Python Retains Its Prominence In Programming Languages on the Internet

Python can be described as a advanced and object-oriented programming language and that is used in functions such as scripting for the web based applications, computing scientific calculations, scripting language for the wide range of many different software applications. Python consists of the different features along with the uses in the scripting language Python that has made it one of the best preference among the software developers. An important characteristic feature in Python is that it has a powerful system and has actually been created with a memory management system that has been automated. Additionally it includes a wide, standard as well as a comprehensive library. Each one of the above mentioned features provide Python an advantage compared to various other programming languages and therefore are the reason of its vast popularity.

Python as a programming language focuses primarily on making its code readable as well as its syntax is made in such a manner to make it possible for computer programmers to easily express his or her ideas within a couple of lines of the coding than it could usually become a possibility in some other computer programming languages like for example C, etc. Making use of Python as a tool software engineers can produce conceptually clear as well as compact programs equally or a large & small scale.

Python is additionally recognized for supporting all types of programming procedures along with the object oriented, functional programming & imperative programming ways or procedural Styles. Python is additionally put to use in the procedure of scripting for example with developing web based applications for instance Drop box and others. In addition, it is put into use for a scripting language into the development of several well known software products such as for instance Three Dimensional Animation Package programs like Blender, Light wave, Motion Builder, & Cinema 4D and so on. This has additionally been put to use widely for the advancement of a number of video games.

One of the latest achievement of the Python is that it has been recognised as among the 3 programming languages that are put to use in building Google Applications Engine along with Go & Java. Python continues to be put to use in the computing scientific calculations within the areas regarding algebra, calculus, numeric maths and so on. The fact is Python has additionally been use within the development with the much anticipated Artificial Intelligence. The majority of Python implementations such as CPython could work like a command line interpreter, to that the user inputs statements sequentially in order to receive the outcomes immediately. Therefore, Python has made the lives of programmers easier and also has spotted its place among the list of most well known programming languages in the internet.

Python was developed during the late 1980s and the execution was actually began in the month of December the year 1989 by Guido van Rossum at CWI from the Netherlands as a successor for the ABC language with the capacity of exclusion handling & interfacing by the Amoeba operating system. Van Rossum known as Python's principal author, and his carrying on with core role on deciding what direction regarding Python is shown with the title given to him from the Python community, Benevolent Dictator For Life (BDFL). You can get in touch with a Python Development company who can help you develop web application within allocated budgets and time schedules.

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Python frameworks and features for working with Hadoop

The software development framework Hadoop has been developed in Apache and used to cluster storage as well as process data-sets on large scale in multiple hardwares. The main reason behind the creation of Hadoop was to enhance the search capability over several servers. This framework uses the factor of distributed computing to enable High Performance Computing. Java is the language in which the framework has been developed but other languages can be used as well like C++ or Python. Looking at the power as well as popularity of Python most people are prompted to use it. However, during that time the framework used must be Python-specific. These make writing in Python really easy as well as convenient. Some of the frameworks along with their associated features are discussed here.

HadoopStreaming- Hadoop Streaming is the number 1 choice for many developers since it is considered the most transparent as well as fastest option. It also encourages great text processing. This canonical method helps supply executables to Hadoop as reducer or mapper. Agreed-upon semantics are used for direct streaming. By default a tab character is used for separation of value and key. This helps in precise as well as clean functioning without the need of configuring a separate framework.

Pydoop- Pydoop script is another popular option which helps in writing of simple MapReduce programs. The reducers as well as mapper functions can be enabled with very few lines of code. When you need more functions than that provided by the Pydoop Script, Pydoop API can be switched over which is far more complete. With this Python RecordWriter, RecordReader as well as Partitioner can be implemented. It has certain unique features; it wraps Hadoop Pipes and claims to provide an interface which is quite rich.

mrjob- This open source framework has been developed actively by Yelp; it wraps Streaming. The operation between Yelp and Amazon Web Services makes integration between mrjob and EMR really easy as well as smooth. A pythonic API is provided by mrjob which enables users to use mappers as well as keys to work with all kinds of objects. The jobs can be run either on Hadoop cluster, locally for testing or on EMR. Multi-step jobs can be written with minimal setup needed.

