Python: The Most Advanced Programming Language for Computer
Science Applications
Akshit J. Dhruv, Reema Patel and Nishant Doshi
Computer Science and Engineering, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat
Keywords: Python, Python libraries, Memory allocation, Data Structure, Framework.
Abstract: In last few years, there has been an advancement in programming languages due to different libraries that
are introduced. All the developers in this modern era prefer programming language that provides a built-in
module/library which can make their work easy. This paper describes the advancement of one such
language “Python” and it’s increasing popularity through different statistical data and graphs. In this paper,
we explore all the built-in libraries for all different computer science domains such as Data Science,
Machine Learning, Image Processing, Deep Learning, Natural Language Processing, Data Visualization,
Cloud Computing, Speech recognition, etc. We have also included Memory management in Python.
Different frameworks for Python which can make the front-end work easier are also mentioned.
1 INTRODUCTION
In 1991, Python language was developed by Guido
van Rossum. There is an interesting story behind
giving the name “Python” to the programming
language. At the time of development of python, the
developer was reading the script “Monty’s Python
Flying which is a BBC series. While reading this
book he got an idea to name the programming
language as “Python” to have a short and unique
name. Python is object- oriented, interpreted, and
interactive programming language. It provides high-
level data structures such as list, tuples, sets,
associative arrays (called dictionaries), dynamic
typing and binding, modules, classes, exceptions,
automatic memory management, etc. It is also used
for parallel computing system and has a
comparatively simple and easy syntax for coding
and still it is a powerful programming language.
Python has the interpreter for java known as
JPython, which is similar to the interpreter for C
language. Python has many advantages over any
other languages, like it has varieties of library which
reduces the code to one-third for programmer and
due to this Python has reached at the +highest peak
in terms of Machine Learning. Difficulty is faced by
many while solving problems(Lawan et al, 2015),
this research will help providing knowledge about
different libraries and motivate them to use Python.
2 DATA STRUCTURE
Data structure means organization, management of
data and also it is a storage format which provides
efficient access and modification. In general, it
contains relation among them, and the functions or
operations that can be applied to the data.
Cython: It helps in improving the speed of the
implementation of the code.
PYTables: It is used in maintaining
hierarchical datasets and is also used to
maintain an extremely large amount of data.
Tree Dict: It works as a container for python
to simplify the bookkeeping surrounding
parameters, variables, and data. It is very
stable and fast at work.
Table 1: Different Data Structure in Python.
Type Definition Symbol Example
List
A list is a
mutable data
structure, ordered
sequence of
element.
It is defined
by square
braces [ ].
List=[1,2,3]
292
Dhruv, A., Patel, R. and Doshi, N.
Python: The Most Advanced Programming Language for Computer Science Applications.
DOI: 10.5220/0010307902920299
In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 292-299
ISBN: 978-989-758-501-2
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Dictionary
Dictionary are
also called
HashMap or
associative arrays,
which means that
an element of the
list is associated
with the
definition, rather
like Map in Java.
It is defined by
brackets {}
Dict=
{1:”a”,2:”b”
}
Set
It is a collection of
unordered and
unique immutable
objects.
It is defined
by brackets
{}
Set={1,2,3
}
3 BUILT-IN LIBRARIES IN
PYTHON FOR COMPUTER
SCIENCE APPLICATIONS
3.1 Data Science
Data Science is to develop a different approach to
record, store, and analyse the data and using this
data to get effective information. Data science aims
at achieving ideas and knowledge from any type of
data.
Python provides number of libraries for the same
as listed below:
Matplotlib: 2D plot graphs can be made using
Matplotlib library.
Pandas: Data analysis in finance, statistics,
social science, and engineering require
different types of data structure and tools
which are provided by Pandas.
(https://pypi.org).
NumPy: It is the basic library for scientific
computing in Python. (https://pypi.org)
Multidimensional arrays and matrices can be
done using objects in NumPy, and also
routines are provided which allows developers
to compute advanced mathematical and
statistical functions on those arrays with code
if possible. It is also used in Data Structure.
