I'm prepping a dataset for an upcoming tutorial and I figured walking through the process of cleaning it would work well for a livestream! We use various Python Pandas functions to accomplish our data cleaning goals. We'll be working off of this repo: https://github.com/KeithGalli/Olympics-Dataset Some topics that we cover: - How you can use web scraping to collect data like this (Python beautifulsoup). - Splitting strings into separate columns - Using regular expressions (regexes) to extract specific details from columns - Converting columns to datetime & numeric types - Grabbing only a subset of our columns Sorry that this was a bit last minute scheduling-wise, will try to give more advance notice in the future! Video timeline! 0:00 - Livestream Overview 4:00 - About the Olympics dataset (source website and how it was scraped) 9:50 - Cleaning the dataset (getting started with code & data) 19:26 - What aspects of our data should be cleaned? 29:08 - Get rid of bullet points in Used name column 34:08 - How to split Measurements into two separate height/weight numeric columns. 1:05:00 - Parse out dates from Born & Died columns 1:25:43 - Parse out city, region, and country from Born column (working with regular expressions) 1:41:15 - Get rid of the extra columns 1:46:08 - Next steps (how would we clean the results.csv) 1:49:41 - Questions & Answers ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #pandas #pandas library #to_numeric #to_datetime #datetime objects #datetime #datetime pandas #regular expressions #regular expression #regex #regexes #data cleaning #dataset cleaning #python pandas #csvs #data science project #data science python #real world #real-world #python practice #practice #exercises #python exercises #data science for beginners #data science course #clean data

In this tutorial, you'll gain hands-on experience with the python pandas library, building experience with data manipulation and analysis skills important for data science. You'll learn how to create, modify, and analyze DataFrames, handle missing data (NaNs), clean messy data, and generate some visualizations. By tackling a variety of problems, from basic data handling to advanced DataFrame techniques, you'll build a solid foundation in managing and interpreting real-world data sets using pandas. Repo we're working off of (credit to Alex Riley who put repo together): https://github.com/ajcr/100-pandas-puzzles My code solutions (use repo above for blank starting template): https://github.com/KeithGalli/100-pandas-puzzles Hope that you enjoy this video. If you do, make sure to like it and subscribe to not miss future videos like this! Video Timeline! 0:00 - Intro & Setup 2:14 - Problems (1-3) Initial pandas setup 4:42 - Problems (4-10) DataFrame operations 4:52 - 4) Create a dataframe from dictionary 5:24 - 5) Display dataframe summary 5:41 - 6) First 3 rows of the dataframe 6:02 - 7) Select ‘animal’ and ‘age’ columns 7:42 - 8) Data in specific rows and columns 9:06 - 9) Rows with visits greater than 3 9:57 - 10) Rows with NaN in age 10:56 - 11) Cats younger than 3 years 11:35 - 12) Age between 2 and 4 12:45 - 13) Change age in row ‘f’ 15:56 - 14) Sum of all visits 16:41 - 15) Average age by animal 20:21 - 16) Modify and revert rows 24:06 - 17) Count by animal type 25:28 - Quick review 26:17 - 18) Sort by age and visits 28:07 - 19) Convert 'priority' to boolean 29:42 - 20) Replace 'snake' with 'python' 30:53 - 21) Mean age by animal and visits 33:49 - Advanced DataFrame techniques 33:57 - 22) Filter duplicate integers 43:18 - 23) Subtract row mean 45:42 - 24) Column with smallest sum 50:39 - 25) Count unique rows 53:17 - 26) Column with third NaN 1:10:27 - Solution review for 26 1:17:13 - 27) Sum of top three values 1:24:01 - 28) Sum by column condition 1:40:11 - Recent problem review 1:42:53 - 29) Count differences since last zero 1:56:19 - 30) Locate largest values 2:08:38 - 31) Replace negatives with mean 2:17:43 - 32) Rolling mean over groups 2:23:10 - Series and DatetimeIndex 2:23:12 - 33) DatetimeIndex for 2015 2:27:56 - 34) Sum values on Wednesdays 2:45:04 - 35) Monthly mean values 2:46:16 - 36) Best value in four-month groups 2:50:26 - 37) DatetimeIndex of third Thursdays 2:59:03 - Cleaning Data 2:59:40 - 38) Fill missing FlightNumber 3:02:45 - 39) Split column by delimiter 3:06:47 - 40) Fix city name capitalization 3:08:30 - 41) Reattach columns 3:13:11 - 42) Fix airline name punctuation 3:17:45 - 43) Expand RecentDelays into columns 3:27:31 - MultiIndexes in Pandas 3:27:34 - 44) Construct a MultiIndex 3:30:37 - Solution review 3:32:44 - 45) Lexicographically sorted check 3:32:58 - 46) Select specific MultiIndex labels 3:34:23 - 47) Slice Series with MultiIndex 3:35:24 - 48) Sum by first level 3:37:47 - 49) Alternative sum method 3:40:08 - Additional solution insights 3:41:22 - 50) Swap MultiIndex levels 3:45:27 - Minesweeper problems 3:45:44 - 51) Generate coordinate grid 4:00:28 - 52) Add 'safe' or 'mine' column 4:03:04 - 53) Count adjacent mines 4:27:33 - Review solution to 53 4:33:02 - Skipped problems 54 & 55 4:33:11 - Plotting 4:33:12 - 56) Scatter plot with black x markers 4:41:26 - 57) Plot four data types 4:52:50 - 58) Overlay multiple graphs 5:03:11 - 59) Hourly stock data summary 5:14:12 - 60) Candlestick plot ------------------ Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #pandas #loc #iloc #python pandas #pd #numpy #np #learn data science #python3 #pandas practice problems #polars #python polars #learn programming #sql #data #data engineering #dataframe #data cleaning #datetimeindex #datetime #multiindex #series #machine learning #artificial intelligence #data scientist #leetcode problems #data structures #pandas library #real world #python interview #nan #clean data

I want to start live streaming on this channel! Figure an AMA is a good way to start. If you have any questions before the live stream starts, leave them in the chat/comments :) Video timeline! 0:00 - Intro & Live Stream Overview 4:58 - How over saturated is the data science job market and will things improve in your opinion? 7:26 - How much maths is needed to get a data science job? 9:55 - Can you share a basic roadmap to learn generative AI and LLMs? 13:08 - What future-proof tech career to should someone focus on who’s looking to change career? 16:52 - Is data structures & algorithms (dsa) necessary to get a job in data science? 19:17 - How to get good at data structures and algorithms? 22:20 - Why don’t you make videos regularly now? 24:18 - How much do you need to know for entry-level roles / college internships? 27:02 - How important is domain knowledge for data science? 29:29 - Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts 32:30 - Any good data projects to increase visibility to companies? 36:05 - Do you think we should all learn vector databases? 39:10 - Is webscraping illegal? what can I do and not do? 43:14 - What are you working on at the moment? 45:25 - How can I turn a financial database I’m building into an interesting portfolio project to showcase work? 49:23 - What advice do you have for data scientists who want to get into freelance/consulting? 55:15 - What are important skills for DS beyond ML & AI? 59:42 - Do I need to become a full-stack programmer to have success in this field? 1:02:31 - If you weren’t allowed to do programming or create content, what would you do? 1:03:39 - How did you achieve your advanced height? Asking for a friend. 1:04:23 - Final thoughts. Thanks for coming! ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #data science #data analysis #machine learning #data scientist #business analyst #data science for beginners #data analyst #Mathematics in Data Science #Generative AI Roadmap #Learning LLMs #Future-Proof Tech Careers #Data Structures & Algorithms #College Internships #Amazon Just Walk Out #Data Science Projects #Vector Databases #Web Scraping Legality #Financial Database Project #Data Science Freelancing #Full-Stack Programming #entry-level #ML #AI

------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming

In this video we walk through the process of analyzing historical documents using Python & Large Language Models. We start by setting up LLMs using both closed-source (OpenAI API) and open-source (Llama 2 via Ollama) options. Next, we walk through how we can leverage the LLMs to parse out entities from text. After this we actually start playing around with our data, loading in a specific subcategory of documents from Kaggle and see how we can connect pages from the same documents together. Once this is completed, we repeat the entity parsing process for our actual data to get pieces of information such as names, ages, and locations from our documents. Finally we analyze these entities to learn some insights from our document database. Kaggle Dataset: https://www.kaggle.com/datasets/keithgalli/freedmens-bureau-historical-documents GitHub Repo: https://github.com/keithgalli/historical-docs-analysis Project Website: https://freedmensbureau.info Contributors: Abdessalem Boukil (NLP Research & Analysis): https://www.linkedin.com/in/abdessalem-boukil-37923637/ Trent Self (Kaggle Dataset Setup): https://www.linkedin.com/in/trentonself/ If you enjoyed this project video, make sure to throw it a thumbs up & subscribe! Let me know in the comments if you have any questions. It would also be helpful for people to upvote the Kaggle dataset for visibility! --------------------------- Video timeline! 0:00 - Video Overview & Reference Material 3:05 - Data & Code Setup 5:04 - Task #0: Configure LLM to use with Python (OpenAI API) 20:10 - Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama) 27:39 - Task #1: Use LLM to Parse Simple Sentence Examples 41:22 - Sub-task #1: Convert string to Python Object 44:29 - Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain 56:24 - Quick note on a benefit of using LangChain (easily switching between models) 58:06 - Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe 1:06:22 - Task #2: Connect Pages that Belong to the Same Documents 1:56:36 - Task #3: Parse out values from merged documents 2:12:44 - Task #4 (setup): Analyze Results 2:17:52 - Fixing up our results from task #3 quickly 2:20:41 - Task #4: Find the average age of apprentices in our merged contract documents 2:30:59 - Other analysis, wlho had the most apprentices? ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #python project #llms #large language model #large language models #llama #llama 2 #openai #chat-gpt #chat gpt #gpt 4 #gpt 3 #openai api #gpt4 #gpt3 #chatgpt #open ai #langchain #ollama #pandas #python pandas #nlp #natural language processing #llm #python3 #programming project #data project #data #jupyter notebook #data scientist #data science full course #data science for beginners #real world

In this video, we look into how you can enhance your GitHub profile to help catch the eye of potential employers. With insights from my experience in hiring for data science and programming roles, we walk through five actionable tips to elevate your GitHub presence. These range from showcasing your coding projects effectively to optimizing your profile's appearance and readability. Whether you're a novice or an experienced coder, these strategies will help you stand out in the competitive job market by demonstrating your ability to build impressive projects with code. Check out Bobby's GitHub! https://github.com/bobbyg603 Python API video Mentioned: https://youtu.be/moi8WPO3Xhs?si=GYQenpZ_ASE8BNqf Ladder's Study on 7 Second Resume Review: https://www.theladders.com/career-advice/you-only-get-6-seconds-of-fame-make-it-count 0:00 - Introduction 1:50 - Tip 1: Show Private Repository Activity 2:57 - Tip 2: Highlight best work using pins 4:13 - Tip 3: Create a Profile README 11:32 - Tip 4: Fill in all Profile Details 13:45 - Tip 5: Fill in READMEs on highlighted repos ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #github #github profile #tech resume #resume review #tech interviews #tech #git #git tutorial #github tips #github portfolio #data science portfolio #data science projects #programming projects #code #coding #learn to code #gitlab profile #github readme #readme #github profile page #project portfolio #portfolio #software developer roadmap #software developer #github tutorial #git hub

