
What is the byte method in Python?
How can you use byte in TensorFlow?
What are some examples of bytes?
Give examples of error handling in bytes.
How can you convert integers into bytes?
What is a byte array?
What is the difference between bytes and byte array methods?
Moving towards our next sequence is the byte method which has interesting applications in Python programming. A byte is the collection or group of byte numbers and it can hold multiple values. The interesting thing about the byte is, these can not hold the negative numbers that are, the values in a byte can not have the minus sign with them. One must keep in mind that these have a range between 0 and 255 only and you can not store more or fewer values than this range. For example, if you want to add 267 or -56 in a byte, this is not possible in it. The main purpose of using this function is to convert an object into an immutable byte-represented object of which, the data and size are specified by the programmer.
To make sure you are getting the whole concept, let us practice it on TensorFlow. To start, have a look at the instructions:
Search for the “Anaconda Navigator” in your windows search panel.
In the environment section, search for the Jupyter Lab.
Launch the lab and wait for the browser to show you the local host on your browser.
Go to the new cell and start coding.
You have seen we made a byte with the different types of elements in it. Now, what if we made the byte with values that exceed its values? Let us check it with the help of the following code:
The byte is a method and to use it well, the syntax must be known. For using it, there must be three parameters that have to be decided before starting, the detail of syntax and the parameters is given next:
byte(src,enc,err)
Here, the three parameters are defined:
src=The object that has to be converted. It is the source object and has superficial characteristics.
enc= it is the encoding that is used only when the case object used in the formula is in the form of a string.
err=If the error occurs during the conversion of the string is not done properly in the step mentioned before then, the logic given here will be used to overcome this error.
Now, using the information given above, we are going to discuss the example to elaborate on the whole process. The bytes when displayed on the output have a small be with them to show it is a byte. Just look at the code below and we are going to tell you the detail after that.
msg = "We are learning Python"
string = bytes(msg, 'utf-8')
print(string)
Here, in the first line, the message is declared and stored in the variable with the name ‘msg’.
The byte function is used in the second line and as the two parameters, we are using the message declared first as the source and the encoding technique is the utf-8.
The result of this function is then stored in the variable ‘string’.
The results are printed at the end. The small b at the start of this message indicates that it is a byte and single quotation marks are the indication that our result is a string.
Here, utf-8 is the indication of the Unicode transformation format, and it is encoding the string into an 8-bit character. So, in conclusion, we can say that a byte is used to convert the string message into an 8-bit character string. The application of the byte function is at a higher level of programming.
Now moving towards the next example, let us check for a relatively large code. This code gives us a demonstration of how we can use different cases of coding in a single piece of code and we will use here the empty byte, number-to-byte conversion, and list-to-byte conversion using single lines.
num = 4
list = [23,76,23,78,34]
#conversion with no argument
print ("Byte conversion with no arguments : ", (bytes()))
# conversion of number into string
print ("The integer conversion results in : ", (bytes(num)))
# conversion of list into string
print ("The iterable conversion results in : " , (bytes(list)))
The number is converted into bytes as expected, and when we try the same method for the list, where a group of numbers are to be converted into bytes, this function is capable of doing so. The output is strange for the non-programmers but the conversion is perfect here.
As we have mentioned earlier, the string is converted with the help of the byte function, but error correction is important in this case. There are some keywords that are suggested as error correction techniques. All of these will be discussed in the next section.
The second parameter of the byte tells us that we have to provide the encoding scheme, but what if the encoding process is not completed due to some error? The method parameter specifies the way to handle that error, and there is more than one way to handle it; therefore, here are the details of each way to handle the error for you. Keep in mind, coding is a vast field and the whole control is in the hand of programmers. Thus, not all errors are handled with the same kind of solution, so you must know all the ways.
The first error handler on the list is the "strict" keyword. It is used when the programmer wants to get the Unicode decoder error when the compiler is not able to convert the string into a byte.
The second error handler in the list is the keyword “ignore." What do you do when any procedure is not under your control and you are not able to do your duty? In the case of the compiler's workings, when it is faced with the same situation, it ignores the part with the error and moves towards the next line. In this way, the result obtained is not complete, but you get the answer to other parts of the code. It works great in many situations, and sometimes, you get interesting outputs.
The third one has a nice implementation of error handling. With the help of this handler, the error that occurred in the string will be replaced by a question mark, and then the compiler will move towards the next character. In short, the error will be gone, and instead of skipping the error, there will be a question mark that will indicate the error.
All the discussions above will be cleared up with the help of the code given in the next line. The error in all the problems is the same, but the outputs—or, in short, the way the compiler deals with the error—are changed.
#Declaring the variable with the string having the error in it.
MyString = 'PythonfÖrDeepLearning'
# using replace error Handling
print("Byte conversion with replace error : " +
str(bytes(MyString, 'ascii', errors='replace')))
# Giving ascii encoding and ignore error
print("Byte conversion with ignore error : " +
str(bytes(MyString, 'ascii', errors='ignore')))
#using strict error Handling
print("Byte conversion with strict error : " +
(bytes(MyString, 'ascii', errors='strict')))
Here, when we talk about the “strict” error handler, we get this error with the pink box because it is just like a simple error, as we are calling it, and we want strict action behind the error. Moreover, in the other two lines of code, the output is a little bit different, as the description tells us.
The conversion of bytes does not end here; instead, it has other conversion techniques as well. Python gives programmers the ease of converting integers into bytes, but it is, somehow, a little bit easier than string conversion because it is a pre-defined function and you just have to insert the values accordingly.
int.from_bytes(bytes, byteorder, *, signed=False)
In the syntax given above, three things are to be noticed;
It is the byte object that has to be converted into an integer.
This function determines the order in which the integer value is represented. The value of byte order can be "little," which stores the most significant bit at the end and the least significant bit at the beginning, or "big," which stores the MSB at the beginning and the LSB at the end. Big byte ordering computes an integer's value in base 256.
