📓 Books, reference guides and resources on Regular Expressions, CLI one-liners, Scripting Languages and Vim.
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Updated
Sep 16, 2024 - Vim Script
A regular expression (shortened as regex or regexp), sometimes referred to as rational expression, is a sequence of characters that specifies a match pattern in text. Usually such patterns are used by string-searching algorithms for "find" or "find and replace" operations on strings, or for input validation.
Regular expression techniques are developed in theoretical computer science and formal language theory. They are used in search engines, in search and replace dialogs of word processors and text editors, in text processing utilities such as sed and AWK, and in lexical analysis. Regular expressions are also supported in many programming languages.
Different syntaxes for writing regular expressions have existed since the 1980s, one being the POSIX standard and another, widely used, being the Perl syntax.
📓 Books, reference guides and resources on Regular Expressions, CLI one-liners, Scripting Languages and Vim.
NoteBook
Python notebooks written while learning some libs
⚗️ An exploratory WhatsApp chat analysis notebook.
Jupyter notebook for Python Regex practice
📓 App to keep track of ideas
Python mastery. OOP | Numpy | Pandas | Jupyter Notebook & more.
Repository for Natural Language Processing lessons, notebooks, and exercises.
Python Regular Express (regex) tutorial and tips with Jupyter Notebooks
Jupyter notebook, where I use nlp and practice regex
Data Cleaning on grades dataset using re and pandas in Jupyter notebook.
Warehouse - storage for notebooks or some other equipment made with Entity Framework.
This notebook explores the application of Regex and embedding techniques in Arabic Natural Language Processing (NLP). It covers the use of regular expressions for text parsing tasks and delves into various word embedding methods, including Word2Vec and FastText, for semantic analysis and representation of Arabic text data.
Data Analysis on Olympics dataset of csv format using re and Pandas in Jupyter notebook.
Portfolio Project.ipynb and Recommendation.py are the finalized Jupiter notebook scripts for this project. Other files are a work in progress to migrate into a web app.
This repository serves as as a practical guide for understanding (NLP) through a Lab. It consists of two Jupyter notebooks, each dedicated to a specific part of the lab.
Personal assistant that can work with address book and notes. Using currying and command parser for Adress Book. Decorator catches all possible errors. Saves data locally in JSON files. Includes Extension File Sorter as a separate sub-application. This specific version was made by team of 6 people as personal project.
Crowd-Quest: ETL Journey for Crowdfunding Data is a repository showcasing the ETL (Extract, Transform, Load) process. It involves extracting data from Excel files, transforming it into CSV format, designing an ERD and database schema, and loading the data into PostgreSQL. Tools used: Jupyter Notebook, VSCode, PostgreSQL, Quick DBD, Excel.