Amr Salah Abd ElGhany’s Post

🛠️ Scraping Data from Wikipedia Using Python Today, I worked on a simple but powerful task: extracting structured data from Wikipedia using Python. With the right approach, Wikipedia becomes a rich data source for: 📊 analysis 📈 visualization 🤖 machine learning practice Using Python libraries like: Requests – to fetch the webpage BeautifulSoup – to parse HTML tables Pandas – to clean and structure the data I was able to convert raw web content into a clean, analysis-ready dataset. This is a reminder that: Data is everywhere — the real skill is knowing how to collect it responsibly and transform it into insight. Web scraping is not about copying data. It’s about automating data collection, ensuring accuracy, and saving time. If you’re learning data analytics or Python, projects like this sharpen: ✔️ data wrangling skills ✔️ automation thinking ✔️ real-world problem solving On to the next dataset 🚀 #Python #WebScraping #DataAnalytics #BeautifulSoup #Pandas #DataEngineering #LearningInPublic #TechSkills

I used to think python was hard for analysis until I started paying attention instead of copy and paste from AI. For example: If you know Excel referencing, you already understand Python's iloc. Here's the connection nobody tells you: Excel cell references = Python iloc positions Excel: When you write "=A1", you're saying "get the value in column A, row 1." "=B5:D10" means "get everything from column B row 5 to column D row 10." Python iloc: "df.iloc[0, 0]" means "get row 0, column 0" (same as A1 in Excel) "df.iloc[4:10, 1:4]" means "get rows 5-10, columns 2-4" (similar to B5:D10) Quick comparison: Excel: `=SUM(C2:C100)` → Sum values in column C from row 2 to 100 Python: `df.iloc[1:100, 2].sum()` → Sum values in 3rd column from row 2 to 100 The logic is the same. The language is different. Pro tip: Excel is 1-indexed (starts at 1). Python is 0-indexed (starts at 0). Excel's A1 = Python's [0, 0] Once you get this, moving between Excel and Python becomes way easier. Learning Python doesn't mean forgetting Excel. It means expanding your toolkit. What is your thoughts with python? Are you finding it difficult to learn? #Python #Excel #DataAnalytics #DataScience #DataAnalyst #PythonProgramming #ExcelTips #Pandas #LearningToCode #DataSkills #TechTransition #Analytics

🚀 Day-9 — Sets in Python Sets are a powerful built-in data structure in Python used to store unique elements. They are especially useful when duplicate values must be automatically removed. 🔹 What is a Set? A set is a collection of items that is: ✔ Unordered ✔ Unindexed ✔ Mutable (can be changed) ✔ Stores only unique values Sets are defined using curly braces { }. 📝 Example: numbers = {1, 2, 3, 4, 4, 5} print(numbers) 📌 Output: {1, 2, 3, 4, 5} 🔹 Important Characteristics Duplicates are automatically removed No indexing or slicing (because sets are unordered) Elements must be immutable (int, str, tuple allowed; list not allowed) 🔹 Creating a Set set1 = {10, 20, 30} set2 = set([1, 2, 3, 4]) print(set1) print(set2) ⚠ Empty set: empty_set = set()  # Correct empty_set = {}   # ❌ This creates a dictionary 🔥 Adding & Removing Elements fruits = {"apple", "banana"} fruits.add("cherry") fruits.remove("banana") print(fruits) Other useful methods: discard() → removes element without error pop() → removes random element clear() → removes all elements 🔹 Set Operations Set operations are very useful for comparisons. ▶ Union a = {1, 2, 3} b = {3, 4, 5} print(a | b) ▶ Intersection print(a & b) ▶ Difference print(a - b) ▶ Symmetric Difference print(a ^ b) 🔹 Looping Through a Set for item in a: print(item) ⚠ Order is not guaranteed. ⚠ Common Beginner Mistakes ❌ Trying to access set elements using index ❌ Expecting order to remain same ❌ Confusing {} as empty set ❌ Adding mutable elements like lists 🌱 Best Practices Use sets when uniqueness matters Use set operations for fast comparisons Avoid relying on order of elements Sets are extremely efficient for handling unique values and comparisons. Once mastered, they simplify logic that would otherwise need complex loops. #Python #PythonProgramming #CodingJourney #LearnTogether #CodeDaily #ProgrammingBasics #TechCommunity

📁 How Python Handles Files Behind the Scenes A Clean Breakdown When Python works with data, it often needs to interact with files logs, configs, text docs, datasets, reports… everything lives in a file somewhere. And Python gives us a simple, elegant way to manage all of it. Here’s a crisp, easy-to-follow guide 👇 🔹 Opening the Door (Opening a File) file = open("notes.txt", "r") Modes like r, w, a, x tell Python what you want to do. 🔹 Checking What’s Inside (Reading) content = file.read() This pulls the entire content into your program. 🔹 Writing New Information file = open("notes.txt", "w") file.write("Writing into the file...") Perfect when you want to replace old content. 🔹 Adding More Data Without Erasing Anything file = open("notes.txt", "a") file.write("\nNew entry added.") 🔹 Closing the Door (Always Important!) file.close() ✨ The Smart Way — Using with with open("notes.txt", "r") as file: data = file.read() Automatically opens and closes — clean and safe. 📌 Quick Reference — Modes "r" → read "w" → write "a" → append "x" → create new "b" → binary "t" → text 💡 Why This Matters Efficient file handling is a core skill — it makes automation smoother and your code more reliable across real projects. 📘 Document Credits: Respect to the original author: PyCode Hubb Found this useful? Repost to help others learn 🔁 Follow Sumaiya for more Python, Data Engineering & coding insights! #python #filehandling #pythonprogramming #codingtips #automation #datascience #dataengineering #backenddevelopment #learnpython #developers #codebetter

