What is Python? Everything you need to know
Dating
from 1991, Python is a relatively new programming language. From the
start, Python was considered a gap-filler, a way to write scripts that
“automate the boring stuff” (as one popular book on learning Python put
it) or to rapidly prototype applications that will be implemented in one
or more other languages.
However, over
the past few years, Python has emerged as a first-class citizen in
modern software development, infrastructure management, and data
analysis. It is no longer a back-room utility language, but a major
force in web application development and systems management and a key
driver behind the explosion in big data analytics and machine
intelligence.
Python’s success revolves around several advantages it provides for beginners and experts alike:
Python
is easy to learn. The number of features in the language itself is
modest, requiring relatively little investment of time or effort to
produce one’s first programs. Python syntax is designed to be readable
and straightforward. This simplicity makes Python an ideal teaching
language and allows newcomers to pick it up quickly. Developers spend
more time thinking about the problem they’re trying to solve, and less
time thinking about language complexities or deciphering code left by
others.
Python is broadly used and
supported. Python is both popular and widely used, as the high rankings
in surveys like the Tiobe Index and a large number of GitHub projects
using Python attest. Python runs on every major operating system and
platform, and most minor ones too.
Many major libraries and API-powered services have Python bindings or wrappers, allowing Python to interface freely with those services or make direct use of those libraries. Python may not be the fastest language, but what it lacks in speed, it makes up for in versatility.
Many major libraries and API-powered services have Python bindings or wrappers, allowing Python to interface freely with those services or make direct use of those libraries. Python may not be the fastest language, but what it lacks in speed, it makes up for in versatility.
Python is not a
“toy” language. Even though scripting and automation cover a large chunk
of Python’s use cases (more on that below), Python is also used to
build robust, professional-quality software, both as standalone
applications and as web services.
What is Python used for?
The
most basic use case for Python is a scripting and automation language.
Python isn’t just a replacement for shell scripts or batch files but is
also used to automate interactions with web browsers or application GUIs
or system provisioning
and configuration in tools such as Ansible and Salt. But scripting and
automation represent only the tip of the iceberg with Python.
Python
is used for general application programming. Both CLI and
cross-platform GUI applications can be created with Python and deployed
as self-contained executables. Python doesn’t have the native ability to
generate a standalone binary from a script, but third-party packages
like cx_Freeze or PyInstaller can be used to accomplish that.
Python
is used for data science and machine learning. Sophisticated data
analysis has become one of fastest moving areas of IT and one of
Python’s star use cases. The vast majority of the libraries used for
data science or machine learning have Python interfaces, making the
language the most popular high-level command interface to for machine
learning libraries and other numerical algorithms.
Python is used
for web services and RESTful APIs. Python’s native libraries and
third-party web frameworks provide fast and convenient ways to create
everything from simple REST APIs in a few lines of code, to full-blown,
data-driven sites. Python’s latest versions have powerful support for
asynchronous operations, allowing sites to handle up to tens of
thousands of requests per second with the right libraries.
Python
is used for metaprogramming. In Python, everything in the language is an
object, including Python modules and libraries themselves. This allows
Python to work as a highly efficient code generator, making it possible
to write applications that manipulate their own functions and have the
kind of extensibility that would be difficult or impossible to pull off
in other languages.
Python is used for glue code. Python is often
described as a “glue language,” meaning it can allow disparate code
(typical libraries with C language interfaces) to interoperate. Its use
in data science and machine learning is in this vein, but that’s just
one incarnation of the general idea.
Also worth noting are the
sorts of tasks Python is not well-suited for. Python is a high-level
language, so it’s not suitable for system-level programming—device
drivers or OS kernels are straight out. It’s also not ideal for
situations that call for cross-platform standalone binaries. You could
build a standalone Python app for Windows, Mac, and Linux, but not
elegantly or simply. Finally, Python is not the best choice when speed
is an absolute priority in every aspect of the application. For that,
you’re better off with C/C++ or another language of that caliber.
The Python language’s pros and cons
Python
syntax is meant to be readable and clean, with little pretense. A
standard “hello world” in Python 3.x is nothing more than:
print(“Hello world!”)
