The Engineering Projects

Hey, peps! Welcome to the new tutorial on the Jupyter notebook. As we are moving towards a higher level of Python, therefore, it is important to understand other ways of doing the same task. As we read in the previous lecture about 2D arrays, this time it is going to be done with the help of a famous library in Python. This time, you are going to learn different, engaging, and advanced-level concepts that not only help you deal with the 2D array but also the installation and working of the unique library of Python, which is NumPy. This lecture is going to be very useful and interesting, and therefore, we suggest you have a glance at all the goals of this lecture.

• Getting the introduction to NumPy

• Successfully installation of NumPy and knowing different methods for it

• Using the arrays with the help of NumPy

• Creation, indexing, flattening, and appending of the arrays using NumPy

• Knowing the different ways in detail to use these methods.

All of these concepts will be clear to you, and we will move on to more details in the next lecture but for this lecture, it is important to learn the basics.

What is NumPy?

NumPy is a fantastic library for dealing with numeric data. You must know that a library in Python is a collection of modules that add functionality to a Python program. Libraries are typically distributed in the form of packages, which can be installed with package managers such as pip. If you remember, at the very first lecture of this course, we installed the packages by using pip. The same is the case with NumPy, and this will be clear in just a bit, but for now, you must know that they said that they could include modules that define functions, classes, and variables, as well as algorithms and other services.

NumPy, Pandas, matplotlib, and sci-kit-learn are some of the most commonly used Python libraries. They can be used for a variety of tasks, including data analysis and visualization, machine learning, and web development. Python programmers can take advantage of pre-existing code by using libraries, which can save time and effort when compared to writing everything from scratch.

"NumPy (Numerical Python) is a Python library that adds support for large, multi-dimensional arrays and matrices, as well as a large collection of high-level mathematical functions for operating on these arrays."

It is a fundamental Python package for scientific computing. The good thing here is, Python provides programmers with several ways to perform the same task so that every person may use the best way according to the requirements and expertise of the programmers. In the previous lecture, a simple method was used to perform the two-dimensional arrays, but you must know that NumPy is the second way to do so, which is the reason why we arranged this lecture.

Installation of NumPy in Python

Here comes the action. We are going to work with NumPy, and therefore, the installation process is necessary. The good news is, the programmers do not have to follow the prerequisites when you need to install the NumPy but only Python is enough. So if you are following our previous course, you are good to go with the Numpy. There are two ways to install NumPy:

1. Installation with the Conda (Advanced level)

2. Installation with the pip (Beginner’s level)

Difference between Conda and Pip

Installation of NumPy Using Conda

Installation with the help of Conda also has different types of methods and for the convenience of all types of users, we are discussing all the basic instructions here.

The best practice for such installations is to use an environment. It means, instead of using the “install” method, the programmers will create the new environment.

conda create -n my-env

Once the environment is created, it's time to activate that particular environment.

conda activate my-env

Another option to perform the same option is to use conda-forge. Many professional programmers prefer this method because conda-forge provides a wide range of software packages, and in our case, NumPy is one of them. If you want to install from conda-forge then the following command is needed:

conda config --env --add channels conda-forge

The methods given above were a little bit tricky, and if the programmer prefers the simplest way, then they must only write the command given next:

conda install numpy

Pip Method for the NumPy

Now, for the installation process at the beginner level, you have to follow the steps listed next. You have to understand that it is recommended to be at this level because all the requirements of this course are designed according to easy and effective ways to code. Hence, the steps given next are enough for all of you:

• In the window’s search panel, write the Anaconda prompt.

• Here, write the following command:

pip install numpy

• Wait for the installation and loading of the library on your personal computer. 

• Now, to check if your NumPy is working well, go to your Jupyter Notebook.

• For this, fire up your Jupyter Notebook by searching for it in your window applications. 

• Go to the new Python project and wait for the new tab to be opened in your browser.

Note: It is essential to notice that every time the programmers want to use Numpy, they have to import it into the code, and that is the first step of every program where the programmer needs to get help from NumPy. 

Using the Arrays with NumPy

The typical easy-to-deal-with arrays are similar when we talk about the Jupyter notebook and MATLAB etc. Yet, with the advancement of techniques, better and updated methods have been introduced. The NumPy is a numeric library and to learn its usage, the best way is to deal with the arrays. We hope you know the basics of arrays and their fundamental information; therefore, we are directly starting the practice of arrays. In this way, the students will learn the details of 2D arrays and NumPy side by side. 

Creating 2D Array Using NumPy

Creating the array is as simple as what the students have seen in the previous lecture. All the steps are easy, and the comments explain the function of each line.

#importing the numpy

import numpy as numPy

#creating the new array from the imported NumPy library

myArray = numPy.array([[34,8,12,4],[90,5,12,78]])

#printing result

print("The 2D array \n ", myArray)

The output of this code is as expected:

Getting Basic Information of Array with NumPy

If you want to make an array automatically with the minimal information provided by you, then it can be done with the help of NumPy (such as declaring the shape and size of the array). There are other ways to do so, but using NmuPy gives the programmers relief. It is because NumPy has built-in functions; they just have to associate their array with that particular function and get the desired output. 

#importing the numpy

import numpy as numPy

#creating the new array from the imported NumPy library

#using the reshape and arrange functions

myArray = numPy.arange(9).reshape(3,3)

#printing the result

print("The 2D array \n ", myArray)

Output:

Here, the important points to be noticed are

• The arrange function is used to provide information about the total number of elements in the array. But this must be declared by keeping the shape of the array in mind. 

• The reshape method has the following syntax:

reshape(numbers of rows, number of columns)

• The total number of elements must match the shape so that the compiler may arrange the numbers easily; otherwise, the programmer observes the error. 

• All of the information is connected with the dot operator in the code. 

• The values in both these functions are enclosed in parentheses.

• The arrangement of both functions is important because if the reshape method comes before the arrange method, there will be a long list of errors and the program will not run.

Indexing and Flattening Using NumPy

The indeed method is not new to us. We all know that the index always starts at zero. But in the case, we are discussing, one must know that when an array is declared, the index is not for only the elements but for the whole set of the row. 

Moreover, flatting is a special method in NumPy in which the output of the resultant elements in an array is not shown in the form of brackets and single rows, but the programmer gets the real form of the array that we have used in our mathematics and other cases of the array. If these two are not clear to you now, do not worry because after looking at the code given next, all things will be clear.

#importing the numpy library

import numpy as numPy

#declaring my array

myArray=numPy.array([[12,56,33,90],[3,89,12,45],[34,89,12,90],[56,12,9,221]])

#using the flatten function

output=myArray.flatten()

print("The array is now = ", output)

#using the index method to get the third-row

a=myArray[2]

print("The third row= ", a)

The output of this program is given next:

Hence, in this way, we have learned two functions in one code. Here you must know that there are many simple and important concepts about arrays. If we start discussing them in separate codes, then it will become boring and lengthy to learn the small functions and methods. 

Appending the Elements in Array 

Once the array is declared, the elements can then be modified. This concept is not applicable to other programming languages, such as C++. Yet, Python gives us this convenience, which is useful for programmers. The appending method is simple and easy, and this code will justify this:

#importing the numpy library

import numpy as numPy

#declaring my array

myArray=numPy.array([[12,56],[3,89],[34,89],[56,12]])

print("\bThe array before appending  \n", myArray )

#declaring the second array to be appended

myArray2=numPy.array([[23,89],[12,90],[78,9],[78,11]])

#appending the second array with the first one and printing the result

print("\bThe result of two matrices \n ", numPy.append(myArray,myArray2,axis=1))

Hence, both of these arrays are merged by simply appending them to each other. Moreover, the “axis=1” instruction tells the compiler that the rows of both of these arrays are to be joined in the respective manner; otherwise, the whole array is fixed after the second one. You should check it on yourself.

Consequently, we have learned a lot today. From the introduction of NumPy to the installation of this library, all the steps were discussed here in detail. Moreover, this library was tested by using the array in different ways, such as creating, changing the dimensions, indexing, flattening, and appending the elements. All of these concepts were discussed thoroughly, and there are many more operations on the arrays. Hence, stay with us for more details in the next lecture.

Hello students! We hope you are enjoying learning Python, and therefore, we are moving forward with the advanced level of Python. For this, you have to keep in mind that you are moving towards complex codes, and therefore, you have to get ready for expert-level problems. Do not worry, we are not going to leave you alone with these complex codes; rather, we are discussing the amazing and interesting libraries in Python so that your work may be done with the pre-defined codes and applications so you get amazing results by following simple operations and statements. If it is confusing for you right now, do not worry. Have a look at the topics that you will learn about today, and after that, we will move towards the details:

• What is meant by a library in programming languages?

• List some of the important libraries that are related to Python.

• Why learning the introduction of important Python libraries is essential for developers and engineers?

• Give us a short introduction of libraries that are specific to some fields and playing important role in the popularity enhancement of Python as a programming language.

• Why most of the libraries are specific for neural networks, machine learning and related fields?

What is a Library in Python?

We all know the proper meaning of the generic name of a library. It is a collection of books of a different kind in a single building. There are different kinds of books that are related to each other but are different in their content and types. The reason why we are discussing this here is, the libraries of Python, or simply any other programming language, can be defined as:

"In programming languages, a library is a collection of gigantic pre-compiled and pre-defined code that makes the programming more efficient, simple, and interesting."

