Best Practices in Django development

Google reinvented Python in past decade through its ambitious project called “Unladen swallow” that give name and fame to the language. Interestingly Google pause the project in the middle due to some internal problem still Python gained a performance boost of around three times than before. As a result, Python became a better choice for the industry to develop enterprise standard software applications. Moreover, Python get a tremendous support from the industry due to its exceptional performance for huge applications and that brought a positive vibe for Python among the software developers. In this situation, Django was born to make the Python development process not only easier but also efficient and faster. Django believes on “perfection within time frame” i.e., it doesn’t compromise on the quality of the Python application but it does the task efficiently within less time and deadline.

Develop efficient Python app in less time: As we discussed above Django is a powerful Python framework that helps the Python developers in developing quality and reliable end product in less time. It provides an awesome platform for Python web developers to develop modern futuristic web solutions for the industry that are backed with modern technologies and services. It provides performance report through various bottleneck calculations for your application like load testing, reliability, page loading time etc. This will definitely help the web developer to develop high performance web applications.

Django is MTV framework: Unlike common MVC architecture for frameworks Django provides a new generation MTV framework i.e., model template view architecture. Well this is not very different from that of MVC pattern. Rather it is the simpler form of the former. In MVC the view describes the interface for user data of the application but, in case of MTV, the same data is projected to the user but in a different form i.e., in a template. So it is just an advanced version of MVC architecture.

Develop asynchronous pages in Django: Django supports AJAX which is nothing but the foundation of asynchronous web application. Now the Python Django developers will get the advantage of AJAX and JSON while developing modern web applications for different devices that run on low memory like smart phones and tablets. Moreover, this will exert a dynamic look and feel to the web page of the application and hence this will improve the user experience as well as performance of the application.

Django templates are thin: As we discussed above about the MTV architecture of Django; now it’s time to spend few lines for the new template concept. Well Django provides a repository of templates for its Python developers. The best part is all these templates are extremely light weight but beautiful. These light weight or thin templates when integrated into the application creates light weight apps that are the requirement of the future generation browsers. Sometimes you may decide not to use any of those thin templates then Django gives you the freedom to integrate your own template for your application!! This proves the flexibility nature of Django.

Migration process is painless: Last but not the least, Django framework provides all types of support when a Python developer wants to migrate to a latest version of Django. This is a seamless process from the developer’s point of view. The best part is you can migrate to a lower version without any issue!!

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Avoid these mistakes while using Python for Big Data

Python – A true friend of web developer: Python is one of those old languages which were developed to give tough competition with C and C++. No need to say, it is highly inspired from the object oriented programming approach of C++ with some enhanced features within it. Due to its similarity with C and C++, it can be used for developing machine programming, mobile application development, desktop application and web application too. Its motto is “Batteries included” indicates its potential to develop huge applications at a very less time due to presence of all necessary and common methods and modules. Hence, Python gets maximum priority from the software developers while developing any big data application. It has the potential to develop big applications in small time with maximum efficiency. At the same time due to its large library, often the web developer miss to use the inbuilt optimized functions which creates trouble in future like performance issue, optimisation issue and synchronization issues. Let us have a quick look over these common mistakes made by the Python developers while dealing with big data projects.

Is the popularity of Python reducing worldwide?

The choice of a programming language is often the result of personal preference and experience. Almost all the modern languages are quite capable of performing most of the major programming tasks as well as include the libraries essential for daily uses. Pyhton is a powerful, easy to use scripting language that is loved as well as hated by programmers across the globe. Although it is powerful but is equally opinionated and idiosyncratic. It is an excellent glue language that is preferred by the Python developers as they find it more productive than others. While there is constant competition from a new and advanced languages, the existence of Python might be threatened. One of the primary reasons why Python is seen as a limited language is because it differs considerably from others.

Has Python surpassed Java in Popularity

According to an analyst, out of the 10 top universities 8 universities currently use Python in order to introduce programming. Python has gone beyond Java as being the top language meant to bring in U.S. students to the programming also computer science, based on the latest study shared by the ACM (Association for Computing Machinery). 8 from the top ten computer science departments nowadays using Python to train coding, and also 27 from the top 39 schools, signifying that it's the preferred language in educating basic computer science programs, as per Philip Guo, a computer science analyst who actually compiled the research for the Association for Computing Machinery.