Hadoopy- This Python wrapper has been written in Cython. This fast as well as simple framework is tested on more than 700 node clusters. Despite its tiny size, it is extremely well documented, transparent, fast as well as efficient. It can efficiently handle even complicated programs and is compatible with dumbo which allows switching back and forth between the two. The debugging feature is quite nice where messages can be written directly to stdout/stderr, that too without any disruptions to the Streaming process.

dumbo- The project for wrapping of Streaming is quite broadly used. This project allows easy writing along with running of Hadoop programs. In fact, it is often considered as a convenient Python API to write MapReduce programs. Its main identifying features are that it is efficient, easy, mature and flexible. Its simplicity does not stand in the way- it can perform low level things along with tricky actions. It relies on typed bytes to communicate with the framework. On top of that, writing resource-intensive elements natively in a job becomes very easy in Java. It is not only easy to write but easy to read too. It even provides several additional features along with boilerplate functionality.

Apart from the ones mentioned here, there are some other frameworks like octopi, Disco, happy, Mortar, Luigi, etc which can be used. These have several features and benefits of using. So considering everything, users have plenty of options when looking for the perfect Python framework to work with Hadoop. You can get in touch with a custom python development company who can help you develop web application within allocated budgets and time schedules.

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Top 10 mistakes that Python programmers should avoid

The high-level interpreted programming language of Python is object-oriented with dynamic semantics. It is combined with dynamic binding as well as dynamic typing and built in data structures. It inspires Rapid Application Development as well as glue or scripting language for connecting of existing services or components. Packages as well as modules are supported by Python which encourages code reuse along with program modularity. The syntax is easy to learn as well as simple which often misleads Python developers, especially if a person is new to the language. So this article attempts to capture 10 of the most common mistakes that they should avoid.

List modifying during iterating- An experienced developer knows the folly of deleting something from an array or list when iterating over it. However, this is often done unintentionally. To handle this there are many elegant programming paradigms in it which have to be used properly to get streamlined as well as simplified code which help them delete this confusion.

Initializing the variable- The language does not allow you to use names within expressions until a value has been assigned to a particular expression. This helps in prevention of common typo mistakes, avoiding the question regarding what exactly automatic defaults should be. So counters should be initialized to 0, list accumulators to [ ], etc.

Remembering the colons- A very common coding mistake often committed by beginners is forgetting the colons. Typing a simple thing as a colon when the compound statement headers end, should never be forgotten as it is very important. Most new programmers do forget to do that; however, after some time it becomes a habit that comes to you unconsciously.

Consistent indenting- In indentation of a particular single block, the spaces and tabs should never be mixed unless the exact reaction between the system and codes are known thoroughly by the developer. Otherwise whatever is seen in the editor just may not be the thing that is seen by Python when the tabs are being counted as number of spaces. So the best thing to do is to use all spaces or all tabs for every block; the number to be used is entirely up to you.

Provision of every argument that is expected of a command- This is another thing which should be followed religiously. Not only for the built-in commands, but even for your own functions and library calls you should provide the proper arguments. If only two arguments are given while three are expected, then an error will be thrown by Python. Similarly if three are expected but four are given then an error will be thrown as Python does not know what exactly needs to be done with the fourth argument.

Starting in 1^st column- One thing which is a must-do thing is starting from the very top-level, unnested code in left, at 1^st column. This includes unnested codes typed at interactive prompt along with unnested code typed in module files. Indentation is used by the language so blocks or columns of nested code can be delimited. This means that white space at your code’s left denotes a nested block. Except for indentation, white space is ignored everywhere.

Using parentheses for function calling- To call a function, a parentheses or ( ) after it is used; this is disregarding the fact whether arguments are taken or not. Normally functions are just simple objects with some special operation called call but it can only be triggered with the use of parentheses.

Capitalization and spelling- Python is very sensitive to capitalization as well as spelling since any change in respect of these two factors will make them unique in its eyes.

Incrementing interation value of while loop- If a while loop’s control value is not incremented then the end result is an infinite loop.

Single equal operator or “ “ to test equality- When a value is being tested, it is mandatory to use double equal operator otherwise it will result in errors.

This flexible and powerful language has many paradigms as well as mechanisms which can improve productivity. However, if the understanding is limited then it might prove to be an impediment. The best way to handle this dilemma is to familiarize yourself with Python’s key nuances, so that it can be used optimally. You can get in touch with a offshore Python development company who can help you develop web apps that are stable, scalable and secure.