SciPy: Manipulation and visualization of data
is done using a high-level command provided
in SciPy. Functions for solving Integrals
numerically, computing differential equations,
and optimization are included in the package.
The library SciPy is also used in Image
processing.
IPython: Using Ipython, an efficient
interactive shell gets added along with the
functionality of Python’s interpreter that has
the capability of adding rich media,
observations, shell syntax, backup of
command history, and tab completion.
(https://pypi.org) It is also used in debugging
by using IPython as fix interpreter. The usage
of Mathematica or MATLAB makes it
comfortable to work with IPython. It is also
used in Data Structure.
Pygame: Video games are created easily using
Pygame. The library has computer graphics
and sound libraries which are specially made
for python programming language.
SQLAlchemy: It provides a common interface
for creating and executing database-agnostic
code without the need of writing SQL
statements. It is also used in data structure.
Scrapy: This library is used to design web
scraping, and also it can be used to get data
using APIs or it is used as a general-purpose
web crawler.
Pywin32: This library is used to create COM
objects and the Pythonwin environment.
wxPython: GUI toolkit for the Python
programming language can be obtained by
this library. Applications made using this has
native appearance on all platforms.
Flask: It allows you to build websites and web
apps very fast and efficiently.
Nose: It runs tests or directories whose name
includes “test” at the end of the word. To ease
out the print- style debugging, it includes
captured stdout output from failing tests.
Sympy: It is used for symbolic mathematics. It
tries to keep the code as simple as possible in
process of making a full-featured computer
algebra system (CAS).
Fabric: Fabric along which is acting as library
for Python, is also a command line interface
tool for increasing the use of SSH for the
application arrangement or systems
administrations. The main use of this library is
to create a module which contains one or more
functions, and then executing them through
fab command-line tool.
Pillow: Python Imaging Library which adds
the support for different options like opening,
manipulating data, and saving images as
different file formats. It is also used in Image
processing.
Statsmodels: Statistical Models can be
estimated using this library. Also it can
explore data and perform statistical test. It is
also used in machine-learning.
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293
3.2 Machine Learning
Machine learning can also be considered as a subset
or part of Artificial Intelligence that can learn
automatically and make changes itself from the
experience without being externally programming it.(
Machine Learning and Deep Learning frameworks
and libraries for large-scale data mining).
Keras: It is a neural networking API and it is to
execute for the machine learning beginners to
build and design neural networks. It is also
used in deep learning.
Shogun: For a wide range of efficient and
unified machine learning methods, Shogun
library is used which is an open source
library.[5]
XGBoost: XGBoost is decision tree that uses
the algorithm to solve the predictive modelling
problems, and this algorithm is efficient and
speedy.
Scikit-learn: It is used for classical ML
algorithms. It supports direct and indirect
learning algorithms and also be used for data
mining and data analysis. It has many
applications but majorly unit-testing and self-
verification are done using Scikit-learn to
detect and diagnose different types of error.
CatBoost: It will read the documents and
analyse the model and data with CatBoost
analysing tools.
PyTorch: PyTorch allows the user to work on
Tensors and GPU accelerations by
implementing it in C with a wrapper in Lua. It
also can create computational graphs.
Eli5: It is a python library which is used to
debug classifiers in machine learning and
explain the predictions.
MlPy: It is the GNU based scientific library
and make the extensive use of Cython
language.
Nilearn: Statistical learning on Neurolmaging
data can easily be learned and understood
using Nilearn library.
TensorFlow: It is used for high-performance
numerical computation. It can develop many
artificial Intelligence applications by
implementing the deep neural networks. It is
also used in Deep Learning.(Machine Learning
and Deep Learning frameworks and libraries
for large-scale data mining)
According to the GitHub survey from January
2018 to December 2018, Python is at the top in
terms of machine learning compared to C++,
Java, JavaScript, etc. (https://github.com).
3.3 Deep Learning
Deep Learning can also be called part of Machine
learning. It has a layer of Artificial Neural Network
which can learn the unstructured or unlabelled data.
(Machine Learning and Deep Learning frameworks
and libraries for large-scale data mining).
