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Python: a language designed for machine learning

Born in the early 90s, Python is one of the most popular programming languages ​​among application developers. When it was created, it made it possible to automate certain recurring scripting tasks. The time saving is complemented by the possibility of managing a large quantity of information flexibly. Which delights Webmasters of large sites with multiple functionalities. However, it is mainly specialists in Machine Learning and Big Data who are served.

Did you say Python?

Python was born from the imagination of Guido van Rossum in 1991. This programmer took inspiration from a television entertainment show of the time to name his project. Unlike other programming languages, it does not require any compilation to work. All you need is native interpreter software to run the codes on a basic computer. This specificity makes it accessible to as many people as possible, even if the speed and performance sometimes leave something to be desired.

With Python programmers focus on what they need to do, not how to complete the task. They use object-oriented software. In other words, the creation work is made as easy as possible so that the application prototype can quickly see the light of day. Moreover, it is an open source tool par excellence for those who want to learn coding without the hassle. Courses are available on OpenClassrooms. Learners study ways to master programming from the developer’s side, but also as ordinary users. A better understanding of tool libraries also allows you to progress quickly.

What are the benefits of learning this programming language?

  • The Python language benefits both beginners and experts. The tool is simplified to allow the user to quickly obtain what they are looking for, that is to say the development of an application. The syntax is readable in addition to being direct.
  • This open source software has the advantage of being compatible with most operating systems. Its versatility but above all its universality well compensates for its relative slowness.
  • The older generation of developers used it for scripting as well as automating computing tasks. Currently, this language also makes it possible to create professional quality software. NASA, EDF and YouTube use it.
  • Many online applications and services are developed and managed in Python. Among the best known are BitTorrent (download), Blender (3D), Miro (Internet TV), Battlefield 2 (video games).
  • Claiming the title of number one programming language in 2021, Python is a must-have for developers of all levels. To ignore it would be a mistake. That said, basics such as JavaScript and challengers like Rust and Ruby also remain essential.
  • A strong community of programmers from all walks of life supports the foundation that manages this free tool. The number of newbies who are interested in this software has increased by 27% in one year. For comparison, C++ saw 10% growth for 2020.
  • The “low-code” interface allows you to do programming without being an experienced coder. There are many intuitive graphic tools in the bookstore. This does not exclude highly competent developers who will always be able to extend the Python library.

How many updates are there for this program?

Turning 30 this year 2021, Python is currently in its third generation. Two versions are available, but developers are invited to work on their application with version 3.8 and above. Indeed, the 2.X edition is supported, but has become an unofficial version since 2020.

With Python 3.X, users benefit from features that are as innovative as they are useful. A better interpreter and a concurrency controller are added to this program. The library provided by third parties is about to be enriched. Compatibility issues have been resolved by the Python Software Foundation and its supporters around the world.

Available since October 2019, Python 3.8.0 introduces the Morse code operator. This salvo includes various improvements, including “if” or “while” type variables in coding. Multiple scenarios and constantly enriched functionalities do not weigh down the applications developed. Debugging of character strings is also included. The ultimate in ease, a single default model invites a healthy installation.

Version 3.9.1 of the programming language is compatible with Mac OS 11. It supports Apple’s M1 processor which uses the ARM structure. The accompanying experimental installer allows you to manage Universal 2.X binary codes. In other words, the most demanding users, in this case Data Scientists, finally have a tool adapted to the challenges they face.

However, it is not a perfect programming language. In 2021, the “CVE-2021-3177” flaw was a real problem for application developers. Since it was possible to execute the codes remotely, hackers found an open door to carry out a proper DDoS attack. Python versions 3.8.8 and 3.9.2 closed the loopholes.

What contribution for machine learning and Big Data?