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Hidden Features of Python That Should be Known to Developers

Unlike other programming languages, Python can be used for both scripting and non-scripting contexts. The high-level programming language is also used widely by developers across the world for general purposes. In addition to enhancing code reusability, Python further enable developers to accomplish complicated tasks without writing longer lines of code. However, programmers often fail to take advantages of all features and capabilities of Python. That is why; it is important for developers to know some of the hidden features of this dynamic programming language.

Seven Useful Tips to Optimize Python Programming

1) Exception Handing: The quality of a software application can be affected hugely if the exceptions are not handled in the most appropriate and graceful way. As a dynamic programming language, Python allows programmers to control the flow of programs through effective error handling. For instance, the EOFError can be used to make the program break out of the loop if the user enters wrong sort of data. The user will be further informed about the error through an error message.

2) User Input Validation: Often developers have to write lengthy and complex code to completely validate user input. However, they have to consider the sorts of information a user can enter while accessing the software. Python enables programmers to validate user input to ensure that the program received only the right sort of information without writing any additional code. Also, they can use exception handlings to implement some of the more advanced techniques of user input validation.

3) String Formatting Code: The programmers also have option to use Python string formatting codes to easily print a number of variables in a single string. For instance, the developers can use several formatting codes including s for string conversion, c for character, I for integer, x for hexadecimal integer, d for signed decimal integer and so on. At the same time, they also have option to modify the output by passing parameters to the basic code.

4) Using Dictionary to Sort a List: Often developers store complex data by using a list of lists. But they have to write additional lines of code to sort or print the list of lists. The Python programmers have option to sort and print the list of lists simply by using a Python dictionary. The use a Python dictionary will include the key that will be used to sort the list. They have further option to specify multiple sorting keys to optimize the process of sorting and printing the list of lists.

5) Optimize the Use of Classes: Python further makes it easier for developers to use classes without writing longer lines of code. They can simply create a separate file that will include the classes and class methods. The program needs to begin with an import class line to call the file stored inside the modules folder. The same process can also be used to import their own modules into the program.

6) Using Python IDE: The programmers can further choose from Integrated Development Environment (IDEs) to work with Python more efficiently. In addition to providing templates for common code, the Python IDEs will further facilitate syntax highlighting, source control, and code completion. Based on the needs of their project, programmers can choose from a range of Python IDEs including Eclipse, Komodo Edit and PyCharm.

7) Basic and Advanced CGI: The web programmers can convert a regular python program into a CGI script without putting any extra effort. Normally, developers use web forms to accomplish interaction with CGI scripts. As Python can also be used as a scripting language, the code can be easily incorporated into a web page. The Python code will process the form input, and return the data send from the server to web browser in HTML format. So the HTML form need not be separated from the Python CGI script.

In addition to knowing the hidden features of Python, the programmers also need to be familiar with several Python development best practices. At the same time, they must explore ways to optimize the usage of the programming language to save their time and efforts.

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What is so great about Python 3?

As a dynamic programming language, Python is being used by developers across the world as a scripting as well as general-purpose language. The open source language further enables developers to express concepts without writing lengthy code, while facilitating code reusability. Python is also used by developers as easy-to-use scripting or automation interfaces required by applications written in other programming languages. A programmer can further avail several new modules by opting for Python 3.

New Models Added to Python 3 to Make It More Powerful

Concurrent.futures: The module provides a high-level interface that can be used by programmers to execute the callables in an asynchronous manner. However, the asynchronous execution of callable can be performed in a number of ways. The developer can use ThreadPoolExecutor to execute the callable through threads. Also, he can use the ProcessPoolExecutor to make the execution a separate process. But each method requires the same interface that is defined by the abstract Executor class.

Faulthandler: As the name indicates, the module includes the function required to explicitly dump Python tracebacks. The functions are executed when the application faces stops performing due to a fault, user signal or timeout. The faulthandler.enable() function is called to install fault handles for SIGFPE, SIGABRT, SIGILL, SIGBUS and SIGSEGV. The functions can be setup during the project startup either by using the X faulthandler command line option or by setting the PYTHONFAULTHANDLER environment variable:

Importlib: The importlib module can be used for two distinct purposes. Firstly, a programmer can implement the import statement within the Python source code. In addition to being portable to the Python interpreter, the module makes it easier for developers to comprehend the implementation. Secondly, the programmer can create custom objects that will participate in the process of importaing the components.