Not only the codes, but the libraries also contain the documentation, message templates, configuration data, etc. In this way, we get the proper package that now only gives us unique outcomes, but these libraries are very specific to their related fields, and therefore, there is no need to have knowledge about all the applications of Python; one or more specific libraries for a specific field are enough.

The good thing about the Python libraries is that they are similar to other entities, such as methods; that is, they make us comfortable reusing the same code again and again with the help of simple commands. Python has been at the top of the programming language for years, and therefore, there are many communities that are working on it and have useful and fantastic features and workings in their company that give motivation to beginners.

The role of Python libraries is remarkable, given their popularity and the number of people working in the fields of data science, deep learning, machine learning, and related concepts. People are interested in the workings of these libraries, and therefore, every year, more and more findings in these fields are obtained using these libraries.

Python Standard Libraries

Let us tell you more about the libraries so that you may understand them better. Python libraries are written in the same syntax as Python semantics, tokens, and syntax. It is surprising to note that most of the Python libraries are written in C language. More than 200 core modules can be used in the Python standard libraries, and this makes Python a terrifically interesting and complex programming language because the programmer has the greed to use more and more features if he is crazy about it.

Without the standard libraries, any programming language is not that efficient, and therefore, we are emphasizing the importance of these libraries. You do not have to know all of these at once, but at this level, we will recommend that you know some very important standard libraries that all Python programmers must know. So here is the introduction of some very essential Python libraries with a very short introduction and the speciality of the related field. 

TensorFlow for Python

No matter which tutorial you follow, the most remarkable Python library is TensorFlow. At this level, we hope that you know about it or have heard its name. The base of this library is found in Google because it was introduced by Google for numerical analysis and machine learning applications. The basic purpose of TensorFlow is to train machine learning and deep learning models and not only this but also helps to deploy these models on different platforms such as CPUs (Central Processing Units), GPU( Graphic Processing Units), or TPU( Tensor Processing Units), where the last one is special for the numerical analysis of the machine and deep learning methods. The good thing about this library is its flexible architecture and you will be amazed to see it works.

NumPy for Python

It is one of the most common libraries that is used for numerical analysis, and therefore, the name stands for Numerical Python. This one is also for machine learning applications and is perfect for large-sized matrices and a great amount of data in the multi-dimensional data processing. For easy computation, the built-in mathematical functions are ideal. 

The importance of NumPy can be understood by the fact that even powerful libraries such as TensorFlow use NumPy internally to perform the computation with the tensors. Another feature that must be mentioned here is the array interface.  

Panda for Python

If your aim is to be a data scientist, then this library is a must-have skill for you. If we talk about machine learning, this library provides professionals with a high-level data structure along with analysis tools for data manipulation and cleaning. The basic operations on data such as indexing, re-indexing concatenation, segregation of data, etc can be done with the help of this data science library. This library functions similarly to NumPy, and the programmers can get a highly responsive data structure using the fantastic features that make it the best tool for solving real-life problems.

PyTourch Library for Python

If you are interested in the computation of tensors in machine learning, then you cannot ignore Pytourch. It is the largest machine-learning library that works for tensor computation and, with strong GPU acceleration, the rich collection of APIs of PyTourch. In solving the complex issues in neural networks, Pytourch has amazing solutions. For the developers of GPU acceleration, PyTourch gives built-in support and therefore, they observe the remarkable speed, efficiency, and working in their field. 

SciPy Library for Python

The full name of SciPy is Scientific Python, which is an open-source library for scientific computation at a high level. Keep in mind, this is an extension of NumPy, and it is used to deal with complex computational problems with simple commands. You can think of the sorting and indexing of the data as the specialities of SciPy. Most of the developers and engineers know the workings of Scipy, and it gives you a big setup as compared to using NumPy alone. 

We have seen that SciPy is more famous for research purposes, and it also offers a C++ interface. Not only with Numpy, but you can have the pair of SciPy with other libraries as well, and it is the favourite library of engineers and developers.

Keras Library for Python

The next library we are discussing is Keras, and  Francois Chollet introduced this library as a powerful tool and flexible API by which deep learning and neural networks are dealt with. To reduce the cognitive load on the processors, Keras is designed simpler yet it gives the programmers the perfect working all the time. 

When dealing with the deep neural network, GPUs are used for better performance, and Keras helps the developers effortlessly scale the models. If you are interested in building model prototypes quickly, then you should have Keras, because no matter if you are interested in the web browser, iOS, embedded devices, or any other platform, Keras shows impressive deployment capabilities. 

PyGame Library for Python

If you are a game lover or want to develop games, then this library is for you. For some people, PyGame is the most popular library in Python because the developers know the importance of this magical library. You will find it at the top of SDL (Simple DirectMedia Layer), and the reason behind this is, it provides low-level access to computer hardware such as keyboards, mice, etc. and graphic units such as GPUs via OpenGL and Direct3D.

PyGame is amazing because it makes industry-standard games so that people can use them as a profession. In simple words, Pygame is a cross-platform that offers developers tons of interesting features through which they can develop games efficiently and with less effort. It makes it easy to have the optimized code, so it is easy to understand. Moreover, it supports a multi-core system that makes the code portable for the convenience of the game developers.

Metaplotlib For Python

Coming back to the numerical data, those who have a large amount of data and want to plot them with minimum effort must install Metaplotlib. Hundreds of companies are using this library because it let the user visualize the data in several ways. Some of the very common uses of this library are histograms, tables, and different types of charts such as bar charts, pie charts, etc. 

The good thing about this library is, it is not just restricted to static visualization, but the experts are working deeply with it to get creative and interactive animations with hard work. Another plus point that must be discussed here is that these give fully customizable results that seem great and can be used with other applications.

PyBrain Library for Python

The name of this library stands for Python Based Reinforcement Learning, and it is a special library for neural networks and artificial intelligence. It is a perfect library for beginners because it is an open-source library, and its easy-to-use algorithm makes it perfect for those who want to get started with learning. Its flexible structure is quickly understood. We recommend this library to those researchers who want to learn the skills and workings of many different fields, including machine learning.

This library has a nice repetition in the student community because artificial intelligence and deep learning are leading fields in many pieces of research, yet not all people are experts in them. In many researches, there is no need to be an expert; the beginner level is more than enough. In such cases, the simplicity and ease of such libraries are a blessing.

The list of Python libraries does not end here, but some others worth discussing are:

• Scikit-Learn

• scrappy

• LightGBM

• Bottle

• Tkinter

• Eli5

• Django

• MoviePy

• NLTK

• Open CV

• Pillow

• Peewee

• Beautiful Soup

• PyQt

• Pytest

All of these are important in specific fields, and you will notice that most of these are specifically optimized for neural networks, artificial intelligence, deep learning, machine learning, etc because these are emerging fields and Python is the most suitable language for these fields. There is so much to talk about in every single library, and you will learn it soon in the coming lectures.

Thus, we saw many useful topics today, and we can say that every library had great value in the information. We started this lecture with an introduction to libraries and kept the compatibility of Python in mind with that introduction. After that, we started introducing the important libraries that every Python programmer must know. It is not necessary that you learn all of these, and it is not wrong to say that a single person cannot be an expert in all these libraries, but the point that we want to make here is that Python is an extremely interesting and useful programming language, and if you want to learn it, you must know the scope of this magical programming language. We will write tutorials on these libraries and their projects in the next lectures, so stay with us for more detailed information about libraries.

Greeting learners! Welcome to the new tutorial on Python, where we are going to discuss the loops. In the previous lecture, our focus was on the primary introduction of the loops in Python. If you are from a programming background, you must know that there is a little bit of difference between the loops in Python and those in other programming languages. At the end of the previous lecture, we saw the details of the basic differences and examined why we consider Python better than other languages when considering loops. In the current lecture, our focus is only on the while loop, and you will get to know its importance soon when we discuss the detail of this loop. But before this, have a look at the list of the major concepts:

• What are the loops?

• Why do we use the while loop in Python?

• What are some major points that must be known about the while loop in Python?

• Can we use the loops while designing the games?

• What is the concept of statements in Python?

• How do break statements work in Python?

• What do you know about the continue statement in Python?

• What does the else statement do in Python when we use it with the while loop?

All of these are important interview questions and we will find the answer to each and every concept in detail with the example. We have been working with the Jupyter Notebook and therefore, we are again using it for the practical implementation of the concepts of the current lecture. So let’s move towards the first concept.

Why Do We use While Loop in Python?

While loop is one of the most common loops in programming languages and if we talk about Python, loops have fundamental importance and while loop is the favorite of many programmers. No one can get excellence in programming in Python without knowing the detail of the loops in it. The reason why we are learning it so deeply is, there is a bit of difference between Python loops and other programming languages. We discussed it in the previous lecture. There are different reasons behind this statement, and some of them are explained next:

Repeating Action in While Loop

As all the loops do, the while loop repeats itself until a certain condition is satisfied. There is no restriction on the data type in the loop, which means, no matter if you have sequences, strings, integers, or other data types, you can use the while loop anywhere. This gives the programmer the independence to use it in a versatile way by using their creativity. Moreover, keep in mind the condition where the programmer wants to get input from the user after a certain condition is met in the code. In such cases, a while loop is perfect to use, and the restriction can be applied to the input as well. 

While Loop on Sequence of Unknown Length

The idea of the condition is so smooth in the while loop that it becomes interesting to use the sequences in the while loop. In some cases, when the length of the sequence is not known, programmers can use the sequence in the while loop to get the required result. In this way, the compiler keeps iterating the sequence until a certain condition is met. 

Gaming and While Loop

Have you ever noticed the condition of a game where the sequence of certain objects keeps on moving in a specific sequence? This happens when the loops are used with special planning, and the while loop is one of those loops that provide the perfect results according to the needs of the program. In the simulations where a certain condition is to be fulfilled, the need for the while loop is always felt. 

In addition, when the need to fulfil a certain condition is felt, a loop can be used, and the compiler uses the condition again and again until the requirements are met; once the mission is completed, the game is then over. This is the very basic idea that how programmers use while looping in gaming, there are many innovations every year where programmers use the loops in a different way to present interesting results. All the discussion can be proved with the help of the example in the jupyter notebook:

Code:

#initializing the while loop with the true condition

while True:

#providing the first condition 

    try:

#using the integers as the condition

        number = int(input("Enter the age of your character: "))

#if the condition is satisfied, the program is terminated

        break

#if the condition is not true, throw the error

    except ValueError:

#printing the error message

        print("The input is not correct, enter only the integers")

 Output:

Discussion:

Once you have seen the working of the code, you might be thinking, "What happens at the backend?" This example is chosen here to tell you that not all conditions of the loops use a comparison between two things only. We can use the boolean conditions as well to get the required output. If you have seen the simple examples of the while loops, then you must be wondering about the output of this program. You can see that the compiler first gives an error when the input does not meet the requirements of the program.

If the user does not give the expected data type for the program, the compiler goes out of the loop and ignores the break statement. In the second part of the program, the compiler goes to the except value and prints the error message that tells the details of the expected input. In this way, the user understands the requirements in detail, and the program does not end, but the iteration is continued until a certain result is obtained. 

Python Statements in While Loop 

In the while loop, there is the ability to use statements of various types, one of which you have just seen. Yet, for the convenience of the learners, we are discussing all the categories in detail. Each of the types has its own example in the Jupyter notebook. To open this, have a look at the steps given next:

• In your Windows search bar, go and search for "Jupyter Notebook.”

• Wait until it opens a new tab in your browser. 

• Once the tab is opened, go to the dialogue box and open a new Python project. 

• It will open a new tab with the cells where you can write your code.

Break Statement in While Loop

In the while loop, the break statement is used many times. As the name suggests, the break statement breaks the iteration, and the compiler stops executing the program as soon as it passes through the break statement. No matter if the body of the code still has to be executed, if the break statement appears in the code, the compiler stops working. Therefore, programmers use the break statement when they have more than one case and just want to execute the one that suits the condition. There are certain benefits to this statement, and one of these is, it gives the programmer all the long codes at once. Here is an example of a break statement in a while loop:”

Code:

#Initializing the variable

a = 4

#Starting a while loop

while a <= 12:

#Printing the required output that we want on the screen

  print(a, ":I am a Programmer of Python")

#Using the if loop to provide the condition to the compiler for break statement

  if a == 9:

    break

#iteration

  a += 1

Output:

Discussion on the Code:

It may seem that the code has some unusual things in it such as the starting point is from 4 instead of 1. This is the way to give you the concept that the starting point is not always zero. It totally depends on the choice of the programmer according to the requirements of the program. Once the variable is declared, the while loop is started, where the maximum number of iterations is shown. Right after declaring the condition, the results are printed.

For the convenience of the reader, I have shown the number of the iteration so that one may understand the whole scenario. Here, to use the break statement, I have shown you the “if” loop. In this way, the compiler understands that whenever the iteration number is 9, it has to stop and jump to the next line instead of completing the iteration. Therefore, after the 9th iteration, the compiler is not showing us any results. 

Another thing to notice is, the value of “a” is 4 at the start, but after every iteration, the compiler checks for the value when it is at the last line, and then it sees that the value of “a” is now one number greater, and therefore, the next iteration starts. 

Continue Statement in While Loop 

If you have understood the previous case, then this one is like a piece of cake because we can say that the working of the continuous statement is somehow opposite to the break statement. 

Code:

#Initializing the variable

a = 0

#Starting a while loop

while a <= 12:

  a += 1

#Using the if loop to provide the condition to the compiler for the continue statement

  if a == 3:

    continue

#Printing the required output that we want on the screen

  print(a,"I am using the continue statement")

Output:

Discussion:

The other pieces of code are the same, but you can see there is a bit of difference in the position of some lines. If you have observed this, then you are on point. The iteration is done in   the body of the while loop, and right after this, we are using the “if” condition. In the output, observe that the third number of iterations is missing, and this is because we told the compiler to jump whenever the iteration is at the third position. Once the iteration is ignored, the print operation gives us the output on the screen, where our value of the variable and the message on the screen are printed. 

else Statement in While Loop

The third and last statement of today is the else statement, and this is quite similar to the cases given above. The else statement has the power to show the result once the condition is no longer true. It means that it can be the ending message that we want to print until the condition is satisfied. As a result, we get two blocks of the code at once that can be printed using the same code. 

Code:

#Initializing the variable

a = 0

#Starting a while loop

while a <= 12:

#Printing the required output that we want on the screen

   print(a,"I am using the continue statement")

#iteration

   a +=1

#Using the else statement to tell the user that the condition is satisfied

else:

    print("The condition is satisfied")

Output:

Discussion:

Here, you can see we are getting a continuous stream of the output where every number of the variable is being used. It is because there were no distractions for the compiler in the code within the while loop. Once we have used it to show that all the iterations are done, the compiler then jumps to the else statement, which tells us the final words of the code.

In the end, we can say that today we have learned a lot about the statements used with the while loop in Python, and with the help of examples, the concept is not completely clear. The focus was on the while loop, but using the example of the statements, we understood how we could get the perfect result by merging the concepts of loops and statements in Python. From the introduction to the ending, the discussion was clear about the loops, and we took care of the minor details as well to know best about the while loop. It was an informative lecture, and in the next lecture, we have to know more about loops in Python, so stay tuned with us and be happy with the practice. 

Greetings Learners! Welcome to the new lecture on Python. Today we are moving towards an interesting and important concept. If you have come from a programming background, then you must know about the workings of loops. To make the repetition of the same sequence, we use loops. We are familiar with the sequences in detail as we have learned them in the previous lectures and have received the concept in the recent lecture. If you want to be a programmer, then you can not move forward without a solid base of loops; therefore, in this lecture, we will learn the concept of loops from scratch and then go on to a deep understanding of each loop in the next episodes. So have a look at the concepts that you will learn today.

• What are the loops?

• How do we understand the concept of loops from scratch?

• What are some conditions of the loops that we have to understand before programming in loops?

• What are the two basic types of loops in Python?

• Discuss the syntax and types of loops.

• What is the speciality of the while loop?

• Why do we prefer the for loop when dealing with for loops?

• What are the major differences that make Python better at looping than other types of programming languages?

Each of these is an important interview question, and will we explore the answers of every question mentioned above. In this lecture, the use of Jupyter notebook is not much but for the practicle implementations, this lecture is very important so let us start finding the answers of each question that we have just read now.

Introduction to Loops in Python

In programming languages, loops are the repetition of the same concept, again and again, to get the required output according to the requirements of the programmer. In other programming languages, there are many types of loops, and programmers use them in different ways. Usually, it has been observed that the programmer uses the same kind of loop most of the time that he finds useful or has a good grasp on the concept of a particular type of loop. Loops are defined as:

"Loops are a fundamental control structure in programming that allows you to execute a set of instructions repeatedly until a certain condition is met."

These are extremely useful to execute the conditions and calculations. The working of loops may be different, and the difference in the syntax is an important thing to be noticed, but keep in mind that the output of the loops is almost the same. No matter which type of loop the programmer uses, the output remains the same, just as at the backend, the working and the logic are the same. 

How Do Loops Work in Python

When you are going to learn the loops, many people ignore the first step, which is to understand how the loops work at the backend of the code. We are not going into details about how the compiler executes the instruction, but just the steps that have to be followed by the compiler when the user runs the loop. Here are three basic steps:

Checking the Condition

Checking for the condition created by the programmer. The user always gives the condition at the start of the loop so that the compiler may understand whether the given loop is possible to execute or not. One must keep in mind that the compiler works with the general truths and facts of the real world. It means that if the programmer, say, tells the compiler that 2 is more significant than 10,000, the compiler will reject the condition.

Making the condition is the most important part of the loop for the programmer, and this is the only step where, if you have a strong base in the concept, the programming will become easy for you. This is the only step that will surely run in the loop no matter what condition the programmer applies. It is usually a single line of code, but it is crucial. 

Execute the Body

Once the condition is satisfied, the body of the code is executed. This is the main step where you will see if you are successful in the loop or not. Once the condition is met and the outcome is true, the compiler enters the body of the loop specified by the programmer and gives the output. Most of the time, the programmer wishes that the condition becomes true, so the remaining code gets the attention of the compiler. 

The whole processing is done in the body, and the compiler can give more than one true condition according to the type of condition and other requirements. 

It is true that programmers created unusual and amazing products, but there is a limitation that the fact is never ignored. The programmers use the facts in a better way by using their creativity, which results in perfect outputs. 

Coming Out of the Loop

It is obvious that many times, the condition is turned false, and then the compiler simply ignores the body and jumps out of the code. This will only happen if the condition is not satisfied, and this step is not connected with the one that we have just discussed. The detail of each of the steps can be understood by observing the flowchart of the loops given next.

Types of Loops in Python

In other programming languages, such as C++ and C#, there are many types of loops. However, in Python, there are primarily two types of loops, each of which has subtypes. In this lecture, you will understand the basic introduction to both of these, and after that, we will move towards the deep concepts and examples of these loops. So, at the most basic level, we can see that there are two types of loops in Python:

• While loop

• For loop

An introduction to both of them is given next.

While Loop in Python

The while loop starts with the keyword "while” and then the condition is written so that the compiler can check the condition and then execute the statements written after it accordingly. We introduce the while loop as:

“In programming, the while loop is one of the fundamental types of the loops in which the condition and the statement are the necessary parts of the syntax”

The condition is checked at the start of each iteration, and if it is still true, the loop executes, and this cycle continues until the condition is found to be true. The syntax of a while loop is given next:

Syntax of the While Loop:

while condition

statement(s)

As you can see, the condition is always given first, and the number of statements may vary according to the complexity of the program. 

Types of While Loop in Python

If the concept is taken to a deeper level, we will see that while a loop is a simple type of looping in Python, it has further subtypes, and the names of these are

1. Infinite loop

2. Else statement

3. Single statement while loop

The details and examples of each of them will be discussed in the next lecture. 

For Loop in Python

The for loop is the basic type of looping, and usually, I find it more convenient to use it. Iterations are usually used with the sequence, where the elements of the sequence are repeated again and again. To control the number of iterations, the programmer sets a condition with the “for” keyword, where the compiler always checks the condition and decides whether it has to enter the body or not. Just like the while loop, it can also have more than one statement, and the control is totally in the hands of the programmer.

Syntax of For Loop in Python

For iterator in sequence:

statement(s)

Here, you have to understand some concepts:

• “For” is the keyword that initiates the loop and tells the compiler to work according to the information fed into the backend. 

• An iterator can be thought of just like the variable name and the programmer may call it anything according to his choice. Let us say, if it is a, then he will call it in the statement with the same name.

• “In” keywords specify that the name of the iterator is now ended and the compiler is now entering into the sequence. 

• The sequence may be of any type no matter if it is a tuple, array, or list. The good thing about for loop is, it is associated with the sequences and according to the requirement, the programmers can access each and every member of the sequence without making much effort. 

• Statements may be one or more and it is important that every word of the sequence is correct so that the compiler will execute the instruction and then move towards the for keyword to check the condition again. 

Difference Between Python Loops with Other Languages

From the beginning, we have been saying that Python is simpler than other languages. Moreover, there are other characteristics that make it unique and better. As we are studying the loops so I have made a little comparison between the loops of Python with other programming languages.

Hence, by using these points, we can conclude that, like every programming language, Python also has many features that make it unique. The loops are more specific and more interesting in Python than in other programming languages, but we have less variety in the loops in Python. 

The range function and other functions that have been mentioned above will be discussed with you in detail in the next episode of this lecture. Till then, let us revise the concepts that we have discussed. The concept of loops is important in Python,n and there are only two major types of loops that we have also introduced. The subtypes of these loops also exist. The working of the loops is easy in Python, and we have seen that not all loops have the same syntax, but the differing syntax is important as it differentiates the languages just like other syntaxes. There are three basic steps that are followed by the loops and while constructing the loops, these steps must be cleared in your mind. This was all for today, and in the next episode, you will see the practical implementations of the loops in Python one after the other. Then, try to practice these concepts and keep learning.

Hi learners! Welcome to the next episode of learning the arrays using string data. We have been working with the arrays and in the previous lecture, we saw the interesting characteristics of the string arrays. In the present lecture, our target is to get knowledge about the built-in functions and we will take the strings in the array so that we may know about both of them together. String arrays are used in many officials uses and therefore, we want to use the built-in functions that every Python programmer must know. Here is the list of the concepts that will be polished in this lecture:

• What are the built-in methods?

• How do you differentiate between methods and functions in programming?

• Give different examples to understand different built-in methods to be used in the same code.

• How do Jupyet notebook use the copy(), clear(), Insert (), Index(), reverse(), range(), and sort() function in Python on the string array?

• How do these functions are related to the working of simple string messages?

For the convenience of the students, we will refresh some important concepts in our mind side by side and whenever required, we will provide the necessary details about the working of the particular method. All these methods have been introduced in Python for a better user experience. One must notice that the same work is also done in other programming languages but Python gives us an uncomplicated way to use them.

Difference between Methods and Functions

Programming is a vast subject and if programmers start making short programs again and again when required, the programming process becomes boring and time taking. For this, the programming languages have built-in methods and functions to be used by simply mentioning them in a specific syntax. Normally, people seem confused between these two because the working and syntax of both of these look alike. So, let's discuss some minor differences between these two.

The working of both of these is similar as the programmer has to input the required data and both of these, after their specific operations, provide the required output as expected.

Built-in Method Used with String Arrays

There are certain methods for the arrays and related concepts that are defined by the Python programming language, but to understand them, we are using the arrays with the string values in them. The method makes the coding easy to use and without knowing them, it is not possible to get the perfect result for a complex task. This will be clear with the help of examples of each of them. A list of such methods is given next:

1. clear()

2. copy()

3. extend()

4. count()

5. insert()

6. index()

7. Reverse()

8. Sort()

Here, we want to compare these functions with some of those that are discussed before in this series. So, let’s move towards the first example:

Copy() and Clear() in Python

If you are interested in programming, you must know to copy the code from one place to another so that you may test it more accurately and produce the better one by using your concepts. By the same token, another method that is important is clear() which is used to clear the memory area where the array is being stored. In this way, the programmer gets the empty space. For convenience, we are using these two in a single code:

Code:

#initializing the array containing the name of programming languages

languages=["C++", "C#", "MATLAB", "ASP.NET", "Flutter"]

print("The original array= " ,languages)

#copying the contents and printing the same results

Languages=languages.copy()

print("The copied array = " ,Languages)

#clear the whole array and printing the results

languages.clear()

print("The clear array= ", languages)

Output:

Here, it is important to notice the difference between the clear() and remove(). The clear() causes the empty locations in the array no matter what the size of an array is whereas, in the remove method, the whole array was removed and the compiler throws the error about the existence of that particular array. In other words, the compiler, after the clear() gives you the chance to add the new data of your own choice and you can think that the container of the element is empty but still, we have that container and can fill the data according to our will. 

Extend() and Cout() Method in Python

The next methods that we are going to discuss are fun to perform. In the previous lecture, we saw the append method, and this time the same task will be performed with the help of another method called extend(). As the name suggests, this function extends the length of the array by adding another array or extra item to it. The modification of the array is an important feature of Python, and practically, it seems like a blessing in some cases. 

The other method that will you see in the code is the count(). It counts the frequency of the mentioned item in the data type, and in our case, we are using it to compare the frequency of the “flutter” before and after the extended array. The element may be any, and the programmer simply has to input it into the method to get the frequency. In programs with a large amount of data where the array or list has 1000+ or even more items, the count() method is a blessing. 

Code

#initializing the array containing the name of programming languages

languages=["C++", "C#", "MATLAB", "ASP.NET", "Flutter"]

print("The original array= ",languages)

result=languages.count("Flutter")

print("Numbers of an element before extending =",result)

#extending the array of string

arrayToBeExtend=["java", "javascript", "Flutter"]

languages.extend(arrayToBeExtend)

print("The extended array= ",languages)

result=languages.count("Flutter")

print("Numbers of element before extending =",result)

Output

Insert() and Index() in Python

The next methods to be discussed are the insert and index methods in Python, and we are expecting that you understand the working of these methods. As in the previous cases, the name exactly matches the working name of the method. 

Code:

#Decalring the array with strings

myStationary=["Notebook", "Pencil", "Erasor", "Pointer", "Papers", "Duster"]

print("Stationaries before insertion= ", myStationary)

#Getting the index of Erasor before insertion

a=myStationary.index("Erasor")

print("Index of Erasor" ,a)

#inserting the new eleemnt at index 2

myStationary.insert(2,"Sharpener")

print("Stationaries after insertion= ", myStationary)

#Getting the index of Erasor before insertion

a=myStationary.index("Erasor")

print("Index of Erasor" ,a)

Output:

In the program given above, we have seen that the list of stationary contained the specific numbers of the elements, out of which, the index of the eraser was 2 at first, but after the addition of the new element, the index function tells us the 3 index number. 

Reverse() and Range() in Python

Now, we are moving towards the situation where we will play with our array. It is interesting to notice that we can retrieve the data in different ways from a single array. Let’s say, the programmer wants results in which the order of the elements in an array is reversed. No matter what the length of the array is, the reverse() method creates the mirror image of the array and represents it. This is important in the field of image processing, where we can invert the images in a simple way. 

On the other side, the range function gives us the elements in a specific length of the array according to our needs. We have been using this in the previous examples in this course, but this time, we are specifying the range() method. It is a professional way to do so. The logic behind this is not compulsory to understand; therefore, to make things simple, we are not going into detail. Have a look at the code where we are using both of these methods. 

Code:

#Declaring the array

myDishes=["chow mien", "sandwiches", "rice", "butter puffs","pizza","meat balls", "rolls",  "momos", "patties", "chicken", "coffee cake", "wings"]

print("\bThe array before reversing= ", myDishes)

#Reversing the whole array using the reverse method.

myDishes.reverse()

print("\bThe array after reversing= ", myDishes)

#Using the range method to get the elements

print("\bThe element from 3rd to 8th position are myDishes", myDishes[3:8])

Output:

Here, the interesting thing is, just like we used the same method in the previous lecture when we were using the same procedure on the simple string, this time, we have used it on the whole array of the string, and the outcomes are perfect. The programmer can easily get the part of the array they only want and perform different operations on that certain list. 

Moreover, the reverse function is also useful in many places, as we have discussed before. The transformation of the array is not just limited to the strings, and in this way, the numbers, when used in the array, can be inverted in their prepositions and useful results can be obtained. 

Sorting of String Array 

We believe that you are from a programming background, and if you are from a C++ background, then you must have experience with the sorting of an array. We define the sorting process as:

"The sorting of the data is defined as the technique to rearrange the elements in a particular order or manner according to the needs of the data."

Hence, as you can see, there are different ways of sorting, but here, we are using the sorting method that arranges the elements of the string array in alphabetical order. This type of sorting follows the ascending order of the alphabet, and as an example of the reverse method, we will use the sorted array in reverse to get the new array with the descending order of the string array.

Code for Sorting String Array:

#Declaring the array

myDishes=["chow mien", "sandwiches", "rice", "butter puffs", "pizza",

          "meat balls", "rolls",  "momos", "patties", "chicken", "coffee cake", "wings"]

print("\bThe array before sorting= ", myDishes)

#sorting the whole array using the sort method.

myDishes.sort()

print("\bThe array after sorting= ", myDishes)

#Reversing the whole array using the reverse method to get the array in descending order

myDishes.reverse()

print("\bThe array after sorting= ", myDishes)

Output:

Hence, it is a perfect way to understand how we can use the different methods together, (in this case, the reverse method) in the same code to get the desired output. Sorting is an important concept, and in programming languages such as C and C++, sorting is done by long codes, but with the help of this uncomplicated method, the programmers can get the desired output easily in just one step.

Consequently, it was a lecture full of practical examples of the methods in Python, and we have seen many daily life problems that can be arranged in a better manner using the built-in methods. We made the pair of methods into single programs so that we could ignore the repetitions of the same data again and again. Moreover, it provided us with a better chance to understand the professional use of the methods in real-life problems. We hope you learned a lot. Stay with us for more learning.

Hola students! Welcome to the new Python tutorial, where we are learning about OOP. We all know why OOP is important in programming. In the previous lecture, a detailed overview of classes and objects was discussed. We are moving towards the next step of learning where our focus will be on inheritance. Yet, programming also contains inheritance. We have all read about inheritance in real life and are aware of the concepts surrounding it. This time, we will discuss these concepts according to the workings of the classes and related entities. The concept of inheritance is the same in both scenarios, and with the help of interesting examples, the practical implementation of inheritance will be shown to you, but first of all, it would be interesting to know about the main headings that will be shared in detail with you later.

• How do you introduce the inheritance in OOP and how do you compare it with the biological inheritance?

• What are the basic types of classes that are used in inheritance?

• Give some examples of parent and child classes.

• How do you explain __init__() function with inheritance?

• What do you know about the self keyword?

• How to override the parent class in inheritance?

What is Inheritance in OOP?

Inheritance is the fundamental concept in OOP, and with the help of Python, the programmers can easily get the required outputs with minimum effort. Keep in mind, OOP is applicable because of its feature of reusability of the code in the perfect way. This helps to curtail the redundancy while working on the development projects. There are different ways of recycling the code in Python, and inheritance is one of the most prominent ways to do so. As in biological inheritance, where the characteristics of the parents are transferred from parents to children, so is it with inheritance in object-oriented programming. The whole concept is about the relationship between different classes and this is important to learn for the large and complex projects of Python.

Types of Classes in Inheritance

We have discussed in the previous lectures that different classes can be used at the same time in the same code, and this is the time to utilize that concept. When there is more than one class, it is important to make connections between these classes. Inheritance is a relationship between different types of classes, and this is the basis of the concept of inheritance. The following are the main classes used in inheritance:

1. Parent class

2. Child class

Parent Class

The parent class is also known as the base class, and it has the main characteristics that can be inherited or copied by the other classes. Any class can be the parent class, and therefore, there is no special syntax for it. The way to form the simple class is considered the parent class. So, we can think in our mind that all classes are parent classes until they are derived from other classes. Here is an example of a parent class.

Example

Let us take an example from the real world so that you may understand it well. We all know about bank accounts. If we consider the bank account as a parent class, then the types of the bank accounts such as business accounts, saving accounts, etc have many attributes that the “bank account” class may have. So we can consider the savings and business account as the child class so whenever we declare them, we do not have to write all the attributes of other types of bank accounts. 

Code for the Parent Class:

#creating the parent class

class Fish:

#defining the attributes

    def __init__(self, firstName="ozzy", LastName="king"):

        self.firstName = first_name

        self.LastName = last_name

#printing the class

    print("Parent class= " , Fish)

Output:

In this code, we have just made a simple class with some attributes and considered it the parent class. As you can see, we can make more objects of this class and use it in different ways, but for now, we are not moving forward because the concept here is clear and one that we want to discuss.

Child Class

As you can guess, the child class is the one that is derived from the parent class and has the same attributes as the parent class. It is a subclass and has a slightly different syntax than its parent class. Let’s say you are talking about bears, All bears can walk, run, eat, and sleep. So, these attributes remain the same, and the parent class, in this case, is "bear,” whereas, the polar bear and brown bear are the subclasses. The same is true for all the animals that have the different kinds of the speicies in them because of the same attributes but have many subclasses. The following code will be a little bit confusing, but the discussion will clarify the concept:

Code For Child Class

#creating a new class with the name penguin so the animal bird specie is used

class Penguin:

 #declaring the attribute and method of the parent class

    name = ""

    def swim(self):

        print("I love swimming")

# a child class that inherits the message from a parent

class tag(Penguin):

    # new method in a subclass

    def display(self):

        # access name attribute of superclass using self

        print("My name is ", self.name)

# creating an object of the subclass

pengName = tag()

#Using the superclass attribute and method 

pengName.name = "tag"

pengName.swim()

# calling the subclass method created above

pengName.display()

Output:

Discussion:

• In the beginning, a parent class is declared with the name "Penguin.” This class has two things declared in it. First of all, it tells us that the “name” is in the form of a string. The second thing is the attribute of swimming. This also gives us the string message that the tag can swim. We have written it here just to show that messages from the child and parent can both be used in the output. 

• In the next step, a child class is introduced in which the particular penguin is declared to have the name “tag”.

• Now, let us use the parent class and get the attribute of the name from it. In this way, the child gets the name "tag." Another thing that happened here is, that a new method was formed to explain swimming. 

• In the end, a built-in method display() is used to display the name of the penguin with a string message that was shown before.

• Keep in mind, the display method is not limited to the inheritance only but can be used with most of the entities of the Python. 

Important Terms Used With Inheritance

When dealing with the codes of inheritance, there are different concepts that must be kept in mind so that you may use them with the inheritance, and once the programmers have a grip on it, it becomes easy and comfortable to use inheritance in multiple ways. At this level, it is important to understand the following methods and constructors, and you will notice some of them have already been used in our codes of inheritance. Yet here, we are discussing it in detail so that you may use it in the practice and get the perfect results according to your requirement. 

Pass Keyword in Classes

Programming is an interesting field, and when you deal with a hundred lines of code, sometimes, when the programmer does not want to use a specific function for some reason ( when you simply declare the function but there is no need to execute it but other code must be run), then to avoid the errors, the “pass” keyword is used to do so. We can define this keyword as:

"A pass keyword is a simple type of keyword that is used to tell the compiler that the line above this keyword has had no usage till now, so go to the next line during execution."

Sometimes the coders are confused between the pass keyword and the comment, but the difference is that the comment is not part of the code but is the explanation to the user. The pass keyword, on the other hand, simply causes the compiler to jump to the line above it, which may be a function that does not yet have an implementation. The syntax of this keyword is to simply type “pass” right after the line to be skipped. 

Example of Pass Keyword in Inheritance

The reason why we are discussing this keyword here is, this helps a lot in inheritance. If there are many subclasses and the programmer simply wants to ignore one line to check whether my other classes are working or not, they use the “pass” keyword in such cases and it seems to be very interesting. 

In the code given above, we are simply placing the pass keyword after the declaration of subclass and the compiler will jump on it and is not able to use it in the methods given in the next lines. 

