Python Data Types Cheat Sheet

Most Python classes I've seen in DS projects do too much! They load data, clean it, transform it, run the model, and log results... all in one place. It feels efficient until you need to change one thing and have to re-test everything else. That's the cost of ignoring the Single Responsibility Principle. 🐍 In my latest article, I break down what SRP actually means for Python data pipelines: https://lnkd.in/esKz_ARk This is post 1 of 5 in a series on SOLID principles applied to Data Science code. What's the messiest class you've inherited on a DS project? 👇 #Python #DataScience #SoftwareEngineering #SOLID #DataEngineering

Working on Real World Data Problems Using Pure Python Recently worked on a project focused on handling and analyzing structured data using core Python without relying on libraries like NumPy or Pandas. The goal was to understand the logic from the ground up. Cleaned and structured raw JSON data Built logic for “People You May Know” (mutual connections) Implemented “Pages You Might Like” recommendations Focused on problem-solving using basic data structures This approach helped me strengthen my core data handling and logical thinking, rather than depending on pre-built tools. Late nights after work, but worth it for the growth. #Python #DataProcessing #DataScience #ProblemSolving #CorePython #Algorithms #NumPy #pandas

🔤 Strings in Python – Quick Guide Strings are used to store text data in Python. They are simple, powerful, and used everywhere — from data cleaning to report generation. Creating Strings s1 = 'Hello' s2 = "Python" s3 = """Multi-line string""" Access & Slicing text = "Python" text[0] # P text[-1] # n text[0:3] # Pyt Common Operations "Hello" + " World" # Concatenation "Hi " * 3 # Repetition Useful String Methods text = " hello world " text.upper() # HELLO WORLD text.lower() # hello world text.strip() # remove spaces text.replace("world","Python") text.split() String Formatting (Best Practice) name = "Maha" print(f"Hello {name}") Important: Strings are immutable (cannot be changed directly) text = "hello" text = "H" + text[1:] #Python #PythonBasics #DataAnalytics #Programming #LearnPython #Coding #DataScience #PythonForBeginners #100DaysOfCode

90% of Data Work = Cleaning. SQL & Python Side‑by‑Side. Cleaning isn’t just prep it’s analysis. Here’s how SQL and Python mirror each other when tackling: Missing values Duplicates Formatting Outliers 👉Full break down here : https://lnkd.in/gUuRJExK #DataScience #SQL #Python #Analytics #BigData #MachineLearning #CareerGrowth

Most Python beginners confuse variables, data types, and casting. Here's the clearest breakdown I know: A variable is just a label on a box: age = 25 name = "Tanvir" active = True Python has 4 core data types you'll use every day: 1. int → whole numbers (25, 100) 2. float → decimals (9.99, 3.14) 3. str → text ("hello", "42") 4. bool → True or False Type casting = converting one type to another. Python does it automatically sometimes (implicit): x = 5 + 2.0  # result is 7.0 (int + float = float) You do it manually when needed (explicit): int("42")   # → 42 str(99)    # → "99" float("3.14") # → 3.14 ⚠️ The gotcha: int("hello")  # → ValueError! Only cast when the value is actually compatible. Understanding this saves hours of debugging type errors in real projects. What Python concept confused you the most as a beginner? Drop it below 👇 #Python #ProgrammingForBeginners #LearnPython #PythonTips #CodingBangladesh

Most Python beginners confuse variables, data types, and casting. Here's the clearest breakdown I know: A variable is just a label on a box: age = 25 name = "Tanvir" active = True Python has 4 core data types you'll use every day: 1. int → whole numbers (25, 100) 2. float → decimals (9.99, 3.14) 3. str → text ("hello", "42") 4. bool → True or False Type casting = converting one type to another. Python does it automatically sometimes (implicit): x = 5 + 2.0  # result is 7.0 (int + float = float) You do it manually when needed (explicit): int("42")   # → 42 str(99)    # → "99" float("3.14") # → 3.14 ⚠️ The gotcha: int("hello")  # → ValueError! Only cast when the value is actually compatible. Understanding this saves hours of debugging type errors in real projects. What Python concept confused you the most as a beginner? Drop it below 👇 #Python #ProgrammingForBeginners #LearnPython #PythonTips #CodingBangladesh

From Raw Websites to Structured Data I recently worked on a project where I extracted real-time data from websites using Python. What I did: - Collected data using BeautifulSoup - Parsed HTML content - Converted unstructured data into a clean dataset using Pandas Why it matters: Data collection is the first step in any data analysis process. Without data, there are no insights! Curious — what kind of data would you scrape? #DataAnalytics #Python #WebScraping #Learning

🚀 Web Scraper Project Built a Python-based web scraper to extract product details like names, prices, and ratings from a website and store them in a structured CSV file. Tech Used: Python, BeautifulSoup, Pandas 📊 Output: products.csv for data analysis This project helped me understand real-world data extraction and handling. #Python #WebScraping #DataScience #SkillCraftTechnology #LearningJourney

Little-Known Ways to Save Time with Python in Power BI It All Started with a Single Script... If you want to perform imputation, run statistical analysis, or dive into machine learning, you need external tools. That is where Python integration changes the game. Python can fetch data without native connectors, perform fuzzy matching, create custom visuals like correlation heatmaps or violin plots, and run machine learning models. Python fills the gaps that standard tools cannot. Here is the link to the article with details: https://lnkd.in/deYr5JWi P.S. I share data analytics tips and my experience in a free newsletter. Join here: https://lnkd.in/d79Zv532

Python makes data cleaning 10x faster. My standard Pandas cleaning workflow: ■ Remove duplicates ■ Handle missing values ■ Fix datatypes ■ Standardize categories ■ Outlier detection Example: ```python df.drop_duplicates(inplace=True) df['date'] = pd.to_datetime(df['date']) df.fillna(0, inplace=True) ``` Clean data = accurate insights. #Python #Pandas #DataCleaning #DataAnalyst #Automation