By default, the value of this parameter is false, and it indicates whether you want to get the 2’s complement of the value or not.
To understand the bytes well, you have to know first that Python is an object-oriented programming language; therefore, it works by creating objects from different pieces of code. While working with the byte function, an immutable byte object is formed. The size of this immutable sequence is just as large as an integer that ranges from 0 to 254, and it prints the ASCII characters. It is a Python built-in function that has many interesting applications in a simple manner. As with the other types of sequences, tuples also contain a number of items, but they can also be empty. The only conditions are that the data should be enclosed in parentheses and the elements should be separated with commas. Another important piece of information about this data type is that it cannot be changed once you have declared it.
As the name of this data type resembles the previous one, both structure and information are also similar. Byte arrays also have the parentheses representation, and you can use different data types in them, but the only difference between the byte array and the simple byte in the sequence is that the former is immutable, and you can change the size of the byte array after you declare it in Python. It makes the byte arrays more useful than the simple bytes in the sequence.
The reason why we are discussing both of these data types here is, there is only a slight difference between the byte and the byte array. You can modify the byteArray and that is not possible in the byte. So, have a look at the code below where we are modifying the array declared by
ourselves.
MyArray=[99,5,1,34,89]
print('The byteArray is: ', MyArray)
#Modifying the ByteArray
MyArray[2]=56
print('The modified array is: ',MyArray)
But for this, you must keep in mind that you can only use values between 0 and 255. All the other conditions are the same as the bytes.
Hence, today we have seen a piece of great information about a special type of method that converts different types of data into bytes in a specific manner. We have seen the details of methods that must be known to a Python programmer. The whole discussion was put into TensorFlow in the form of codes, and we get the outputs as expected. The next lecture is also related to this, so stay with us for more information.
We're glad you could join us for another lesson in our comprehensive Raspberry Pi programming guide. I will show you how to install and connect the RFID card chip to your Raspberry Pi through step-by-step instructions.
Modern security systems would only be complete using radio frequency (RFID) devices. To control who can enter a facility or which rooms they can access, RFID chips and card readers are employed. The RFID card's unique identification number can be read wirelessly with a wall-mounted RFID reader. A door will only unlock and allow entry if the RFID card's unique identification number matches a list of approved cards.
It's fun to tinker with this circuit, and it may be used in many other applications, from opening locks to taking attendance. The MFRC522 microcontroller underpins the RFID RC522, a cheap RFID (Radio-frequency identification) reader/writer. The RFID tags can connect with this microcontroller using an electromagnetic field it generates at 13.56MHz and sends to them via the SPI protocol. If you want to use your RFID RC522 with tags, you must ensure that they are 13.56MHz compatible. We'll walk you through the wiring of the RC522 and the creation of Python programs to communicate with the chip, allowing you to read and write RFID tags. Adding a 16x2 LCD to the Raspberry Pi is a simple extension of this tutorial, and it can be helpful if you need to show the user some information or provide a visual prompt.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Breadboard | Amazon | Buy Now | |
| 2 | Jumper Wires | Amazon | Buy Now | |
| 3 | Raspberry Pi 4 | Amazon | Buy Now | |
Raspberry Pi
Micro SD Card
Power Supply
RC522 RFID Reader
Breadboard
Breadboard Wire
An RFID reader reads the tag's data when a Rfid card is attached to a specific object. An RFID tag communicates with a reader via radio waves.
In theory, RFID is comparable to bar codes because it uses radio frequency identification. While a reader's line of sight to the RFID tag is preferable, it is not required to be directly scanned by the reader. You can only read an RFID tag up to three feet away from the reader. The RFID tech quickly scans many objects, making it possible to identify a specific product rapidly and effortlessly, even if it is sandwiched between several other things.
Major components of Cards and tags include an integrated circuit (IC) that stores the unique identification value and a copper that acts as the antenna.
Inside the Rfid reader is another copper wire coil. This coil produces a magnetic field when current flows through it. Magnetic flux creates a current inside the wire coil when the card is brought close to the reader. This current can power the card's internal integrated circuit. The reader then takes in the card's serial number. A card reader will send the card's serial number to a central processing unit (CPU) like a Raspberry Pi for further processing.
When you buy an RFID RC522 Reader, you may discover that 90% of them do not have the header pins pre-installed. Due to a lack of pins, you'll have to solder them yourself; however, this is a relatively easy task, even for amateurs. Assuming the header pins that came with your RC522 are too large, you may snap them in half to reduce them to a single column of eight.
Start by inserting the header pins into the RC522 from the top. The circuit may be easily placed on top of the connector pins by inserting the large side of the pins onto a breadboard. The breadboard's secure holding of the pins will make soldering them to the RFID circuit much simpler.
Solder each pin individually by carefully heating your soldering iron and applying it to the pins. Remember that heating the junction slightly before to solder application increases the solder's adhesion and decreases the likelihood of generating a cold joint. When using solder, we advise you to be conservative. When you've finished soldering the header pins onto your RFID circuit, you'll be ready to move on with the guide.
There are eight different connectors on the RFID RC522. Except for the IRQ, we need to connect all these to the GPIO pins on our Raspberry Pi.
This guide shows how to connect an RFID RC522 to a Breadboard and then to the Raspberry Pi's GPIO Pins, although you could also wire the components straight to the Pi.
Simply connecting 7 of the Raspberry Pi's GPIO pins to the RFID RC522 reader is all needed to get it up and to run. Refer to the GPIO pin locations detailed in our tutorial and the table below when deciding how to wire your RC522.
SDA connects to Pin 24.
SCK connects to Pin 23.
MOSI connects to Pin 19.
MISO connects to Pin 21.
GND connects to Pin 6.
RST connects to Pin 22.
3.3v connects to Pin 1.
