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Python is so widely used and so user-friendly, a lot of newbie programmers study it as their first language. Currently, Python 2 and Python 3 are the two versions of this programming language in use. Even if there are some parallels between these Python 2 to Python 3 Migration, there are also some significant variances.
When choosing which version of Python to use, developers and novices alike should carefully analyse the trade-offs between versions 2 and 3, taking into account things like compatibility with existing code, the availability of third-party libraries, and the newest features available in the language. This article compares and contrasts Python 2 and Python 3, pointing out their advantages and disadvantages as well as how to switch from Python 2 to Python 3.
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Introduction
One of the computer languages that has completely changed the face of technology is Python. General-purpose programming languages are used in many different fields, such as software engineering, artificial intelligence, natural language processing, data analysis, and more.
The public domain was opened to Python 2 and Python 3, two of the most noteworthy versions of the language. Despite being just two different versions of the same computer language, it might be interesting to compare and contrast Python 2 and Python 3. Unexpectedly, there are significant differences between the two. There has been a lot of controversy in the past about which of the two versions is better to use.
What is Python 2?
Python 2.0 was made available to the computer community in 2000. The BeOpen Python Labs team released Python 2 with the intention of making programming more approachable and available to a wider audience.
The technical elements of the Python Enhancement Proposal (PEP) were successfully implemented in Python 2. However, Python 2’s popularity in the tech sector declined significantly once Python 3 was released, and the last version of Python 2 was released in 2020; Python 2.7 being the last one.
What is Python 3?
When Python 3 was first published in 2008, it was more than just a polished version of Python 2 after debugging. Eliminating redundancy—the practice of writing the same code again or repetitively—was the main driving force for Python’s introduction. Python 3 aims to address the problems that inexperienced programmers go into when learning the language with its backward incompatibility.
Differences Between Python 2 and Python 3
We are well-versed in the background of Python’s two main versions, let’s examine their main distinctions and determine which version emerges victorious in the contest: Python 3 and Python 2.
There were multiple versions of Python available for a variety of reasons. First of all, converting all industrial code from Python 2 to Python 3 would have been a laborious and time-consuming task because so much Python 2 code had previously been produced.
Comparison Python 2 and Python 3
Comparison Parameter | Python 2 | Python 3 |
Released Year | In the year 2000, Python 2 was released. | In the year 2008, Python 3 was released. |
Usage of Keyword “Print” | Print is seen as a statement in Python 2, not a function. | Print is regarded as a function rather than a statement in Python 3 |
String values storages | Strings are automatically stored as ASCII in Python 2. | Strings are automatically stored as UNICODE in Python 3. |
Integers Division | In Python 2, we obtain an integral value on the division of two numbers. For example, in Python 2, 7/2 produces 3. | In Python 3, we obtain a floating-point result when we divide two integers. For example, in Python 3, 7/2 returns 3.5. |
Exceptions | Python 2 uses notations to enclose exceptions. | Python 3 puts parenthesis around exceptions. |
Variable leakage | In Python 2, using global variables inside a for-loop causes their values to change. | In Python 3, variables’ values are constant. |
Iteration | The xrange() function has been defined for iterations in Python 2. | To execute iterations, Python 3 added a new method called Range(). |
Ease of Syntax | The syntax of Python 2 is more intricate than that of Python 3. | The syntax of Python 3 is simpler than that of Python 2. |
Libraries | Many Python 2 libraries are not forward compatible. | Many libraries are written in Python 3 and are intended only to be used with Python 3. |
Use in the modern era | Since 2020, Python 2 has not been used. | Python 3 is still in use today and has more popularity than Python 2. |
Compatibility | It takes a lot of work to port Python 2 routines to Python 3. | Python 3 and Python 2 are not backwardly compatible. |
Application of Python | The primary tool utilised to become a DevOps Engineer was Python 2. After 2020, it is no longer in use. | Numerous professions, including software engineering and data science, employ Python 3. |
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Which Python Migration Method to Choose: Manual or Automated?
Selecting between the two strategies—manual and automated—is necessary when migrating to the most recent version of Python. A thorough grasp of the project’s state in terms of its size, complexity, type of application, and other factors is one of the main factors influencing this decision.
- For example, just the top-level scripts need to be rewritten if you intend to move an application to Python 3. Very few internal modules require modification because they are not dependent on an application.
- On the other hand, many plug-ins and apps rely on a framework, which must be moved. Numerous modules and applications will be impacted by a minor technical modification in the framework. Therefore, your migration process would be different depending on whether your embedded programme is a library provider or a consumer.
- Your Python-based software’s user base and income model are important considerations to make while deciding on a migration plan. If the programme is only meant for commercial use, you might want to expedite the transfer process or give some modules more priority than others. The migration plan may be less strict if your application is an open-source project that is utilised by a group of people working for a company.
- You have two options after you are certain on the type of applications you are making. One involves starting from scratch and writing the entire code again, while the other makes use of an automated tool.
- Occasionally, it is not possible to automate the entire Python migration process, and some manual code rewriting is required to ensure that the programme completes the work at hand. To reduce the cost of the tool, a manual migration can also be performed if the code base is not too big.
Steps Involved in Migration from Python 2 to 3
Certain toolboxes and frameworks are necessary for Python migration, regardless of whether you choose to migrate Python manually or automatically. The most apparent ones are the more recent Python 3.X version and the older 2.X version that you want to switch to. Depending on the user population you are serving, a few other frameworks will also require preparation.
Basic tasks to be completed for the Python 2 to 3 migration include:
- Frameworks and libraries to support earlier Python 3 versions
- Details regarding compatibility should be included in the project’s readme file.
- Installing a virtual environment manager is necessary to oversee the many installed Python versions.
- It’s important to have a Python code analysis tool on hand, like Pylint, since it can help determine the syntactic modifications needed to complete the migration correctly.
- Code coverage after migration requires Pytest, another Python code testing package.
One of the options, manual code rewriting, can be implemented once the toolboxes are prepared.
Manual Method for Python 2 to 3 Migration
- The goal of the manual code rewrite approach is to update the programme to comply to Python 3.
- You say farewell to the previous iteration and its legacy code, but the earlier code continues to serve as a source of unit testing test cases and references.
- Moving the outdated unit test cases to the Python 3 environment is the first step towards building the new code. New code is written in response to the results of these tests, whether they pass or fail.
- It is necessary to redo the codes that don’t pass the unit test. The ones that survive can be kept safe.
- It is imperative to address any latent flaws discovered in the outdated code, as they may not present amicably in the Python 3 environment as they did in the previous iteration.
- It is occasionally necessary to rewrite some new unit tests for Python 3. This is because the mock libraries that are integrated into the more recent version of Python make the unit tests easier to write.
- The tests need to be in line with the updated software architecture as well. But, you can leave simpler unit tests alone if they don’t depend on special features of Python 3.
Automated Way for Python 2 to 3 Migration
It is nearly hard to write every line of code again and test iteratively when you have a large code base. That would take a tremendous amount of time, money, and effort. It is advised to use automated migration for such projects.
Additionally, tools that enable automation make this feasible. Similarly, we have a programme for automated migration to Python 3 that goes by the simple moniker 2to3.
There are 6 steps involved in the automated migration from Python 2 to 3. Let’s take a closer look at each of them:
1. Establishing unit test cases:
Since it is essential to writing functional code, this is one of the most important steps in the migration process. But occasionally, this can become difficult. Sometimes unit tests are not written for the legacy code, or they do not address specific migration problems such as data type conversions. Worse even, they might have been created in a syntax that has completely altered since then. Tools like 2to3 and Six come in pretty handy in these kinds of situations.
2. Fixing syntactic changes-related problems:
The actual migration begins at this point. This stage addresses the problems that result from changes in syntax. The Pylint tool assists with this procedure by pointing out Python 3 version issues.
3. Executing the test cases within the contexts of Python 2 and Python 3:
The legacy code is subjected to unit testing. Typically, every unit test fails during the first iteration. Having a method to continuously run unit tests until you get it correct is the key.
4. Making the migration with the 2to3 tool:
The available code will be migrated by the 2to3 tool based on the unit test failure report. While the migrated code should function as intended, there may be times when minor grammatical changes and revisions to the unit test cases are necessary. Rerunning the unit tests and performing many migrations are necessary if there are errors until the code achieves its intended goal.
5. Retest after fixing the issues:
The effort is not even half finished until the code has been completely converted to Python 3! The Python 3 environment will now be used to test the code. It could be necessary to modify the Python 2 code and carry out the migration again. Alternatively, you would have advanced past that point and Python 3 code modifications would be sufficient. Thus, these little changes will need to be made repeatedly until the code is fully functional and free of errors and has undergone a full migration.
6. Post-migration optimisation:
Occasionally, certain undesired parts are introduced into the code during an automatic conversion. Anything from an additional float function to a list function could be among them. It is necessary to tidy up these components. And additional testing is required to be extra cautious.
Strategies for Migration
Although the transition from Python 2 to Python 3 may appear difficult, it may be a simple and fruitful process if planned and carried out carefully. By using these techniques, you may take advantage of Python 3’s most recent features and enhancements while ensuring a successful transfer.
Only supporting Python 3
- When you only need to support one version of Python at a time, it is the easiest scenario to handle. In certain situations, you can simply abandon Python 2 and switch your work to Python 3.
- Using this method, you will first automatically convert the majority of the modifications using the 2to3 tool, and then you will manually correct any remaining issues in the Python 3 code.
- Since the 2to3 conversions might not always be the best option for your situation, you should definitely go through and clean up all of the converted code.
Converting Python 2 to Python 3
Since Python 2 and Python 3 differ significantly from one another, porting code from one language version to another can be difficult. Testing imported code is essential to make sure it functions as intended. You can use the resources listed below to convert Python 2 to 3.
- Python 2to Python 3 is a Python script that uses a set of transformations known as fixers to automate the conversion of Python 2 code to Python 3 code. While portion of the converting process is automated by this command-line script, further manual adjustments could be necessary.
- With the help of this compatibility module, you may write code that works with Python 2 and 3. It assists you in balancing out the variations between the two Python versions. The package can be imported into a Python script and installed using pip.
- With the help of the library Python-Future, code written for Python 2 and Python 3 can be written. With the help of this library, users can support both versions with little overhead by using a single codebase. Cheat sheets for writing Python 2 and 3 compatible code are also available on Python-Future.
Python 2 and Python 3 have different Branches.
- Having a branch in your source tree containing the code for Python 2 and another branch containing the code for Python 3. It is the easiest scenario if you must continue to support Python 2.
- Subsequently, all modifications must be made on both branches, which adds a little extra work but is manageable if code changes seldom.
- Having two distributions and having to make sure that Python 3 users receive the Python 3 version of your package. And Python 2 users receive the Python 2 version is one issue with this technique. It also makes your distribution more complex.
Similarities between Python 3 and Python 2
- Python 2 and Python 3 have nearly the same core syntax and structure.
- The variables and data types in Python 2 and Python 3 are identical.
- Both versions continue to utilise indentation to denote code blocks.
- A sizable library of modules covering a range of topics, including file input and output, regular expressions, and networking, is included in both versions.
- You can enter code in the interactive mode of both versions to see the results right away.
- Programming that is object-oriented is supported by both versions.
- Variables do not need to be defined with a specific data type in either version thanks to dynamic typing.
- A sizable and vibrant group of developers actively participates in both versions’ development and offers assistance.
Python 2 and Python 3 without Conversion
- Although you must use a few strategies to avoid the incompatibilities between Python 2 and Python 3. it is frequently entirely possible to adapt the code so that it runs under both Python 2 and Python 3 without the need for any conversions.
- Because of their great forward compatibility, Python 2.6 and 2.7 are far easier to support than Python 2.5 and Python 3, which makes 2.6 and 3 considerably more manageable.
- You have to use extra methods to support versions 2.5 or even older.
- The u” literal for strings is also back in Python 3.3, which helps with one of the main challenges of supporting Python 3.
- In certain situations, you may not be able to use Distribute or may choose not to. It could be necessary to release your code in a format that cannot be distributed using Distribute because it cannot be installed using Distutils.
- When that happens, you are unable to utilise Distribute’s Python 2 to Python 3 support, which makes using Python 2 to Python 3 more difficult and making the decision to forgo using Python 2 to Python 3 more alluring.
Using Python 3 to Python 2
- Because of the Python 2 to Python 3 tool’s flexibility, you can specify what modifications need to be made by creating “fixers.”
- This place allows for almost any kind of Python code conversion, and Joe Amenta’s Python 3 to Python 2 fixers perform conversions between Python 3 and Python 2.
- This allows you to write your Python 3 code and, before it is released, convert it to Python 2.
- Nevertheless, Python 3 to Python 2 does not have Distribute support, and Python 2.5 and below do not provide with the necessary library Python 2 to Python 3 module.
- As a result, Python 3 to Python 2 is still simply an intriguing experiment at this time, however this could change in the future.
Which Version is Best to learn: Python 2 or Python 3?
It’s important to keep in mind that learning Python 3 is simpler than learning Python 2, but neither is inherently superior.