Scripting and automation in Python works well for web browsers or mobile applications. This language is also essential for Machine Learning players. They will be able to exploit the different possibilities of the tool thanks to a well-stocked code library. Many robotics companies are counting on this thirty-year program to make their products more “intelligent. This is the case of Aldebaran. This Softbank brand has chosen an easy-to-access platform to allow DIYers to tinker with their robots quickly. In addition, it is a tool compatible with most known operating systems. Developers on Windows, Mac OS, Linux and UNIX use it every day. Then, the open source program includes not just one, but several framework libraries.

Currently, Python remains the most used programming language in Data Science. The simplicity of the interface, the readability of its syntax and above all its great flexibility are qualities appreciated by data management professionals. Drilling, classification and analysis can be easily programmed. With Tensor Flow, Scipy as well as Numpy it becomes possible to perform an infinite number of tasks. These tools are accessible to a wide audience, even people without a background in software engineering or engineering. For Data Scientists, Scrapy and Beautiful Soup are recommended for extracting information from the Internet. Seaborn and Matplotlib are designed for their Visualization. For the development of Deep Learning type models, you will need to rely on Theano and Khera. If you absolutely must have artificial intelligence algorithms, then you will have to turn to Scikit-Learn.

What about the toolboxes available in the library?

Data science is a favorite field for programmers proficient in Python. They benefit from a wide choice of frameworks which allow them to exploit the data.

  • Pandas no longer needs to be introduced to application developers in R and Python. It launches reliable scientific analyzes on a database. Its features are multiple, including the ability to obtain responses to a specific query. It is even possible to generate graphic visualizations to transpose them into an Excel workbook.
  • Agate addresses the issues encountered in aggregated data analysis. It suggests a simple interface with meaningful statistics. The encrypted information can be copied to an office spreadsheet.
  • Bokeh further leverages database visualization. Compatible with Agate and Panda, it is commonly used with Python. The user does not even need to write a line of code.
  • NumPy is a Python calculator. It is the tool for those who want to understand data through algebraic formulas. It is aimed at beginners who want to compile multiple databases without headaches.
  • Scipy is a technical calculator. This toolbox includes several modules to carry out Big Data engineering and exploitation tasks. This is a program dedicated to interpolation, FFT and signal processing.
  • PyBrain or Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library brings together an army of algorithms specially designed for Machine Learning.
  • The Python library still includes an infinite number of programs. Developers will be able to try Cython, the C code translator and PyMySQL, the connector to the MySQL environment. BeautifulSoup reads data in XML and HTML.
  • Finally, Google Atheris is an open source tool for diagnosing Python bugs. Larry Page’s team did the same thing with applications in C or C++.

Python: Everything you need to know about the most popular Big Data and Machine Learning programming language.

python language

Python is the most popular programming language in the fields of machine learning, big data, and data science.

Learn everything you need to know about it, including its definition, benefits, and use cases.

The Python programming language emerged in 1991 as a technique for automating the most tedious aspects of writing scripts or quickly prototyping applications.

However, this programming language has become one of the most used in recent years in software development, infrastructure management and data analysis.

It is one of the driving forces behind the explosion of Big Data.

What is Python programming language?

Guido van Rossum, a programmer, designed Python in 1991 as an open source programming language.

It takes its name from Monty Python’s flying circus. As it is an interpreted programming language, it does not require compilation to work.

You can run Python code on any computer using an “interpreter” application.

This allows you to quickly see the effects of a code change.

On the other hand, it slows down the language compared to a compiled language like C.

Python, as a high-level programming language, allows programmers to focus on what they do rather than how they do it.

Therefore, writing applications in Python takes less time than any other language. It’s an excellent first language.

What are the main advantages of the Python programming language?

Python’s success is due to a number of advantages that help both beginners and experts. First of all, it is simple to learn and apply.

Its functionality is limited, which allows you to write programs quickly and easily. Additionally, its syntax is designed to be readable and simple. Another advantage is the popularity of Python.