Few Python programming problems

Python is a high level programming language that has in built data structures and dynamic semantics. It is a preferred language for Rapid Application Development as well as a glue language for connecting existing services or components. Needless to say, Python is an impressive language but it has its own sets of problems that might lead the programmers to bugs and poor performance. On the other hand, developers are human beings as well and to err is human. There are certain common mistakes that Python programmers commit while coding software. However, if you are aware of these common problems, then it might help you to avoid falling into the trap when coding with Python.

Use of wrong indentation

There are several Python features that are dependent on indentation. For instance, if a developer is creating a new class then everything in there will be indented under the class declaration. Similar rule applies to loop, decision and other structural statements. In case you find that a code is executing a task that it should not be doing, then it is time to review the indentation that you have used.

Data and Markup Mixed
This is another common mistake committed by the programmers. If you mix up markup and data then it leads to a number of problems and finally to unmanageable codes. It might happen that you have data in a string and this string will have a particular format. If the format is in prosaic text, you would want to preserve the newlines and at the same time collapse the whitespace into a single space. The problem is the data appearing on a website is in HTML format. Generally it is suggested to escape the input text and convert the newlines to simply. This is a wrong approach. One of the main reasons why this is wrong is due to the context. Owing to the API concept web applications are growing more complicated nowadays. Thus, there are other forms, like, JSON in which the web functionalities are also available. So the best way to be on the safe side is to store data as it arrives instead of flipping it.

Choosing assignment operator
There are many developers who rely on the assignment operator while performing comparison between two values or objects. The problem is that the assignment operator simply places the value or an object within a variable. It, however, does not perform any kind of comparison.

Problem with choosing the framework
It often happens that more than the desired time is spent on choosing framework. When you have a small application, there are no problems with framework. Even if there are more codes switching systems is no difficulty when need arises. On the contrary if you want to switch a core component like the ORM, it is conveniently achievable. This process was more difficult when the systems were incompatible. But now choosing the right framework is quite an easy job. Hence, there is no point in spending much time over its choice.

Monolithic systems have no value
In this world of agility, building monolithic systems make no sense at all. The web developers have good advantage as they can work with HTTP. You can write small services with HTTP and combine them together with another application. You can also choose to use the load balancer between the individual components. In case you do not have the Python library required for the job, you can also make use of Java or Ruby for writing a part of the System. However, it is important that the programmers do not use 8 different languages and with various runtime environments. This would completely jeopardize the life of the system administrator.

Count by one errors
This is created on the loops. What a developer needs to keep in mind is that a loop will not count the last number that is specified in a range.

Spelling errors
Now, this is really unfortunate. Even some of the most experienced developers commit this error. It is better that they adopt a common approach for naming functions, variables and classes. All it requires is to be a little careful while typing the naming schemes.

If you are interested in working with Python, it is imperative to program it without any errors. The above mentioned points will help you to avoid some of the most common mistakes in Python programming.

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Why is Python preferred over other languages?

Large number of software engineers, business owners, as well as the users choose Python rather than some other programming languages. Python is a general purpose interpretative, user friendly, object oriented and high-ranking programming language which was designed by Guido Van Rossum in the year 1991. It is actually created from a number of other languages, such as ABC, Modula-3, C, C++, SmallTalk, Algol-68, and UNIX shell along with other scripting languages. In a short time frame, Python has drawn interest of several developers. Google, YouTube, Facebook (PHP & Python), Reddit (Pylon), FriendFeed, SuggestionBox (Django), Tabblo (Django), Disqus, Bitbucket (Django), and Quora are among the well known websites which are with the Python programming language. A lot of companies are offering Python Development Services at a very affordable price to the people who run businesses to obtain the entire advantages of Python. With the help of these facilities, businessmen can produce their own website in a very professional and rich way.

Jython - Things to know about

Jython, an open source, implements the Python programming language that is integrated with the Java platform. To run this object-oriented and dynamic language a programmer has to compile Python source code with Java bytecode. When a programmer integrates Python and Java, it gives him an access to all the Java libraries. By using the libraries, Applets and JavaBeans can be used by assigning the classes of any of these languages as sub-classes under the other. Jython is written in Java and seamlessly integrates Python so that it can run on any Java platform. It has been designed to perform various kinds of tasks, like, embedded scripting. Its original version was known as JPython in 1997. Jython 2.0 is a later version that has been developed through a combined volunteer effort and it is available without any cost for both non-commercial and commercial uses.

