How to Program with Lambda, Map, Filter & Reduce in Python
Speaker(s): Parul Pandey
Topic/Title: Programming with Lambda, Map, Filter & Reduce in Python
Description: There are multiple programming languages in the world with multiple ways they can be classified. A programming paradigm classifies programming languages based on their features or their coding style. Different paradigms (e.g. functional, imperative, object-oriented) describe different styles or modes you can write code in.
Most of the time, Pythons paradigm is seen as object-oriented, a style where we model our data in the form of classes, objects, and methods. But Python supports alternatives to that OOP paradigm - Functional Programming being a very popular choice.
Functions like lambda , filter, map, & reduce fully support the Functional Programming style, and in this live coding session we'll demonstrate how they can be effectively & efficiently used in our data analysis tasks.
About the Speaker
Parul is a Data Science Evangelist at H2O.ai. She combines Data Science, evangelism & community service in her work: people. She's an active writer & speaker and has contributed to publications like TDS, Analytics Vidhya, KDNuggets & Datacamp. She's also the organizer of the Women in Machine Learning & Data Science(WIMLDS) Hyderabad Chapter whose mission is to organize meetups supporting & promoting women & gender minorities in data science, machine learning, and AI.
Linkedin : / parulpandeyindia ,
Twitter : / pandeyparu
Material
If you would like to follow along, it'll be good that they have a Collaboratory Notebook, Jupyter Notebook, or Binder Notebook up and running. I will be live coding from scratch so, this session will be good for everybody who has a basic knowledge of Python.
Here are some good reads on Functional Programming, to get started :
1. https://www.smashingmagazine.com/2014...
2. https://docs.python.org/3.7/howto/fun...
Github: https://github.com/parulnith/Elements...
Written Tutorials:
Part1:
https://towardsdatascience.com/elemen...
Part2: https://towardsdatascience.com/compre...
Repo access https://github.com/WomenWhoCode/WWCod...
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