Python for Programmers is written for those who already have some object-oriented programming background and are interested in Python, Artificial Intelligence, and Data Science.
Book Review – Python for Programmers, by Paul Deitel and Harvey Deitel
Building a Recommendation System Using Deep Learning Models
In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.
The recommendation systems can be classified into 4 groups:
WebScraping With Python, Beautiful Soup, and Urllib3
In this day and age, information is key. Through the internet, we have an unlimited amount of information and data at our disposal. The problem, however, is because of the abundance of information we as the users become overwhelmed. Fortunately, for those users, there are programmers with the ability to develop scripts that will do the sorting, organizing, and extracting of this data for them. Work that would take hours to complete can be accomplished with just over 50 lines of code and run in under a minute. Today, using Python, Beautiful Soup, and Urllib3, we will do a little WebScraping and even scratch the surface of data extraction to an excel document.
Research
The website that we will be working with is called books.toscrape.com. It's one of those websites that is literally made for practicing WebScraping. Before we begin, please understand that we won't be rotating our IP Addresses or User Agents. However, on other websites, this may be a good idea, since they will most likely block you if you're not "polite." (I'll talk more on the concept of being polite in later posts. For now, just know that it means to space out the amount of time between your individual scrapes.)
Implement a Command-Line Shell by Using Command Dispatcher in Python
Let's implement a command Shell by using a command dispatcher. The objective is to have an event loop, which is to dispatch input commands and implement some handlers who are going to handle those commands. And, we don't want to change in the event loop whenever some new command comes in future. So, our design should be scalable enough to support adding new commands without affecting the event loop.
Decorator in Python
I hope you have seen Timeit implementation using Decorator.
Playing With TOTP (2FA) and Mobile Applications With Ionic
Today I want to play with Two Factor Authentication. When we speak about 2FA, TOTP comes to mind. There are many TOTP clients (e.g. Google Authenticator).
My idea with this prototype is to build one mobile application (with Ionic) and validate one TOTP token in a server (in this case a Python/Flask application). The token will be generated with a standard TOTP client. Let’s start
Automatic Sharing and Loading RDS Snapshots Using Lambda
We have a scenario where we have a production and a staging account. In these accounts, we have RDS databases deployed. The RDS is set up to create an automatic snapshot once a day. For testing and validation purposes we want to keep the data in the staging environment current we want to load the daily snapshot from the production account into the staging account.
To accomplish this I created two lambdas. The first one runs in production and copies the latest snapshot and then shares it with the staging account. The second lambda runs in the staging account and renames the current database, loads the snapshot into a new database and then deletes the old database.
Pandas Dataframe Functions
Pandas is a Python library that allows users to parse, clean, and visually represent data quickly and efficiently. Here, I will share some useful Dataframe functions that will help you analyze a data set.
First, you have to import the library. Conventionally, we use the alias, "pd," to refer to Pandas.
Web-Testing Automation in Python
Python is becoming the most widely used programming language.
Different Types of Testing
- Unit Test — you can picture this being at the bottom of the pyramid. It is mostly used to pinpoint bugs in your code. It cannot be used for integration testing, unit tests should essentially just give the function that’s tested some inputs, and then check what the function outputs are correct.
- UI Testing — the user interface testing, on the other hand, it can be pictured at the top of the pyramid. The purpose is to test UI elements and features, to replicate a user experience. Usually, this is the most time consuming and expensive type of testing on your web application.
- Service/API Layer Testing — the aim is to split UI testing from Unit testing and test functionality in terms of services.
Efficient UI testing
I'm going to go ahead and assume that you know what Selenium is and that you are familiar with "Unit Test" or "Pytest" in python. If you are not, I would suggest that you read an article about either, or try them out yourself!
Package Signing in PIP
A few days ago, I made this DEV.to post about how Python's PIP lacks GPG package signing. Well, it turns out that I'm wrong! It does have a package signing process after all. Except it's one of the most manual, archaic, and cumbersome security practices I've seen to date.
I discovered this method when I landed on this blog post by a core python developer yesterday. To test package signing in the way described, I created a test package called siterank, a small script to fetch Alexa ranking of given websites.
Top 10 Python Libraries You Must Know in 2019
In this article, we will discuss some of the top libraries in Python that can be used by developers to prase, clean, and represent data and implement machine learning in their existing applications.