Code:

#creating a new class with the name penguin so the animal bird specie is used

class Penguin:

 #declaring the attribute and method of the parent class

    name = ""

    def swim(self):

        print("I love swimming")

# a child class that inherits the message from a parent

class tag(Penguin):

#using the pass keyword to jump from the line above

 pass

    # new method in a subclass

def display(self):

        # access name attribute of superclass using self

        print("My name is ", self.name)

# creating an object of the subclass

pengName = tag()

#Using the superclass attribute and method 

pengName.name = "tag"

pengName.swim()

# calling the subclass method created above

pengName.display()

Output:

You can see only the parent class is used here, and the child class is jumped. The code is not able to be executed well because of this jump condition.

The __init__() Function

Since we are using the concepts of inheritance, have you noticed the usage of __init()? If yes then you must be wondering what is it. This is the constructor of Python and it is used to initialize the data member. That is required when the object of the class is created. At the time of the creation of the object, this function contains the collection of instructions or statements that it contains. 

Self Keyword in Inheritance

In the previous lectures, we also used another keyword to be noticed, which is the “self” keyword. In simple words, we can say that when we are using the concept of classes, the “self” keyword is used to access the attributes and methods of the class. In some other programming languages, the @ is used to refer to the instant attribute.

Overriding Method in Python

Another method that is used in Python, especially with the inheritance concepts, is the overriding method. Till now, we have said that the child class gets the attributes from the parent class, but what if the programmer wants to introduce an attribute with the same name but different features in the child class? For this, we use the overriding method, which is introduced with the statement given next:

"In inheritance, the overriding method allows the child class to have the implementation of the specific method that is already declared in the parent class."

It is important to keep the flow of the code in mind while using the overriding method because incorrect overriding can cause errors or redundancy. Moreover, attributes and classes can both be overridden using this method.

In this lecture, we have learned many simple and smart concepts to deal with inheritance and have learned a lot about them. At the start, we saw the basic definition of inheritance in OOP and compared it with biological inheritance. After that, we have seen what is the child class and parent class. Some important terms about te inheritance was also discussed that were different kind of constructors and methods that we useful in their functioning. In the next lecture, we will talk more about inheritance so stay with us and happy coding.

In the realm of PCB manufacturing, the Gerber file format plays an important role in the design and production processes. Understanding and inspecting these files are crucial to ensuring the accuracy and quality of the printed circuit board (PCB). JLCPCB is a leading PCB fabrication and assembly service provider. Fortunately, it  offers an exceptional online tool. It is called the JLCPCB Online Gerber Viewer. It  simplifies and enhances the inspection of PCB layouts. In this article, we will discuss the significance of Gerber files. We will  explore the reasons for inspection. We will  also showcase the powerful functionalities of JLCPCB's Online Gerber Viewer.

What is a Gerber file?

A Gerber file, named after the famous Gerber Scientific Instruments, is a standard file format used in the sector of printed circuit board (PCB) manufacturing. It serves as a crucial bridge between the design stage and the physical realization of a PCB. Essentially, a Gerber file is a collection of instructions and data that communicates the precise specifications and details of the PCB design to the manufacturing equipment.

In more technical terms, a Gerber file is generated from the PCB design software and contains a series of commands that describe each layer of the PCB. These commands include information about the placement of copper traces, solder masks, silkscreen markings, drill holes, and other crucial elements that contribute to the final PCB layout. Each layer is represented as a separate Gerber file, allowing manufacturers to accurately interpret and translate the design into a physical PCB.

The Gerber file format has been widely adopted as an industry standard due to its versatility and compatibility with various PCB manufacturing processes and equipment. It provides a concise and standardized representation of the design, enabling seamless communication and collaboration between design engineers and manufacturers.

One of the key advantages of the Gerber file format is its flexibility in accommodating different PCB layer configurations. Whether a PCB design requires single-sided, double-sided, or multi-layer boards, the Gerber file format can adapt to convey the necessary information. Each layer is defined within its respective Gerber file, allowing for clear differentiation and precise control over the manufacturing process.

Furthermore, Gerber files incorporate not only the geometric layout of the PCB but also important manufacturing details. This includes parameters like trace widths, pad sizes, drill hole dimensions, and material specifications, ensuring that the manufacturing equipment performs the necessary operations accurately and consistently.

In summary, a Gerber file is a standardized file format that encapsulates the critical information required to produce a PCB. It serves as a vital communication medium between the PCB design software and the manufacturing equipment. By conveying precise instructions and specifications, Gerber files enable manufacturers to bring PCB designs to life with the utmost accuracy and fidelity.

Design for Manufacturability (DFM) Analysis:

By analyzing the Gerber files, manufacturers can identify potential manufacturing issues, such as inadequate spacing between components, unachievable trace widths, or insufficient clearances. This helps in optimizing the design for efficient and cost-effective manufacturing. Addressing DFM concerns at the early stages of the design process saves time, reduces manufacturing risks, and streamlines the production timeline.

Error Detection and Correction: 

Detecting errors early in the manufacturing process saves time and resources. Inspecting Gerber files helps identify errors such as missing or overlapping features, incorrect layer assignments, or mismatches in the solder mask and silk screen layers. With the assistance of an advanced online viewer like JLCPCB's, design engineers can quickly spot these errors and rectify them before initiating the manufacturing process, minimizing rework and production delays.

Collaboration and Communication: 

Gerber files act as a common language between designers, manufacturers, and assemblers. Inspecting the files ensures clear communication and effective collaboration, reducing misunderstandings and streamlining the entire production cycle. With the availability of the Online Gerber Viewer, stakeholders can easily share and discuss design iterations, enabling smoother collaboration and efficient decision-making.

Exploring JLCPCB's Online Gerber Viewer:

JLCPCB's Online Gerber Viewer is a user-friendly and feature-rich tool that simplifies the inspection process of Gerber files. Let's explore some of its notable features:

User-Friendly Interface:

The viewer boasts an intuitive and easy-to-navigate interface, making it accessible to both beginners and experienced users. The clean layout allows for effortless file upload and exploration. Users can quickly familiarize themselves with the tool, eliminating the need for extensive training or technical expertise. There is a clear-cut set of instructions for ordering.

Below is how the interface looks like. You have to open an account. Then you can login and start uploading your gerber files. You can login anytime to see your upload history.

Comprehensive File Format Support:

JLCPCB's Online Gerber Viewer supports a wide range of file formats, including Gerber (RS-274X), Excellon drill files, and even ZIP archives containing multiple Gerber layers. This versatility ensures compatibility with various PCB design software and simplifies the viewing process. Users can confidently upload their Gerber files without worrying about format limitations.

After uploading the zip folder, it can detect the number of layers.

Take a look at the image below. Here the Online Gerber Viewer detected a 2-layered board.

Similarly, in case of this design, the same tool detected 4 layers.

All uploads are secure and confidential. 

Layer Selection and Visualization:

The viewer enables users to selectively display individual PCB layers or combinations of layers, making it easier to analyze specific components, copper traces, solder masks, silk screens, and other essential elements. This feature provides a comprehensive view of the PCB, allowing users to examine the design from different perspectives and assess its integrity.

You can analyze your PCB layer by layer.

This is when all the layers are visible:

Then you can select the Top layer only:

And this is the bottom layer:

There are other parts of the PCB on the left side of the console. Such as-Top silk, Top solder mask, Top paste mask etc. You can check and uncheck them to view. It makes your observation much more convenient.

Zooming and Measurement Tools: 

The viewer offers zooming capabilities and measurement tools, empowering users to examine intricate details, measure distances accurately, and ensure precise component placements and dimensions. The ability to zoom in and out helps users identify minute details, ensuring that every aspect of the design is thoroughly inspected.

In the image below, we can see the zoom option.

Analysis result:

As soon as you upload your gerber files, the  Online Gerber Viewer gives you an analysis result. It is done within the shortest possible time.

The following figure shows how an analysis result looks like. You can see pretty much everything. Number of layers, trace width, drill size and lots more. If there is an error in your design, you will see that too. The result shows warnings in yellow.

3D Visualization:

In addition to 2D views, JLCPCB's Online Gerber Viewer also provides an optional 3D visualization feature. This immersive experience facilitates a better understanding of the PCB's physical appearance, component heights, and spatial relationships. Users can rotate, pan, and zoom in the 3D view to gain a comprehensive understanding of the PCB's three-dimensional structure.

Features offered by Online Gerber Viewer by JLCPCB at a glance:

To wrap up:

The JLCPCB Online Gerber Viewer is an invaluable tool that simplifies the inspection and verification of Gerber files, ensuring the accuracy and quality of PCB layouts. By utilizing this user-friendly and feature-rich tool, design engineers, manufacturers, and assemblers can effectively identify errors, optimize designs for manufacturability, and streamline collaboration. The power of JLCPCB's Online Gerber Viewer enhances the efficiency of the PCB manufacturing process, ultimately leading to higher-quality products and faster time-to-market.

With its comprehensive file format support, advanced visualization features, and collaborative capabilities, JLCPCB's Online Gerber Viewer revolutionizes the way PCB layouts are inspected and analyzed. The user-friendly interface and intuitive navigation make it accessible to users of all levels of expertise, eliminating the need for extensive training or technical proficiency. The ability to selectively display and visualize individual PCB layers or combinations of layers provides a comprehensive view of the design, allowing for a thorough assessment of the PCB's integrity.