We need to adjust the Raspberry Pi's settings before we can use the RFID RC522. Inconveniently, our RFID reader circuit relies on the Raspberry Pi's SPI (Serial Peripheral Interface), which is disabled by default. Worry not, though, as it is easy to restore this interface; follow our instructions below to set up your RPi and Raspbian to use the SPI port. Launch the raspi-config utility by opening a terminal and typing the following command.
sudo raspi-config
A menu of choices will appear when you use this tool. You may read up on all of these options in the raspi-config documentation. Choose "5 Interfacing Options" using the arrow keys. Select this choice, and then hit the Enter key. Once "P4 SPI" is selected in the next screen, press Enter once more to confirm your selection. To continue, use the arrow keys to choose "Yes" and then press Enter when prompted to confirm that you want to activate the SPI Interface. For the raspi-config utility to finish enabling SPI, you'll have to be patient for a while.
The raspi-config tool's success in enabling the SPI interface will be shown by the display of the message "The SPI interface is enabled." Activating the SPI Interface requires a full reboot of the Raspberry Pi. Press Enter, and then ESC, to return to the terminal. If you want to restart the RPi, enter the following Unix instruction into the terminal.
sudo reboot
It is time to verify that Raspberry Pi has been activated now that it has rebooted. Checking if spi bcm2835 is available is as simple as running the following command.
lsmod | grep spi
If you get spi bcm2835, you're good to go with the rest of the tutorial. If you tried the preceding command and it didn't work, try the following three things. If the SPI component is not enabled, we can manually modify the boot config file by issuing the following code to our RPi.
sudo nano /boot/config.txt
You can use CTRL + W to search the configuration file for "dtparam=spi=on" If you think you have discovered it, look if it has a number in front of it. If there is, delete it because it disables the code. If you cannot find the line, add "dtparam=spi=on" to the very end of the file. To commit your modifications, use CTRL + X, followed by Y and Enter. You can double-check that the module has been activated by restarting your Raspberry Pi, as in Step 5.
After connecting our RFID circuit to the RPi, we can turn it on and start writing Python scripts to communicate with the chip. You'll learn how to read and write information to RFID chips by composing scripts like the ones we'll provide. These will serve as the foundation for future RFID RC522 tutorials and provide you with a fundamental understanding of how data is handled. The Raspberry Pi must be brought up to date with the most recent software versions before we can begin programming. Get the latest version of Raspbian for your Pi by running these two commands.
sudo apt update
sudo apt upgrade
Installing the python3-dev, python-pip, and git packages is the last thing to do before moving forward. To get your RFID reader set up with this guide, type the following command into your Raspberry Pi's terminal.
sudo apt install python3-dev python3-pip
Now that we have python "pip" installed on our Raspberry Pi, we can install the spidev Python library. An integral part of this guide, the spidev library allows the RPi to communicate with the RFID via the SPI. Run the following command to get spidev set up on your Raspberry Pi via pip. It's important to remember that we're using sudo to guarantee that the package gets installed for everyone's usage, not just the logged-in user.
sudo pip3 install spidev
After getting the spidev library up and running on our Raspberry Pi, we'll move on to setting up the MFRC522 library with pip. Two files, in particular, are used by us, both of which are part of the MFRC522 library:
This library, MFRC522.py, implements the RC522 interface for communicating with RFIDs via Raspberry Pi's SPI port.
Simplifying the MFRC522.py file so that you only need to work with a small subset of its many functions, SimpleMFRC522.py is a significant time saver.
Enter this command into your terminal to have pip setup the MFRC522 library on your Pi 4:
sudo pip3 install mfrc522
Now that the library has been transferred to the Pi, we can start writing code for the RFID RC522. First, we'll explore how to use the RC522 to program your RFID cards. Move on to the following part, where we will write our first Python code.
In this first Python script, we'll go over the steps needed to send information from the RC522 to RFID tags. This is made more accessible by the SimpleMFRC522 script, but we'll still break down the code's individual components for you. To begin, let's create a directory to hold the scripts we'll be using. Create the "pi-RFID" folder by using the following command.
mkdir ~/pi-rfid
To get started, navigate to the folder you just cloned and create the Write.py script in Python.
cd ~/pi-RFID
sudo nano Write.py
Add the following blocks of code to this file. This code prompts you for some text, which it then uses to update the RFID Tag.
#!/usr/bin/env python
import RPi.GPIO as GPIO
from mfrc522 import SimpleMFRC522
The very first line of the code snippet instructs the terminal to use Python rather than another scripting language like Bash to parse and run the file. To guarantee that the GPIO Pins are reset when the script terminates, we must first import the RPi.GPIO package contains all the necessary functions for communicating with the GPIO Pins. The second import is our SimpleMFRC522 library, which will be used to communicate with the RFID RC522. Compared to the standard MFRC522 library, it dramatically simplifies working with the chip.
reader = SimpleMFRC522()
In this line, we make a new instance of the SimpleMFRC522 object, use its setup function, and save the result in our readers variable.
try:
text = input('New data:')
print("Now place your tag to write")
reader.write(text)
print("Written")
We enclose the following section of code with a try statement to ensure that any unforeseen problems are handled, and the code is cleaned up correctly. Python is whitespace sensitive; it uses tabs to distinguish between code sections, so keep them after trying. In this case, the second line reads a command-line input and stores it in a text variable using Python 3's input function.
The third line makes advantage of print() to prompt the user to set the RFID tag onto the reader. After that, on line 4, we utilize our scanner object to instruct the RFID Circuit to write the text field's contents to a certain sector of the RFID tag. On line 5, after successfully writing to the RFID tag, we call print() once more to inform the user.
finally:
GPIO.cleanup()
The script will terminate in the last two lines of code. The finally statement always follows the try statement. Thus the GPIO.cleanup() method is called after each iteration of the try block. These lines are essential because improper cleanup can disrupt the functionality of other programs. Upon completion, your script should be like the example given below.