We will be considering the following 10 libraries:
Getting Started With Python
In this article, we will install Python and create the first “Hello World” application using Python.
What Is Python?
Python is a high-level, general-purpose, interpreted, Cross-Platform, Case Sensitive, and Object-Oriented programming Language. Python was created by Guido van Rossum and first released in 1991. It uses an Object-Oriented approach which makes the programmer write clear, logical code for projects. Python has become one of the most popular programming languages used in the world. It is easy to learn because its syntax is similar to the English language. It uses a new line to complete commands and indentation to indicate a block of code. Python is derived from C, C++, Algol-68, Unix Shell, and other scripting languages, etc. The latest version of the Python is Python 3.
Best Practices for Using Maybes and Creating Default Values
Introduction
You’ve learned that using Maybes allows you to get rid of null pointer exceptions (i.e. “undefined is not a function”). However, now your application fails and gives no indication as to why. Errors would have left a stack trace that could have provided a hint as to where the problem originated, but, clearly here, you don't have that to rely on. What should you be doing instead?
Null Pointers in Python vs. Ruby, Lua, and JavaScript
Let’s define what we mean by null pointers and how you usually encounter them. Most null pointers you’ll run into are from either accessing a property of an object to show on the screen or calling a method on an object.
How to Protect Dataset Privacy Using Python and Pandas
Working with datasets that contain sensitive information is risky, and as a data scientist, you should be extremely careful whenever this type of data is present in a dataset. People dealing with sensitive information are often under the misunderstanding that by removing names, ID’s, and credit card numbers that the privacy risk is eliminated. While removing direct identifiers can help, there are more information elements in a dataset that can be used to re-identify an individual. For example, Latanya Sweeney, Director of the Data Privacy Lab in the Institute of Quantitative Social Science (IQSS) at Harvard, proved that 87 percent of US population can be re-identified using zip code, gender, and date of birth.
In this post, I am going to show you how to effectively reduce the privacy risk of a dataset while maintaining its analytical value for machine learning.
Pyleri Tutorial: Parsing with Ease
Welcome to a tutorial on Pyleri, a.k.a. the Python Left-Right Parser, a simple parsing tool used you need something more robust than a regular expression but less dense than a full-parser generator. In this tutorial, we are going to show you how to use the tool to quickly parse through your text files.
Why Learn Pyleri?
We chose Pyleri because of its ease of use and its overall effectiveness. It makes creating parsers easy. It can also quickly support features like auto-completion. In addition, the same grammar can also generate parsers for multiple languages: JavaScript, C, Python, Go and Java. It is also well-tested given that it was designed to be used with SiriDB, a highly-scalable and fast time-series database.
Programmer Levels [Comic]
Originally published October 2016
Google Cloud Vision With Spring Boot
In this post, we will take a look at how we can use Google Cloud Vision from a Spring Boot application. With Google Cloud Vision it is possible to derive all kinds of things from images, like labels, face and text recognition, etc. As a bonus, some examples with Python are provided too.
1. Introduction
A good point to start experimenting with Cloud Vision is the Cloud Vision API Documentation. The documentation is comprehensive and the examples actually do work ;-) . In the next paragraphs, we will explore some of the image processing capabilities. We will do the following:
Marker Tracking via WebSockets With a Raspberry Pi
Imagine you intend to automatically move a couple of robots within a room. You need accurate position and orientation (direction frontside is pointing to) of each robot. Apparently, outdoor systems like GPS don't work and you have a small budget. What do you do?
After some research for easy-to-apply solutions, my students and I decided to visually track our robots. We put a camera at the ceiling continuously streaming a video of our robots below. What remained to be done was capturing the frames of the video stream, searching for the objects of interest inside and serving the findings.
Using Scikit-Learn for Machine Learning Application Development in Python
Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start. They should look into the scikit-learn library, which is one of the best for developing machine learning applications. It is free and relatively easy to install and learn.
Why Machine Learning Programmers Should Be Familiar With Scikit-Learn
If you are trying to develop machine learning applications, then you were going to need a robust toolkit. Scikit-learn is just the solution that you need. This library was developed in 2007 as part of a Google project. Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies.