In this article you’ll pick up the following basic concepts of OOP in Python: Python Classes Object Instances Defining and Working with Methods OOP Inheritance Free Bonus: Click here to get access to a free Python OOP Cheat Sheet that points you to the best tutorials, videos, and books to learn more about Object-Oriented Programming […]
Category: Data Analytics
8 World-Class Software Companies That Use Python
There are over 500 current programming languages, with more being written every day. Admittedly, the majority of these overlap and a large number were never meant to be used outside of a theoretical or lab setting. But for the programming languages that are used in everyday coding and businesses, you have to make a choice. […]
Modern Web Automation With Python and Selenium
In this tutorial you’ll learn advanced Python web automation techniques: Using Selenium with a “headless” browser, exporting the scraped data to CSV files, and wrapping your scraping code in a Python class. Motivation: Tracking Listening Habits Suppose that you have been listening to music on bandcamp for a while now, and you find yourself wishing […]
Shallow vs Deep Copying of Python Objects
Assignment statements in Python do not create copies of objects, they only bind names to an object. For immutable objects, that usually doesn’t make a difference. But for working with mutable objects or collections of mutable objects, you might be looking for a way to create “real copies” or “clones” of these objects. Essentially, you’ll […]
Introduction to AWS for Data Scientists
These days, many businesses use cloud based services; as a result various companies have started building and providing such services. Amazon began the trend, with Amazon Web Services (AWS). While AWS began in 2006 as a side business, it now makes $14.5 billion in revenue each year. Other leaders in this area include: Google—Google Cloud […]
Python + Memcached: Efficient Caching in Distributed Applications
When writing Python applications, caching is important. Using a cache to avoid recomputing data or accessing a slow database can provide you with a great performance boost. Python offers built-in possibilities for caching, from a simple dictionary to a more complete data structure such as functools.lru_cache. The latter can cache any item using a Least-Recently […]
Introduction to Functional Programming in Python
Most of us have been introduced to Python as an object-oriented language; a language exclusively using classes to build our programs. While classes, and objects, are easy to start working with, there are other ways to write your Python code. Languages like Java can make it hard to move away from object-oriented thinking, but Python […]
Practical Introduction to Web Scraping in Python
Web Scraping Basics What is web scraping all about? Consider the following scenario: Imagine that one day, out of the blue, you find yourself thinking “Gee, I wonder who the five most popular mathematicians are?” You do a bit of thinking, and you get the idea to use Wikipedia’s XTools to measure the popularity of […]
Local Interpretable Model-agnostic Explanations – LIME in Python
When working with classification and/or regression techniques, its always good to have the ability to ‘explain’ what your model is doing. Using Local Interpretable Model-agnostic Explanations (LIME), you now have the ability to quickly provide visual explanations of your model(s). Its quite easy to throw numbers or content into an algorithm and get a result […]
Python – TechEuler
Python – TechEulerUnOrdered Linked list – Prepend, Append, Insert At, Reverse, Remove, SearchUse of __slots__ in Python ClassUsage of Underscores before and after function name in Python Warning: Cannot modify header information – headers already sent by (output started at /home/manikandancsea/public_html/index.php:4) in /home/manikandancsea/public_html/wp-includes/feed-rss2.php on line 8http://www.techeuler.com A Tech blog for nerds Thu, 28 Jul 2016 […]
Introduction to Python Ensembles
Stacking models in Python efficiently Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them. Put simply, ensembles combine predictions from different models to generate a final prediction, and the more models we […]
Postgres Internals: Building a Description Tool
In previous blog posts, we have described the Postgres database and ways to interact with it using Python. Those posts provided the basics, but if you want to work with databases in production systems, then it is necessary to know how to make your queries faster and more efficient. To understand what efficiency means in […]
Learning Curves for Machine Learning
Diagnose Bias and Variance to Reduce Error When building machine learning models, we want to keep error as low as possible. Two major sources of error are bias and variance. If we managed to reduce these two, then we could build more accurate models. But how do we diagnose bias and variance in the first […]
Will’s Noise
Will’s NoiseBowl Game Pick ’em ResultsOn taking things too seriously: holiday editionElote: a python package of rating systemsRipyr: sampled metrics on datasets using python’s asyncioCategory Encoders v1.2.5 ReleaseStanding Peachtree ParkData Science Things Roudup #11Modernizing Pedalwrencher: whatever that means.Git-pandas caching for faster analysisCategory Encoders v1.2.4 Release http://www.willmcginnis.com Data Science, Technology, Atlanta Mon, 25 Dec 2017 00:52:27 […]
Bowl Game Pick ’em Results
If you haven’t read my previous post on picking bowl game winners with elote, this may not make a whole lot of sense, but basically I wrote a rating system, trained it on the college football season thus far, and used it to predict winners for every bowl game. In this post, I’m tracking how […]