The zooming and measurement tools offered by the viewer enable users to examine intricate details, ensuring precise component placements and dimensions. The cross-sectional views and optional 3D visualization feature provide an in-depth understanding of the PCB's internal structure, aiding in the identification of potential issues and improving overall manufacturing quality.

By inspecting Gerber files with the JLCPCB Online Gerber Viewer, design engineers can verify the accuracy of their designs, address design for manufacturability (DFM) concerns, and detect errors early in the manufacturing process. This results in optimized designs, reduced manufacturing risks, and improved overall product quality.

In conclusion, the JLCPCB Online Gerber Viewer empowers users to conduct comprehensive inspections of PCB layouts, ensuring the successful realization of design intent. Its intuitive interface, extensive feature set, and collaborative capabilities make it an indispensable tool for anyone involved in the PCB manufacturing process. By harnessing the power of the JLCPCB Online Gerber Viewer, designers and manufacturers can achieve greater efficiency, accuracy, and collaboration, ultimately leading to superior PCBs and accelerated time-to-market for their products.

Artificial intelligence plays a crucial role in commerce by providing personalized recommendations based on previous searches and online behavior. With AI, businesses can optimize products, plan inventory, and improve logistics, which leads to reduced operating costs and increased margins. Whether you aim to scale personalized experiences, delight customers, or create new revenue streams, building an AI solution is essential. While priorities may differ among business owners, AI remains integral to implementing a variety of solutions. In this article, we discuss how AI experts can help develop and grow businesses.

What Are AI Solutions?

Artificial intelligence solutions offer pre-built or customizable options to tackle specific use cases and overcome business challenges. These solutions enable increased efficiency and growth, provide new methods of operation, and drive transformative innovation. They do so at three times the speed of traditional product development, making them a game-changing asset for any organization. Furthermore, while each solution is already optimized for a particular industry and function, it can be easily personalized to address the unique hurdles of individual clients.

AI Bots

Companies can deploy an AI bot to swiftly automate customer conversations with staff via live chat. Such tools allow them to enhance customer service and alleviate agent overload by automating interactions throughout the entire customer journey.  If you are seeking to collaborate with an AI solution provider with a focus on building AI-based chatbots, look no further. Discover more here to aid you in making an informed decision. Chatbots facilitate task automation based on client intent, operating 24/7.

Invoice Automation Solutions

Such cloud-based solutions offer an effortless and streamlined invoicing experience for sellers and buyers alike. With an easy-to-navigate invoice generator, users can create professional invoices with ease and improve their cashflow. Using such tools, you can stay organized and enhance efficiency due to the intuitive step-by-step process.

IoT Solutions

Internet-of-Things and AI operate on the concept of transmitting real-time information and physical data into the cloud using advanced technology. This stored data can then be processed for various purposes at the application level, including monitoring, alarms, and predictive analytics. Additionally, the integration of machine learning, the usage of ERP or CRM systems, and the utilization of IoT-generated data in blockchain projects are becoming increasingly prevalent. With faster, event-based processes and improved transparency, such solutions have numerous use cases for customers seeking greater efficiency.

How Are AI Solutions Developed?

AI-development teams consist of highly qualified professionals with extensive programming experience and proficiency in multiple computer languages. These professionals hold BA degrees in fields such as mathematics, data science, statistics, and computer science, which equips them with the necessary skills to excel in building AI solutions. The process for creating these solutions involves defining future goals, recommending necessary tools, as well as devising budgets and timelines for the project. Following this, teams collect data, develop and train algorithms, and ultimately deliver the finished product.

What Expertise is Required

The challenge of developing artificial intelligence expertise is significant due to the high demand for skilled practitioners and the limited availability of qualified individuals. This is largely due to leading tech companies monopolizing the most talented engineers at salaries that many organizations cannot compete with. Successful AI professionals utilize a variety of technologies, such as Python, Django, Oracle, JS, React, and more. 

AI Experts That You Need for Building a Project

Companies have two options: forming an in-house AI development team or outsourcing to a software development team or consultant with proven AI expertise. Artificial Intelligence encompasses a diverse range of technologies, frameworks, and tools. To effectively solve your business problems, it is crucial to have a proficient external engineering team comprising experienced data scientists and data engineers.

For businesses lacking in-house AI expertise, assembling the required skill set and identifying suitable candidates can be daunting. Thus, engaging an AI solution provider who can supply tech experts or delivery managers to guide you on team composition and recruitment could prove invaluable.

Big Data Specialists

To properly analyze data, it is essential to first collect, organize, and process it. Artificial intelligence heavily relies on large amounts of data, therefore it is crucial to have experienced engineers who are skilled in working with both structured and unstructured data. Additionally, they must be capable of constructing a secure data platform and have a proficient understanding of Hadoop, Pig, Spark, R, Hive, and other technologies.

Machine Learning Professionals

The experts in question employ machine learning libraries and implement ML solutions for practical use. Moreover, they ensure the reliability and expandability of data science coding.

Data Scientists

They are an indispensable component of an AI group. Through their expertise in data manipulation, they craft sophisticated models, scrutinize and analyze data, and get valuable insights using state-of-the-art mining procedures. With their exceptional abilities, they can tackle complex business issues and provide practical solutions.

NLP Professionals

Numerous AI initiatives involve Natural Language Processing, hence requiring the expertise of NLP specialists. NLP functions as a mediator between human communication and machine interpretation, enabling computers to comprehend and translate human language.

The role of an NLP engineer entails utilizing NLP techniques to convert natural language data into utilizable features for classification algorithms. To excel in this position, specialists must possess exceptional proficiencies in statistical analysis, machine learning methodologies, and text representation techniques.

Computer Vision Experts

They excel in image recognition by linking each image to a specific metric, rather than matching metrics with one another. Through computer vision, they create models of objects and environments, providing innovative solutions for medical image analysis and identification tasks.

AI projects require the expertise of a variety of professionals across a range of disciplines. From hiring data scientists to voice recognition specialists, assembling an experienced team is essential for the successful development and deployment of any AI project. With the right combination of talent, businesses can enjoy the benefits that artificial intelligence has to offer. Having the right AI team in place is essential for businesses that want to maximize the potential of artificial intelligence. By understanding the different roles required and sourcing the right talent, they can ensure that their project is a success.

Targeting customers and niche demographics online continues to grow in relevance as more companies adopt telecommuting practices. For this purpose, mass emailing is superior to less extensive forms of advertising in terms of both obtaining prospects and reaching the intended customer base. Amid the growth of social networking sites and other promotional channels, email marketing efforts stay at the forefront of the industry.

Using mass emailing, businesses may find valuable and potential customers for their products and services. Promoting both products and services with the help of such a service increases the likelihood of generating leads and improving ROI.

To help our readers find the easiest options for this, we have compiled a list of easy ways to send a mass email in Gmail below. 

1. By Use of BCC(Blind-Carbon-Copy) in Gmail 

Google Mail's BCC feature is a fantastic way to streamline your communication and make sure the proper individuals receive important notifications. The amount of time spent forwarding individual emails to several people reduces and the process is streamlined.

Follow the guideline below on how to do mass email in gmail easily.

• Simply access the Google inbox from a computer and choose the Compose button to launch the message composition box.

• Create another message addressing it to the initial receiver by typing their full email handle into the For box. 

• When sending to a large group of people, divide their mailing addresses via punctuation, spaces, or using the 'Enter' button.

• You may fill in more people to this BCC group by using the Bcc icon in the header information. 

• When you use this option, an additional space labeled BCC may display. 

• Fill up the box with the e-mail address for every anonymous receiver. It's time to write up the email and send it. 

2. By Use of a Mail Merge

An effective method to save energy when sending emails to a large group of individuals who all need unique content is to utilize a mail merge. This is a useful tool to do mass email in Gmail with personalized contact information pre-filled via an integrated database.

Among its many handy functions is the ability to bring in all of your Google Contacts with just one keystroke. This makes it quick how to create an email blast in Gmail and easy to start passing out targeted emails. It additionally lets you evaluate the campaign settings prior to releasing the newsletter to clients ensuring everything looks satisfactory.

If you want to utilize this tool with a Gmail mailbox, you'll require downloading it as a Gmail extensions add-on from Google's marketplace. The free version of Mail Merge is limited to sending to Fifty people every single day. While customers who purchase the paid version may send an email to a maximum of 400 individuals at once.

You can use the following steps on how to use a mail merge effectively.

• Ensure that you've downloaded and set up Right Inbox. The premium plan is required for the mail merging features for more than 300 recipients.

• Log in to your Google Mail account. At the left end of the Google mailbox a mail merging option will appear; choose it. 

• Make sure it includes the correct receivers. Users have the option of either uploading a spreadsheet called CSV containing the recipient's email accounts or entering them individually.

• Join this by adding your own email address. Turn on the promotion's mail follow-up notifications. 

• When you want to boost response rates, review the email and customize each individually.

• Modify the pace at which emails are sent and whether or not they are being tracked. You may initiate the mail merging after you have validated what you want.

3. By Use Of An Effective Mass Email Marketing Service 

Another efficient method how to mass email in Gmail at once is to employ a reliable email marketing service. Email template builders like Stripo make it easy to how to send out a mass email on gmail quickly and efficiently.