The file can be saved by pressing CTRL Plus X, Y, then ENTER once you've double-checked the code and are convinced it's correct. Now that the script is written, we need to put it through some testing. Get an RFID tag ready before running the script for testing. When you're ready, open the terminal on your Raspberry Pi and enter the following command.
sudo python3 Write.py
In this situation, we're just going to type in "any word" because it's easy to remember and short. Press the Enter key when you have finished writing and are ready to send. After that, your RFID Tag can be placed directly above your RFID circuit. It will immediately update the tag with fresh information when it does. You'd see the word "Written" on the command prompt if it worked. Now that you have your Write.py script completed, we can move on to explaining how to read information from the RFID RC522.
We have successfully programmed our RC522 to print to RFID tags and can now move on to writing a script to retrieve the data from the tags. First, we'll make sure we're in the correct location by switching directories, and then we'll use nano to start drafting the Read.py script.
cd ~/pi-rfid
sudo nano Read.py
Incorporate the following code into this document. When an RFID tag is placed in the RFID reader, the script will wait until the tag's data has been read before displaying the results.
This file's first line of code instructs the operating system on how to proceed when the user clicks the "Run" button. If you don't specify that it's a Python file, it'll try to run it like any other script. An initial RPi.GPIO import is made. Importing this library ensures that the Raspberry Pi's GPIO pins are cleaned up after script termination, as it contains all the necessary functions. SimpleMFRC522 is the second import. With the assistance functions included in this script, reading and writing to an RFID RC522 is a breeze, whereas, with them, the scripts would quickly grow to be manageable.
This line is crucial because it invokes SimpleMFRC522's creation method, which returns an object that is subsequently stored in our reader variable.
try:
id, text = reader.read()
print(id)
print(text)
The following code section will be encapsulated in a try block to allow us to handle any unforeseen errors gracefully. Because Python is sensitive to whitespace, you must use the 'tabs' as displayed following try:
In this scenario, the second line of this code block initiates a call on our scanner object, instructing the circuits to begin scanning any Rfid card that is positioned on top of the reader. On the third and forth lines, we use print() to display the data we gleaned from the RFID Chip; this includes the tag's unique identifier and any text it may consist of.
finally:
GPIO.cleanup()
The script ends with the last two lines of code. No of what happens inside the try block, the final statement is always executed afterward. No matter what, the GPIO.cleanup() code will be executed thanks to this try statement. It's vitally important, as not doing so can disrupt the proper operation of other scripts that rely on the GPIO. Your completed Read.py script for the RFID RC522 should resemble the example below.
When you've double-checked your code and are satisfied with it, press Ctrl + X, then Y, and finally ENTER to save the file. The time has come to put our completed Read.py script to the test. Get ready to test the script by picking up any of the RFID tags. If you're all set, enter this command into the terminal on your Raspberry Pi.
sudo python3 Read.py
Now that the script is active, you can set your RFID Tag atop your RFID circuit. When the RFID tag is placed on top, the Python program will immediately begin reading the information from the tag and display the results on the screen. What a finished product might look like is shown below as an illustration.
To test whether your Raspberry Pi is properly connected on the RFID RC522 Circuit, run the Read.py script and see if it returns any data that matches the text you wrote to the card in the Write.py script.
Connecting an RC522 RFID module to a Pi 4 makes reading MIFARE chips and cards is now possible. This might be very useful in security systems and other applications where identifying an item or person is required without the user having to physically interact with the device by pressing buttons, switching, or activating any sensors. Eventually, you should be able to use this to decipher the UID encoded on your MIFARE tags. You should know that these cards can be duplicated and assigned a new unique identifier (UID) if you plan on employing this technique in a security system. To ensure the safety of your system, you must ensure that no one learns your UID or gains remote access to your devices. The contactless tags are convenient because they can be attached to a keychain, and the cards are convenient because they can be carried in a wallet. Both things can be concealed inside others to give them a hidden identifier that the Pi can access. With the help of our Pi 4-powered RFID attendance systems guide, you can learn how to set up your RFID Reader/Writer for use in checking attendance. Our exploration of the RFID chip and the scripts above will continue in subsequent guides. A door security system is one of the fantastic DIY Pi ideas we'll look into. The next lesson will teach you how to connect a 16x2 LCD screen to a Raspberry Pi 4.
No matter your company's industry, you need fleet maintenance software. It’s something you need if your engineering firm has a fleet of vehicles. Failing to get the necessary tools is a bit like driving blind -- not the best course of action by any stretch of the imagination.
If you’re not sold on the benefits of investing in fleet maintenance software, keep reading to learn more about four reasons you need it.
Fleet maintenance software will help your engineering firm with preventative maintenance. The software will make it easy to schedule maintenance so that your fleet of cars stays in good shape.
One of the worst things you can do is delay or ignore preventative maintenance. That will ultimately cost you in terms of downtime stemming from repairs. Preventative maintenance won’t eliminate the possibility of repairs, but it will reduce the odds of breakdowns . And breakdowns can become expensive if you also find yourself unable to use the company vehicles for business. Unplanned downtime is a no-no.
When your business invests in fleet maintenance software, you’ll also enjoy lower vehicle repair costs. Putting off maintenance means that small problems can morph into major issues that cost an arm and a leg to fix. So, it’s more cost-effective to invest in preventive maintenance than to postpone doing so and having to spend many times more to fund major repairs.
When considering the importance of your fleet, you can appreciate the need to ensure the vehicles are available and in good repair whenever they’re needed. You can increase uptime and reduce downtime by tackling issues as soon as they’re discovered so that they don’t worsen.
When you get one of the best fleet maintenance software applications, you’ll also benefit from better fuel efficiency. Proper maintenance and repairs will ensure your fleet of vehicles operates more efficiently, resulting in better fuel efficiency. You can also optimize routes so that drivers don’t take longer routes than necessary. And by keeping track of driver behavior, like speeding or hard braking, you can notify drivers of necessary changes. You can save a lot on fuel just by getting drivers to operate the fleet vehicles more responsibly.
Who has time to worry about their fleet of vehicles? You have other things to worry about such as operating your engineering firm. When you’re taking good care of your cars, you won’t be left worrying about them. Instead of having to make frequent arrangements to have your inoperational cars towed to automotive facilities for repairs, you’ll be able to use them for business purposes. They’ll spend more time on the road and less time out of commission.