This language is compatible with all major operating systems and computer platforms. Additionally, while it’s certainly not the fastest language, its diversity makes up for its slowness. Finally, while this language is mainly used for scripting and automation, it is also used to build professional-quality applications.

Python is used by a large number of developers to build software ranging from applications to web services.

What are the differences between Python 2 and Python 3?

Python comes in two versions: Python 2 and Python 3. There are many distinctions between these two versions.

Python 2.x is the previous version, which will be supported and receive official updates until 2020. It will most likely continue to exist informally after that date.

Python 3.x is the current version of the language. It includes a number of extremely useful new features, such as better concurrency control and a more efficient interpreter. However, the lack of supporting third-party libraries has delayed Python 3 adoption for a long time.

Many of them were only compatible with Python 2, making the switch difficult. However, this problem is almost resolved and there are few compelling reasons to continue using Python 2.

Python is a programming language used for big data and machine learning.

python logo

The main applications of Python are scripting and automation.

Indeed, this language makes it possible to automate interactions with online browsers or application graphical interfaces.

However, scripting and automation are far from the only applications of this language.

It is also used for application programming, web service or REST API development, metaprogramming, and code creation. Additionally, this language is employed in data science and machine learning.

With the advent of data analytics across industries, it has become one of its most important applications.

The vast majority of data science and machine learning libraries integrate Python interfaces.

As a result, this language has emerged as the most widely used high-level command interface for machine learning libraries and other numerical methods. There are many introductory books available on the Internet.

Finally, robotics companies such as Aldebaran use this language to program their robots.

This programming language was adopted by the company acquired by Softbank to simplify the design of applications by third-party companies and amateurs.

Why do Data Scientists use Python?

Python is the most widely used data science programming language.

It is simple, readable, clean, adaptable and platform independent.

Its numerous libraries, including TensorFlow, Scipy and Numpy, allow you to carry out a wide range of work.

According to a 2013 O’Reilly survey, 40% of data scientists use Python on a daily basis. Thanks to its easy syntax, it can be used by people who do not necessarily have engineering experience.

It allows rapid prototyping, and the code can be executed anywhere: Windows, macOS, UNIX, Linux… Its flexibility allows it to build Machine Learning models, data exploration, classification and many other tasks faster than other languages.

The Scrapy and BeautifulSoup libraries allow you to extract data from the Internet, while Seaborn and Matplotlib make data visualization easier.

Tensorflow, Keras, and Theano help create deep learning models, while Scikit-Learn makes it easy to create machine learning algorithms.

Top Python and Big Data Libraries and Packages

python programming

Python’s many data science packages and tools have allowed it to establish itself as the ideal programming language for Big Data.

Here are some of the most popular ones.

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Pandas

One of the most used data science libraries is Pandas.

Created by data scientists who were familiar with R and Python, it is now used by a large number of scientists and analysts.

It has many very practical native features.

It is possible, for example, to read data from multiple sources, generate big dataframes from those sources, and run aggregated analyzes based on the questions you want to answer.

One of the most used data science libraries is Pandas.

Created by data scientists who were familiar with R and Python, it is now used by a large number of scientists and analysts. It has many very practical native features.

It is possible, for example, to read data from multiple sources, generate big dataframes from those sources, and run aggregated analyzes based on the questions you want to answer.

You can also create graphs from the analysis results or export them to Excel using the visualization functions.

It is also capable of manipulating numerical tables and temporal data. You can also create graphs from the analysis results or export them to Excel using the visualization functions. It is also capable of manipulating numerical tables and temporal data.

Agate

Agate, a newer Python package than Pandas, also aims to solve data analysis challenges.

It allows you to review and compare Excel tables, as well as perform statistical calculations on a database. Overall, Agate is easier to learn than Pandas.

Additionally, its data visualization features make it easy to see analysis results.

Bokeh

Bokeh is a great tool for making data visualizations.

It is compatible with Agate, Pandas, and other data analysis libraries. It can also be combined with pure Python.