Python
provides many syntactical elements that make it possible to concisely
express many common program flows. Consider a sample program for reading
lines from a text file into a list object, stripping each line of its
terminating newline character along the way:
with open(‘myfile.txt’) as my_file:
file_lines = [x.strip(‘\n’) for x in my_file]
file_lines = [x.strip(‘\n’) for x in my_file]
The with/as construction is a “context manager,” which provides an efficient way to instantiate a given object for a block of code and then dispose of it outside of that block. In this case, the object in question is my_file, instantiated with the open() function. This takes the place of several lines of boilerplate to open the file, read individual lines from it, then close it up.
The [x … for x in
my_file] construction is another Python idiosyncrasy, the “list
comprehension.” It allows a given item that contains other items (here,
my_file and the lines it contains) to be iterated through and to allow
each iterated element (that is, each x) to be processed and
automatically appended into a list.
You could write such a thing
as a formal for… loop in Python, much as you would in another language.
The point is that Python has a way to economically express things like
loops that iterate over multiple objects and perform some simple
operation on each element in the loop or work with things that require
explicit instantiation and disposal. Constructions like this allow
Python developers to balance terseness and readability.
Python’s
other language features are meant to complement common use cases. Most
modern object types—Unicode strings, for instance—are built directly
into the language. Data structures—like lists, dictionaries (i.e.,
hashmaps), tuples (for storing immutable collections of objects), and
sets (for storing collections of unique objects)—are available as
standard-issue items.
Like C#, Java, and Go, Python has
garbage-collected memory management, meaning the programmer doesn’t have
to implement code to track and release objects. Normally garbage
collection happens automatically in the background, but if that poses a
performance problem, it can be triggered manually or disabled entirely.
An
important aspect of Python is its dynamism. Everything in the language,
including functions and modules themselves, are handled as objects.
This comes at the expense of speed (more on that below), but makes it
far easier to write high-level code. Developers can perform complex
object manipulations with only a few instructions, and even treat parts
of an application as abstractions that can be altered if needed.
Python’s
use of significant whitespace has been cited as both one of Python’s
best and worst attributes. The indentation on the second line shown
above isn’t just for readability; it is part of Python’s syntax. Python
interpreters will reject programs that don’t use proper indentation to
indicate control flow.
Syntactical white space might cause noses
to wrinkle, and some people do reject Python out of hand for this
reason. But strict indentation rules are far less obtrusive in practice
than they might seem in theory, even with the most minimal of code
editors, and the end result is code that is cleaner and more readable.
Another
potential turnoff, especially for those coming from languages like C or
Java, is the way Python handles variable typing. By default, Python
uses dynamic or “duck” typing—great for quick coding, but potentially
problematic in large code bases. That said, Python has recently added
support for optional compile-time type hinting, so projects that might
benefit from static typing can make use of it.
Python 2 versus Python 3
Python
is available in two versions, which are different enough to trip up
many new users. Python 2.x, the older “legacy” branch, will continue to
be supported (i.e. receive official updates) through 2020, and it might
even persist unofficially after that. Python 3.x, the current and future
incarnation of the language, has many useful and important features not
found in 2.x, such as better concurrency controls and a more efficient
interpreter.
Python 3 adoption was slowed for the longest time by
the relative lack of third-party library support. Many Python libraries
supported only Python 2, making it difficult to switch. But over the
last couple of years, the number of libraries supporting only Python 2
has dwindled; most are now compatible with both versions. Today, there
are few reasons against using Python 3.
Python: A “batteries included” experience
The
success of Python rests on a rich ecosystem of first- and third-party
software. Python benefits from both a robust standard library and a
generous assortment of easily obtained and readily used libraries from
third-party developers. Python has been enriched by decades of expansion
and contribution.
Python’s standard library provides modules for
common programming tasks—math, string handling, file and directory
access, networking, asynchronous operations, threading, multi process
management, and so on. But it also includes modules that manage common,
high-level programming tasks needed by modern applications: reading and
writing structured file formats like JSON and XML, manipulating
compressed files, working with internet protocols and data formats (web
pages, URLs, email). Most any external code that exposes a C-compatible
foreign function interface can be accessed with Python’s ctypes module.
The default Python distribution also provides a rudimentary, but useful,
cross-platform GUI library by way of Tkinter, and an embedded copy of
the SQLite 3 database.