Ipaddress: The module enables developers to create, operate and manipulate on IPv4 and IPv6 addresses and networks. They can simply use the functions and classes included in this module to handle a variety of IP address related tasks including validating a network definition or IP address, and checking if multiple hosts are on a single subnet.

Unittest.mock: The module makes it easier for developers to test the Python application. The QA professionals can replace certain parts of the application with mock objects while testing the application, and make assertions about their usage. The core Mock class provided by the module helps programmers in not creating additional stubs throughout the testing suite. Along with being easy to use, the module is also designed with the features required for unit testing.

Selectors: The selectors module is designed based on select module primitives. It can be used by Python developers for high-level and efficient I/O multiplexing. Further, the programmers can exercise tight control over the OS-level primitives used. In addition to the BaseSelector abstract base class, the module also defines a set of concrete implementation for I/O multiplexing. However, the types of file object supported by the module with vary from one operating system to another.

Statistics: The module provides the functions required for calculating mathematical statistics of numeric data. These functions further support a wide range of numeric data types including int, float and decimal. However, currently the module does not support the behavior of several data types. The developers have to define mixed types specifically, and implement them according to the requirements of the project.

Recently, Python 3.4 was released with a set of new features to complement the latest trends in software development. The most recent version of the open source programming language comes with a new feature called asyncio that provides Asynchronous IO support. Further, Python 3.4 comes with a set of advanced features to meet the requirements of Cloud developers. You can get in touch with a custom Python Development company who can help you develop web apps that are stable, scalable and secure.

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Why start-ups prefer Python over Ruby on Rails?

For high level application development two very popular as well as dynamic programming languages that gain much favor are Ruby and Python. During the early 1990s, these two languages were released. The two of them are similar in many respects since some design elements of Python was used to build Ruby. The main aim of building these two languages was not web development initially. However after the creation of some big web destinations by virtue of these two, they became quite popular. Rails, the full-stack open source framework helps simplify the building of web platform with Ruby that lies underneath. This was used for the creation of big sites such as Hulu along with Twitter. On the other hand Python is used by YouTube as well as Google which speaks for its popularity.

There are several advantages and disadvantages to both the languages. Ruby on Rails or Rails as simply known, allows the writing of web applications which can talk to backend database for retrieval as well as rendering of data on client side. Plenty of implicit default conventions are provided by Rails so that programmers may rely on it. However, Rails makes the assumption that there can be a single way to do some specific thing, which makes it extremely hard for any programmer to modify behavior and logic of Rails code.

The projects of Ruby on Rails have sensible defaults along with predefined layout. Standard sub-directories contain all components like controllers, static JavaScript and CSS files and models; so customized implementation files can be simply dropped into the directories for Rails to pick them up automatically. Developers win big time since a lot of time is saved from not having to type the very same configuration code time and again. This full stack framework is MVC based which enables the controller to call functions from model so that the data can be returned back to view. In fact, it supports the REST protocol fully and that too out-of-the-box. You can easily access as well as handle all model objects uniformly through the use of standard HTTP verbs such as POST, DELETE, PUT and GET.

There are no doubts that Rails has a lot of benefits and advantages. However, when we think of starting to learn and apply a framework, a lot of people prefer using Python over it. This can be ascertained from the fact that Python has entered the list of top five most used as well as popular programming languages, with the percentage of usage increasing with each passing day. In comparison Ruby is still in the list of top ten with the percentage of growth quite low in comparison.

One of the reasons for people preferring this language is the simplicity of learning that it offers. Beginners can thus start to build powerful sites very fast. In fact, Python can evolve in complexity with you as you become better programmer. Thus it suits both the new as well as the matured developer but the easy comprehension feature will accompany both.

The syntax of Python is extremely regular looking and pure by virtue of natural language used with a lot of indenting as well as white spaces. This makes the code easily as well as clearly readable in comparison to Rails. In fact, this makes it very easy to do updates as well as fix mistakes. On top of that there are several pre-built modules which can be used to get on with the programming fast. The programming is object oriented and functional which is yet another virtue that speaks for its preference over other languages. In terms of speed of code execution, Python wins hands down.

Ideologically speaking, there may not be huge differences between Ruby on Rails and Python since both come with their own individual sets of pros and cons. But people who aim to conduct diverse tasks often choose Python over the other language. Ultimately, the choice is personal as well as subjective. You can get in touch with a Custom Python Development company who can help you develop web apps that are stable, scalable and secure.

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