Apache MxNet: It permits to use mix
symbolic and crucial coding to increase
productivity and efficiency Inside MxNet
there is a modern dependency scheduler that
will help to automatically parallelizes both
symbolic and imperative operations quickly.
Caffe: Expression, modularity, and speed are
the key features of this library.
Fastai: It simplifies the training of neural nets
very quickly and with accuracy and using the
latest technique. It includes the support for
text, vision, and tabular models. (Machine
Learning and Deep Learning frameworks and
libraries for large-scale data mining).
CNTK: Neural networks are defined in a
directed graph by a series of computational
steps that are described by the toolkit of
CNTK. In this graph, input values are
represented by leaf nodes or network
parameters and other represent matrix
operation which took place on the inputs.
TFLearn: High level API is provided to
TensorFlow using this library. This library
helps to get the quick outputs of the
experimentation and also the process is
transparent.
Lasagne: It is used to build and train neural
networks using Theano. Convolutional Neural
Networks (CNNs), recurrent networks are
supported by this library.
Elephas: Deep learning’s distributed models
can run at
scale with the use of spark using this library.
Theano: Mathematical expression for multi-
dimensional arrays can be optimized, evaluate
and can also be defined in this library. Theano
is also used in Machine Learning.
3.4 Image Processing
Image processing is specially used to do some
operations on an image to get a better-quality image
or to find some useful information from it. It works
like signal processing in which we take input as
image and output may vary, like it can be image or
characteristic features which is associated with that
image.
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Scikit Image: It is a collection of algorithms
for image processing and uses NumPy arrays
as image objects. It includes algorithms for
geometric transformation, segmentation,
colour space, analysis, manipulation, filtering,
morphology, feature detection, etc.
Open cv-python: It is developed by Intel for
real-time image & video analysis and
processing.
Mahotas: Functions such as morphological
operations, modern computer vision functions
and filtering for the advanced computation
and includes the interest point detection also.
Cairo: It acts as a 2D graphics library for
python and also supports many output devices.
Using display hardware acceleration, it gives
continuous output on all connected devices.
3.5 Game Development
Game Development is used to create games and
describes the design, development, and release of a
game. Before game development, it is important to
think about the game mechanics, rewards, player
engagement, and level designing.
Pyglet: It is a cross-platform windowing and
multimedia library for Python, developed to
create games and other visually rich
applications. It has feature which can load
images, sound, music, and video in almost any
format.
Arcade: It is used to create 2D video games. It
helps the developer to create the 2D games
without learning complex frameworks.
Rabbyt: It is a sprite library for python which
provides fast performance with an easy to use
but flexible API.
Pymunk: It is a pythonic 2D physics library
that can be used whenever there is a need for
2D rigid body physics from Python.
Pybox 2D: It is purely 2d engine written
primarily for games. It includes features like
circles, up to 16 sided polygons, thin line
segments, controllers, basic breakable bodies,
and pickling support.
Panda 3D: It is used for 3D rendering and
game development. It also support automatic
shader generation, which means that we can
use normal maps HDR, gloss maps, cartoon
shading glow maps.
3.6 Networking
Python provides two-level access to networking.
One low level, in which one can access the basic
socket support in the same OS that permits
implementation for clients and servers to do
connection-orientation and connectionless protocols.
asyncio: It provides base for writing the
existing code in a single sequence using
coroutines, multiplexing I/O access over
sockets and different resources, which are
running network clients, servers, and other
related primitives. To detect common issues
during development, debug mode is enabled.
Tftpy: It includes client and server classes and
create a TFTP server/client to receive/send
files.
Telnet lib: It provides a telnet client
implementation, so it represents a connection
to a Telnet Server.
Parmiko: It is an implementation of SSHv2
protocol, providing the functionality of client
and server both. SFTP client and server mode
are supported.
Requests: All kinds of HTTP requests are sent
in python using this module.
3.7 Natural Language Processing
Natural Language Processing shows the connection
between human language and computers. It is used
in businesses and it is a very important term in every
engineer’s life.
Gensim: It is used for topic modeling,
similarity revival and document indexing with
large corpora. It includes features like all the
algorithms are memory- independent
compared to the intuitive interfaces, corpus
size, efficient multicore implementations of
popular algorithms, distributed computing etc.
