Airbnb Data Analysis Challenges Intuition

I’ve been exploring Python visualization tools recently, and realized I was choosing libraries based on habit more than understanding. This helped me a lot: https://lnkd.in/dRWuftDb It gives a simple, clear view of the ecosystem and when to use each tool. Sometimes all you need is the right map. What’s your go-to visualization library in Python? #Python #DataVisualization #DataScience #MachineLearning #SoftwareEngineering #Plotly #Matplotlib #Seaborn #Streamlit #Dash

Learn web scraping with Python and BeautifulSoup. Extract data from websites with ease and inform business decisions. https://lnkd.in/gQAkwAqK #WebScrapingWithPython Read the full article https://lnkd.in/gQAkwAqK

Learn web scraping with Python and BeautifulSoup. Extract data from websites with ease and inform business decisions. https://lnkd.in/gQAkwAqK #WebScrapingWithPython Read the full article https://lnkd.in/gQAkwAqK

Learn web scraping with Python and BeautifulSoup. Extract data from websites with ease and inform business decisions. https://lnkd.in/gQAkwAqK #WebScrapingWithPython Read the full article https://lnkd.in/gQAkwAqK

Learn web scraping with Python and BeautifulSoup. Extract data from websites with ease and inform business decisions. https://lnkd.in/gQAkwAqK #WebScrapingWithPython Read the full article https://lnkd.in/gQAkwAqK

Exploratory Data Analysis (EDA) in Python ===================================== Before building dashboards or models, I always run EDA to answer: ■ What’s the trend? ■ Which category dominates? ■ Are there missing values? ■ Any outliers? Python makes EDA quick with Pandas + Matplotlib. EDA = understanding the story behind the data. #Python #EDA #DataAnalytics #DataAnalyst

Python: sort() vs sorted() Have you ever had to pause for a second and think: “Do I need sort() or sorted() here?” 😅 This is the common Python confusions. Let’s clear it up. 🔹 list.sort() ◾ A method (belongs to list objects) ◾ Works only on lists ◾ Sorts the list in-place ◾ Changes the original list ◾ Returns None Example: numbers = [3, 1, 4, 2] numbers.sort() print(numbers) # [1, 2, 3, 4] 🔹 sorted() ◾ A function (built-in Python function) ◾ Returns a new sorted list ◾ Does NOT change the original ◾ Works on any iterable Example: numbers = [3, 1, 4, 2] new_numbers = sorted(numbers) print(new_numbers) # [1, 2, 3, 4] print(numbers) # [3, 1, 4, 2] The key difference: sort() → changes your original data sorted() → keeps your original data safe 💡 Quick way to remember: 👉 If you want to keep the original, use sorted() 👉 If you want to modify the list directly, use sort() #Python #Programming #LearnPython #DataScience #LearningJourney #WomenInTech

🚀 #python #Ep 2: Understanding #Data Types in Python In Python, everything is an object, and every object has a data type. Data types define what kind of value a variable holds and what operations you can perform on it. 🔗 Code reference: https://lnkd.in/ei6STRqT 🧠 Why Data Types Matter? Prevent errors in your code Help Python understand how to store and process data Make your programs efficient and readable 📌 Common Python Data Types 🔢 Numeric Types int → Whole numbers (10, -5) float → Decimal numbers (3.14) complex → Complex numbers (2+3j) 📝 String (str) Used to store text Example: "Hello Python" ✅ Boolean (bool) Only two values: True or False 📦 Sequence Types list → Ordered & mutable → [1, 2, 3] tuple → Ordered & immutable → (1, 2, 3) 🗂️ Mapping Type dict → Key-value pairs → {"name": "Hari"} 🔁 Set Types set → Unordered & unique values → {1, 2, 3} 💡 Pro Tip Python is dynamically typed, meaning you don’t need to declare data types explicitly — Python figures it out at runtime 🔍 Example x = 10 # int y = 3.14 # float name = "Hari" # str is_active = True # bool 📣 Final Thought Mastering data types is the foundation of Python programming. Once you understand them, everything else becomes easier! #Python #Coding

PYTHON: Most analysts jump into analysis too quickly. The real step is: Clean Explore Understand Then analyze. EDA is underrated. Do you spend enough time exploring data? #PythonData #EDA

A quick refresher on Statistics in Python! From basics like mean & median to advanced topics like hypothesis testing and distributions, this guide neatly covers the key functions every data analyst should know. Definitely a handy reference for real-world data analysis 💡 #DataAnalytics #Python #Statistics