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: 10.5220/0010307900003051

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

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.

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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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