This application allows you to produce high-quality charts and visualizations without requiring code.

NumPy

NumPy is a Python module for performing scientific calculations.

It is useful for linear algebra, Fourier transforms, and random number calculations. It is a generic multi-dimensional data container. Additionally, it easily integrates with a wide range of databases.

Scipy

Scipy is a technical and scientific computing library.

It has modules for data science and engineering activities like algebra, interpolation, FFT, and signal and image processing.

Scikit-learn

Scikit-learn is extremely useful for classification, regression and clustering methods integrating decision tree forests, gradient boosting and k-means.

This machine learning Python library works in conjunction with other Python libraries such as NumPy and SciPy.

PyBrain

Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library are all acronyms for PyBrain.

It is, as the name suggests, a library that provides basic but powerful algorithms for machine learning tasks. It can also be used to test and compare algorithms in a wide range of predefined scenarios.

TensorFlow

TensorFlow is a machine learning library created by Google Brain.

Its data flow graphics and flexible architecture enable operations and calculations to be performed on multiple CPUs or GPUs from a PC, server, or even mobile device through a single API.

Cython is another Python library that allows code to be transformed to run in a C environment to minimize execution time.

PyMySQL, on the other hand, allows you to connect to a MySQL database, get data, and run queries.

BeautifulSoup supports reading XML and HTML data.

Finally, interactive programming is possible with the iPython notebook.

Learn Python with OpenClassrooms

If you want to learn Python gradually and for free, the introductory course offered by OpenClassrooms is a good option for beginners.

This course is divided into five sections.

After an in-depth introduction to Python, you will learn to understand object-oriented programming, both from the user side and the developer side.

The standard library will then be discovered, and the course will end with some additional appendices.

The OpenClassrooms solution has the advantage of being free, accessible to novices, and allowing you to progress at your own pace.

In addition, if you pass the test exercises after following the program, you will be able to acquire a certification recognized by specialists.

Some resources to help you learn Python on your own.

Several people have shared PDFs or videos of Python tutorials for newcomers.

These materials may be useful if you are self-taught. Dominique Liard has created a series of Python learning videos on YouTube for those who prefer the video format.

Version 3.9.7 available since August 2021

The eponymous Python language has been updated to version 3.9.7 by the Python Software Foundation. This is the sixth maintenance since the major version 3.9 upgrade in October 2020.

Python is natively compatible with macOS 11 on the Apple M1.

Python core developers released version 3.9.1 of the Python language in December 2020.

This is the first version of macOS 11 Big Sur that is natively compatible with Apple’s new Arm-based M1 processor.

The Core Python teams have created macos11.0, an experimental installation.

It is possible to develop Universal 2 binaries that run on Apple Silicon processors using Xcode 11.

Binaries can be created on current versions of macOS and deployed on previous versions of the operating system. Following Apple’s decision to change the architecture, this is a relief for data scientists.

Google Atheris is a free and open source tool for locating Python issues.

The Atheris tool was “made freely available” by Google security specialists.

It helps detect security flaws and vulnerabilities in Python code and resolve them before it is too late as well.

The “fuzzing” technique is used in this utility.

This technique involves feeding a huge volume of random data to an application and analyzing the results to detect potential failures or anomalies. Developers can then look for defects in the application code.

This new tool joins a long list of open-source “fuzzers” launched by Google since 2013: OSS-Fuzz, Syzkaller, ClusterFuzz, Fuzzilli and BrokenType.

These earlier solutions, on the other hand, were used to find flaws in software in C or C++.

While Python is currently the third most popular language, according to the TIOBE Index, Google is meeting the growing demand with Atheris.

The tool, which was created during an internal hackathon in October 2020, allows you to fuzz Python 2.7 and 3.3+ programs as well as native extensions created with CPython.

However, it is recommended to use it with Python 3.8 or higher code, as the new features of the language allow Atheris to identify more problems. Atheris code can be found on GitHub or PyPi.