That means you’ll have fewer headaches with your fleet of vehicles. Using a fleet maintenance software solution, you’ll be able to stay on top of things. And if things don’t get out of control, you’re less likely to have headaches over something going horribly wrong.
If your engineering firm has a fleet of vehicles, it’s essential to consider the benefits of fleet maintenance software. Protecting your assets is a must if you want them to remain in good repair over the long haul. Getting a fleet of vehicles requires a tangible investment. If you want to protect your investment, then investing in fleet maintenance software is a good idea.
Before settling on a platform, you’ll want to do research to find something that meets your company’s needs. When you find the right software, you’ll see up close and personal what the benefits of fleet maintenance software are all about.
Hey peeps! Welcome to another tutorial on data types in Python. Our purpose in Python education is to get a grip on the basic concepts so that we may work on deep learning easily. In the previous lecture, we read a lot about lists as we are working on the subtypes of the sequence data type. In the present lecture, you are going to understand more types of sequences. If you know the list well, this lecture will be a piece of cake for you. We will start the introductions in just a bit, but before that, there must be a quick review of the topics that you are going to understand:
How do you introduce the tuples in Python?
What are some important characteristics of a tuple that must be kept in mind when we are dealing with it?
How can you practically perform the tuples in TensorFlow?
How do you associate tuples with the list?
Can you delete, update, add, or delete the elements in a tuple? If yes, then how?
Which is the way to delete the tuple entirely?
All of these are important interview questions about the tuple and if you are dealing with these data types, you must know and perform these by yourself in TensorFlow. All the information about tuples will be discussed with you and you have to practice more and more in the code to understand the concepts.
For the concept of a list, we have given you the reference of an array. This time, we will be mentioning the name of the list for better understanding. If we talk about the topic of today, a tuple is also a group of items that are arranged when required in Python. The working and the definition of the tuple seem like a list because both of them are types of a single data type, which is the sequence. But there are some differences that make them ideal for different kinds of situations. The following points will make this more clear:
The tuple is represented by enclosing the data in parentheses.
A tuple is immutable, which means that once you declare the items in the tuple, you can not change them as you can in a list.
When you try to change the value of the element in the tuple, an error is shown on the screen, so while declaring the new tuple, you have to be very clear about what is on your mind and what you want to do with the tuple.
In a tuple, you can also use the function in the tuple and it becomes easy to perform different tasks through codes.
A single element in the tuple is necessary to build the tuple. In other words, unlike the strings, the tuple must contain at least one element in it.
In the tuple, any data type can be saved whether it is boolean, int, or string. Moreover, there is a possibility to add different types of data in a single tuple. So we can conclude that we can have a homogeneous or heterogeneous tuple according to our choice.
As we have mentioned, when using the tuple, we get the ordered list of objects, which means we get the specific order, and this order, once mentioned, can not be changed, unlike other data types of the sequence.
Not just the order, but the size, sequence, and entries are unchangeable, so during the initialization and declaration of the tuple, the concept must be clear about what you want from the particular tuple.
Another thing that must be kept in mind is, the tuple has the index form, and therefore, every element has a specific number of indexes. This is the reason, the elements are easily accessible, and hence, you can also have duplication in the tuple. The elements are recognized by the index number, and therefore, the compiler knows when which element is being called.
The length is the number of elements in the tuple, and we have read about the length function in the previous lecture. Similar to the list, a tuple can also be used in the length function, and the programmer gets the length of the tuple. Right now, this function is not looking very attractive because we are dealing with small tuples. But take the case in your mind when the tuple contains hundreds or thousands of elements and get the length of the tuple in just a few moments.
It is now time to go over some basic tuple-related code using TensorFlow. The steps for starting it are the same as those we always follow; have a look at these steps:
Open your Anaconda Navigator by browsing it from your window panel.
Search for the Jupyter lab in the environment section.
Wait for the PC to open the new tab in your browser.
Go to the new cell.
Start coding.
With the help of code, we will try to create a tuple, and then, by using the function on it we will try to retrieve the data from the tuple in different ways. So, have a look at the code given next and then guess the output in your mind.
print('Make a tuple with stationaries item')
myTuple=('pen', 'paper', 'eraser', 'pencil', 'sharpener', 'notebooks')
print('The tuple has following items: ',myTuple)
print()
print('print only the third item of the tuple:', myTuple[3])
print()
print('print the item by using the index: ',myTuple[1])
Now, have a look at the output. Is it exactly the same as you were expecting?
You can see how simple it is to get the item from the tuple when it is declared in the tuple. This is the same as what we did with the list. But what if we want more than one element from the tuple at the same time?
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
print()
print('print only the forth fruit of the fruit shop:', myTuple[-7])
print()
print('print the name of only fruits: ',myTuple[0:5])
Looking at the code, you will observe that the negative number is used in the index. This is the way to tell the compiler that we are counting from the end of the index. Moreover, the index on the end side starts at 1 instead of zero. Another point to notice is that the start and end limits are specified by separating them with the colon, and as a result, we get the whole tuple in the output.
A new concept that is to be shared is that when you are providing the limits to the tuple, you have to take care that the right numbers are being used because it will then not work properly. You must be thinking that the compiler will throw the error when you feed the wrong limits in the tuple, but instead, the output will be empty, and you will get the parentheses with nothing in them.
Another thing that must be mentioned here is that you can check whether the tuple has a specific element or not. For this, we get the help of a statement. We know that, until now, we have not learned about the statements, and therefore, we suggest just looking at the code and output to understand the concept.
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
if "apple" in myTuple:
print("Yes, 'apple' is in the present in the fruit shop")
This program searches for the elements specified in the “if condition” and then prints the result on the screen.