History of Python - Versions, Features, and Upgrades

Python is a hugely popular general-purpose as well as high-end programming language that is based on code readability. The syntax of Python provides the programmers the unique option of expressing concepts by writing new codes that are better than what is needed in case of other languages like C or C++. The language helps in creating constructs that are designed to develop clearer programs on a large scale as well as a small scale.

It can well be termed as an interpreted, high level, object oriented programming language, that comes up with a series of dynamic semantics. The high end data structures, that are combined with dynamic binding as we’ll as typing, make the language so lucrative, especially when it comes to RAD or Rapid Application Development or using the language for scripting or as a glue language for connecting the existing components with each other. It is simple and its syntaxes are all extremely easy to learn. They put more focus on readability. Hence, they come in handy in reducing the expenses of maintaining the program. The language also supports modules as well as packages. This encourages program modularity as well as reuse of codes. The interpreter and its extensive customary library are present in source as well as in the binary form and they are freely distributable.

Python is designed to support multiple programming models that include object-oriented, functional, as well as imperative programming patterns. It also comes up with automatic as well as system memory management along with a comprehensive standardized library.

Python is generally used for scripting for web based apps and for facilitating these apps, a standard API has been designed with WSGI or Web Server Gateway Interface. Various web app frameworks such as Pylons, TurboGears, Tornado, Zone, Flask, and the likes, support the developers when it comes to designing or maintaining the complex applications, while IronPython and Pyjamas are used for developing the client side of the Ajax-based apps.

Like any other dynamic programming language, Python can be used for scripting though it is also possible to use it as a non-scripting language. The codescan can also be used as a standalone program, especially with the use of certain third party tools like Py2exe and Pyinstaller.

Python, technically speaking can be termed as a multi-paradigm language that is used for structured, aspect-oriented, as well as object-oriented programming. It uses dynamic typing technique along with a combination of cycle-detecting garbage collector and reference counting for the purpose of memory management. Another very significant feature of Python is its dynamic name resolution mechanism that binds method as well as the variable names when executing a particular program.

Python saw the daylight for the first time towards the end of 1980, while the implementation commenced from the month of December, 1989 and was considered as a successor to ABC programming language.

Python 2.0 was ultimately released on October 2000. This version came up with a number of fresh features that also included a full garbage collector along with Unicode support. However, the most significant feature that this particular version came up with was the development mechanism that was followed. It involved a major tilt towards a process that was more transparent as well as community-backed.

The next version, P 3.0 was released in December, 2008 and this version came up with a lot of features that have been back ported to the 2.6 and 2.7 versions.

Then came the next version, P3.0, which is also called Py3K or Python 3000, which was designed and developed for rectification of the fundamental designing loopholes that were earlier detected in the language.

The language has successfully been embedded in quite a number of products like Abaqus alongside a few 3D animation languages like Max, Cinema 4D, Houdini, Maya, etc.

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Python Development - Attain Robust App Building

Many programmers opt for Python to express concepts without writing longer lines of code. As a dynamic programming language, Python is also used for both scripting and general purposes. The simple syntax and semantic further makes the programming language highly readable and easier to learn. You also have option to use the large standard library provided by Python to accomplish a variety of common programming tasks including establishing connection with the web server, reading and modifying files, and searching for text with regular expressions. However, you can always consider some simple tips to attain robust application building without putting any extra time and effort.

5 Simple Tips to Effectuate Your Python App Development

1) File Handling

Python enables you to handle your files efficiently without importing additional modules. You can simply use a built-in called as files to accomplish file handling tasks. The object allows you to manipulate files by using a set of basic functions and methods. For instance, you can use the built-in open() function by passing the file name as parameter to open the particular file in read mode. Likewise, you can manipulate the file by using a variety of functions like read(), readline() and readlines(), write(), writelines(), append() and close() functions.

2) Language Rules
You can easily enhance the readability and aesthetics of your code by using PEP8. The style guide for Python code will further help you in maintaining consistency within individual functions and modules. It can also be used to maintain consistency throughout the project. For instance, based on the requirement of your project you can consider using imports, packages, exceptions and generators. Similarly, you need to avoid using global variables, and use list comprehensions only for simple cases.

3) Python Style Rules
The PEP8 style guide for Python can also be used to customize your code. However, you must remember certain styling rules while customizing the lines of code. You must avoid using semicolons (;) to put two commands in the same line or terminate lines. The length of each line must not exceed 80 characters. You have to indent the code blocks with 4 spaces. It is also important to use the most appropriate style for method, function, module and in-line comments.