Check out AnalystBuilder! https://www.analystbuilder.com/?via=keith Join me as I dive into the Analyst Builder platform created by fellow YouTuber @AlexTheAnalyst! In this video, we tackle a series of Python programming challenges, demonstrating real-time problem-solving and coding skills. We navigate through various tasks, from identifying high-risk heart attack patients using data analysis to manipulating strings and solving complex data queries. Whether you're a seasoned programmer or just starting, this video offers insights into Python programming and practical data analysis techniques. Highlights - Overview of Analyst Builder platform and its user-friendly interface. - Coding session solving Python problems related to data analysis and manipulation. - Detailed walkthrough of three distinct challenges: heart attack risk assessment, customer data anonymization, and sales data analysis for a bakery contest. Python & Programming Skills Used: - Writing efficient Python queries to extract specific data from data frames. - Utilizing Pandas for data analysis, including conditional filtering, sorting, and grouping. - Implementing string manipulation techniques for data anonymization. - Leveraging pivot tables for comparative analysis of sales data. - Dynamic problem-solving and debugging using Python in a real-world scenario. If you have any questions, let me know in the comments! If you enjoyed this video, make sure to throw it a like & subscribe for all future content :) Video timeline! 0:00 - Overview & Getting Started 0:50 - 1. Predicting Heart Attack Risk (Easy Problem) 6:44 - 2. Data Anonymization (Medium Problem) 11:53 - 3. Dessert Duel (Hard Problem) Keywords: #Python #Pandas #DataAnalysis #AnalystBuilder #ProblemSolving #Coding #Tutorial #DataScience ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #analystbuilder #data analyst #stratascratch #practice problems #python3 #sql #interview problems #ai #artificial intelligence #machine learning #python pandas #pandas library #pandas #numpy

We continue where we left on in the last video and implement two REST endpoints for a book review API. We implement a Get request to retrieve all our reviews and implement a Post request to add a new review to our Airtable database. We use the Flasgger library to create interactive documentation for our endpoints. We use ChatGPT to help us write our endpoints Python code. We use Render.com to deploy our API to a publicly accessible URL Link to first video: https://youtu.be/moi8WPO3Xhs Source code: https://github.com/KeithGalli/python-api-example/tree/part1-code Final source code: https://github.com/KeithGalli/python-api-example/tree/book_review Gitlog command info can be found in this article: https://medium.com/posh-engineering/mastering-the-terminal-to-improve-development-speed-fd234149efb6 How to set environment variables (all operating systems: https://www.twilio.com/blog/how-to-set-environment-variables-html More information on the Flasgger library: https://github.com/flasgger/flasgger Handling API keys with a Python decorator: https://coderwall.com/p/4qickw/require-an-api-key-for-a-route-in-flask-using-only-a-decorator If you enjoyed this video, be sure to give it a like and subscribe :). If you have any questions, let me know in the comments! Video timeline! 0:00 - Overview 0:41 - Getting started on the Book Review API 2:20 - Set up Airtable as our database & connect to it with Python 10:44 - Implement code to add reviews and view all reviews 31:40 - Adding a POST request to our API 36:40 - Trying out our new endpoints (using documentation & requests library of Python) 41:32 - Commit code to Github & deploy live to Render.com ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #python3 #flask #flask library #API #Rest API #API Endpoints #requests #Book review api #create api #build api #how to build an api in python #flasgger #SwaggerUI #Swagger #render.com #deploy api python #deploy api #Post request #get request #real world example #python project #python projects #python code #flask api #deploy flask api