If you are reading this lecture from the beginning, you must be thinking we have mentioned again and again that a tuple is immutable and unchangeable. Yet, programmers have the logic and solutions to every problem, and if the programmers have specified the tuple and want some changes made to it, they can do so with the help of the list. It is one of the reasons why we have discussed the list before in this series. Have a look at the following code and output, and then we will discuss it in detail.
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
myList=list(myTuple)
print(myList)
myList[2]='pear'
print(myList)
myTuple=tuple(myList)
print(myTuple)
Copy this code and run it on your TensorFlow, you will get the following output:
First, look at the brackets carefully and check the output in a sequence with the points given next:
At the start, the string message is shown, which shows the main title of the tuple.
The name of the tuple here is “myTuple” and it contains the fruits’ names and prices.
To make the changes in the tuple, we have to use another approach, and we know the reason why. For this, we are using the list. It is possible to convert the list into a tuple and vice versa, and we are doing the same in this. We are just using the name of the data type as a function and inputting the name of the data type to be changed. So, we changed the tuple into a list and then made the changes according to our choice.
By using the index number and feeding the value into the list, the list is updated. This can be observed with the help of square brackets.
Once we have seen the updated list, we can now easily convert it into a tuple again.
The conversion is done with the same procedure, and we get the updated tuple.
This was a simple example, other operations such as the addition of the new element, deleting the elements from the tuple, removing the single elements from the tuple, etc. are done with the help of lists.
Now that you know all the ways to initiate, use, and update the tuple in detail, a last method for the tuple is ready for you. In some cases, when you do not want to use a particular tuple for any reason, such as if you are no longer using it, you can do so in just a simple step. This is used in the programs when long calculations and collections of data types are needed for a particular time and then there is no need for them anymore. So for this, we use the delete operation. The programmers have to simply use the del keyword before the name of the tuple to be, and the compiler has to do its job well. Have a look at the example given next:
Tuple = ("Artificial Intelligence", "Machine Learning", "Deep Learning")
print(Tuple)
del(Tuple)
print(Tuple)
So, when the compiler was on the second line, it showed us the results, but on the next line, when we deleted the declared tuple, the compiler was not able to show us the result because it had been removed from its memory.
So, it was an informative data types lecture in which we clarified many concepts about tuples. These are the data types that belong to the class of sequences. We read a lot about it and started the discussion with the introduction. The tuple characteristics were thoroughly discussed, and then, with these in mind, we tested the functions of the tuple using TensorFlow examples. We attempted similar examples and carefully observed the operation of tuples, which also involved the list. In the next lecture, you will know more about the data types in Python, so stay connected with us.
Hey, peep! This is a connected tutorial from the previous one where we saw the detail of numeric data types. This time, we are moving forward with the other data types in Python. We are understanding all these concepts with the help of examples and practising the little but understandable codes in TensorFlow. Different types of operations are also performed on these data types so that you may have an idea of why we are differentiating all these data types and how we can categorize all of them into different groups. Keep in mind that all of these concepts are for deep learning, and we want to make sure that we will not face any problems in the complex work of deep learning; therefore, we are moving slowly and steadily in Python. So, have a look at the content you are learning in this tutorial, and after that, we’ll start the introduction.
What are the strings in Python?
How do you declare the string in different ways while working in Python?
What are escape sequences in Python?
How can you use the triple quotation in Python and why it is useful?
Each concept will be discussed with the help of simple and easy codes and the description of each of them is discussed in detail in this lecture. This is a connected part of the lecture that was discussed in the last lecture and other data types will be mentioned in the next lecture.
A string is nothing but the combination of different alphabets in a specific sequence. In programming, the concepts are a little bit different from those in the real world. The way we speak in English is said to be "string" in the programming language, and it is an important data type in any programming language for non-programmers, anything that comes on the screen must be easy to understand, and string is the best way to print the message on the screen. Let us define the string in simple words.
"The string is the sequence of characters or alphabets that specify the message on the screen and it is denoted by 'str' in Python."
We always say that python is simpler and easier than other programming languages, and it is true for the concept of string as well. In Python, the string can be denoted by using single or double quotation marks and the usage of commas is according to the choice of the programmer. In other words, you can use the single or double inverted commas around the alphabet to represent the string. Moreover, you must know that string has no limited length same as in the case of integers. The only thing that limits the length of the string is the memory space of the system you are using. Have a look at the syntax of the string while you are working on Python.
First of all, you have to look at the syntax of the string. The syntax of all the data types is the same; therefore, we are mentioning just this one, and after that, you will have an idea of how to implement the other data types. We have mentioned in the previous lectures that you just need the name of the variable and then the value of the variable in the form of any data type you want, so the syntax is given as
string = "I am learning Python for Deep learning."
The name may be anything, but it is important to use inverted commas (either single or double). TensorFlow will make this clearer, but there is a short procedure to get TensorFlow up and running.
Search for the “Anaconda Navigator” on your PC.
In the environment section, click on the Jupyter lab and launch TensorFlow.
The new tab will open in your browser with the name “Local host.”
Go to the new cell and start coding there.
Now, you have to write the following code in the new cell and run the program to get the output.
string="I am learning at TheEngineeringProjects"
print(string)
a=" "
print(a)
b='Python is best for Deep learning'
print(b)
The output of this code is given as:
From the code and output, we can conclude with the following point:
The name of the variable may be anything.
We can use single or double inverted commas for the string and the result will be the same.
A string may be an empty space so we can say that length of the string is zero to positive infinity.
The output of each print function is always shown in the next line in normal conditions.
So, the best way to show any message to non-programmers is in the form of a string.
In most high-level programming languages, there are certain words that are chosen to be used for a special task in Python with the help of their special sequence. These are known as the escape sequence. These are defined as:
"The escape sequence in Python is the combination of characters that, when used inside a string or character, does not show itself but does the specific task according to its functionality."
It is important to notice that these are important concepts in Python because it is very difficult and in some cases, impossible to do the same task without using the escape sequence. Now, have a look at some of these in the next section and you will get the detail of some important escape sequences.