Textblog: This library is used for processing
written data. It provides API for part-of-
speech tagging, sentiment analysis, noun
phrase extraction, classification, translation,
WordNet integration, word inflection, parsing,
add new models or languages through
extensions.
SpaCy: It is a Natural language Processing
library of Python which contains pre-trained
statistical models, word vector and also it has
support tokenization for 49+ languages.
Vocabulary: It is a Python library, which is
used to get the meaning, synonyms, opposites,
part of speech, translations for a given word.
Python: The Most Advanced Programming Language for Computer Science Applications
295
PyNLPl: PyNLPI is also read as “Pineapple”,
which is used in extracting a sequence of n
items in a list and also used in building a
simple language model. This library covers a
large area in the field of working with FoLiA
XML.
NLTK: Lexical resources such as WordNet
requires an interface to run in Python which is
provided by NLTK and also provides library
called text processing for tokenization,
classification and stemming. It has built- in
function which provides practical ideas to
programming for language processing.
CoreNLP: Using this library it is easy to apply
groups of linguistic analysis tools to a piece of
text. It includes many tools such as: Part-of-
speech recognizer (POS) tagger, the
conference resolution system, the parser, the
named entity recognizer, sentiment analysis,
the open information extraction tools, and
bootstrapped pattern learning.
Pattern: It has tools for data mining, natural
language processing, machine learning,
network analysis using graphs centrality and
visualization.
3.8 Cloud Computing
Cloud Computing uses a network of remote server
which is hosted on the Internet to store, process, and
manage the data, rather than a local server or a
personal computer.
Apache Libcloud: It is a python library for
cloud computing and it does work of hiding
difference between different cloud provider
APIs and allows us to manage different cloud
resources through a unified and easy to use
API.
google-api-python client: It allows us to work
with Google APIs such as Google+, YouTube
or Drive on our server. They are officially
supported by Google.
3.9 Data Visualization
Data visualization is used to represent the
information in the form of a chart, diagram, and
pictures.
Seaborn: It is a matplotlib based library that
provides an interface of high-level for making
catchy and informative statistical graphics.
Bokeh: It is used as an interactive
visualization library that can target web
browsers for representations. We can pass all
types of data such as Python lists, tuples,
NumPy arrays or Pandas Data Frames to make
the plots.
Py.gal: Formats for the vector graphic are
SVG whose charts can be made from python
using a library called PyGal.
Ggplot: It is a very helpful library for the
programmers who are coming from the R
background and used ggplot2 in it, as the same
Ggplot is used in python.
Plotly: It is an interactive library use for
plotting which supports 40 different types of
graphs covering a wide range of financial,
statistical, geographic, scientific, and 3
dimensional.
Missingno: Many times the datasets are
missing and they are represented by NaN (not
a number). So this library provides a way to
visualize the distribution of NaN values. It is
compatible with Pandas.
Leather: It makes the work done fast but not
with perfection, like someone needs charts but
doesn’t expect any type of accuracy or
perfection can use this library.
Pydot: It provides a complete interface to
create, handle modify and process graphs.
3.10 Speech Recognition.
Speech recognition is used to convert the human
voice to computer understandable language using
different software or different hardware. It has many
applications, like to give command to computer do
perform any particular task without even writing or
working physically.
CMU sphinx: It contains the best toolkit with
many different tools used to build speech
applications.
Google Speech Recognition: Provides facility
likes
o Configure Microphone.
o Set chunk size.
o Set the sampling rate.
o Set device id to selected microphone.
o Allow adjusting for unknown noises.
Microsoft Bing Voice Recognition:
o Microsoft Heera
o Microsoft Zira
o Microsoft David
o Microsoft Mark
o Microsoft Ravi
are some of the in-built voice used in speech
recognition app
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Houndify API: It is used in creating a client
that converts speech to text in under a few
minutes. It provides a simple way for
developers to use the platform for its speech to
text capabilities through the speech to text
only domain.
Api.py: It is used to combine speech
recognition with natural language processing.
Ppytsx3: It is a text to speech python library
which works without internet connections or
any type of delay.
