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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.