 PYTHON JOURNEY - Day 38 / 50..!! TOPIC – Python Sets Today I explored Sets — a unique data collection type in Python that is all about uniqueness and mathematical operations! 1. Creating a Set Sets use curly braces {} just like dictionaries, but they only contain single values, not pairs. Python numbers = {1, 2, 3, 4, 5, 5, 5} print(numbers) # Output: {1, 2, 3, 4, 5} (Duplicates are automatically removed!) 2. Unordered & Unindexed Unlike lists, sets do not have a fixed order. You cannot access items using an index like [0]. Python fruits = {"Apple", "Banana", "Cherry"} # print(fruits[0]) # This will cause an ERROR 3. Set Operations (The Power of Math) Python sets allow you to perform powerful operations like Union and Intersection. Python set1 = {1, 2, 3} set2 = {3, 4, 5} print(set1.union(set2)) # Output: {1, 2, 3, 4, 5} (Combines both) print(set1.intersection(set2)) # Output: {3} (Items present in both) Why Use Sets? Remove Duplicates: The easiest way to clean a list of repeating items is to convert it to a set. Membership Testing: Checking if an item exists in a set is much faster than in a list. Data Comparison: Perfect for finding what two groups of data have in common (or what makes them different). Mini Task Write a program that: Creates a list with duplicate numbers: [1, 2, 2, 3, 4, 4, 5]. Converts that list into a Set to automatically remove the duplicates. Creates a second set {4, 5, 6, 7} and prints the Intersection between the two sets. #Python #PythonLearning #50DaysOfPython #DailyCoding #LearnPython #CodingJourney #PythonForBeginners #LinkedInLearning #DeveloperCommunity

If you tired of Using long complex if-else Chains in Python. Stop it right now. !! Start leveraging Dictionary Mapping Instead: ______________ For example: Let's suppose a messy data column as values like - "mgr", "manager", "Manager", "Sr Manager", "MGR". Old technique ( Lengthy to write & difficult to manage in longer run)- if x == "mgr":   role = "Manager" elif x == "Manager":   role = "Manager" elif x == "MGR":   role = "Manager" ... New technique ( Dict Mapping) - 1. First create a mapping: mapping = {  "mgr": "Manager",   "manager": "Manager",   "sr manager": "Senior Manager" } 2. Map the correct role: role = mapping.get(x.lower().strip(), "Unknown") Done!! _______________ This small implementation can save efforts & complexity of the code. #python #coding_ideas #data #dataengineering #datascientist #dataanlayst #python_learning

📁 How Python Handles Files Behind the Scenes A Clean Breakdown When Python works with data, it often needs to interact with files logs, configs, text docs, datasets, reports… everything lives in a file somewhere. And Python gives us a simple, elegant way to manage all of it. Here’s a crisp, easy-to-follow guide 👇 🔹 Opening the Door (Opening a File) file = open("notes.txt", "r") Modes like r, w, a, x tell Python what you want to do. 🔹 Checking What’s Inside (Reading) content = file.read() This pulls the entire content into your program. 🔹 Writing New Information file = open("notes.txt", "w") file.write("Writing into the file...") Perfect when you want to replace old content. 🔹 Adding More Data Without Erasing Anything file = open("notes.txt", "a") file.write("\nNew entry added.") 🔹 Closing the Door (Always Important!) file.close() ✨ The Smart Way — Using with with open("notes.txt", "r") as file: data = file.read() Automatically opens and closes — clean and safe. 📌 Quick Reference — Modes "r" → read "w" → write "a" → append "x" → create new "b" → binary "t" → text 💡 Why This Matters Efficient file handling is a core skill — it makes automation smoother and your code more reliable across real projects. 📘 Document Credits: Respect to the original author: PyCode Hubb Found this useful? Repost to help others learn 🔁 Follow Srilaxmi Nelluri for more Python, Data Engineering & coding insights! #python #filehandling #pythonprogramming #codingtips #automation #datascience #dataengineering #backenddevelopment #learnpython