As you can see, in the previous code, we made a space between two lines with the help of an empty string. But, what if we want to print a new line in the message? For this, we use a special operator in the string message and it is used in places when the output is long or there is the need for more than one line to represent the message more clearly. The operator is a backslash with an “n” that can be used at any place in the text. Have a look at one example to do so.
print('The backslash with "n" is used to \nprint a new line')
The output of this single-line program is interesting.
It is important to notice that if you want to work with this operator, you have to use it properly. You can not use any additional space between the text and this new line operator; otherwise, you will get an error message from the compiler.
We all use the tab on the keyboard but what if you want a space between the text? You can do so by applying the space between the text while you are printing the message but it is not the professional way. To do this with convenience, just like some other programming languages, Python has a special operator. Same as we use the n in the new line operator, if you use the t with the backslash, you can print the space between the text that is equal to eight space bars.
print('The backslash with "t" is used to \tprint a tab space in the line')
Let’s see what we have in the output of this code.
Same as these operators, we also have some other commands that do the work similar to this and the syntax of all of these is the same. For the convenience of the reader, we have made a table that contains all the information about these operators. Have a look at the table and after that, we will run all of these at once in our code in TensorFlow.
Name |
Representation |
Description |
New line |
\n |
It creates a new line in the text even if you are using it in the middle of the line. |
Tab space |
\t |
It is used for the space tab between the text. |
Bullet |
\a |
For the bullets in the text, we use this operator. |
Delete space |
\b |
To delete the space in the text, we use this operator. In this way, the text obtained only removes the space from the place where this operator is being used and the other text remains the same. |
Ignore the line |
\r |
By using the working of this operator, the text before the operator is deleted, and you will get the text after this operator only in the output. |
Arrow |
\v |
If you want to show a small arrow in the text, you will use this operator. |
To test each of the commands discussed before, we are rushing towards the TensorFlow, where we are using all of these in a similar ways to show the difference between all of these. Keep in mind, the syntax of each of them is the same. These are not the proper functions but are the pre-defined commands that are used less commonly in Python. Here is the homework task for you. You have to look at the code we are giving below and without cheating will guess the output.
print('We use \ncups for tea')
print('The tab create \teight spaces in the text')
print('\aBullets can be made using this operator in Python')
print('If you want to delete the \bspace, you can use the operator in Python')
print('This part will be ignore \ronly this will be printed on the screen')
print('\vThis small arrow looks cute')
Once you have guessed the output, now check this in your compiler, and then match the output with the one that is given next in the image.
The last arrow can also be used as the bullet in the text you want to show on the screen. Another task for you is use the different text and practice the code and string with your own message. It is the simplest and interesting task that you must try on your TensorFlow.
Here is another way to indicate that you want to declare the string. This is a less common way to represent the string, but if you are studying the string, you must know this. You can use double and single inverted commas around the text at the same time, and this will not cause any errors. Let me tell you about the workings of this kind of string. After that, I’ll show you how you can use it for different purposes.
print('''Deep learning is the subclass of the artificial intelligance''')
So, we are using three quotation marks around our text and getting the same output as for the single and double inverted commas. Here is the output:
The advantage of using this method is, you do not need any new line operators to start the text on a new line, but you will write the code as you want the output.
This is a more interesting and convenient way to write your text. Right now, you must be wondering why we are highlighting such uses and creating this hype. Yet, you must know, the aforementioned ways of writing the codes will help you a lot when you will go to the complex and long codes for the deep learning. One of the application of this way to declare the string is in this lecture. We can use this way to declare the string and can perform all the escape sequence command by declaring a single string in different lines so that we may not have to write “print command” every time when we are working with a new escape sequence.
Hence, we have read a lot about strings today. It was an interesting lecture where we saw what strings are and how we can use them in our coding in different ways. We say the representation, working, and the escape sequence between the string. The syntax of each case was clarified with the help of examples in TensorFlow. You will get information about more data types in the next lecture.
Hey fellow! Welcome to the next episode of the Python series, where we are learning the basics of Python to implement them in deep learning. In the previous lecture, our focus was on string data types. With the practical implementation of TensorFlow, many interesting points were discussed in depth. I hope you completed the home task that I assigned you during that lecture. Today, we are moving forward with the next data type, which is a sequence. You will know the different sub-groups of this data type as well in the next lecture, but today, the focus will be totally on the list because, once you understand them well, other data types of the sequence will be at your fingertips. Yet before starting, it's time to look at the content that you will learn today:
What is a sequence?
How do you classify the sequence into different types?
How do you understand the list?
What are some characteristics of this list that make it useful and different from the others?
The workings and characteristics of the list are interesting, and these will be well understood when we start our TensorFlow for different examples taken from our daily lives. We hope you have a strong grip on the integers, strings, and floats because, in our example, we will use them and will try to make changes in the list to show the difference in detail. So, without using your time, I am going to start the learning phase in Python.
In programming languages, we use a lot of items, objects, classes, and related concepts, and when we talk about the sequence, that is a data type that provides us with the concept of a collection of things. These are useful concepts that allow us to work with them in a useful way. We define the sequence in the following way:
"The sequence in Python is a generic term that defines the ordered set of items in such a way that any of the items can be easily referred to."
Here, the word “ordered set” is important to notice. We’ll talk about it in detail in just a bit, but before that, let me tell you that the string is also considered to be the sequence because it contains the sequence of the characters. We have read a lot about the string, and therefore, we know that the ordered sequence of the characters, or alphabets, is called the string. Yet, I had a lot of data to tell you about these concepts; therefore, I have discussed them separately. There are two natures of the sequence that are listed below:
Homogeneous sequence
Heterogeneous sequence
The difference between these two is that a homogeneous sequence contains a group of items of the same kind.
Homogeneous Sequence |
Heterogeneous Sequence |
A homogeneous sequence contains a group of items of the same kind. |
A heterogeneous sequence contains a group of items of different kinds. |
{"Apple", "Banana", "Cherry"} |
[Cup, knife, banana] |
It is important to know this difference because, on the basis of it, you will get the further types of the sequence.