Python is the most popular programming language

table best programming languages

The number of programming languages ​​is increasing to the point that developers are struggling to determine which one to master to advance their careers.

O’Reilly’s new analysis, “Where Programming, Ops, AI, and the Cloud are Headed in 2021,” predicts which languages ​​will be most popular in 2021.

The data used by analysts comes from O’Reilly online training, partner training, and virtual events.

This year, Python remains the most popular programming language. Developer interest increased 27% from the previous year.

We can observe that this frenzy is mainly related to the advantages of Python for machine learning.

In fact, use of the scikit-learn library increased by 11%.

The PyTorch framework, which is used for deep learning, has seen a 159% growth in popularity.

However, other languages ​​are gaining popularity.

Compared to 2020, JavaScript usage increased by 40%, C by 12%, and C++ by 10%.

Other lesser-known programming languages, such as Go, Rust, Ruby, and Dart, are growing in popularity.

Rust could become the language of choice for systems programming, such as developing new operating systems and tools for cloud operations.

Similarly, Go has established itself as a leading competing programming language.

O’Reilly also highlighted the acceptance of “low-code” or “no-code” programming, which allows people without computer coding skills to build applications using simple, simple tools. graphical interfaces.

Professional developers, meanwhile, are not at risk of losing their jobs.

New languages, libraries, and tools for this type of programming will always require the creation and maintenance of expert developers.

Artificial intelligence and machine learning are also gaining popularity.

Developer interest in AI jumped 64%, compared to 14% for ML.

At the same time, natural language processing increased by 21%.

TensorFlow is the most popular machine learning platform, with an expected 6% increase in interest by 2020.

More and more developers want to learn how to take advantage of cloud computing.

In one year, interest in AWS increased by 5%.

Amazon’s cloud remains the most popular, but interest in Microsoft Azure increased by 136%. As for Google Cloud, the increase is 84%.

This trend indicates that an increasing number of businesses are migrating their data and applications to the cloud. Finally, adoption of online learning jumped 96%.

This is hardly surprising, given that the COVID-19 outbreak prevents face-to-face training.

The use of educational books increased by 11%, while educational films increased by 24%…

Python: FSP fixed two vulnerabilities that allow remote code execution.

IT security

Two vulnerabilities affecting current versions of Python were discovered in early 2021.

The “CVE-2021-3177” vulnerability affected the buffer and could lead to remote code execution in Python programs.

Fortunately, PSF clarifies in a blog post that remote code execution requires a number of conditions.

However, this flaw allows DDoS attacks. In order to crash a program, a cyber-attacker could overload the buffer. The second vulnerability, CVE-2021-23336, allows poisoning of the web cache.

Following the discovery of these issues, the Python Foundation fixed both bugs with the release of Python 3.8.8 and 3.9.2. So, it is essential to update your Python version to eliminate this security hazard.

For the first time, Python will overtake Java and C in the TIOBE index.

TIOBE presents a monthly ranking of the most popular programming languages.

This monthly ranking helps us gauge changes in the coding field over time.

The percentage rating system is based on the volume of searches for each programming language on Bing, Amazon, YouTube, Wikipedia, Google, Yahoo and Baidu.

In June 2021, the C language is ranked first with a score of 12.54%. This score, however, reflects a decrease of 4.65% compared to June 2020.

The Python language, for its part, occupies second place with a score of 11.84%. Therefore, the difference between these two languages ​​is only 0.7%. Python’s score increased by 3.48% last year.

Next in third place is Java, with a score of 11.54%, which is 4.56% lower than in June 2020. At the time, Java was in second place.

Python, according to Paul Jansen, CEO of TIOBE Software, will soon take the top spot in the rankings. This rise is possible in July 2021, when the TIOBE index will celebrate its 20th anniversary.