Day 8: Unpacking the Power of Python Sets! 🐍 Today, we dove into one of Python's most unique data structures: the set. Sets are incredibly useful when you need a collection of unique, distinct items. They enforce data integrity by automatically eliminating duplicates. Here’s a quick breakdown of the key properties and operations we covered: 🔑 Key Characteristics of Sets: 📍Unordered: You can't rely on the order items appear in. 📍Unindexed: You cannot access elements using set1[0]. 📍Mutable (Sort of): You can add or remove items, but you can't change existing ones in place. 📍No Duplicates: This is their superpower! {"John", "John"} becomes just {"John"}. 🛠️ Common Operations: We reviewed how to manage a set effectively: Accessing/Iterating: # Use a for loop, not an index set1 = {"Jack", True, 1, "John", "John"} for x in set1: print(x) Adding Items: set1.add(235) # Adds a single item set1.update(set2) # Merges another set (or list, tuple) into the current one print(set1) Removing Items set1.remove("John") # Raises an Error if the item isn't found set2.discard(123) # Does nothing if the item isn't found. (Safer bet!) Clearing: set1.clear() # Empties the entire set del set2 # completely delete the set 💡 Why does this matter? Sets are essential for operations like: 🔸Efficiently checking membership (item in my_set). 🔸Finding unique user IDs in a list. 🔸Performing mathematical set operations (unions, intersections, differences). 🔸Mastering these core data types is crucial for writing clean, efficient Python code! What other Python data structures are you currently working with? Share your insights below! 👇 #Python #PythonLearning #Coding #TechSkills #DataStructures #Day8ofLearning #Coding #LogicBuilding #DataAnalyst #AnalyticalJourney

𝗗𝗮𝘆 𝟯𝟴: 𝗪𝗵𝘆 𝗬𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝗱𝗲 𝗪𝗼𝗿𝗸𝘀… 𝗯𝘂𝘁 𝗙𝗲𝗲𝗹𝘀 𝗦𝗹𝗼𝘄. Have you ever written Python code that gives correct results, but takes way too long to run? Most of the time, the problem isn’t Python itself. It’s how we use it. Here are the most common performance mistakes I’ve learned to avoid 👇 𝟭. 𝗨𝘀𝗶𝗻𝗴 𝗹𝗼𝗼𝗽𝘀 𝘄𝗵𝗲𝗿𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Python loops are slow - especially over large datasets. ❌ Looping row by row ✅ Using Pandas / NumPy vectorized operations Vectorized code is not just shorter, it’s significantly faster. 𝟮. 𝗔𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗿𝗼𝘄-𝘄𝗶𝘀𝗲 𝗶𝗻 𝗣𝗮𝗻𝗱𝗮𝘀 Using .apply() feels convenient, but it often behaves like a hidden loop. Before using apply, ask: • Can this be done with built-in Pandas functions? • Can it be expressed as a vectorized operation? Most of the time - yes. 𝟯. 𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 Reading entire tables or files when only a few columns are required wastes: • Memory • Time • Compute resources Always filter: • Columns • Rows • Date ranges as early as possible. 𝟰. 𝗥𝗲𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗹𝗼𝗴𝗶𝗰 𝗿𝗲𝗽𝗲𝗮𝘁𝗲𝗱𝗹𝘆 If the same computation runs inside a loop or function multiple times: • Cache it • Store it once • Reuse the result Repeated computation silently kills performance. 𝟱. 𝗜𝗴𝗻𝗼𝗿𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲𝘀 Wrong data types slow everything down. Examples: • Using an object instead of a category • Using float where int is enough • Storing dates as strings Correct dtypes = faster operations + lower memory usage. Python is fast enough for most data tasks; inefficient patterns are usually the real bottleneck. Writing efficient code matters as much as writing correct code. 𝗪𝗵𝗮𝘁 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀𝘀𝘂𝗲 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝗱 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝘁? 𝗟𝗲𝘁’𝘀 𝘀𝗵𝗮𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 👇 #Python #DataScience #PerformanceOptimization #Pandas #NumPy #Analytics #Learning #CodingTips

📊 Hypothesis Testing in Python — From Business Questions to Statistical Decisions Hypothesis testing is often taught as formulas and theory but applying it correctly to real data is where true analytical thinking shows. I recently completed an end-to-end hypothesis testing project in Python, using a real-world used-car dataset, where I translated business questions into statistical decisions, not just p-values. 🔍 What I implemented (from scratch): One-sample t-test to validate whether average car prices have changed over time. One-sample z-test for proportion to test shifts in automatic transmission adoption. Two-sample mean comparison using: Variance testing with F-distribution Welch’s t-test for unequal variances Two-sample proportion z-test to compare fuel-type trends across time periods 📐 What I focused on beyond library functions: Clear hypothesis formulation (H₀ vs H₁) Correct distribution selection (t, normal, F) Manual calculation of critical values using PPF Interpreting both p-values and rejection regions Linking statistical results back to business meaning 🧠 Key takeaway: Hypothesis testing is not about calling .ttest() — it’s about choosing the right test, the right distribution and making defensible decisions under uncertainty. This project strengthened my understanding of: Sampling theory Distribution assumptions Why different tests exist for means vs proportions Why variance matters before comparing averages 📌 Full Python implementation includes structured data cleaning, reproducible sampling and decision logic aligned with statistical theory. #DataAnalytics #HypothesisTesting #Statistics #Python #DataScience #LearningInPublic #AnalyticsProjects