PyAudio: It is the cross-platform audio I/O
library. This library helps to play and record
audio streams on several platforms.
3.11 Cryptography
This library provides a set of procedure to decrypt
messages and to secure communications among
computer system, matplotlib, etc.
PyNacl: It is a bounded library called
libsodium which is a collection of the
Networking and cryptography library.
PocketProtector: It is a library that contains a
secret management system.
Pycryptodome: It is low-level security
providing library but still used for home
purposes.
4 MEMORY MANAGEMENT IN
PYTHON
Memory management is an important factor that
everyone checks before choosing any programming
language. Memory management is allocating
particular block to programs and reduce the overall
space required and increases system performance.
Comparison in memory allocation in other
languages and Python: int a = 10; int b= 10;Then in
C language, it is stored as variable and both a and b
will be provided a different memory space.
While in Python it is stored as reference. When
we enter value as 10 the reference count of 10
becomes 1 and when the value of b is added as 10
and the reference count becomes 2.
Table 2: Difference between Python and C.
P
y
thon C Lan
g
ua
g
e
No data is stored during
compile time.
Data is stored in “Stack”
during compile time.
During run time the
data
is stored in “heap”.
During run time the data
is
stored in “heap”.
There is no use of
variable in storage.
Variable is also stored in
memory.
All the values are stored
as reference making the
memory optimized.
All the values are stored
using memory location as
well as variable in which
it is stored.
After the execution of
The program, if the
Reference count is 0
then The garbage
collector will release the
memor
y
on its own.
After the execution also
the memory area stays
occupied unless the
command is not given
externally to free the
memor
y
.
4.1 Second Section
Figure 1: Survey of 2017.
Figure 2: Survey of 2019
Comparing the graph of 2017 and 2019 we can
see it has raised its position from 5th rank to 4th
rank. Also, the number of users of Python in 2017
was 32% which increased to 41.7% in 2019.
Python: The Most Advanced Programming Language for Computer Science Applications
297
4.2 Usage of Python
Figure 3: Graph showing usage of Python
4.3 Average Python Programmers
Salary by States: 2019
Figure 4: Salary chart by states.
Here we can see that California has the highest
paid salary to the programmer and slowly there will
be others joining
them.(https://www.daxx.com/blog/development-
trends/python-developer-salary-usa)
4.4 Python’s Rating
Figure 5: Rating of Python from time it was developed.
This graph shows the increment in rating since
the time it is developed.
4.5 Top Websites Built using Python
The top websites built are Instagram, Spotify,
Netflix, Google, Uber, Dropbox, Pinterest,
Instacart, Reddit, and
Lyft.(https://learn.onemonth.com/10-famous-
websites-built-using-python/).
5 THIS SECTION INCLUDES
INFORMATION ABOUT THE
DIFFERENT FRAMEWORKS
USED IN PYTHON
Frameworks: A collection of different
modules/packages which are used by developer to
write web-applications or services without
requirement of handling minor details such as
protocols, socket, or process management. (Kumar,
Dahiya)
5.1 Full Stack Framework
It is a framework that tries to provide nearly
everything i.e. from web serving to database
management right down to HTML generation —
which a developer could need to build an
application.(Nithya et al).
Few Full Stack Frameworks :
Django
Turbo Gears
web2py
Cubic web
Tornado
Giotto
Grok
Pylon
Reahi
wheezy.web
Zopez
kiss. Py
Lino
Nagare
Pylatte
5.2 Non-full Stack
These frameworks do not provide extra
functionalities and features to the developers. They
have to add huge code and components manually
here.
Few Non-Full Frameworks are:
Bottle
Cherry.Py
Flask
Hug
P
y
ramid
AppWsgi
BlueBream
More Path
Bobo
Bocadillo
Clastic
Divmod
N
evow
Falcon
Growler
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6 CONCLUSION
Python is growing rapidly and has reached to 3rd
rank in terms of best programming language. It is
seen that; Python has many libraries which makes it
unique from other programming languages. It’s
popularity and ratings are increasing day by day
along with the demand of Python programmers all
over the world.
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