The thousands of third-party libraries,
available through the Python Package Index (PyPI), constitute the
strongest showcase for Python’s popularity and versatility. The
BeautifulSoup library provides an all-in-one toolbox for scraping
HTML—even tricky, broken HTML—and extracting data from it. Frameworks
like Flask and Django allow rapid development of web services that
encompass both simple and advanced use cases. Multiple cloud services
can be managed through Python’s object model by way of Apache Libcloud.
NumPy, Pandas, and Matplotlib accelerate math and statistical operations
and make it easy to create visualizations of data.
Python distributions for all
The
most straightforward way to get Python is by downloading a release for
your platform from the Python Software Foundation, the creators of the
language. CPython, as this edition is called, is used as the stock
Python runtime in every major Linux distribution as well as MacOS. That
said, a wealth of other Python distributions exist to serve specific
audiences.
Python for enterprise developers. ActiveState markets
its own Python distribution, ActivePython, to enterprise users who want
support and a rich set of development tools, such as ActiveState’s own
Komodo IDE.
Python for data scientists. The Anaconda distribution,
created by Continuum Analytics, includes a slew of common libraries for
machine learning and data wrangling. Installing those libraries by hand
can be tricky, especially on Windows. Anaconda saves you that trouble
and provides mechanisms for keeping them up to date and installing other
libraries in the same vein. See also the Intel Distribution for Python,
a repackaging of Anaconda using Intel’s custom math acceleration
extensions.
Python for developers with a need for speed. PyPy
accelerates Python applications by way of just-in-time compilation, a
handy way to amp up an existing Python app without having to rewrite it.
The biggest limitation with PyPy is that it works best with Python apps
that don’t use external C libraries, but its development team has been
addressing that problem.
Python for.Net and Java developers.
Editions of Python exist that run the.Net and Java Virtual Machine
runtimes—IronPython and Jython, respectively. Both of them allow Python
to interoperate with other languages on their respective runtimes—such
as an IronPython app can interoperate with.Net classes. Jython
development hasn’t budged much in the last couple of years, but work on
IronPython has been rejuvenated with a new development team.
Is Python too slow?
One
common caveat about Python is that it’s slow. Objectively, it’s true.
Python programs generally run much more slowly than corresponding
programs in C/C++ or Java. Some Python programs will be slower by an
order of magnitude or more.
Why so slow? It isn’t just because most Python runtimes are interpreters rather than compilers.
It is also due to the fact that the inherent dynamism and the
malleability of objects in Python make it difficult to optimize the
language for speed, even when it is compiled. That said, Python’s speed
may not be as much of an issue as it might seem, and there are ways to
alleviate it.
Python has many routes for speed optimization. It
isn’t always the fate of a slow Python program to be forever slow. Many
Python programs are slow because they don’t properly leverage the
functionality present in Python or its standard library. Math and
statistics operations can be boosted tremendously by way of libraries
such as NumPy and Pandas, and the PyPy runtime can provide
orders-of-magnitude speedups for many Python apps.
A common adage
of software development is that 90 percent of the activity for a program
tends to be in 10 percent of the code, so optimizing that 10 percent
can yield major improvements. With Python, you can selectively convert
that 10 percent to C or even assembly, by way of projects like Cython or
Numba. The end result is often a program that runs within striking
distance of a counterpart written entirely in C, but without being
cluttered with C’s memory micromanagement details.
In Python,
developer time is usually far more valuable than machine time. Or to put
it another way: For many tasks, the speed of development beats the
speed of execution.
A given Python program might take six seconds
to execute versus a fraction of a second in another language. But it
might take only ten minutes for a developer to put that Python program
together, versus an hour or more of development time in another
language. The amount of time lost in the execution of the Python program
is more than gained back by the time saved in the development process.
Obviously,
this is less true when you’re writing software that has
high-throughput, low-concurrency demands, such as a trading application.
But for many real-world applications, in domains ranging from systems
management to machine learning, Python will prove to be rich enough and
fast enough for the job. And the flexibility and pace of development
that Python enables may allow for innovation that would be more
difficult and time-consuming to achieve in other languages.
When
the speed of development and programmer comfort are more important than
shaving a few seconds off the machine clock, Python may well be the best
tool for the job. For more comprehensive tutorials on Python
programming language with beautifully annotated screenshots, follow the
link: HERE
This story, “What is Python? Everything you need to know” was originally published by InfoWorld.