The collection of the data can be represented in different ways, and on the basis of these types, we can divide the sequence into six major types. All of these are important to understand because we use the sequence often while programming and when dealing with complex subjects such as deep learning (in which we are interested), and the sequence plays a vital role in organizing the data in an understandable way. So, have a look at the types of sequences:
String
List
Tuple
Byte sequence
Byte array
Range objects
Out of them, the strings have been discussed in detail in the previous lecture. So, we are skipping that for now. You will be familiar with the remaining ones in depth, and things will become clearer to you with the help of examples. Thus, have a look at them:
In some other programming languages, such as C++ and C#, there is the concept of arrays that we study a lot. Yet, in the Python programming language, this concept is not used; instead, lists are used. The list is indicated by the square brackets just like the arrays, but it is a little bit different from them. The list is the type of sequence that contains an ordered group of different items. More information about the list can be found in the table below:
Name |
Attribute |
Representation |
Square brackets |
Nature |
Heterogenous sequence |
Example |
[‘cups’, ‘eggs’, ‘yolk’, ‘tea’] |
Here, it is important to notice the representation of the list with the square bracket because the bracket is the only way to differentiate it from the other types of sequence that you will learn in the coming lectures. The operation of the list will be explained in simple steps in a moment, but first, let us open the Python workspace for practical implementation. For this, you simply have to follow the steps again that we always mention:
Search for the Anaconda Navigator on your personal computer.
Go to the environment section and look for the “Jupyter lab” or “Jupyter notebook." For this tutorial, we are using the Jupyter lab.
Go to the new cell and get ready for the coding.
The list's information does not end with the table mentioned above. Certain characteristics make the lists useful, and you will learn about them in the following section.
One of the most significant features of Python is its mutable nature. To know the meaning of the line we have just said, you must know that when we declare a list, we define the size of that particular list by mentioning the number or directly feeding the elements in it. The list's advantage is that the programmer can change the elements at any time to meet the needs of the situation. In addition to this, if you want to change the individual value of the list, you can do so easily. Not only this, but you can also change the order of the elements while working on the code. It means if you want to swipe or change the position of elements 1 and 3, you can do so easily. In short, you can say that, while working with the list, all the controls are in your hands.
MyIntList=[1,56,8,12,56,90,3,67]
print('The homogenous integer list is : ', MyIntList)
MyFloatList=[1.2,56.2,8.2,12.2,56.2,90.2,3.2,67.2]
print('The homogenous float list is : ', MyFloatList)
You can see that all the elements belong to the same data type, so we are calling it the homogeneous list. In the second code, we want to do something different, so have a look at the code.
list=['eggs', 2.0, 34, 'plates']
print('The elements of the list are : ', list)
print('The element at position 2 is : ', list[2])
print('The elements before the second position are : ', list[:3])
list=[ 34,2.0, 'eggs', 'plates']
print('Now the new list is: ', list)
By looking at the code given above, we can conclude the following points:
To declare more than one type in a single list, the programmer simply has to use the accurate way to declare them in the list, and nothing special has to be done during the declaration of the list.
To get a specific element from the list, simply the position of the element is mentioned.
The position in the elements start from the 0 just like the arrays and you have to declare the element accordingly.
The order of the elements can be changed easily and to do this, the programmer have to merely change the elements according to the will with the same number. Here we have used two types of lists with the same name but by changing the order of the elements and the code is working well.
To get the elements of the list to a certain limit, there is a need of mentioning that number of position along with a colon. Keep in mind, here we specify the position 2 and all the elements before the third position are shown to us. Same can be done to get the elements after the mentioned number and it is your homework to know how can you do so.
When you compare the list to the array, you can better understand Python's dynamic nature. Let me remind you that arrays have a fixed size and cannot be changed once declared in your code. But in the case of the list, once you start working on it and need to change its size, you can increase or decrease its size by merely mentioning it in just one line. In other words, your list can grow or shrink according to your choice. To understand this you must know about a built-in function in Python.
The length is a built-in function in Python that provides the length of the functions made in the code. You simply have to input the name of the function in it and it will show you the number of elements specified in that particular function. The syntax of the length function is:
len(name)
By using this function in the list mentioned above, the number of elements can be obtained with the help of this code:
list=['eggs', 2.0, 34, 'plates']
print('The length of list is : ',len(list))
list=[34,2.0, 'eggs']
print('The new length of list is : ',len(list))
The output of the code is given next:
Keep this in mind the next time you go grocery shopping and want to buy something else; make a list for it. It will contain vegetables, meat, cloth, spices, and other things of the natural world. Because of its heterogeneous nature, the list is the same. It feels like a relief that you can make a list that contains strings, integers, and any other data types in one place. In this way, you can deal with multiple types of data at once. In arrays, this is not possible. You can also add functions to the list that contain elements of different types. You have understood it well with the previous examples in TensorFlow, but I want to show you this by mentioning a built-in function in it.
Mylist=[23,'decoration', False ]
print(Mylist)
Yet, there is a bit of difference between the keywords and loops; therefore, you can not mention the loops in the list; otherwise, you will face the same error as given next:
Here, the error is demanding that you write the code in the valid syntax. It is because the compiler is not able to understand that the for loop is an element of the list, but it demands the proper syntax for the for loop and is expecting an iterative procedure. Another thing to notice here is the color of the word “False” in the list. All the keywords, while coding, are shown in green, showing that it is a built-in function or keyword and that the compiler has understood the default functionality of that particular keyword.
Hence, it was an interesting tutorial, through which we learned a lot about the list data type. We initially understood what list data types were and why we were learning them. After that, a detailed overview of the list was discussed, through which we found the characteristics of the list. It was easy to understand as we practically performed each step of the TensorFlow with the help of different examples that were related to the general examples, so we were able to understand it well. If you have a clear understanding of the list, the following lectures will be simple to grasp because they are related concepts. So, stay tuned with us to get more information about Python.