In this video, we work through the process of creating and deploying a Python3 based API using libraries such as Flask, Flask-Restful, & Flasgger. Link to code: https://github.com/KeithGalli/python-api-example Part 2: https://youtu.be/rCrDYRBOuNw?si=bBeT9orOpjr-089S In this first video, we start with the basics, setting up GET endpoints and learning how to deploy our API to the cloud (using Render.com). This part is crucial for understanding the core functions of APIs and making them accessible worldwide. In the second video, we progress to implementing POST requests and constructing a full-fledged Book Review API, similar to services like Goodreads. This involves integrating API functionality to post and retrieve book reviews from a database, with practical examples using Airtable for database interaction. Flasgger plays a crucial role in our tutorial, providing interactive documentation through its SwaggerUI integration. This feature allows you to visualize and interact with your API directly at http://localhost:5000/apidocs, offering a clear and user-friendly overview of your API's capabilities. Additionally, we cover essential practices like working with GitHub repositories, managing Python libraries, and employing Flask and Flask-Restful for API development. The skills in crafting effective GET and POST requests are emphasized, highlighting the importance of efficient system communication. This video is ideal for anyone keen on enhancing their Python programming, API development, and cloud deployment skills. Whether you're a beginner or an experienced developer, this tutorial provides valuable experience in developing well-documented and user-friendly APIs. Video timeline! 0:00 - Video overview 1:18 - What we're building 3:20 - How to get setup with Github template code 7:00 - Taking a look at the Flask, Flasgger Python3 code 8:38 - Testing some API requests (GET) locally 13:09 - Building another GET request endpoint (with multiple parameters) 14:34 - Using ChatGPT to help us build another endpoint 22:43 - Deploying our API to a live public URL endpoint (using render.com) ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #Keith Galli #python #programming #python 3 #data science #python programming #python3 #API #application programming interface #API development #Get request #Post request #requests library #Flask #Flask-restful #Restful API #flasgger #render.com #cloud deployment #build an api #deploy an api #airtable #api keys #python api #swagger #documentation #python documentation #swagger docs #openapi #swaggerui #api docs #api documentation #github #fastapi #gunicorn #render #cloud #api creation

Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith In this video we go through all the fundamentals of using regular expressions (regexes) to match patterns in programming. In this video we cover the following: - Character Sets [a-zA-Z0-9] - Quantifiers *, +, ?, {3,5} - Metacharacters ^ . | $ - Character Classes \b \s \w \d - Groups - Lookahead & Lookbehind assertions - and more! There is a detailed timeline down below. In a future video we will apply what we learn in this one to the Python programming language (re library) -- subscribe to not miss that! Resources! Source code & txt files: https://github.com/keithgalli/regular-expressions Practice exercise video: https://youtu.be/Qv_RYpREz5k Cheat sheet: https://cheatography.com/davechild/cheat-sheets/regular-expressions/ Regex golf: https://alf.nu/RegexGolf Javascript Info: https://javascript.info/regexp-lookahead-lookbehind ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. ------------------------- Video timeline! 0:00 - Video overview & topics covered 1:43 - Basic regex syntax (building up an intuition) 4:23 - Character Sets Overview ([A-Za-z0-9]) 5:57 - Quantifiers Guide (*, +, ?, {3,5}) 9:30 - Guided Exercise: Find all words that don't use vowels 11:08 - Helpful cheat sheet to remember regex syntax in the real-world 12:47 - Matching words/patterns of a specific length ({3,5}) 14:58 - OR operator overview 17:14 - Guided Exercise: Match valid sentences (starts with capital letter, ends with period) 21:18 - Character classes overview (\w, \b, \d, \s) 23:13 - Escaping Characters 25:02 - Practice Exercise #1: Write a regular expression to match meme text format 30:39 - Practice Exercise #2: Write a regular expression to match a specific date format 39:03 - Groups overview 50:16 - Lookahead & Lookbehind Assertions 1:00:18 - Practice Exercise #3: Detect if same word pops up multiple times in a sentence 1:06:04 - Practice Exercise #4: Password matching with rules 1:16:16 - Some final recommendations! (additional practice, chatgpt, etc.) #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #regex #javascript #web development #softare engineer #software development #software engineering #engineering #regexes #regular expression #regex tutorial #practice regex #advanced #beginner #programming skills #regex groups #character sets #look ahead #lookahead #grouping #regular expressions #quantifiers #metacharacters #software #reg ex #pattern matching #patterns #matching #chatgpt #chat gpt