- The majority of new projects are created in Python 3, but many businesses continue to use Python 2. because it takes work to move code between the two versions or because they are currently in the process of moving their existing code to Python 3.
- Keep in mind that Python 2 is no longer being actively developed or maintained, thus no bug patches, security upgrades, or new features will be added.
- Because of its popularity, newcomers who wish to learn any or both versions of Python have access to a wealth of instructional resources.
- For assistance in learning Python, look for books, articles, courses, bootcamps, and video tutorials.
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Reasons for Migration from Python2 to Python3
1. Third-party libraries
One of Python’s biggest benefits is the abundance of user-friendly third-party packages it provides. It’s difficult to envision a situation in which this isn’t regarded as an unquestionably advantageous feature of Python at first glance.
- Ironically, though, the main causes of Python 3’s sluggish acceptance have been its abundance and easy accessibility to modules and frameworks. To make matters worse, they’re suddenly turning into Python 2’s nemesis.
- When interpreters reach the end of their useful lives, Python packages often discard them. The goal of this is to lessen the workload for developers, who are frequently compelled to contribute to those projects during their own free time.
2. Software security
It should be rather easy to secure an interpreter written in Python. If you find a security flaw in Python 2.7 after it has reached its end of life, you will need to install unofficial security updates.They could come from Linux distributions that have a ten-year support window. Applying them manually will be required if you’re not currently using a distribution with this level of long-term support.
- Maintaining third-party dependencies will become increasingly difficult because, if they haven’t already, they will undoubtedly lose support for Python 2.
- You’ll be on your own in the (quite likely) case that you find a weakness in any of them. For this reason, this is perhaps the biggest risk to Python 2 application security.
- It is legally required to ensure software security, particularly when working with personal data under GDPR regulations.
- It seems sense because maintaining software updates is high on the list of security best practices, and using an out-of-date Python interpreter would almost certainly raise red flags in a security assessment.
3. Developer support
One of the highest rates of employee turnover in the industry is seen in software companies. Therefore, it should come as no surprise that bringing in new developers and retaining your current staff for as long as feasible are top priorities.
- They’re constantly seeking the next big thing and up for a challenge. You run the danger of losing them in the process and their interest if you don’t give things to them.
- Python 2 is, in the meantime, gradually becoming obsolete or, at most, a niche language.It’s a fast train that will soon render obsolete before we know it.
- Resolving issues related to obsolete and unsupported technologies doesn’t seem like a smart use of the mental prowess that developers claim to possess.
- Python 2 is becoming boring and uninteresting. Rather, it has turned into a death sentence for your engineers’ creativity and a headache for the people working on the project and the stakeholders.
- To put it briefly, the future of your Python projects is not looking good if you don’t migrate to Python 3, if only because you may soon find it difficult to even get engineers to work on them.
4. Missed opportunities
Third-party libraries have already been discussed, have we not? Apart from that, however, Python 3’s standard library alone offers a lot.
First off, there was a significant improvement in type safety.This addresses the most well-known modification, which is that strings are now explicitly of a different type than bytes and are by default unicode. We can attest that this saves a tonne of time compared to the numerous hours you would otherwise spend trying to find UnicodeDecodeErrors.
However, that is by no means the end of it. Additionally, Python 3 provides you with:
- type annotations that you can use to enhance usability generally and static code analysis;
- programming in asynchronous native mode;
- chained exceptions, which come be particularly handy for debugging
- additional beneficial features that greatly increase the efficiency of Python coding.
5. Tangible benefits
Additionally, there are more concrete advantages to Python 3. For example, Instagram, one of the most well-known Python-using software firms, claimed a 30% improvement in memory resource usage and a 12% increase in CPU speed following their move to Python 3 and a Django upgrade.
- In relation to that, it’s also important to note that, although Django had long supported Python 2, it didn’t switch to Python 3 until Django 2.0 was launched.
- It should be obvious that an interpreter upgrade for Python 2 projects may be necessary to enable the kind of transformation that Instagram experienced, especially if other libraries decide to follow suit.
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Frequently Asked Questions
Is it easy to learn Python 3 after Python 2?
Because of its emphasis on code readability, Python 3 is simpler to learn and use than its predecessors.–
Is Python 2 compatible with Python 3?
The fact that Python 3 is incompatible with Python 2 is another benefit of using it. Although it may seem like a drawback, there are certain advantages. Writing code particularly for Python 3 is simpler because Python 3 is incompatible with Python 2.