4) Browsing with Mechanize
As a useful Python module, Mechanize makes it easier for you to navigate through the web forms. You can use the browse-like object provided by the module to interact with the web pages. To use Mechanize, you have to create a browser object and specify its settings. Then you can open a web page, and list the forms included in it. After selecting the web form, you can use the browser object provided by Mechanize to iterate through the web form controls. You also have option to select text controls, and set these as a string or access in read only mode.

5) Using Python for the Web
As noted earlier, Python can also be used as a scripting language for the web. You can write Python code to interact with other applications. Your application will use application programming interface key (API key) to access a variety of services on the internet including Facebook, Google and Twitter. Further, it can exchange information with other systems through JavaScript Object Notation (JSON). JSON can also be used in a variety of forms including string, array, number, value or objects. Thus, you can easily optimize the performance of your application by accessing and integrating with a variety of online services.

You also have option to use Python development frameworks to rapidly build robust applications. The tools provided by these frameworks will make it easier to accomplish specific programming tasks without writing complex code. Also, you can access various online developers’ forums to know some of the Python development best practices.

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Moving from Python 2.7 to 3- Some pros & cons

It has been quite some time that Python 3 has been introduced in the software community, but due to some technical glitches, it was not widely accepted. Earlier, it did not have the basic Python libraries like SciPy and NumPy but now they have been imported. In fact, it is an incremental release and that is why the casual programmers may not have many new features to use. But it has now ironed out the glitches as well as quirks which were there in the earlier versions, making the syntax consistent. So before moving to version 3, here are some pros and cons to know about.

Pros

Using compatible syntax- There are many common features between versions 2.7 and 3 due to backporting which essentially allows you to use them in the old scripts, subsequently making the transition very easy. Like % operator that works in version 3; only the format method is different, and that gives increased flexibility.

Unicode support- The version’s text model has been revised which makes the primary string type totally Unicode-capable. Thus the models used in Unicode-capable Windows APIs, JVM, .NET CLR, etc. and Python 3 are brought closer. Its interpreter core is more tolerant to paths containing Unicode characters, which leads to less chances of problems when running scripts on Windows from home directory if, for example, the user name has non-ASCII characters. In fact, the support is more deeply embedded in the language itself. Since a huge variety of Unicode characters are now allowed, developers can use other languages, apart from English when naming; they do not have to use names which suit the fixed character set.

Set literals- Sets in the version have new literal syntax. {1, 2, 3} can be written instead of the set([1, 2, 3)]; the former is quite clean. In fact, both work perfectly, but using the new one gives less problems.

Abstract base classes- Support to make abstract base classes is contained by abc module; in it a property or method may be marked as “abstract” on base classes and implemented in subclasses. This is for definition of interfaces with the creation of classes which has no concrete method(s). One example of the concept is the mathematical class hierarchy.

Cons

Using ported libraries- The pure libraries may be converted semi-automatically to Python 3 but those depending on C extensions are really hard to port. Many libraries have been already ported, but there are some which have not been ported yet. However, this may not be of much problem if the project is small.However, in case of large scale projects this might pose a problem.

Differences in builtin functions- All functions of the previous version are not compatible or rather related to the Python 3 counterparts. Like when you use filter function, an iterator is returned in version 3 but the previous versions return a list. The same is applicable for .keys method.

Filesystem access- It is assumed by the software that all the filesystems have encoding, but then as a matter of fact, there is no encoding in many Linux systems as well as OS X. In fact, different rules for Unicode normalization are applied for filesystem on OS X than for applications. So the filename has to be normalized manually to separate encoding when filenames have to be compared in file system.

Beginners in the world of programming can benefit if they learn Python 3, especially if they do not know C. All in all, there is no huge difference between the implementation of Python 2.7 and 3. In fact, the usage of version 3 seems to be a complicated procedure for many, especially if the project is a big one and there is less time for unit testing. When unit testing is conducted the ultimate code turns out to be well tested as well as reproducable. Porting to Python 3 will yield in plenty of minor and innocent changes, but this will make it more dangerous, making it necessary to test particular and individual functions in the scripts. This will ultimately lead to profit in the long run, but you will have to waste some of your precious time which could have been devoted in bettering the application and other such things.

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