Python is the most widely used programming language in Machine Learning, Big Data and Data Science. Find out everything you need to know about it: definition, advantages, use cases…
Created in 1991, the Python programming language appeared at the time as a way of automate the most annoying elements of scripting or to quickly build prototype applications.
In recent years, however, this programming language has become one of the most widely used languages for software development, infrastructure management and data analysis. It is a driving force behind the Big Data explosion.
Python language: what is it?
Python is an open source programming language. created by programmer Guido van Rossum in 1991. It’s named after Monty Python’s Flying Circus.
This is a interpreted programming languagewhich therefore does not need to be compiled to work. An “interpreter” program allows Python code to be executed on any computer. This allows you to quickly see the results of a change in the code. On the other hand, this makes this language slower than a compiled language like C.
As thehigh-level programming anglePython allows programmers to focus on what they do rather than how they do it. Thus, writing programs takes less time than in other languages. It is an ideal language for beginners.
Python language: what are the main advantages?
The Python language owes its popularity to several advantages that benefit beginners as well as experts. First of all, it is easy to learn and use. Its features are few in number, allowing programs to be created quickly and with little effort. In addition, its syntax is designed to be readable and straightforward.
Another advantage of Python is its popularity. This language runs on all major operating systems and computer platforms. Moreover, although it is clearly not the fastest language, it compensates for its slowness with its versatility.
Finally, even if it is mainly used for scripting and automation, this language is also used for create professional quality software. Whether applications or web services, Python is used by a large number of developers to create software.
Python 2 vs Pyton 3: what are the differences?
On distinguishes two versions of Python: Python 2 and Python 3. There are many differences between these two versions. Python 2.x is the older version, which will continue to be supported and thus receive official updates until 2020. After this date, it will probably continue to exist unofficially.
Python 3.x is the current version of the language. It brings many new and very useful featuressuch as better competition control and a more effective interpreter. However, the adoption of Python 3 has long been slowed down by the lack of supported third-party libraries. Many of them were only compatible with Python 2, which made the transition complicated. However, this problem is now almost solved and there are few good reasons to continue using Python 2.
The Python language for Big Data and Machine Learning
The main use case for Python is scripting and automation. Indeed, this language allows to automate interactions with web browsers or application GUIs.
However, scripting and automation are far from being the only utilities of this language. It is also used for application programmingThe software can be used to create web services or REST APIs, or for metaprogramming and code generation.
This language is also used in the field of data science and Machine Learning. With the growth of data analysis in all industries, it has also become one of its main use cases.
The vast majority of libraries used for data science or Machine Learning have Python interfaces. Thus, this language has become the most popular high-level control interface for Machine Learning libraries and other numerical algorithms. Many introductory books are available on the Web.
Finally, companies specializing in robotics such as Aldebaran use this language to program their robots.. The company acquired by Softbank chose this programming language to facilitate the design of applications by third party companies and amateurs.
Python and Big Data: top of the best libraries and packages
If Python has established itself as the best programming language for Big Data, it’s thanks to its various data science packages and libraries. These are the most popular.
Pandas is one of the most popular data science libraries. It has been developed by Data Scientists accustomed to R and Pythonand is now used by a large number of scientists and analysts.
It offers to many useful native features. In particular, it is possible to read data from many sources, create large dataframes from these sources, and perform aggregate analyses based on the questions to be answered.
Visualization features also allow you to generate graphs from the results of the analyses, or export them to Excel format. It can also be used to for manipulating numerical vectors and time series.
More recent than Pandas, Agate is also a Python library designed to solve data analysis problems. It offers features such as the ability to analyze and compare Excel tables, or perform statistical calculations on a database.
On the whole, he is easier to learn to master Agate than Pandas.. In addition, its data visualization features make it easy and quick to view the results of the analyses.
Bokeh is an ideal tool for create dataset visualizations. It can be used in conjunction with Agate, Pandas and other data analysis libraries.
It is also possible to use it with pure Pyton. This tool allows you to create excellent graphics and visualizations without the need for extensive coding.
NumPy is a package used for scientific calculations in Python. It is ideal for operations related to linear algebra, Fourier transforms, or random number crunching.
It can be used as a multi-dimensional container for generic data. In addition, it can be easily protected with many different databases.
Scipy is a library for technical and scientific calculations. It includes modules for data science and engineering tasks such as algebra, interpolation, FFT, or signal and image processing.
Scikit-learn is very useful for classification, regression or clustering algorithms such as decision tree forests, gradient boosting, or k-means.
This library of Machine Learning for Python reveals itself to be complementary for other libraries such as NumPy and SciPy.
PyBrain is actually the acronym for
It can also be used to test and compare algorithms using a variety of predefined environments.
Developed by Google Brain, TensorFlow is a Machine Learning library. Its data flow graphs and flexible architecture allows you to perform operations and data calculations using a single API on multiple CPUs or GPUs from a PC, server or even a mobile device.
Among other Python librariesAnother example is Cython, which converts code to run in a C environment to reduce runtime. Similarly, PyMySQL allows to connect a MySQL database, extract data and execute queries. BeautifulSoup allows to read XML and HTML data. Finally, the iPython notebook allows interactive programming.
Learning Python with OpenClassrooms
If you want to learn the Python language gradually and for free, a solution adapted to beginners is the introductory course offered by OpenClassrooms.
This course is divided into five parts. After a complete introduction to Python, you will learn how to master object-oriented programming on the user side and then on the developer side. You will then discover the standard library, and the course concludes with a few additional appendices.
The advantage of the OpenClassrooms solution is that it is free, accessible to beginners, and allows you to progress at your own pace. Moreover, once the training is completed, you will be able to receive a certification recognized by professionals provided that you pass the test exercises.
A new version planned for October 2019
UPDATED 07/2019: The Python Software Foundation is preparing to release version 3.8.0 of the eponymous language. After two beta releases in June and July 2019, it will be available in October 2019. It introduces the Morse code operator. It allows to assign variables of type if or while in expressions. With the single position parameters, the encoders define scenarios or a function that only affects the elements concerned. Strings can be debugged. Similarly, the Python initialization C API will make it easier to configure the code and the applications that use it. A single default template facilitates a sound installation.