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Presentations
Presentations

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An overview of data
and web-application
development with
PYTHON
Sivaranjan Goswami
Team Lead, Data Tech

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CONTENTS
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Python – what and why?
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Common applications of Python
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Is Python Slow?
●
Pandas and Numpy
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What makes Python a great choice for web back-end?
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Web Frameworks in Python – Django
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Web Scrapping
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Data Science and Data Engineering

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Python
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Python is a general purpose programming language released by Guido van
Rossum, Dutch programmer in 1991.
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Major Timeline:
– First release: 0.9.0 in 1991
– Released Python 2.0 in 2000
– Released Python 3.0 in 2008
– Discontinued Python 2 (2.7.18) in 2020
– Latest version: 3.11 (2022-10-24)
Most commonly used versions nowadays: Python 3.7, Python 3.8

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Some Characteristics of Python:
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Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc).
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Python has a simple syntax similar to the English language.
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Python has syntax that allows developers to write programs with fewer lines
than some other programming languages.
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Python runs on an interpreter system, meaning that code can be executed as
soon as it is written. This means that prototyping can be very quick.
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Dynamically typed language
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Object oriented programming language – everything in Python is an object
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Supports functional programming.

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Common applications of Python
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Web applications
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Workflow
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Interact with database
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Implement complex mathematical expressions for data processing
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Image processing and computer vision
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Machine Learning
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Data Science and Engineering

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A Linkedin Survey

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Is Python Slow?
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It depends on how you use Python
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Loops are slow in any interpreted programming language
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Use in-built functions and libraries as much as possible

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Pandas and Numpy
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Numpy is a fundamental library in Python for performing scientific
computing.
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Numpy provides high-performance multidimensional arrays and
tools to deal with them.
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Pandas is built on the numpy library and written in languages like
Python, Cython, and C.
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Pandas provide high performance, fast, easy-to-use data structures,
and data analysis tools for manipulating numeric data and time
series.

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What makes Python a great choice for web
back-end?
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Modern web applications are much more than just record
keeping and CRUD operations.
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There are dashboards that need to aggregate data
dynamically to support multiple charts and graphs.
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There are web applications that use image processing,
computer vision, machine learning etc.
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Python comes with one of the most popular web development
framework – Django.

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Python web Framework - Django
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The web framework for perfectionists with deadlines.
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Django is a high-level Python web framework that enables rapid
development of secure and maintainable websites.
– Complete
– Versatile
– Secure
– Scalable
– Maintainable
– Portable

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Django MVC
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Model: Django ORM that allows you to perform complex
database operations with Python Code. Django models
have in-built support for filter, sort, pagination etc.
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View: Python class or functions that implements the
business logic.
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Template: HTML templates where contents can be
manipulated dynamically on server side using Python-Like
code.

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Django Template Looks Like

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Django REST Framework
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A Django project except the Templates
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The view returns JSON response that any front-end
framework can work with.
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Ideal for projects where front-end and back-end are
completely decoupled.
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Enables front-end developers to use libraries or frameworks
of their own choice such as React, Angular, React Native,
Flutter etc.

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Popular Websites powered by Django:
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Google Search
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Youtube
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Instagram
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Spotify
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Dropbox
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NASA
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The Washington Post

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Web Scrapping
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Web scraping is the process of collecting and parsing raw data from the Web, and
the Python community has come up with some pretty powerful web scraping tools.
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Web scrapping is a popular application of Python.
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Python comes with a variety of tools to scrap websites and extract data form the
websites.
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Web scrapping is useful for collecting data for training machine learning models.
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Web scrapping may be used for generating leads for sales and marketing.
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However, scrapping is usually a complex task and every website may require a
different approach for scrapping.

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Data Engineering
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Acquire datasets that align with business needs
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Develop algorithms to transform data into useful, actionable information
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Build, test, and maintain database pipeline architectures
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Collaborate with management to understand company objectives
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Create new data validation methods and data analysis tools
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Ensure compliance with data governance and security policies

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Tools used for Data Engineering
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Data Warehouse
– Snowflake, AWS Redshift
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Data Stage
– AWS S3
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Data Transformation
– Platforms: AWS EC2, AWS Glue, AWS Lambda, DBT
– Tools: Pandas, PySpark, SQL
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Data Analytics and Visualization
– Tableau, Sisense

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An overview of data and web-application development with Python

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