Only C and Java have held the top spot over the past two decades. The supremacy of Python would therefore represent a decisive moment in the history of computing…

C++, C#, Visual Basic, JavaScript and PHP remained in the ranking from fourth to ninth position since June 2020. Assembly is ranked eighth with a score of 2.05 percent.

This represents a gain of 1.09 percent compared to June 2020, where this language was ranked 14th.

SQL, with a score of 1.88%, completes the top 10. This represents an increase of 0.15% compared to June 2020.

Outside of the top 10, Visual Basic Classic rose eight places in one year.

Groovy, at 12th, moved up 19 places, while Fortran, at 17th, moved up 20 places.

R and Swift, on the other hand, fell five places each, reaching 14th and 16th respectively. MATLAB, which dropped four spots, and Go, which dropped eight, round out the top 20.

Dart, Kotlin, Julia, Rust, TypeScript and Elixir are some of the promising languages ​​for the future.

For now, these new languages ​​remain far from the top and have not moved significantly in the rankings over the past year.

According to SlashData, Python and JavaScript have the largest developer communities.

The global developer community has seen rapid growth in the first six months of 2021. This is highlighted by a report published by SlashData.

According to the survey, there were 24.3 million developers worldwide in the first quarter of 2021. This represents an increase of 14% compared to the 21.3 million recorded in October 2020.

JavaScript attracted around 1.4 million new developers in six months. This language has the largest developer community, with 13.8 million developers.

It also saw the fastest growth, with 4.5 million new developers added between Q4 2017 and Q1 2021.

Even in fields where it is not the preferred language, such as data science, about a quarter of engineers use JavaScript.

developer communities chart

Python comes in second, with a development community of 10.1 million people.

This community is growing at a rate of 20%, which is the fastest growth rate among all programming languages.

Python’s popularity, according to the study, is mainly due to the rise of data science and machine learning.

In fact, Python is used by around 70% of data scientists and machine learning engineers.

By comparison, only 17% of people use R. Java comes in second with 9.4 million developers, followed by C/C++ with 7.3 million and C# with 6.5 million.

With 2.6 million and 2.5 million developers respectively, Android’s Kotlin language is slightly ahead of iOS’ Swift.

Python 4.0, according to its developer, may never see the light of day.

Guido Van Rossum

Python founder Guido Van Rossum has said that version 4 of the language may never see the light of day. This is mainly due to the many challenges encountered during the transition from Python 2.0 to Python 3.0 in 2008.

In an interview with Microsoft Reactor, Van Rossum noted that due to the many obstacles encountered during the previous major update, neither he nor the core team of Python developers feel motivated to create a 4.0 version.

Because Python 3 is incompatible with Python, developers who built dependencies on Python 2 software libraries were unable to upgrade them to Python.

A long phase of migration followed, which lasted several years and left a painful memory for the initiator of the language. As a reminder, the Python 2 lifecycle ended in April 2020 with version 2.7.18.

The only reason Python 4.0 would see the light of day would be a significant change in terms of C compatibility. The update would then be necessary. Other than that, Python will continue to adhere to a rigid annual release schedule.

Versions 3.x will be maintained until 3.99, at which point another digit will be added after the decimal point if necessary.

Python will become 5 times faster in about 5 years

speed

Python’s slowness is one of its fundamental flaws, despite its many strengths.

This language interpreted with a high level of abstraction is much slower than C++ or Java. However, this could change in future editions.

Guido Van Rossum, the language designer, said at the Python Language Summit that the speed will be doubled with version 3.11, due in October 2022.

And that’s just the beginning.

Each year a new edition will be published, and the current pace is expected to increase fivefold within five years.

  1. Van Rossum explains how he plans to accomplish this feat in a presentation shared on GitHub.

Among the approaches examined are an adaptive interpreter, frame stack optimization, and overhead-free exception support.

Other improvements, such as an Application Binary Interface (ABI) or a standalone code generator, are intended to further speed up Python. Therefore, speed seems to be the main concern of Python designers.