More than 1300 professionally-designed Email templates are already included, and customers may test out the tool's drag-and-drop functionality before committing to a mass email strategy.

When you're done with an email, save it as a draft in Gmail and hit the "Send" button to send it out to the targeted audience.When you use one of these services, you can be certain that your mass email will reach its intended recipients safely and in the most effective way possible.

Using the help of the resources provided by mass email marketing firms, advertisers can monitor the success of their campaigns and adjust their strategies accordingly.

Conclusion

Many businesses and email marketers rely on mass emailing as a primary strategy for keeping up with the fierce competition in today's online marketplace. Any business may benefit from using a mass emailing service because of the time and effort it saves. Specifically, it allows for the tracking of metrics like audience size and monetary gain to gauge the success of advertising campaigns.

Using a bulk email solution is essential for even the greatest small businesses in this industry. We've compiled a number of strategies for using Gmail to send out mass emails, which you can view above. Hopefully, you found some useful information in this article.

Hello pupil! We hope you are doing well with object-oriented programming with Python. OOP is an extremely important topic in programming, and the good thing about Python is that the concept of OOP can be implemented in it. In the previous lecture, we made a basic introduction to all the elements that are used in the OOP, and right now, we are moving forward with the detail of each concert with examples. The imperative nature of the OOP is useful for us because it uses the statements to change the programming state, and in this way, the working of the code becomes easy and effective. To use the applications of Python, the imperative nature of OOP will give us the tools we need to get the perfect results by using our creativity. We will prove this in just a bit, but here are the highlights of the topics that will be discussed. 

• How can you describe the OOP by using an example?

• Why are classes important in OOP?

• Discuss the example and syntax of code when we use classes in OOP.

• What are objects?

• Give us an example where multiple objects are made if one class and the information are given by the user.

• Discuss the basic features of objects in OOP.

• How do you classify the attributes of objects?

• What are the behaviours in OOP?

All of these are important to learn so pay attention to each and every concept and if you have any confusion, you can contact us directly.

Example of the OOP with Python

Before going into the details of the OOP, now is the time to discuss why we are emphasizing the importance of the OOP and how we explain it with Python. The whole concept will be clear with the help of the following example:

Think of a dog that will be taken as an example by a class. Here, we have taken a general example that is common in the real world. Think of all the characteristics of the dog that may be either physical or natural habits of the dog. The general class of dog may have many types, such as the breed of the dog. There are hundreds of breeds of dogs, and these are considered the basic members of the class "dog." 

Moreover, different dogs have different kinds of attributes such as their size, colour, skin type, age, and other characteristics that are said to be their instance attributes. On the basis of these attribute, the division of the dogs is done into different classes. 

Moreover, the way the dogs bark, their favourite food, and their daily routine make them more specific, and these are referred to as the methods of that particular class.

Importance of Classes in Python OOP

The class is the basic unit, without which, the programmers may not think about the OOP in any way.  This is the prototype with which the whole OOP process is carried out. You can think of a class as the blueprint of the house (let's say). In this way, the information about the floor, walls, rooms, windows, doors, etc. In this way, the whole house is made by following this blueprint(class) whereas, the result, that is the house is the object. Let us prove all this information with the help of this example.

Syntax of the Class

The class is simply made in Python with the keyword “class” and the name that we want for our class:

class myClass:

    # class definition

Here,

class=keyword

myClass= the name of the class given by the programmer.

In this way, with the help of simple lines, the creation process of the class is carried out. Now, the definition of the class is in the new line. To make a clear difference, a colon is used after the name of the class. You must keep in mind, the class name is a single word, no matter what the length of the name, and there must be no space between the name of the class. 

Example of Class in Python

In the example of the class given above, where we were talking about dogs. We are using the same example and will show you the result by using them in the example. To start with a simple example, the class with only three attributes. 

Code for Class

#declaring the class

class myDog:

#defining attributes

    def __init__(self, name, age):  

#using the attributes and storing them in the new names

        self.name = name

        self.age = age

#usign the stored name to print the results

tinku = myDog("Tinku", 2)

print("The name of the dog= ",tinku.name)

print("The age of the dog= ",tinku.age)

Output:

Let's see what happens during this code:

• First of all, we created a class with the class keyword, and this time, we named it “myDog” so you are providing the information about your dog( if you do not have it, just imagine it) and we are writing the particular features of that dog. So, the class is myDog, and all the other features are its attributes. 

• In the next step, we are defining the characteristics with which we want to describe our dog. Here, name and age are the attributes. That is taken as one of the two arguments we are defining to use later.

• The "self" keyword will tell us that name and age are the special attributes that we want to use here.

• In the end, a new unit is formed, where this time, we are using the values of attributes instead of the names. The first step was only to give the base of what we wanted, and this time, we will assign values to these attributes. 

• You can see, the name is in the form of a string, therefore we have used a double quotation. In this way, we do not have to specify the data type but just represent it in a specific way. 

• In the end, it's time to print the result and to make the message clearer, we used the written message and then declared the value we wanted.

We can add more and more attributes to make the class clearer and store data about the dog, such as its colour, fur type, etc. This is a simple example to show you how the class works. 

Objects in Python OOP 

The workings of the class are not just limited to one product. This is the good thing about the OOP is, it provides us with the versatility to work in a different way with the same code. In the example given above, if more than one breed of dog is to be stored, then we will make the objects of the class "dog."

Code:

To understand the objects in interesting ways, we are moving towards real-time applications where different objects are made and then used in a specific way. Let us take an example where we have different types of dogs with different ages, and the combination of the ages of the first two dogs is the age of the third dog. So here is the question that can be asked of you in the examination. 

Question:

Write the code to get the age of a dog that is the combination of two dogs and get the input from the user for it. 

Answer:

#declaring the class

class myDog:

#defining attributes

    def __init__(self, name, age):  

#using the attributes and storing them in the new names

       self.name = name

       self.age = age

#using attributes in string message

    def dogInfo(self):

            print(self.name + " is " + str(self.age) + " year(s) old.")

#printing queries and getting input from user            

print("What is the age of happy")

AgeOfHappy=input()

print("What is the age of sunny")

AgeOfSunny=input()

AgeOfFancy=int(AgeOfHappy)+int(AgeOfSunny)

#using the inputs to guess the age of fancy 

print("Age of fancy= ",AgeOfFancy)

#printing results

happy = myDog("Happy", AgeOfHappy)

sunny = myDog("Sunny", AgeOfSunny)

fancy = myDog("Fancy", AgeOfFancy)

happy.dogInfo()

sunny.dogInfo()

fancy.dogInfo()

Output:

Discussion:

This program has many interesting features, and these will become clear with the help of this discussion. 

• At the beginning of the program, the class “myDog” is created so that we may give the same attributes to different objects. 

• To declare the attributes we are using here the “def” keywords to define the keyword then the 

• Attributes are mentioned so that we may use them later.

• The next step is to define the message, in which we will use the attributes and get the result in the form of a message. Here, the point to notice is, we are using the string keyword for the "age,” but how can age be a string? Actually, we are using it in the string message, and if we declare the “int” here, the compiler, when it reaches this point at the output, will throw the error and not print the whole string. 

• After that, the whole mechanism is used in which we are printing the message to input the age of the first two dogs and get the output in the form of integers. 

• Here, if the user inputs a data type other than an integer, it will also result in an error. 

• Both these ages are then added, and the result is saved in the “AgeOfFancy” variable.

• The object we have just used to print the results.

So with the help of this discussion, we come to know that the object consists of the following features:

Attributes of Objects:

Each object has its own set of attributes, which are variables that store information about the object's current state. We can say that objects are made of attributes. Yet, there are different types of attributes in OOP, and the following are some of the most important characteristics of attributes:

• Instance variables: These are defined within the class but are unique to every instance of the class.

• Class variables: These are also defined within the class but are shared with all the instances.

• Methods: These are used to manipulate the object’s state and are defined within the class to operate the attributes. 

• Special methods: These are also referred to as the magic methods and these tell the compiler how objects will behave under certain conditions. These are denoted by the double score in the name as we have just used in the previous code of dogs. 

• Inherited attributes: These are used to reuse the modularity in the code as these are inherited by a subclass or a superclass. So it makes working easy.

The Behaviour of Objects in OOP

When an object is created in OOP, it has its own set of behaviours, which are defined by the methods in its class. These methods can be used to change the state of an object. Another function of the behaviour is to carry out other operations related to the object's behaviour. For example, if we declare the object of a car, we might have methods such as:

• start engine()

• accelerate()

• brake()

• turn()

These methods define the behavior of the car object and enable it to perform the actions that are expected of a car. These are just simple examples, and we can add more and more behaviors, just as a car can do hundreds of tasks.

Hence, in this lecture, we read a lot about objects and classes. There are different entities that are used in OOP, and objects and classes are the basics. We must know that objects and classes are used simultaneously, and for a better understanding, we learned the classes first and then examined where and how objects are being used. Moreover, different types of objects and fundamental knowledge about their pillars are discussed here. We have a lot of information to share with you, and you must have noticed that Python is now more complex but useful when we use the concepts of OOP. We have some mini Python projects to share with you in this tutorial, so stay with us for more programs.