[1]
Python is among the fastest-growing and most popular programming
languages out there today. Here are a few ways to use the coding
language across industries.
https://www.techrepublic.com/article/python-5-use-cases-for-programmers/
1. Insurance
Top use: Creating business insights with machine learning
Case study: One American multinational finance and
insurance corporation faced competition from smaller companies that were
introducing services driven by machine learning. To compete, the
insurer allowed teams to develop new applications and services using
machine learning; however, with too many sets of data science tools
involved, a number of different versions of Python and compatibility
issues arose. The company settled on one version of Python to deliver
all of the machine learning capabilities needed.
2. Retail banking
Top use: Flexible data transformation and manipulation
Case study: A
large American department store chain with an in-store banking arm
collects data centrally in a warehouse, and then shares it with multiple
applications to enable its supply chain, retail banking, and analytics
and reporting needs. While the company standardized on Python for data
manipulation, each team created its own version, which created problems.
The company decided on a single, standard Python build to increase
engineering speed and decrease support costs.
3. Aerospace
Top use: Meeting software system deadlines
Case study: An
American multinational aerospace, military, and defense corporation was
contracted to provide a number of systems for the International Space
Station. While aerospace software focused on critical safety systems is
typically written in a language like Ada, those older languages do not
lend themselves well to scripting tasks, GUI creation, or data science
analysis. Selecting a single Python version offered a larger contract
value and no exposure.
4. Finance
Top use: Data mining identify cross-sell opportunities
Case study:
An American multinational financial services corporation wanted to mine
complex customer and prospect behavioral data as part of a digital
transformation project. The company used Python to initiate different
data science and machine learning initiatives to examine the structured
data it had been collecting for years, and correlated it with
unstructured data from the web and social media to increase
cross-selling and reclaim resources.
5. Business services
Top use: API access to financial information
Case study:
A privately-held financial data and media company had previously
provided partners with access to financial information through different
electronic resources. Partners wanted to build desktop applications in a
variety of languages, including Python, to incorporate the customer's
API directly into their own, and created a Python Software Development
Kit (SDK) for their financial information API, leading to increased
revenue and customer satisfaction.
[2]
https://stackabuse.com/functional-programming-in-python/
Functional Programming is a programming paradigm with software
primarily composed of functions processing data throughout its
execution. Although there's not one singular definition of what is
Functional Programming, we were able to examine some prominent features
in Functional Languages: Pure Functions, Immutability, and Higher Order
Functions.
Python allows us to code in a functional, declarative style. It even
has support for many common functional features like Lambda Expressions
and the
map and
filter functions.
However, the Python community does not consider the use of Functional
Programming techniques best practice at all times. Even so, we've
learned new ways to solve problems and if needed we can solve problems
leveraging the expressivity of Functional Programming.
[3]
Historical evaluation of python evolution
https://python-history.blogspot.com/2009/04/origins-of-pythons-functional-features.html
"..It is also worth nothing that even though I didn't envision Python as a
functional language, the introduction of closures has been useful in the
development of many other advanced programming features. For example,
certain aspects of new-style classes, decorators, and other modern
features rely upon this capability.
Lastly, even though a number of functional programming features have
been introduced over the years, Python still lacks certain features
found in “real” functional programming languages. For instance, Python
does not perform certain kinds of optimizations (e.g., tail recursion).
In general, because Python's extremely dynamic nature, it is impossible
to do the kind of compile-time optimization known from functional
languages like Haskell or ML.
.."
[4]
Tutorial: Python Functions and Functional Programming
In this post, we will:
- Explain the basics of functional programming by comparing it to object-oriented programming.
- Cover why you might want to incorporate functional programming in your own code.
- Show you how Python allows you to switch between the two.
- The Lambda Expression
- The Map Function
- The Filter Function
- The Reduce Function
- Rewriting with list comprehensions
- Writing Function Partials
[5]
Another link to get started with language syntax
Concluding thoughts
Getting to know your programming language
of choice well by exploring its features, libraries and internals will
undoubtedly help you debug and read code faster. Knowing about and using
ideas from other languages or programing language theory can also be
fun, interesting, and make you a stronger and more versatile programmer.
However, being a Python power-user ultimately means not just knowing
what you *could* do, but understanding when which skills would be more
efficient. Functional programming can be incorporated into Python
easily. To keep its incorporation elegant, especially in shared code
spaces, I find it best to use a purely functional mindset to make code
more predictable and easy, all the while maintaining simplicity and
idiomaticity.
[6] Another good and concise one to get started
A practical introduction to functional programming
